4.7 Article

Relationship between chemical composition and oxidative potential of secondary organic aerosol from polycyclic aromatic hydrocarbons

期刊

ATMOSPHERIC CHEMISTRY AND PHYSICS
卷 18, 期 6, 页码 3987-4003

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-18-3987-2018

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资金

  1. Natural Sciences and Engineering Research Council Discovery Grant
  2. Canadian Foundation for Innovation John R Evans Leaders Fund
  3. Ontario Early Researcher Award

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Owing to the complex nature and dynamic behaviors of secondary organic aerosol (SOA), its ability to cause oxidative stress (known as oxidative potential, or OP) and adverse health outcomes remains poorly understood. In this work, we probed the linkages between the chemical composition of SOA and its OP, and investigated impacts from various SOA evolution pathways, including atmospheric oligomerization, heterogeneous oxidation, and mixing with metal. SOA formed from photooxidation of the two most common polycyclic aromatic hydrocarbons (naphthalene and phenanthrene) were studied as model systems. OP was evaluated using the dithiothreitol (DTT) assay. The oligomerrich fraction separated by liquid chromatography dominates DTT activity in both SOA systems (52 +/- 10% for naphthalene SOA (NSOA), and 56 +/- 5% for phenanthrene SOA (PSOA)). Heterogeneous ozonolysis of NSOA was found to enhance its OP, which is consistent with the trend observed in selected individual oxidation products. DTT activities from redox-active organic compounds and metals were found to be not additive. When mixing with highly redox-active metal (Cu), OP of the mixture decreased significantly for 1,2-naphthoquinone (42 +/- 7 %), 2,3-dihydroxynaphthalene (35 +/- 1 %), NSOA (50 +/- 6 %), and PSOA (43 +/- 4 %). Evidence from proton nuclear magnetic resonance (H-1 NMR) spectroscopy illustrates that such OP reduction upon mixing can be ascribed to metal-organic binding interactions. Our results highlight the role of aerosol chemical composition under atmospheric aging processes in determining the OP of SOA, which is needed for more accurate and explicit prediction of the toxicological impacts from particulate matter.

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