From Inshore To Offshore: Microplastic Flows in Three Areas of The MSFD Italian Subregions

From Inshore To Offshore: Microplastic Flows in Three Areas of The MSFD Italian Subregions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Inshore To Offshore: Microplastic Flows in Three Areas of The MSFD Italian Subregions Alice Sbrana, Tommaso Valente, Jessica Bianchi, Simone Franceschini, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1044167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract An increasing number of ecologists and researchers have addressed microplastics pollution in marine waters to explain the source, the transport, and the fate of these pollutants. Further data on the concentration of microplastics in the water column are crucial for understanding the impacts on ecosystems and to implement monitoring programs. This study provides information on the concentration and composition of sea water surface microplastics in three Italian subregions of the MSFD. We examined the flow of MPs from coastal to offshore areas, comparing their densities. We tested the efficiency of two sampling methodologies to evaluate the abundance and typology of MPs between marine layers. The results of this study confirm the high values of this pollutant in the Mediterranean Sea (0.029 ± 0.033 items · m -2 ), a MPs gradient from coastal to offshore areas, and a difference between the surface and subsurface marine layers. Environmental Chemistry Marine litter Sea surface Mediterranean Sea Depth Coastal distance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The production and the use of plastic materials increased exponentially during the 20th century (Barnes et al., 2009 ). The plastic development process has led to inevitable consequences, with obvious repercussions on waste management and negative impacts on terrestrial and aquatic ecosystems (Lebreton et al., 2019 ; Thompson et al., 2009 ). A study by Koelmans et al. ( 2017 ) suggested that 99.8% of plastics that entered the marine environment since 1950 have degraded into micro and nanoplastics. According to Matiddi et al., ( 2021 ) microplastics (MPs) are defined as “ all sort of small particles of plastic, less than 5 mm in two of their three dimensions that pass through a 5 mm mesh sieve but are retained by a 330 µm mesh sieve ”. MPs are widespread in seas and oceans, mostly found on the surface but also in the water column and marine sediments (Ryan et al., 2009 ). The spatial distribution of this pollutant is influenced by multiple interacting factors (Franceschini et al., 2019 ). In particular, floating microplastics are carried by sea water movements and their distribution will reflect the surface and winds circulation (Iwasaki et al., 2017 ; Reisser et al., 2015 ). Even if, in coastal areas, multiple anthropic factors can affect their accumulation and dispersal (Suaria and Aliani, 2014 ; Thiel et al., 2013 ). In fact, proximity to big cities and anthropic activities ( e.g. , fishing, aquaculture) can significantly contribute to the amount of marine litter in the marine environments (Araújo and Costa, 2007 ; Jambeck et al., 2015 ; Rech et al., 2014 ; Thiel et al., 2013 ; Galgani et al., 2013; Lusher et al., 2017). The abundance, persistence, and ubiquity of MPs represent a crucial factor in environmental pollution and a serious threat to marine organisms. Indeed, the smaller size of MPs increases the probability of plastic ingestion by individuals (Auta et al., 2017 ; Kühn et al., 2015 ; UNEP/MAP SPA/RAC, 2018 ; Werner et al., 2016 ; Sbrana et al., 2020 ; Tsangaris et al., 2020 ; Valente et al. 2019). On 2008, the European Union approved the Marine Strategy Framework Directive (MSFD, 2008/56/EC) to protect more effectively the marine environment across Europe. The overall achieve of the MSFD is the Good Environmental Status. One of the major objectives is to reduce the loss of marine biodiversity and to monitor the concentration of pollutants in marine environments, and their impact on marine biota. In particular, the Marine Litter survey ( D10 Marine Litter ) is aimed at protecting the marine environment against harm caused by litter. Although there is no regular regional monitoring, several scientific studies show the existence of considerable amounts of micro-litter in the MSFD sea waters (European Commission, 2020 ), which is directly linked to the occurrence of litter in the terrestrial and riverine environment. The Directive requires Member States to define GES at the level of the region or subregion, planning to set threshold values, perform regular assessments, and implement programs of measures (2017/848/EU). The assessment of the current environmental status requires a comparison between a reference (expected/usual/normal) state and an impacted one. (Werner et al., 2020 ). Thus, it is necessary to define the reference values for indicators against which the actual or potentially changed situation can be compared. Nowadays, the definition of threshold value is computed only for beach litter (Van Loon et al., 2020 ). Regarding microlitter there is a lack of coherence within the same marine region or subregions and harmonized sampling and laboratory methods need to be agreed (Werner et al., 2020 ). Method of sampling and laboratory analysis conducted by different countries are often inconsistent and there is a fundamental absence of comparability among data (Hermsen et al., 2018 ; Covernton et al., 2019). It is therefore important to increase marine MP concentration data and to develop standardized sampling protocols to be comparable across studies and regions, as well as useful for predicting and assessing the effects of MP contamination on marine organisms. This pilot study aims to quantify MP concentrations in the sea water surface. The results may contribute to increasing data and assess potential implications for future MSFD monitoring programs. We examined the flow of MPs from coastal to offshore areas, comparing their densities. Furthermore, we tested the efficiency of two sampling methodologies to evaluate the abundance and typology of MPs between marine layers. We conducted sampling at three areas within the MSFD Italian subregions in the Ligurian Sea, the Adriatic Sea, and the Ionian Sea. We collected 249 sea water samples and MP concentrations were determined following the same methodological protocol. The distribution of microlitter was analyzed in terms of MPs concentration, polymer types, shapes, and colors, to explore patterns of aggregation and to further investigate their potential relationships. Methods Sampling location The Marine Strategy Framework Directive (MSFD) divides the Mediterranean Sea into specific subregions, including Italian territorial waters and contiguous zones: The Adriatic Sea (MAD). The Western Mediterranean Sea (MWE). The Ionian Sea and the Central Mediterranean Sea (MIC). The Italian national microlitter monitoring protocol for MSFD was developed by the Ministry of the Ecological Transition (MiTE), the Italian National Institute for Environmental Protection and Research (ISPRA), and the Regional Environmental Protection Agencies (ARPAs). The study included sampling at coastal and offshore areas of the three Italian subregions (MWE, MAD, MIC) during the period 2019-2020. Coastal samplings were performed by the 3 ARPAs in the Italian administrative regions (Liguria, Calabria, Puglia). Coastal sampling was carried out at different distances along a line orthogonal to the coast (0.5, 1.5, and 6 NM). Offshore samplings (12, and 24 NM from the coast) were performed by ISPRA, following the tracks of the coastal ones (Fig. 1 ). Sampling activities Samples from the surface layer of the sea were collected using the manta trawl net (mouth: 50 x 25 cm, net: 330 µm mesh size). The variability of the sampling method was verified during offshore sampling using simultaneously two manta nets, one to the left (port side; Fig. 2 a) and one to the right (starboard; Fig. 2 b) of the stern ship. Moreover, offshore subsurface samples were collected at a depth of 10-20 m using a plankton net wp2 (Ø = 50 cm, net: 330 µm mesh size; Fig. 2 c). The sampling involved three consecutive withdrawals in each station, both inshore and offshore. Nets were trawled for 20 minutes along rectilinear transects, with a speed of 1-2 knots, in the opposite direction to the surface current and the wind direction. For each trawl, the GPS coordinates at the beginning and end of sampling were recorded in WGS 84 UTM 32. The sampled area during each trawl was calculated using the