Assessing Electronic Cigarette Emissions: Linking Physico-Chemical Properties To Product Brand, E-Liquid Flavoring Additives, Operational Voltage And User Puffing Patterns

INHALATION TOXICOLOGY(2018)

引用 56|浏览15
暂无评分
摘要
Users of electronic cigarettes (e-cigs) are exposed to particles and other gaseous pollutants. However, major knowledge gaps on the physico-chemical properties of such exposures and contradictory data in published literature prohibit health risk assessment. Here, the effects of product brand, type, e-liquid flavoring additives, operational voltage, and user puffing patterns on emissions were systematically assessed using a recently developed, versatile, e-cig exposure generation platform and state-of-the-art analytical methods. Parameters of interest in this systematic evaluation included two brands (A and B), three flavors (tobacco, menthol, and fruit), three types of e-cigs (disposable, pre-filled, and refillable tanks), two puffing protocols (4 and 2s/puff), and four operational voltages (2.2-5.7V). Particles were generated at a high number concentration (10(6)-10(7) particles/cm(3)). The particle size distribution was bi-modal (approximate to 200nm and 1 mu m). Furthermore, organic species (humectants propylene glycol and glycerin, nicotine) that were present in e-liquid and trace metals (potassium and sodium) that were present on e-cig heating coil were also released into the emission. In addition, combustion-related byproducts, such as benzene and toluene, were also detected in the range of 100-38,000ppbv/puff. Parametric analyzes performed in this study show the importance of e-cig brand, type, flavor additives, user puffing pattern (duration and frequency), and voltage on physico-chemical properties of emissions. This observed influence is indicative of the complexity associated with the toxicological screening of emissions from e-cigs and needs to be taken into consideration.
更多
查看译文
关键词
Electronic cigarette, particulate matter, smoking, vaping, exposure assessment, inhalation toxicology
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要