1. Scope

Keywords

Rationale

There is a growing body of evidence that microplastics may have a negative impact on human health (1, 2). Hence, there are a growing number of federal, state, and local environmental bodies that are interested in understanding the types and magnitude of microplastic pollution in ambient air. Measurement methods and regulations currently exist for particulate matter (PM) sized PM1, PM2.5, PM10, and TSP (total suspended particles), but no current standardized method exists for measuring microplastics in PM filter samples. A major issue with the sampling of microplastics is their unique behavior; microplastics occur as non-homogeneously distributed particulates and fibers composed of various polymeric materials. This Py-GC/MS method enables users to achieve polymer identification and mass quantitation directly from the sample with minimal pretreatment. Py-GC/MS is not constrained by lower particle size (for example, 0.1 to 5 um) limitations typical with IR and Raman count-based imaging identification methods. The proposed test method is designed to accurately identify the polymer types and mass of particles and fibers in the collected sample This method is intended for use in conjunction with other ASTM International microplastic standards, such as two from Committee D19 on Water: ASTM 8401 and WK87463. 1) Damià Barceló, Yolanda Picó, Ahmed H. Alfarhan, Microplastics: Detection in human samples, cell line studies, and health impacts, Environmental Toxicology and Pharmacology, Volume 101, 2023, 104204, https://doi.org/10.1016/j.etap.2023.104204. 2) Chartres, Nicholas, Cooper, Courtney B., Bland, Garret, Pelch, Katherine E., Gandhi, Sheiphali A., BakenRa, Abena, Woodruff, Tracey J., Effects of Microplastic Exposure on Human Digestive, Reproductive, and Respiratory Health: A Rapid Systematic Review, Environ. Sci. Technol. 2024, 58, 52, 22843–22864, https://doi.org/10.1021/acs.est.3c09524