Plastic debris is thought to be widespread in freshwater ecosystems globally1. However, a lack of comprehensive and comparable data makes rigorous assessment of its distribution challenging2,3. Here we present a standardized cross-national survey that assesses the abundance and type of plastic debris (>250 μm) in freshwater ecosystems. We sample surface waters of 38 lakes and reservoirs, distributed across gradients of geographical position and limnological attributes, with the aim to identify factors associated with an increased observation of plastics. We find plastic debris in all studied lakes and reservoirs, suggesting that these ecosystems play a key role in the plastic-pollution cycle. Our results indicate that two types of lakes are particularly vulnerable to plastic contamination: lakes and reservoirs in densely populated and urbanized areas and large lakes and reservoirs with elevated deposition areas, long water-retention times and high levels of anthropogenic influence. Plastic concentrations vary widely among lakes; in the most polluted, concentrations reach or even exceed those reported in the subtropical oceanic gyres, marine areas collecting large amounts of debris4. Our findings highlight the importance of including lakes and reservoirs when addressing plastic pollution, in the context of pollution management and for the continued provision of lake ecosystem services.
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The datasets generated and/or analysed during this study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7824882 .
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This manuscript benefited from conversations at meetings of the Global Lake Ecological Observatory Network (GLEON; supported by funding from US NSF grants 1137327 and 1702991). This work was supported by the University of Milano-Bicocca (UNIMIB). Raman facilities were provided by the Department of Earth and Environmental Sciences (DISAT, UNIMIB) and the Interdepartmental Network of Spectroscopy (UNIMIB). We gratefully acknowledge G. Candian and E. Caprini for their assistance in the laboratory activities and data analysis. A.M.A.-G. acknowledges the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020). R. Bao acknowledges support from Project IMPACOM (PID2019-107424RB-I00) of the Spanish Ministry of Science and Innovation. M.C.-A. was supported by a Ramon y Cajal contract financed by the Spanish Ministry of Science and Innovation (RYC2020-029829-I). M.C. acknowledges support from Cátedra EMALCSA-UDC (industrial chair). R.C. was supported by a Juan de la Cierva contract and Project FJC (FJC-2021-046415-I) of the Spanish Ministry of Science and Innovation financed by the Next Generation EU. Z.E. and M.G.M. acknowledge support from the Portuguese Science and Technology Foundation (FCT) project no. PTDC/CTA-AMB/30793/2017 (AdaptAlentejo—Predicting ecosystem-level responses to climate change). H.F. acknowledges support from the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCaPE programme delivering National Capability. H.-P.G. and S.P. were supported by the European Union Horizon 2020 Research and Innovation 772 programme under grant agreement number 965367 (PlasticsFatE). D.P.H. acknowledges support from the Australian Research Council (DP190101848). S.N.M. acknowledges support from Rhodes University and the University Capacity Development Programme. K.K. acknowledges support from grant PRG 1266 of the Estonian Research Council. S.N. and S.S.S.S. acknowledge support from PAPIIT UNAM IG200820. A.P. acknowledges support from the Institute of Nature Conservation (Polish Academy of Sciences). P.R. acknowledges support from Portuguese Science Foundation (FCT) (DL57/2016/ICETA/EEC2018/25). E.-I.R. acknowledges support from grant PUT1598 of the Estonian Research Council. C.S. acknowledges support from the Flemish Interuniversity Council through the VLIR-UOS/UB Programme. G.A.W. acknowledges support from the Swedish Research Council (VR; grant no. 2020-03222) and Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS; grant no. 2020-01091). N.W. acknowledges support from the National Natural Science Foundation of China (grant no. 52279068). F.S. acknowledges support from an IAI-CONICET special grant.
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Comparison of the density distribution of the features of our study lakes and reservoirs (in yellow) to the box plot of freshwater systems included in the HydroLAKES global dataset.
The features being compared are: lake area in km2 (a); mean depth in m (b); lake volume in km3 (c); residence time of lakes in years (d).
Extended Data Fig. 2 Means and s.e. of plastic concentration (particles m−3) resulting from the three trawls collected in each lake.
The lakes are ranked in descending order based on their particle concentration, from highest to lowest.
Cluster plot showing the different lakes included in the study divided on the basis of the percentage occurrence of the plastic shapes, colours and polymeric compositions.
Extended Data Fig. 4 Scaling 1 of redundancy analysis between plastic concentration in lakes, features of plastics and environmental and anthropogenic drivers.
The dots are coloured on the basis of the concentration of plastics (particles m−3) detected.
Extended Data Fig. 5 Density plots and histogram of the Feret’s diameter (width, mm) of the 9,425 particles identified in the 38 lakes analysed.
The median trend is indicated by the dashed blue line.
The pictures show the shape categories used in the study: fragment (a–c); fibre (d–f); filament (g–i); film (j,k); sphere/pellet (l).
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Nava, V., Chandra, S., Aherne, J. et al. Plastic debris in lakes and reservoirs. Nature 619, 317–322 (2023). https://doi.org/10.1038/s41586-023-06168-4
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