instrument’s length dimensions and the distance covered during the sampling. The collected material was detached from the collection sock of the net and the sample was poured, using 330 µm metal mesh sieves, into 500 ml glass jars for subsequent qualitative and quantitative analyses. The samples were stored in refrigerators at 4°C or room temperature and protected from light and heat. Laboratory analyses In the laboratory, samples were treated with 15% H 2 O 2 to digest organic substances. Hydrogen peroxide solution was added at a 1:1 volume, sample: solution ratio, and stored 5 days at room temperature. Afterward, samples were vacuum filtered through a Whatman GF/D TM filter (47 mm in diameter, pore size 2.7 µm). MPs were visually sorted and identified under a stereomicroscope. MP recognition was performed following the MEDSEALITTER protocol (MEDSEALITTER deliverable 4.6.1 “Final common monitoring protocol for marine litter”) considering: i) the resistance of the particles to the contact with tweezers; ii) the absence of cell structures; iii) either typical skewed shapes and crooked edges or uniform thickness; iv) distinctive colours. Furthermore, we considered plastic items those showing a dark sticky mark when touched with a hot needle (Hermsen et al., 2018 ). Polymer characterization was performed by taking a 10% sub-sample of offshore plastic particles (Total items = 300) and using Nicolet iS10 Fourier Transform Infrared Spectroscopy with Attenuated Total Reflection (ATR-FTIR) (Thermo Fisher Scientific, Madison, WI, USA). All the collected particles were sub-divided into 6 shape categories (fiber, filament, foam, sheet, fragment, pellet) and colors. To avoid secondary contamination, a Tyvek® protective suit was used during all laboratory phases, and samples were processed under a laminar flow cabinet. Filters were stored in covered petri dishes and all laboratory instruments and tools were washed with ultra-pure water and checked under a stereomicroscope, to prevent cross-contamination. Procedural blanks were used in all steps (digestion, filtration, and identification) for each batch of processed samples (about 10 samples). Data analyses Microplastic concentration was expressed as the number of particles per surveyed area (items· m −2 ). Firstly, a data exploration was performed to detect outliers, assess the collinearity of the explanatory variables, and relationships between the response variable and the explanatory ones (Zuur et al., 2009 ). The normality of the data was tested using the Shapiro-Wilks test, and non-parametric data log-transformed where applicable. Comparison among subregions were assessed using a Kruskal-Wallis and Mann-Whitney-U test for non-parametric data. Generalized Linear Models (GLMs) with gamma distribution were used to evaluate the interaction among procedural and environmental variables (gear, distance from the coast, subregions, depth, wind speed) and MP concentrations. Model selections were based on the information-theoretic approach (Burnham and Anderson, 2007 ), by comparing models AICs (Akaike's Information Criterion; Akaike 1974 ). A significant difference was attributed where p < 0.05. A nonmetric multidimensional scaling (nMDS) plot was used to examine the shape of microplastics (i.e., by particle typology - fiber, filament, foam, sheet, fragment, pellet) among surface - subsurface sea waters. The plot considered both the number of microplastics as well as the shape of particles within each sample. It grouped samples with similar patterns based on both amount and typology. For nMDS plots two-dimensional ordinations using “metaMDS” and Bray-Curtis dissimilarity was made. Statistical analyses were conducted with R 4.0.4 (R Core Team, 2021), using ggplot2 (Wickham, 2016 ), ggmap (Kahle and Wickham, 2013 ), and vegan (Oksanen, 2009) packages. Results During the surveys, 249 water samples were collected. Most of the samples showed the presence of plastic-like microparticles in each of the three subregions (total items · m −2 : MWE= 2.505; MAD= 2.302; MIC= 3.116) with a mean total concentration of 0.029 ± 0.033 items · m −2 . Sea water concentration of microplastics did not show significant differences among subregions (Fig. 3 a, Table 1 ). The coastal sites (inshore) had the highest concentration of microplastics (MPs average: inshore = 0.062 ± 0.054; offshore = 0.020 ± 0.017), which differed significantly from the offshore samples ( p < 0.05, Table 1 ). Among offshore waters, we found a significant difference between MWE and MIC subregions ( p < 0.05, Table 2). Table 1 Chi-squared and p-values with significant code (*) of no-parametric Kruskal-Wallis and Mann-Whitney-U tests of significance among subregions and coastal distance (inshore – offshore). Concentration (mean ± SD and %) of items · m −2 found in sea water in areas of the three Italian MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. Kruskal-Wallis Chi-squared p-value Subregions 2.7144 0.2574 Coastal distance 31.878 1.7e-08* Mann-Whitney test Inshore Offshore MIC MWE MIC MWE MAD 0.04* 0.45 MAD 0.13 1.00 MIC 0.81 - MIC 0.02* - MIC MWE MAD MIC MWE MAD Mean ± SD 0.042 0.062 0.090 0.025 0.017 0.018 ± 0.032 ± 0.050 ± 0.075 ± 0.019 ± 0.015 ± 0.014 % 21% 32% 47% 42% 28% 30% Table 2 Summary of the results from the best fit GLM with a gamma distribution (including only coastal distance) in areas of the three Italian MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. Significance codes: *p<0.05; **p<0.01; ***p<0.001. Sea Estimate Std. Error T value P value MAD log (MP/m2) 0.4318152 0.0164854 26.194 *** Dist. Coast -0.0039383 0.0008703 -4.525 *** MIC log (MP/m2) 0.3841899 0.0132278 29.044 *** Dist. Coast -0.0008016 0.0006869 -1.167 0.246 MWE log (MP/m2) 0.3928887 0.0131887 29.790 *** Dist. Coast 0.0022711 0.0007992 -2.842 ** The best fit GLM model, with the lowest AIC (Fig. 3 b, Table 2), included only distance from the coast as a predictor of MPs concentration. The summary of the model showed a significant decrease in microplastics with increasing distance from the coast in samples from the Adriatic Sea (Fig, 1-MAD) and from the Ligurian Sea (Fig. 1 -MWE), while it is not detected in samples from the Ionian Sea (Fig. 1 -MIC). The concentration of microplastics in offshore waters was significantly different among vertical marine layers (Fig. 4 b, p < 0.05). Surface water samples had a mean abundance of 0.027 ± 0.016, while subsurface samples had a mean abundance of 0.007 ± 0.006. Particles varied by shape and color (Fig. 4 a). Fragments were the predominant types (39%) followed by sheet (28%), fiber (17%), filament (11%), foam (3%) and pellet (2%). We found a great variety of colors, ranging from dominant blue items (29%) to black (20%), white (18%), red (14%), green (12%), and others (7%). FT-IR identification confirmed that all isolated particles were plastic polymers: 72% polyethylene (PE), 24% polypropylene (PP), and 4% polystyrene (PS). The nMDS plot suggested that subsurface samples were less similar in microplastics typology compared to surface starboard and port side samples (Fig. 5 ). While surface samples collected with manta nets (both on starboard and port side) are characterized by a similar proportion of different MP types ( e.g. , fragments, sheets, filaments and fibers), subsurface samples had a predominance of fibers. Discussion Our results confirm the high abundance of MPs in the Mediterranean Sea (Caldwell et al., 2020 ; Cincinelli et al., 2019 ; de Lucia et al., 2018 ), and not reveal a particular pattern of MPs accumulation or more impacted areas. Consistently with previous studies (Atwood et al., 2019 ; Coll et al., 2012 ; Desforges et al., 2014 ; Pini et al., 2019 ), the concentration of MPs in the Ligurian and the Adriatic Sea follows a gradient with distance from the coast. Sampling sites close to the coast show significantly higher MP concentrations than offshore waters (12 – 24 NM). On the contrary, results from the Ionian Sea are not significantly affected by coastal distance. In this area (Gulf of Taranto), the mapping of the mesoscale and large-scale geostrophic circulation shows the presence of an anticyclonic gyre occupying the central open sea (Pinardi et al., 2015 ). Sea currents could influence the accumulation and transport of MPs (Liubartseva et al., 2018 ; Mansui et al., 2020 ; Zhang, 2017 ) and, the generation of eddies in the Gulf of Taranto could alter the flow of microplastics, hiding the gradient found in other sampled areas. Furthermore, the coastal samples show a high inter-variability of MPs compared