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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

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Published on: December 16, 2016

An Accurate Size-Probability Distribution Method for Converting Microplastic Counts to Mass.

Hongyu Chen^1,2,3, Jianguo Tao^1,2,3, Teng Wang⁴

¹School of Geography and Ocean Science, Nanjing University, Nanjing 210093, China.

Environmental Science & Technology

|December 25, 2025

Summary

Estimating microplastic (MP) mass is crucial for environmental studies. A new size-probability distribution method accurately calculates MP mass from particle counts, offering a reliable alternative when detailed data is missing.

Keywords:

Yangtze River Basin microplastic pollution monitoring methodology particle size distribution volume-density conversion

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Published on: July 28, 2018

Area of Science:

Environmental Science
Analytical Chemistry
Polymer Science

Background:

Microplastic (MP) mass is vital for environmental fate and transport studies.
Current methods for estimating MP mass are limited, especially without detailed morphological data.

Purpose of the Study:

To develop and validate a reliable method for estimating microplastic mass using particle count data.
To integrate size distribution characteristics with volume-density models for accurate mass quantification.

Main Methods:

A size-probability distribution method was developed, combining a conditional fragmentation distribution (CFD)-based size model with volume approximations.
The method's optimal configuration was determined by evaluating against measured mass data from balance and mass spectrometry.
Performance was compared against coefficient-based conversion and direct volume-density calculations.

Main Results:

The proposed size-probability distribution method demonstrated superior performance compared to coefficient-based conversion approaches.
The method achieved accuracy comparable to direct volume-density calculations.
Application to Yangtze River data yielded annual microplastic mass fluxes between 1950.00 and 12,655.58 tons (mean 6895.90 ± 3763.24 tons).

Conclusions:

The developed method offers a reliable and efficient approach for estimating microplastic mass from particle count data.
It provides accurate and comparable mass estimates across various microplastic size classes.
This technique enhances the understanding of microplastic pollution dynamics in aquatic environments.