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How many microplastics do you need to (sub)sample?

Win Cowger1, Laura A T Markley2, Shelly Moore3

  • 1Moore Institute for Plastic Pollution Research, 120 N Marina Drive Long Beach, CA 90803, USA; University of California, Riverside, USA.

Ecotoxicology and Environmental Safety
|March 24, 2024
PubMed
Summary
This summary is machine-generated.

Characterizing every microplastic particle is often infeasible. This study proposes a statistical method to determine the minimum number of particles that need subsampling for accurate polymer distribution analysis.

Keywords:
Detection limitsErrorMicroplasticsStatisticsUncertainty

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Statistics

Background:

  • Microplastic analysis requires polymer characterization, which is time-consuming and expensive.
  • High particle counts in samples often prevent complete characterization due to resource constraints.
  • Characterizing every particle is unnecessary for describing overall sample properties.

Purpose of the Study:

  • To develop a statistical approach for determining the optimal subsample size for microplastic characterization.
  • To enable accurate assessment of polymer distribution in environmental samples with high microplastic loads.
  • To provide a method applicable to various microplastic properties including polymer type, color, size, and morphology.

Main Methods:

  • Development of an a priori statistical equation based on sampling theory.
  • Validation of the equation using published microplastic data and simulations.
  • Calculation of minimum subsample sizes for specific error thresholds and confidence levels.

Main Results:

  • A minimum of 386 particles should be subsampled for accurate polymer distribution analysis (95% confidence, 5% error).
  • Characterizing polymer, color, size, and morphology simultaneously requires a minimum of 620 particles.
  • The proposed method is applicable for determining minimum counts in samples for diversity analysis.

Conclusions:

  • The proposed statistical method optimizes microplastic characterization efforts.
  • Accurate environmental sample characterization can be achieved with statistically determined subsample sizes.
  • This approach addresses the limitations of time and cost in microplastic research.