to offshore ones. The concentration and distribution of MPs in the water surface are highly variable due to seasonal changes in river outflows, currents, mechanisms of degradation and fragmentation, changes in litter size, shape, buoyancy, and movement to and from other compartments (Atwood et al., 2019 ; Cózar et al., 2015 ; GESAMP, 2019 ; Jambeck et al., 2015 ; Mansui et al., 2020 ). The abundance of MPs can be influenced by processes operating over hours, days, weeks, or months; including tidal conditions, short-term wind and rain events, and seasonal extremes (GESAMP, 2019 ). In coastal areas, these phenomena are stronger and more variable than in the open sea (Hamid et al., 2018). Marine litter inputs come mainly from land-based sources, where anthropogenic pressures persist locally (Jambeck et al., 2015 ), and disperse and decrease widely in the open sea (Gorman et al., 2020 ). Decreasing abundance from inshore sample to offshore sample could be done to a dilution process from the input point, and MPs sink to the bottom. Several studies estimated the vertical distribution of MPs in the water column depending on environmental variables (Kukulka et al., 2012; Kooi et al., 2016 ), instrumental mesh size (Green et al., 2018 ; Karlsson et al., 2020 ; Viršek et al., 2016 ; Zheng et al., 2021 ) or particle densities (Choy et al., 2019 .; Lenaker et al., 2019 ), considering different methods to sample sea water MPs. The most common approach is the surface net tow, using a manta trawl, to collect floating MPs, while for sampling in subsurface waters, a plankton net is typically used (Cutroneo et al., 2020 ). In the offshore sampling waters, we made a comparison between surface and subsurface marine layers. Our results show that a significant difference occurs in the abundance of MPs collected in the two layers. The surface samples had a greater concentration of microplastics compared to the subsurface waters (0.027 ± 0.016 vs 0.007 ± 0.006), suggesting that MPs decrease drastically with depth, as also highlighted by Kooi et al., 2016 and Reisser et al., 2015 . In line with most of the published studies (Barrows et al., 2018 ; Goldstein et al., 2013 ; Llorca et al., 2020 ; Suaria et al., 2016 ) shape and colors of MPs are highly heterogenous in all three sampling areas. The study confirms the predominance of fragments blue or black in sea water surface, as still reported by some authors (Alomar et al., 2016 ; Pini et al., 2019 ; Suaria et al., 2016 ; de Lucia et al. 2018 ; Güven et al. 2017). Furthermore, the nMDS analysis reveals differences in MP types among water layers. For most types, the concentration of particles is consistent with the above results (i.e., a decrease with increasing depth). Conversely, the greatest concentration of fiber particles is found in subsurface waters. This phenomenon is probably due to the lower density of this particle than sea water density. Additionally, fibers are most susceptible to wind mixing than other MP types, because of their low buoyancy (Choy et al., 2019 ; Kooi et al., 2016 ). As suggested by GESAMP ( 2019 ), “it is important to establish a baseline by carrying out an initial survey, which will form the basis for monitoring future changes in the type, abundance, and distribution of plastic marine litter”. In recent years, much importance has been dedicated to implementing monitoring programs for the management and evaluation of MPs in surface waters. This study shows an increase of microplastics in the coastal zones, and this result suggests a greater input of plastic from the mainland (Auta et al., 2017 ; Jambeck et al., 2015 ). Have a deep knowledge of marine litter sources is essential to monitor the spatial and temporal variability of MPs. As the main input of plastic in the marine environment comes from beaches, riverside or inland (Campanale et al., 2020 ), the importance of planning clean-up actions and program of measures ( e.g. , best waste management) is crucial to achieve a decreasing trend of MPs concentration. In offshore surface waters, MPs are widely dispersed and could be extremely conditioned by the hydrodynamic forcing (winds, tides, and currents) (Liubartseva et al., 2018 ; Zhang, 2017 ) or the specific properties of plastic particles (Choy et al., 2019 ; Kooi et al., 2016 ). To date, the data acquired so far are not sufficient to establish the primary causes of the accumulation of MPs on sea water surfaces. The assumptions made by different authors to model the transport and the accumulation of floating marine litter leads to more or less significant discrepancies ( e.g. higher density of MPs in the coastal areas)(Liubartseva et al., 2018 ; Mansui et al., 2020 ). Understanding dynamics of aggregation on surface microplastics is essential to improve the monitoring strategy for MSFD programs. Several studies highlight that proximity to hotspots of seafloor macroplastic is significantly related to microplastics ingestion by marine organisms (Alomar et al., 2020 ; Franceschini et al., 2021 a). However, only few studies combine an accurate characterization of MPs isolated from sediment and water samples with the analysis of MP ingested by marine animals. A full comprehension of mechanisms and patterns that define the fate of MPs will be useful to support the global action plan. This becomes crucial in the sea water surface where researchers reported higher concentrations of MPs ingested by planktonic feeders compared with other commonly studied taxonomic families (Covernton et al., 2021 ). The knowledge behind microplastic distribution in the column water is a key component in the view of future understanding about microplastic transportation and fate in the Mediterranean Sea. This is not only to identify the seabed areas with high accumulation potential, but also to carry out future studies on the interactions of these contaminants with biota and the quality of the product fished at sea (Franceschini et al., 2021 ). For these reasons, contribute to data sharing on surface microplastics is important in increasing knowledge on marine litter, reducing the risk of bias, and developing further research. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: This work was supported by the Italian Ministry of the Ecological Transition (MiTE) as the Competent Authority for the implementation of the MSFD in Italy. Authors’ contributions: All authors contributed to the study conception and design, material preparation, data collection and analysis. The first draft of the manuscript was written by AS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: The authors would like to thank all the staff of the National System for Environmental Protection involved in Descriptor 10 implementation. A special thanks go to the ARPA staff involved in data collection on coastal microlitter. 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Prog Oceanogr 182. https://doi.org/10.1016/j.pocean.2020.102268 Matiddi M, Pham CK, Anastasopoulou A, Andresmaa E, Avio CG, Bianchi J, Chaieb O, Palazzo L, LastNameDarmon G, de Lucia GA, Deudero S, Sozbilen D, Eriksson J, Fischer E, Gómez M, Herrera A, Hattia E, Kaberi H, Kaska Y, LastNameKühn S, Lips I, Miaud C, Gambaiani D, Nelms S, Piermarini R, Regoli F, Sbrana A, Setälä O, Settiti S, Soederberg L, Tomás J, Tsangaris C, Vale M, Valente T, Silvestri C (2021) Monitoring micro-litter ingestion in marine fish: a harmonized protocol for msfd & rscs areas monitoring micro-litter ingestion in marine fish: a harmonized protocol for MSFD and RSCS areas Oksanen J (2020) Vegan:ecological diversity Pinardi N, Zavatarelli M, Adani M, Coppini G, Fratianni C, Oddo P, Simoncelli S, Tonani M, Lyubartsev V, Dobricic S, Bonaduce A (2015) Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis. 