0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Sign in to save

Characterization and potential influence of laboratory airborne particle fallout on microplastics analysis

Journal of Hazardous Materials 2025 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Wenjian Lao, Sydney Sauers, Charles S. Wong

Summary

Airborne microplastics generated in laboratory settings were characterized for their size, shape, and polymer type to understand potential contamination of experimental samples. Identifying and controlling laboratory-generated airborne microplastics is essential for ensuring the reliability of environmental microplastic research.

Attenuating background contamination is essential in analytical methods, particularly for analysis of microplastics (MPs). While measures to mitigate airborne particle interference exist, long-term laboratory fallout remains understudied. We conducted a 28-month monitoring study in a trace organics laboratory where a background contamination control protocol was implemented at the outset. Airborne particles were passively collected on polycarbonate track etch (PCTE) membrane filters at six locations over periods ranging from one to several months. Deposition rates decreased significantly from 82.3 ± 47.6-6.2 ± 5.5 (count / h / 8-inch sieve) within the first eight months and stabilized at a low level (4.21 ± 3.74) with sustained protocol adherence. Estimated intrusion of airborne particles into sample containers during MP sample preparation ranged from 14.1 to 0.2 particles, representing only 2-8 % of the lowest procedural blank, indicating minimal contamination potential. Polyvinyl chloride (PVC) and polytetrafluoroethylene (PTFE) were the most frequently detected MPs. The particle size ≥ 6 μm (counts ≥ 2) was well characterized by log-normal and linear log-log distributions. These findings demonstrate effective contamination control, providing a robust framework for laboratories engaged in MP analysis.

Share this paper