Issue 2, 2024

The missing small microplastics: easily generated from weathered plastic pieces in labs but hardly detected in natural environments

Abstract

Small microplastics (SMPs, 1–20 μm) and nanoplastics (NPs, 1–1000 nm) are contaminants of high concern, but they were only documented in a few studies due to challenges during pre-treatment and characterization in environmental samples. In this study, weathered plastic pieces and surrounding sediments were collected from 3 areas of Yangtze Estuary, China. A top-down method was used to generate SMPs/NPs from plastic pieces using an ultrasonic cleaner in the lab, and the abundance and size distribution of SMPs/NPs generated, as well as those found from the surrounding sediments of the pieces in the field were measured and subsequently compared after verifying polymer types using Raman spectroscopy. The results revealed that each plastic piece generated an average of 3 × 104 particles of MPs, and NPs with size down to 620 nm in lab samples. However, the number of SMPs found in surrounding sediments was almost 3 times lower than that generated from one plastic piece. Furthermore, the particle size ranges do not align with those generated in the lab. It indicated that smaller and more abundant SMPs/NPs could be generated from the weathered plastic pieces, but few SMPs were found in surrounding environments. We assume that the current sampling and identification methods limit the representativeness of samples and the accuracy of SMP/NP detection.

Graphical abstract: The missing small microplastics: easily generated from weathered plastic pieces in labs but hardly detected in natural environments

Supplementary files

Article information

Article type
Paper
Submitted
19 9月 2023
Accepted
24 11月 2023
First published
12 12月 2023
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Adv., 2024,3, 227-238

The missing small microplastics: easily generated from weathered plastic pieces in labs but hardly detected in natural environments

F. Du, H. Cai, L. Su, W. Wang, L. Zhang, C. Sun, B. Yan and H. Shi, Environ. Sci.: Adv., 2024, 3, 227 DOI: 10.1039/D3VA00291H

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