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Environ Pollut 263. https://doi.org/10.1016/j.envpol.2020.114429 Shahul Hamid F, Bhatti MS, Anuar, Norkhairiyah, Anuar, Norkhairah, Mohan P, Periathamby A (2018) Worldwide distribution and abundance of microplastic: How dire is the situation? Waste Manag. https://doi.org/10.1177/0734242X18785730 . Res Suaria G, Aliani S (2014) Floating debris in the Mediterranean Sea. Mar Pollut Bull 86:494–504. https://doi.org/10.1016/j.marpolbul.2014.06.025 Suaria G, Avio CG, Mineo A, Lattin GL, Magaldi MG, Belmonte G, Moore CJ, Regoli F, Aliani S (2016) The Mediterranean Plastic Soup: synthetic polymers in Mediterranean surface waters. Nat Publ Gr 1–10. https://doi.org/10.1038/srep37551 Thiel M, Hinojosa IA, Miranda L, Pantoja JF, Rivadeneira MM, Vásquez N (2013) Anthropogenic marine debris in the coastal environment: A multi-year comparison between coastal waters and local shores. Mar Pollut Bull 71:307–316. https://doi.org/10.1016/j.marpolbul.2013.01.005 Thompson RC, Swan SH, Moore CJ, vom Saal FS (2009) Our plastic age. Philos Trans R Soc B Biol Sci 364:1973–1976. https://doi.org/10.1098/rstb.2009.0054 Tsangaris C, Digka N, Valente T, Aguilar A, Borrell A, de Lucia GA, Gambaiani D, Garcia-Garin O, Kaberi H, Martin J, Mauriño E, Miaud C, Palazzo L, del Olmo AP, Raga JA, Sbrana A, Silvestri C, Skylaki E, Vighi M, Wongdontree P, Matiddi M (2020) Using Boops boops (osteichthyes) to assess microplastic ingestion in the Mediterranean Sea. Mar Pollut Bull 158:111397. https://doi.org/10.1016/j.marpolbul.2020.111397 UNEP/MAP SPA/RAC (2018) Defining the most representative species for IMAP Candidate Indicator 24., SPA/RAC. ed Van Loon W, Hanke G, Fleet D, Werner S, Barry J, Strand J, Eriksson J, Galgani F, Gräwe D, Schulz M, Vlachogianni T, Press M, Blidberg E, Walvoort D (2020)A European Threshold Value and Assessment Method for Macro Litter on Coastlines. Publ. Off. Eur. Union Viršek MK, Palatinus A, Koren Å, Peterlin M, Horvat P, Kržan A (2016) Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis. https://doi.org/10.3791/55161 Werner S, Budziak A, Van Franeker J, Galgani F, Hanke G, Maes T, Matiddi M, Nilsson P, Oosterbaan L, Priestland E, Thompson R, Veiga J, Vlachogianni T (2016) Harm caused by Marine Litter, JRC Technical report. https://doi.org/10.1590/S1517-83822014005000038 Werner S, Fischer E, Fleet D, Galgani F, Hanke G, Kinsey S, Mattidi M (2020) Threshold Values for Marine Litter marine litter. https://doi.org/10.2760/192427 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York Zhang H (2017) Transport of microplastics in coastal seas. Estuar Coast Shelf Sci. https://doi.org/10.1016/j.ecss.2017.09.032 Zheng Y, Li J, Sun C, Cao W, Wang M, Jiang F, Ju P (2021) Science of the Total Environment Comparative study of three sampling methods for microplastics analysis in seawater. Sci Total Environ 765:144495. https://doi.org/10.1016/j.scitotenv.2020.144495 Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM, Springer (2009) https://doi.org/10.1007/978-0-387-87458-6 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Dec, 2021 Reviewers invited by journal 30 Nov, 2021 Editor invited by journal 18 Nov, 2021 Editor assigned by journal 05 Nov, 2021 First submitted to journal 02 Nov, 2021 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. 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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-1044167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":66960558,"identity":"2782364e-35ab-44ca-b098-5a5a9d811f70","order_by":0,"name":"Alice Sbrana","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-7916-7580","institution":"University of Rome Tor Vergata: Universita degli Studi di Roma Tor Vergata","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Sbrana","suffix":""},{"id":66960559,"identity":"b4fa655d-c40b-40a9-8118-03a76b6e824f","order_by":1,"name":"Tommaso Valente","email":"","orcid":"","institution":"University of Rome: Universita degli 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Oristano","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"Giuseppe","lastName":"de Lucia","suffix":""},{"id":66960565,"identity":"c68315b0-8330-430b-93b5-016624285d95","order_by":7,"name":"Andrea Camedda","email":"","orcid":"","institution":"IAS CNR Oristano: Istituto per lo Studio degli Impatti Antropici e Sostenibilita in Ambiente Marino Consiglio Nazionale delle Ricerche Sede di Oristano","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Camedda","suffix":""},{"id":66960566,"identity":"3a09a6ce-4938-4692-88b5-941fb52462c7","order_by":8,"name":"Marco Matiddi","email":"","orcid":"","institution":"ISPRA: Istituto Superiore per la Protezione e la Ricerca Ambientale","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Matiddi","suffix":""},{"id":66960567,"identity":"0ff6ec4f-fc7e-4801-a31c-e38b93384ae0","order_by":9,"name":"Cecilia Silvestri","email":"","orcid":"","institution":"ISPRA: Istituto Superiore per la Protezione e la Ricerca Ambientale","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cecilia","middleName":"","lastName":"Silvestri","suffix":""}],"badges":[],"createdAt":"2021-11-02 16:01:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1044167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1044167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16156408,"identity":"f0478a62-856b-4ee3-9cd6-73b2d8a2945b","added_by":"auto","created_at":"2021-12-03 19:14:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":478181,"visible":true,"origin":"","legend":"Italian sea water (coastal and offshore samples) in areas of the three MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. ","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/a78e365dce1be96c6b0e6713.png"},{"id":16156412,"identity":"c11a36e2-0a26-4c32-9427-46510fbf8b27","added_by":"auto","created_at":"2021-12-03 19:14:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":686494,"visible":true,"origin":"","legend":"Sampling method for sea water microplastics using simultaneously two manta nets, one to the left (port side; a), one to the right (starboard; b), and a plankton net (c) at a depth of 10-20 m. ©ISPRA, 2020.","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/c6b403a1ca4c7457e9fcfeef.png"},{"id":16156410,"identity":"4afc4d65-5214-4159-ad6a-85188eaca8b7","added_by":"auto","created_at":"2021-12-03 19:14:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53799,"visible":true,"origin":"","legend":"Sea water microplastics differences among subregions (a) and coastal distance (b) in areas of the three Italian MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. Offshore waters range from 12 to 24 NM; inshore waters range from 0.5 to 6 NM.","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/bcd5d7f092362a0b6ede7073.png"},{"id":16156411,"identity":"f2dc0bf5-b3b4-42c6-9cf3-21fe21c10445","added_by":"auto","created_at":"2021-12-03 19:14:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69292,"visible":true,"origin":"","legend":"Shape categories, colors (a), and layers (b) of microplastics collect in sea water in areas of the three MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. ","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/3671f329fde8360a9ad9a386.png"},{"id":16156409,"identity":"b4c63133-9d2a-47d1-806a-17a3c232a643","added_by":"auto","created_at":"2021-12-03 19:14:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82803,"visible":true,"origin":"","legend":"A nonmetric multidimensional scaling plot created using the number of microplastics typologies within each sea water layers (starboard – port side surface, and subsurface waters) in areas of the three Italian MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea. We used the Bray-Curtis dissimilarity metric and plot it in two dimensions. Samples close together in space on the plot suggest they have a more similar type of microplastics. The colors stand for the 2D density plot of sampling gears.","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/17cbfe164954500276c550c6.png"},{"id":16156413,"identity":"ef02f16e-b7a9-4c9e-a9c0-2f4d3e207623","added_by":"auto","created_at":"2021-12-03 19:14:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1044167/v1/e6e8e3f6-23f8-4429-9604-25c4fa007428.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eFrom Inshore To Offshore: Microplastic Flows in Three Areas of The MSFD Italian Subregions\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe production and the use of plastic materials increased exponentially during the 20th century (Barnes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The plastic development process has led to inevitable consequences, with obvious repercussions on waste management and negative impacts on terrestrial and aquatic ecosystems (Lebreton et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study by Koelmans et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggested that 99.8% of plastics that entered the marine environment since 1950 have degraded into micro and nanoplastics. According to Matiddi et al., (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) microplastics (MPs) are defined as \u0026ldquo;\u003cem\u003eall sort of small particles of plastic, less than 5 mm in two of their three dimensions that pass through a 5 mm mesh sieve but are retained by a 330 \u0026micro;m mesh sieve\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e \u003cp\u003eMPs are widespread in seas and oceans, mostly found on the surface but also in the water column and marine sediments (Ryan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The spatial distribution of this pollutant is influenced by multiple interacting factors (Franceschini et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In particular, floating microplastics are carried by sea water movements and their distribution will reflect the surface and winds circulation (Iwasaki et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reisser et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Even if, in coastal areas, multiple anthropic factors can affect their accumulation and dispersal (Suaria and Aliani, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thiel et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In fact, proximity to big cities and anthropic activities (\u003cem\u003ee.g.