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Tracing microplastics in aquatic environments based on sediment analogies.

ABSTRACT: Microplastics (MP) data collection from the aquatic environment is a challenging endeavour that sets apparent limitations to regional and global MP quantification. Expensive data collection causes small sample sizes and oftentimes existing data sets are compared without accounting for natural variability due to hydrodynamic processes governing the distribution of particles. In Warnow estuarine sediments (Germany) we found significant correlations between high-density polymer size fractions (?500?mm) and sediment grain size. Among potential predictor variables (source and environmental terms) sediment grain size was the critical proxy for MP abundance. The MP sediment relationship can be explained by the force necessary to start particle transport: at the same level of fluid motion, transported sediment grains and MP particles are offset in size by one to two orders of magnitude. Determining grain-size corrected MP abundances by fractionated granulometric normalisation is recommended as a basis for future MP projections and identification of sinks and sources.

SUBMITTER: Enders K

PROVIDER: S-EPMC6811616 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Tracing microplastics in aquatic environments based on sediment analogies.

Enders Kristina K Käppler Andrea A Biniasch Oliver O Feldens Peter P Stollberg Nicole N Lange Xaver X Fischer Dieter D Eichhorn Klaus-Jochen KJ Pollehne Falk F Oberbeckmann Sonja S Labrenz Matthias M

Scientific reports 20191023 1

Microplastics (MP) data collection from the aquatic environment is a challenging endeavour that sets apparent limitations to regional and global MP quantification. Expensive data collection causes small sample sizes and oftentimes existing data sets are compared without accounting for natural variability due to hydrodynamic processes governing the distribution of particles. In Warnow estuarine sediments (Germany) we found significant correlations between high-density polymer size fractions (≥500 ...[more]

PMID: 31645581

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Project description:The mitigation of diffuse sediment pollution requires reliable provenance information so that measures can be targeted. Sediment source fingerprinting represents one approach for supporting these needs, but recent methodological developments have resulted in an increasing complexity of data processing methods rendering the approach less accessible to non-specialists. A comprehensive new software programme (SIFT; SedIment Fingerprinting Tool) has therefore been developed which guides the user through critical data analysis decisions and automates all calculations. Multiple source group configurations and composite fingerprints are identified and tested using multiple methods of uncertainty analysis. This aims to explore the sediment provenance information provided by the tracers more comprehensively than a single model, and allows for model configurations with high uncertainties to be rejected. This paper provides an overview of its application to an agricultural catchment in the UK to determine if the approach used can provide a reduction in uncertainty and increase in precision. Five source group classifications were used; three formed using a k-means cluster analysis containing 2, 3 and 4 clusters, and two a-priori groups based upon catchment geology. Three different composite fingerprints were used for each classification and bi-plots, range tests, tracer variability ratios and virtual mixtures tested the reliability of each model configuration. Some model configurations performed poorly when apportioning the composition of virtual mixtures, and different model configurations could produce different sediment provenance results despite using composite fingerprints able to discriminate robustly between the source groups. Despite this uncertainty, dominant sediment sources were identified, and those in close proximity to each sediment sampling location were found to be of greatest importance. This new software, by integrating recent methodological developments in tracer data processing, guides users through key steps. Critically, by applying multiple model configurations and uncertainty assessment, it delivers more robust solutions for informing catchment management of the sediment problem than many previously used approaches.

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Dataset Information

Tracing microplastics in aquatic environments based on sediment analogies.

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Tracing microplastics in aquatic environments based on sediment analogies.

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