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Related Concept Videos

Sampling Plans01:23

Sampling Plans

163
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
163
Sampling Methods: Overview01:06

Sampling Methods: Overview

265
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
265

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Related Experiment Video

Updated: May 23, 2025

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
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Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

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Size selection in sampling nets leads to underestimation of microplastic pollution.

Mengjie Yu1, Bent Herrmann2, Hui Liang1

  • 1Fisheries College, Ocean University of China, 266003, Qingdao, Shandong, China.

Environmental Pollution (Barking, Essex : 1987)
|March 7, 2025
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Summary

Sampling nets used for microplastic (MP) monitoring can underestimate MP concentrations due to mesh selectivity. This study introduces a new model to correct for these biases, improving marine pollution assessment accuracy.

Keywords:
Mesh sizeMicroplasticsNeuston netSize selectivitySurface water

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Area of Science:

  • Environmental Science
  • Marine Biology
  • Analytical Chemistry

Background:

  • Microplastic (MP) contamination is a significant global environmental concern.
  • Current monitoring methods using sampling nets suffer from mesh selectivity, leading to biased MP concentration data.
  • This bias is particularly pronounced for smaller microplastic particles, complicating data comparability across studies.

Purpose of the Study:

  • To develop a novel selectivity model to account for mesh size biases in MP sampling.
  • To provide a robust framework for correcting MP concentration data obtained from different net mesh sizes.
  • To improve the accuracy and comparability of microplastic pollution assessments.

Main Methods:

  • Size selectivity analyses were performed to model the retention probabilities of MP fibers and fragments.
  • The study examined nets with mesh sizes ranging from 100 to 500 μm.
  • A new model was developed to correct for size-dependent retention biases.

Main Results:

  • Microplastic fibers and fragments exhibit distinct size selectivity patterns based on net mesh size.
  • Larger mesh sizes significantly underestimate MP concentrations due to size-dependent retention.
  • A 330 μm mesh net underestimated MP fiber and fragment concentrations by approximately 45% and 30%, respectively, compared to a 92 μm mesh.

Conclusions:

  • This study presents the first systematic approach to address and correct for net mesh selectivity biases in microplastic monitoring.
  • The developed framework enhances the accuracy of MP pollution assessments.
  • Improved data comparability across studies is achieved by correcting for sampling biases.