\u003c/em\u003e, fishing, aquaculture) can significantly contribute to the amount of marine litter in the marine environments (Ara\u0026uacute;jo and Costa, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jambeck et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rech et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thiel et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Galgani et al., 2013; Lusher et al., 2017).\u003c/p\u003e \u003cp\u003eThe abundance, persistence, and ubiquity of MPs represent a crucial factor in environmental pollution and a serious threat to marine organisms. Indeed, the smaller size of MPs increases the probability of plastic ingestion by individuals (Auta et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; K\u0026uuml;hn et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; UNEP/MAP SPA/RAC, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Werner et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sbrana et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tsangaris et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Valente et al. 2019).\u003c/p\u003e \u003cp\u003eOn 2008, the European Union approved the Marine Strategy Framework Directive (MSFD, 2008/56/EC) to protect more effectively the marine environment across Europe. The overall achieve of the MSFD is the Good Environmental Status. One of the major objectives is to reduce the loss of marine biodiversity and to monitor the concentration of pollutants in marine environments, and their impact on marine biota. In particular, the Marine Litter survey (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eD10 Marine Litter\u003c/span\u003e) is aimed at protecting the marine environment against harm caused by litter. Although there is no regular regional monitoring, several scientific studies show the existence of considerable amounts of micro-litter in the MSFD sea waters (European Commission, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is directly linked to the occurrence of litter in the terrestrial and riverine environment.\u003c/p\u003e \u003cp\u003eThe Directive requires Member States to define GES at the level of the region or subregion, planning to set threshold values, perform regular assessments, and implement programs of measures (2017/848/EU). The assessment of the current environmental status requires a comparison between a reference (expected/usual/normal) state and an impacted one. (Werner et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, it is necessary to define the reference values for indicators against which the actual or potentially changed situation can be compared. Nowadays, the definition of threshold value is computed only for beach litter (Van Loon et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regarding microlitter there is a lack of coherence within the same marine region or subregions and harmonized sampling and laboratory methods need to be agreed (Werner et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Method of sampling and laboratory analysis conducted by different countries are often inconsistent and there is a fundamental absence of comparability among data (Hermsen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Covernton et al., 2019).\u003c/p\u003e \u003cp\u003eIt is therefore important to increase marine MP concentration data and to develop standardized sampling protocols to be comparable across studies and regions, as well as useful for predicting and assessing the effects of MP contamination on marine organisms.\u003c/p\u003e \u003cp\u003eThis pilot study aims to quantify MP concentrations in the sea water surface. The results may contribute to increasing data and assess potential implications for future MSFD monitoring programs.\u003c/p\u003e \u003cp\u003eWe examined the flow of MPs from coastal to offshore areas, comparing their densities. Furthermore, we tested the efficiency of two sampling methodologies to evaluate the abundance and typology of MPs between marine layers. We conducted sampling at three areas within the MSFD Italian subregions in the Ligurian Sea, the Adriatic Sea, and the Ionian Sea. We collected 249 sea water samples and MP concentrations were determined following the same methodological protocol. The distribution of microlitter was analyzed in terms of MPs concentration, polymer types, shapes, and colors, to explore patterns of aggregation and to further investigate their potential relationships.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eSampling location\u003c/h2\u003e\n \u003cp\u003eThe Marine Strategy Framework Directive (MSFD) divides the Mediterranean Sea into specific subregions, including Italian territorial waters and contiguous zones:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe Adriatic Sea (MAD).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe Western Mediterranean Sea (MWE).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe Ionian Sea and the Central Mediterranean Sea (MIC).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eItalian national microlitter monitoring protocol\u003c/span\u003e for MSFD was developed by the Ministry of the Ecological Transition (MiTE), the Italian National Institute for Environmental Protection and Research (ISPRA), and the Regional Environmental Protection Agencies (ARPAs).\u003c/p\u003e\n \u003cp\u003eThe study included sampling at coastal and offshore areas of the three Italian subregions (MWE, MAD, MIC) during the period 2019-2020. Coastal samplings were performed by the 3 ARPAs in the Italian administrative regions (Liguria, Calabria, Puglia). Coastal sampling was carried out at different distances along a line orthogonal to the coast (0.5, 1.5, and 6 NM). Offshore samplings (12, and 24 NM from the coast) were performed by ISPRA, following the tracks of the coastal ones (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eSampling activities\u003c/h2\u003e\n \u003cp\u003eSamples from the surface layer of the sea were collected using the manta trawl net (mouth: 50 x 25 cm, net: 330 \u0026micro;m mesh size). The variability of the sampling method was verified during offshore sampling using simultaneously two manta nets, one to the left (port side; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea) and one to the right (starboard; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) of the stern ship. Moreover, offshore subsurface samples were collected at a depth of 10-20 m using a plankton net wp2 (\u0026Oslash; = 50 cm, net: 330 \u0026micro;m mesh size; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). The sampling involved three consecutive withdrawals in each station, both inshore and offshore. Nets were trawled for 20 minutes along rectilinear transects, with a speed of 1-2 knots, in the opposite direction to the surface current and the wind direction. For each trawl, the GPS coordinates at the beginning and end of sampling were recorded in WGS 84 UTM 32.\u003c/p\u003e\n \u003cp\u003eThe sampled area during each trawl was calculated using the instrument\u0026rsquo;s length dimensions and the distance covered during the sampling.\u003c/p\u003e\n \u003cp\u003eThe collected material was detached from the collection sock of the net and the sample was poured, using 330 \u0026micro;m metal mesh sieves, into 500 ml glass jars for subsequent qualitative and quantitative analyses. The samples were stored in refrigerators at 4\u0026deg;C or room temperature and protected from light and heat.