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Quality Assurance and Quality Control in Microplastics Processing and Enumeration
Summary
This study evaluated the quality control practices used in microplastics research, specifically testing how different water sources (tap, filtered, deionized) used for rinsing lab equipment affect contamination measurements. It found statistically significant differences between water sources, meaning that the choice of blank water can substantially change how many microplastic particles appear to be present in a sample — a critical finding for ensuring the reliability of microplastic studies.
Despite six decades of microplastic contamination research, this field struggles to establish clear, universally accepted methods and techniques. Furthermore, scant published work scrutinizes the application, effectiveness, and utility of positive and negative controls. This study examines three common practices involved in microplastic processing, enumeration, and particle characterization. The first component evaluates four filtered water sources that are commonly used to run procedural laboratory blanks and rinse laboratory glassware and instruments. A statistically significant difference was found between tap water and three sources of filtered water and between two sources of filtered water. This suggests the magnitude of correction applied to samples is dependent on the type of filtered water chosen for blanks. The second component chronicles particle loss, specifically particle adhesion to the filtration apparatus. Water samples spiked with plastic standards representing three distinct morphologies and vacuum filtered through a two-piece borosilicate glass filtration apparatus yielded a notable difference in recovery rates compared to a two-piece filtering apparatus made of stainless steel. The steel filter had significantly higher recovery rates of irregular polyethylene fragments compared to glass, although there was no statistical difference in the recovery of nylon fibers or symmetrical polyester fragments. The final component compares the effectiveness of ImageJ, a popular imaging software program, with Material Image Processing and Automated Reconstruction (MIPAR), a new program with deep learning capabilities. Both systems analyzed identical images captured from a set of polycarbonate filters that contain environmental media spiked with plastic standards. While ImageJ is capable of reporting particle enumeration, as well as basic measurement and categorization, it grossly overestimated fragments and frequently mischaracterized fibers. Particle count summaries from MIPAR, however, were in accord with the known quantity of standard spiked into each sample. These findings underscore the importance of quality controls when developing new methods or when modifying established methods.
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