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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

Automated method for routine microplastic detection and quantification.

Matteo Giardino¹, Valentina Balestra², Davide Janner¹

¹Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; INSTM, Consorzio Interuniversitario Nazionale per la Scienza e Tecnologia dei Materiali, Via G. Giusti 9, 50121 Florence, Italy.

The Science of the Total Environment

|November 15, 2022

Summary

This study introduces a fast, semi-automatic image processing method for quantifying microplastics (MPs). The Python-based tool significantly reduces errors and time compared to manual counting, improving environmental monitoring accuracy.

Keywords:

Fluorescence Image analysis Microplastic pollution Staining dye Water and sediment monitoring

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Area of Science:

Environmental Science
Analytical Chemistry
Polymer Science

Background:

Microplastics (MPs) are pervasive environmental contaminants (<5 mm) requiring accurate detection and quantification.
Current visual counting methods are subjective, time-consuming, and prone to errors.
Existing automated methods have limitations in area coverage or particle size detection.

Purpose of the Study:

To develop and validate a semi-automatic image processing method for microplastic quantification on filter membranes.
To compare the accuracy and speed of the new method against traditional visual counting.
To assess the impact of Nile Red staining on microplastic detection.

Main Methods:

Development of an innovative image processing algorithm implemented in a Python script.
Testing the algorithm with artificial microplastic images and real environmental samples (cave sediment, river water).
Comparison of results with visual counts by an experienced operator; analysis with and without Nile Red staining.

Main Results:

The semi-automatic method demonstrated high accuracy and speed (approx. 10 s/image).
Average deviation from visual counts was below 10%, reduced to ~8% with Nile Red staining.
The method effectively quantifies and measures microplastics on filter substrates.

Conclusions:

The developed Python script offers an accurate and efficient alternative to manual microplastic analysis.
Nile Red staining enhances the performance of the image analysis method.
This tool aids in more reliable environmental microplastic monitoring.