Representative subsampling methods for the chemical identification of microplastic particles in environmental samples

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Chemical identification of microplastics is resource-intensive, often necessitating subsampling.
• Current subsampling protocols lack standardization, hindering comparability and data reliability.
• Representative subsampling is crucial for accurate microplastic analysis.

Purpose of the Study:

• To establish best practices for subsampling microplastics (>100 μm) for chemical identification.
• To evaluate subsampling methods based on two objectives: material proportion and material diversity.
• To enhance the representativeness and comparability of microplastic research.

Main Methods:

• Utilized published datasets with fully chemically identified microparticles.
• Tested subsampling strategies involving selection from individual or pooled samples.
• Assessed random particle selection against other methods for representativeness and effort.

Main Results:

• Random particle selection emerged as the most efficient method for obtaining representative subsamples.
• Fewer particles are needed to accurately quantify material proportions versus material diversity.
• Understanding environmental matrix diversity is key for accurate particle diversity representation.

Conclusions:

• Random subsampling offers a standardized and efficient approach for microplastic chemical identification.
• Research objectives dictate the necessary subsample size for accurate representation.
• Harmonized subsampling practices will improve study comparability, data transparency, and scientific conclusions.