Atmospheric Chemistry and Physics (May 2020)

Characterization and comparison of PM<sub>2.5</sub> oxidative potential assessed by two acellular assays

  • D. Gao,
  • D. Gao,
  • K. J. Godri Pollitt,
  • J. A. Mulholland,
  • A. G. Russell,
  • R. J. Weber

DOI
https://doi.org/10.5194/acp-20-5197-2020
Journal volume & issue
Vol. 20
pp. 5197 – 5210

Abstract

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The capability of ambient particles to generate in vivo reactive oxygen species (ROS), called oxidative potential (OP), is a potential metric for evaluating the health effects of particulate matter (PM) and is supported by several recent epidemiological investigations. Studies using various types of OP assays differ in their sensitivities to varying PM chemical components. In this study, we systematically compared two health-relevant acellular OP assays that track the depletion of antioxidants or reductant surrogates: (i) the synthetic respiratory-tract lining fluid (RTLF) assay that tracks the depletion of ascorbic acid (AA) and glutathione (GSH) and (ii) the dithiothreitol (DTT) assay that tracks the depletion of DTT. Yearlong daily samples were collected at an urban site in Atlanta, GA (Jefferson Street), during 2017, and both DTT and RTLF assays were performed to measure the OP of water-soluble PM2.5 components. PM2.5 mass and major chemical components, including metals, ions, and organic and elemental carbon were also analyzed. Correlation analysis found that OP as measured by the DTT and AA depletion (OPDTT and OPAA, respectively) were correlated with both organics and some water-soluble metal species, whereas that from the GSH depletion (OPGSH) was exclusively sensitive to water-soluble Cu. These OP assays were moderately correlated with each other due to the common contribution from metal ions. OPDTT and OPAA were moderately correlated with PM2.5 mass with Pearson's r=0.55 and 0.56, respectively, whereas OPGSH exhibited a lower correlation (r=0.24). There was little seasonal variation in the OP levels for all assays due to the weak seasonality of OP-associated species. Multivariate linear regression models were developed to predict OP measures from the particle composition data. Variability in OPDTT and OPAA were not only attributed to the concentrations of metal ions (mainly Fe and Cu) and organic compounds but also to antagonistic metal–organic and metal–metal interactions. OPGSH was sensitive to the change in water-soluble Cu and brown carbon (BrC), a proxy for ambient humic-like substances.