Exposure to (micro/nano)-plastics and their combustion products studied by cyclic ion mobility-mass spectrometry

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Keywords

microplastics, nanoplastics, mass spectrometry, cyclic ion mobility, indoor air

Degree Level

masters

Advisor

Degree Name

M. Sc.

Volume

Issue

Publisher

Memorial University of Newfoundland

Abstract

Degradation of plastics in the environment has led to formation of micro/nano-plastics (MNPs). Currently, there are only a few studies measuring plastic particles smaller than 1 µm in air. As such, the goal of this study was to develop a method for identification and quantification of MNPs in indoor air. Particulate matter (PM) from two indoor environments was size-resolved using a Micro-Orifice Uniform Deposit Impactor (MOUDI) model 110 cascade impactor ranging from 56 nm to 18 µm in size. The GCxcIM-MS method was then developed to characterize four common plastics: polystyrene (PS), polyethylene (PE), polypropylene (PP), and polymethyl methacrylate (PMMA). The results indicated that approximately 57-67% of MNPs had particle diameters >2.5 µm, and these microplastics constituted 50-60% of the total particulate matter in private residences. Moreover, the comprehensive two-dimensional separation provided by the developed method enabled us to analyze other polymers and plastic additives. For instance, plastic additives such as TDCPP (Tris (1, 3-dichloro-2-propyl) phosphate) was detected, and its concentration correlated with polyurethane (PU). Plastic can also pose a risk to human health when they are combusted. The goal of second chapter was differentiation between toxic and non-toxic halogenated of polycyclic aromatic hydrocarbons (HPAHs) isomers that were released during combustion of plastics. The geometry of cIM-MS allows ions to travel multiple passes through cyclic cell such that, the greater of pass numbers, the better resolution of isomers. When a complex real sample was studied in this way, the toxic 2367-tetrachloroanthracene (2367-TCA) was separated from a mix of 17 other isomers with the assistance of an advanced “unwrapping” data analysis technique.

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