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eLaboratory analyses\u003c/h2\u003e\n \u003cp\u003eIn the laboratory, samples were treated with 15% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e to digest organic substances. Hydrogen peroxide solution was added at a 1:1 volume, sample: solution ratio, and stored 5 days at room temperature. Afterward, samples were vacuum filtered through a Whatman GF/D\u003csup\u003eTM\u003c/sup\u003e filter (47 mm in diameter, pore size 2.7 \u0026micro;m).\u003c/p\u003e\n \u003cp\u003eMPs were visually sorted and identified under a stereomicroscope. MP recognition was performed following the MEDSEALITTER protocol (MEDSEALITTER deliverable 4.6.1 \u0026ldquo;Final common monitoring protocol for marine litter\u0026rdquo;) considering: i) the resistance of the particles to the contact with tweezers; ii) the absence of cell structures; iii) either typical skewed shapes and crooked edges or uniform thickness; iv) distinctive colours. Furthermore, we considered plastic items those showing a dark sticky mark when touched with a hot needle (Hermsen et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Polymer characterization was performed by taking a 10% sub-sample of offshore plastic particles (Total items = 300) and using Nicolet iS10 Fourier Transform Infrared Spectroscopy with Attenuated Total Reflection (ATR-FTIR) (Thermo Fisher Scientific, Madison, WI, USA).\u003c/p\u003e\n \u003cp\u003eAll the collected particles were sub-divided into 6 shape categories (fiber, filament, foam, sheet, fragment, pellet) and colors.\u003c/p\u003e\n \u003cp\u003eTo avoid secondary contamination, a Tyvek\u0026reg; protective suit was used during all laboratory phases, and samples were processed under a laminar flow cabinet. Filters were stored in covered petri dishes and all laboratory instruments and tools were washed with ultra-pure water and checked under a stereomicroscope, to prevent cross-contamination. Procedural blanks were used in all steps (digestion, filtration, and identification) for each batch of processed samples (about 10 samples).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eData analyses\u003c/h2\u003e\n \u003cp\u003eMicroplastic concentration was expressed as the number of particles per surveyed area (items\u0026middot; m\u003csup\u003e\u0026minus;2\u003c/sup\u003e). Firstly, a data exploration was performed to detect outliers, assess the collinearity of the explanatory variables, and relationships between the response variable and the explanatory ones (Zuur et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The normality of the data was tested using the Shapiro-Wilks test, and non-parametric data log-transformed where applicable.\u003c/p\u003e\n \u003cp\u003eComparison among subregions were assessed using a Kruskal-Wallis and Mann-Whitney-U test for non-parametric data.\u003c/p\u003e\n \u003cp\u003eGeneralized Linear Models (GLMs) with gamma distribution were used to evaluate the interaction among procedural and environmental variables (gear, distance from the coast, subregions, depth, wind speed) and MP concentrations. Model selections were based on the information-theoretic approach (Burnham and Anderson, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), by comparing models AICs (Akaike\u0026apos;s Information Criterion; Akaike \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e). A significant difference was attributed where \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n \u003cp\u003eA nonmetric multidimensional scaling (nMDS) plot was used to examine the shape of microplastics (i.e., by particle typology - fiber, filament, foam, sheet, fragment, pellet) among surface - subsurface sea waters. The plot considered both the number of microplastics as well as the shape of particles within each sample. It grouped samples with similar patterns based on both amount and typology. For nMDS plots two-dimensional ordinations using \u0026ldquo;metaMDS\u0026rdquo; and Bray-Curtis dissimilarity was made.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were conducted with R 4.0.4 (R Core Team, 2021), using ggplot2 (Wickham, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), ggmap (Kahle and Wickham, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), and vegan (Oksanen, 2009) packages.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the surveys, 249 water samples were collected. Most of the samples showed the presence of plastic-like microparticles in each of the three subregions (total items \u0026middot; m\u003csup\u003e\u0026minus;2\u003c/sup\u003e: MWE= 2.505; MAD= 2.302; MIC= 3.116) with a mean total concentration of 0.029 \u0026plusmn; 0.033 items \u0026middot; m\u003csup\u003e\u0026minus;2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSea water concentration of microplastics did not show significant differences among subregions (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The coastal sites (inshore) had the highest concentration of microplastics (MPs average: inshore = 0.062 \u0026plusmn; 0.054; offshore = 0.020 \u0026plusmn; 0.017), which differed significantly from the offshore samples (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among offshore waters, we found a significant difference between MWE and MIC subregions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Table 2).\u003c/p\u003e\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChi-squared and p-values with significant code (*) of no-parametric Kruskal-Wallis and Mann-Whitney-U tests of significance among subregions and coastal distance (inshore \u0026ndash; offshore). Concentration (mean \u0026plusmn; SD and %) of items \u0026middot; m\u003csup\u003e\u0026minus;2\u003c/sup\u003e found in sea water in areas of the three Italian MSFD subregions: MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003eKruskal-Wallis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eChi-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSubregions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2.7144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e0.2574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eCoastal distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e31.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e1.7e-08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003eMann-Whitney test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cem\u003eInshore\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eOffshore\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMWE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026plusmn; 0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026plusmn; 0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn; 0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026plusmn; 0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026plusmn; 0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026plusmn; 0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eSummary of the results from the best fit GLM with a gamma distribution (including only coastal distance) in areas\u0026nbsp;of the three Italian MSFD subregions:\u0026nbsp;MWE, the Western Mediterranean Sea (including the Ligurian Sea and Tyrrhenian Sea); MAD, the Adriatic Sea; and MIC, the Ionian Sea and Central Mediterranean Sea.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificance codes: *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.1993769470405%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.109034267912772%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.289719626168225%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.445482866043612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.445482866043612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.510903426791277%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.1993769470405%\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\"\u003e\n \u003cp\u003e\u003cem\u003elog (MP/m2)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.289719626168225%\"\u003e\n \u003cp\u003e0.4318152 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e0.0164854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e26.194 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.029739776951672%\"\u003e\n \u003cp\u003e\u003cem\u003eDist. Coast\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63197026022305%\"\u003e\n \u003cp\u003e-0.0039383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e0.0008703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e-4.