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

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

Graphical Abstract

EDITOR:

• Mark Swihart

Disclosure statement

The authors declare no competing financial interest.

Additional information

Funding

This research was supported by the UCOP Tobacco-Related Disease Research Program (T32IP5141). Wonsik Woo and Alexa Canchola were supported in part by an NRSA T32 training grant (T32ES018827).