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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Marine & Wildlife Nanoplastics Policy & Risk Sign in to save

Plastics in Freshwater Bodies

2022 14 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Laurent Lebreton, Merel Kooi, Thomas Mani, Svenja M. Mintenig, Tekman, Mine, Tim van Emmerik, Helen Wolter

Summary

This review provides a comprehensive assessment of methods for monitoring and modeling plastics -- from macroplastics to nanoplastics -- in freshwater bodies including rivers and lakes, mapping global study distributions. It identifies four key knowledge gaps warranting greater attention: temporal variation, cross-compartment transfer, harmonization of size ranges across monitoring and modeling studies, and data quality control.

Study Type Environmental

Considering everything between macroplastics and nanoplastics, this chapter aims to provide a comprehensive and detailed assessment of the available methods to monitor and model plastics in freshwater bodies and to visualize the geographical distribution of studies reporting plastics in rivers and lakes. Both in marine or freshwater ecosystems, five steps can be distinguished for monitoring, namely, sampling, extraction, analysis, identification, and extrapolation. Despite the rapidly increasing number of publications on plastic occurrences in freshwater bodies, several aspects still carry high levels of uncertainty. The chapter highlights four key aspects that deserve more attention when designing and performing monitoring campaigns and when developing and interpreting numerical models. These four aspects are: temporal variation, transfer between environmental compartments, harmonization of monitoring and modeling investigations regarding plastic size ranges, and, finally, data quality control and validation of results.

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