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.702602230483272%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.1993769470405%\"\u003e\n \u003cp\u003eMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\"\u003e\n \u003cp\u003e\u003cem\u003elog (MP/m2)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.289719626168225%\"\u003e\n \u003cp\u003e0.3841899 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e0.0132278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e29.044 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.029739776951672%\"\u003e\n \u003cp\u003e\u003cem\u003eDist. Coast\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63197026022305%\"\u003e\n \u003cp\u003e-0.0008016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e0.0006869 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e-1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.702602230483272%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.1993769470405%\"\u003e\n \u003cp\u003eMWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\"\u003e\n \u003cp\u003e\u003cem\u003elog (MP/m2)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.289719626168225%\"\u003e\n \u003cp\u003e0.3928887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e0.0131887 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.445482866043612%\"\u003e\n \u003cp\u003e29.790 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.029739776951672%\"\u003e\n \u003cp\u003e\u003cem\u003eDist. Coast\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63197026022305%\"\u003e\n \u003cp\u003e0.0022711 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e0.0007992 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.817843866171003%\"\u003e\n \u003cp\u003e-2.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.702602230483272%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe best fit GLM model, with the lowest AIC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table 2), included only distance from the coast as a predictor of MPs concentration. The summary of the model showed a significant decrease in microplastics with increasing distance from the coast in samples from the Adriatic Sea (Fig, 1-MAD) and from the Ligurian Sea (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e-MWE), while it is not detected in samples from the Ionian Sea (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e-MIC).\u003c/p\u003e\n\u003cp\u003eThe concentration of microplastics in offshore waters was significantly different among vertical marine layers (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, p \u0026lt; 0.05). Surface water samples had a mean abundance of 0.027 \u0026plusmn; 0.016, while subsurface samples had a mean abundance of 0.007 \u0026plusmn; 0.006.\u003c/p\u003e\n\u003cp\u003eParticles varied by shape and color (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). Fragments were the predominant types (39%) followed by sheet (28%), fiber (17%), filament (11%), foam (3%) and pellet (2%). We found a great variety of colors, ranging from dominant blue items (29%) to black (20%), white (18%), red (14%), green (12%), and others (7%). FT-IR identification confirmed that all isolated particles were plastic polymers: 72% polyethylene (PE), 24% polypropylene (PP), and 4% polystyrene (PS).\u003c/p\u003e\n\u003cp\u003eThe nMDS plot suggested that subsurface samples were less similar in microplastics typology compared to surface starboard and port side samples (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). While surface samples collected with manta nets (both on starboard and port side) are characterized by a similar proportion of different MP types (\u003cem\u003ee.g.\u003c/em\u003e, fragments, sheets, filaments and fibers), subsurface samples had a predominance of fibers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results confirm the high abundance of MPs in the Mediterranean Sea (Caldwell et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cincinelli et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Lucia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and not reveal a particular pattern of MPs accumulation or more impacted areas.\u003c/p\u003e \u003cp\u003eConsistently with previous studies (Atwood et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Coll et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Desforges et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pini et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the concentration of MPs in the Ligurian and the Adriatic Sea follows a gradient with distance from the coast. Sampling sites close to the coast show significantly higher MP concentrations than offshore waters (12 \u0026ndash; 24 NM).\u003c/p\u003e \u003cp\u003eOn the contrary, results from the Ionian Sea are not significantly affected by coastal distance. In this area (Gulf of Taranto), the mapping of the mesoscale and large-scale geostrophic circulation shows the presence of an anticyclonic gyre occupying the central open sea (Pinardi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sea currents could influence the accumulation and transport of MPs (Liubartseva et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mansui et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and, the generation of eddies in the Gulf of Taranto could alter the flow of microplastics, hiding the gradient found in other sampled areas.\u003c/p\u003e \u003cp\u003eFurthermore, the coastal samples show a high inter-variability of MPs compared to offshore ones. The concentration and distribution of MPs in the water surface are highly variable due to seasonal changes in river outflows, currents, mechanisms of degradation and fragmentation, changes in litter size, shape, buoyancy, and movement to and from other compartments (Atwood et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; C\u0026oacute;zar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; GESAMP, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jambeck et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mansui et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The abundance of MPs can be influenced by processes operating over hours, days, weeks, or months; including tidal conditions, short-term wind and rain events, and seasonal extremes (GESAMP, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In coastal areas, these phenomena are stronger and more variable than in the open sea (Hamid et al., 2018). Marine litter inputs come mainly from land-based sources, where anthropogenic pressures persist locally (Jambeck et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and disperse and decrease widely in the open sea (Gorman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Decreasing abundance from inshore sample to offshore sample could be done to a dilution process from the input point, and MPs sink to the bottom.\u003c/p\u003e \u003cp\u003eSeveral studies estimated the vertical distribution of MPs in the water column depending on environmental variables (Kukulka et al., 2012; Kooi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), instrumental mesh size (Green et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Karlsson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Viršek et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or particle densities (Choy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e.; Lenaker et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), considering different methods to sample sea water MPs. The most common approach is the surface net tow, using a manta trawl, to collect floating MPs, while for sampling in subsurface waters, a plankton net is typically used (Cutroneo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the offshore sampling waters, we made a comparison between surface and subsurface marine layers. Our results show that a significant difference occurs in the abundance of MPs collected in the two layers. The surface samples had a greater concentration of microplastics compared to the subsurface waters (0.027 \u0026plusmn; 0.016 \u003cem\u003evs\u003c/em\u003e 0.007 \u0026plusmn; 0.006), suggesting that MPs decrease drastically with depth, as also highlighted by Kooi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e and Reisser et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn line with most of the published studies (Barrows et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Goldstein et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Llorca et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Suaria et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) shape and colors of MPs are highly heterogenous in all three sampling areas. The study confirms the predominance of fragments blue or black in sea water surface, as still reported by some authors (Alomar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pini et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Suaria et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; de Lucia et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; G\u0026uuml;ven et al. 2017).