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Updated: Apr 12, 2026

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
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Physically-based mesh selectivity correction model for standardized microplastic abundance estimates in aquatic

Bu Zhao1, Ruth E Richardson2, Yilin Huang1

  • 1Department of Environmental and Sustainable Engineering, University at Albany, State University of New York, NY 12222, USA.

Water Research
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new physically-based model to accurately quantify microplastic (MP) abundance in aquatic environments. The model corrects for mesh selectivity, improving data reliability and enabling better ecological risk assessments.

Keywords:
Abundance correctionMesh selectivityMicroplasticsProbability distributionStandardization

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Ecotoxicology

Background:

  • Accurate microplastic quantification is vital for ecological and health impact assessments.
  • Methodological inconsistencies, especially mesh size, hinder data comparability.
  • Existing correction models inadequately address particle properties and mesh effects.

Purpose of the Study:

  • Develop a physically-based mesh selectivity correction model for microplastics.
  • Mechanistically account for particle size, shape, and deformation in sampling.
  • Enable reliable adjustment and standardization of microplastic abundance data.

Main Methods:

  • Simulated microplastic capture across various mesh sizes and particle properties.
  • Developed a physically-based model linking environmental characteristics to field abundances.
  • Validated the model against multiple published datasets.

Main Results:

  • The new model significantly improves estimation accuracy (up to 70.6%) and reduces logarithmic error (83.7%).
  • It substantially decreases systematic underestimation compared to empirical and power-law methods.
  • Established a direct, physically interpretable link between microplastic properties and observed abundances.

Conclusions:

  • The physically-based model enhances the standardization and comparability of microplastic data.
  • This framework supports more accurate quantitative assessments and risk evaluations in aquatic systems.
  • Advances global microplastic monitoring efforts through rigorous data correction and unification.