Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols

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Abstract

E-cigarette aerosols contain a complex mixture of harmful and potentially harmful chemicals. Once released into the environment, they evolve and become new sources of indoor air pollutants that could pose a significant threat to both users and non-users. However, current understanding of the physicochemical properties of e-cigarette aerosol constituents that govern gas-particle partitioning in the atmosphere is limited, making it difficult to estimate the health risks associated with exposure. Here, we used correlation gas chromatography (C-GC) and two-dimensional volatility basis set (2D-VBS) methods to determine the vapor pressures and volatility for commonly reported toxic and irritating e-cigarette aerosol constituents. The vapor pressures of target compounds at 298 K were estimated from the Antoine-type linear relationship between the vapor pressure of reference standards and their retention times. Our C-GC results showed an overall positive correlation (R = 0.84) with estimates using the EPI (Estimation Programs Interface) Suite. The volatility calculated by 2D-VBS correlates well with the calculated vapor pressure from both C-GC (R = 0.82) and EPI Suite (R = 0.85). The volatility distribution also indicated fresh e-cigarette aerosol constituents are mainly more volatile organic compounds. Our case study revealed that low-vapor-pressure compounds (e.g., σ-dodecalactone, γ-decalactone, and maltol) become enriched in the e-cigarette aerosols within 2 h following vaping emissions. Overall, these findings demonstrate the applicability of the C-GC and 2D-VBS methods for determining the physiochemical properties of e-cigarette aerosol constituents, which can aid in assessing the dynamic chemical composition of e-cigarette aerosols and exposures to vaping emissions in indoor environments. Copyright © 2024 American Association for Aerosol Research.

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APA

Tian, L., Woo, W., Canchola, A., Chen, K., & Lin, Y. H. (2024). Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols. Aerosol Science and Technology, 58(6), 630–643. https://doi.org/10.1080/02786826.2024.2326547

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