\u003c/p\u003e \u003cp\u003eFurthermore, the nMDS analysis reveals differences in MP types among water layers. For most types, the concentration of particles is consistent with the above results (i.e., a decrease with increasing depth). Conversely, the greatest concentration of fiber particles is found in subsurface waters. This phenomenon is probably due to the lower density of this particle than sea water density. Additionally, fibers are most susceptible to wind mixing than other MP types, because of their low buoyancy (Choy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kooi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs suggested by GESAMP (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003e\u0026ldquo;it is important to establish a baseline by carrying out an initial survey, which will form the basis for monitoring future changes in the type, abundance, and distribution of plastic marine litter\u0026rdquo;.\u003c/em\u003e In recent years, much importance has been dedicated to implementing monitoring programs for the management and evaluation of MPs in surface waters. This study shows an increase of microplastics in the coastal zones, and this result suggests a greater input of plastic from the mainland (Auta et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jambeck et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHave a deep knowledge of marine litter sources is essential to monitor the spatial and temporal variability of MPs. As the main input of plastic in the marine environment comes from beaches, riverside or inland (Campanale et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the importance of planning clean-up actions and program of measures (\u003cem\u003ee.g.\u003c/em\u003e, best waste management) is crucial to achieve a decreasing trend of MPs concentration.\u003c/p\u003e \u003cp\u003eIn offshore surface waters, MPs are widely dispersed and could be extremely conditioned by the hydrodynamic forcing (winds, tides, and currents) (Liubartseva et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or the specific properties of plastic particles (Choy et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kooi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To date, the data acquired so far are not sufficient to establish the primary causes of the accumulation of MPs on sea water surfaces. The assumptions made by different authors to model the transport and the accumulation of floating marine litter leads to more or less significant discrepancies (\u003cem\u003ee.g.\u003c/em\u003e higher density of MPs in the coastal areas)(Liubartseva et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mansui et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding dynamics of aggregation on surface microplastics is essential to improve the monitoring strategy for MSFD programs. Several studies highlight that proximity to hotspots of seafloor macroplastic is significantly related to microplastics ingestion by marine organisms (Alomar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Franceschini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003ea). However, only few studies combine an accurate characterization of MPs isolated from sediment and water samples with the analysis of MP ingested by marine animals. A full comprehension of mechanisms and patterns that define the fate of MPs will be useful to support the global action plan. This becomes crucial in the sea water surface where researchers reported higher concentrations of MPs ingested by planktonic feeders compared with other commonly studied taxonomic families (Covernton et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe knowledge behind microplastic distribution in the column water is a key component in the view of future understanding about microplastic transportation and fate in the Mediterranean Sea. This is not only to identify the seabed areas with high accumulation potential, but also to carry out future studies on the interactions of these contaminants with biota and the quality of the product fished at sea (Franceschini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor these reasons, contribute to data sharing on surface microplastics is important in increasing knowledge on marine litter, reducing the risk of bias, and developing further research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eNot applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis work was supported by the Italian Ministry of the Ecological Transition (MiTE) as the Competent Authority for the implementation of the MSFD in Italy.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions: \u003c/strong\u003eAll authors contributed to the study conception and design, material preparation, data collection and analysis. The first draft of the manuscript was written by AS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eThe authors would like to thank all the staff of the National System for Environmental Protection involved in Descriptor 10 implementation. A special thanks go to the ARPA staff involved in data collection on coastal microlitter.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkaike H (1974) A New Look at the Statistical Model Identification 215\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4612-1694-0_16\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlomar C, Deudero S, Compa M, Guijarro B (2020) Exploring the relation between plastic ingestion in species and its presence in seafloor bottoms. 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Sci Total Environ 765:144495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.144495\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM, Springer (2009) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-0-387-87458-6\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Marine litter, Sea surface, Mediterranean Sea, Depth, Coastal distance","lastPublishedDoi":"10.21203/rs.3.rs-1044167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1044167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn increasing number of ecologists and researchers have addressed microplastics pollution in marine waters to explain the source, the transport, and the fate of these pollutants. Further data on the concentration of microplastics in the water column are crucial for understanding the impacts on ecosystems and to implement monitoring programs.\u003c/p\u003e\u003cp\u003eThis study provides information on the concentration and composition of sea water surface microplastics in three Italian subregions of the MSFD. We examined the flow of MPs from coastal to offshore areas, comparing their densities. We tested the efficiency of two sampling methodologies to evaluate the abundance and typology of MPs between marine layers. The results of this study confirm the high values of this pollutant in the Mediterranean Sea (0.029 ± 0.033 items · m\u003csup\u003e-2\u003c/sup\u003e), a MPs gradient from coastal to offshore areas, and a difference between the surface and subsurface marine layers.\u003c/p\u003e","manuscriptTitle":"From Inshore To Offshore: Microplastic Flows in Three Areas of The MSFD Italian Subregions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-03 19:14:39","doi":"10.21203/rs.3.rs-1044167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-12-01T14:39:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-30T14:00:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2021-11-18T21:15:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-05T05:44:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-11-02T12:01:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6cd036da-f86a-406c-a4d2-c2391ab3837b","owner":[],"postedDate":"December 3rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8931730,"name":"Environmental Chemistry"}],"tags":[],"updatedAt":"2022-10-08T10:06:55+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-03 19:14:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1044167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1044167","identity":"rs-1044167","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}