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Particulate organic emissions from incense-burning smoke: Chemical compositions and emission characteristics

化学 烟雾 微粒 左旋葡糖 环境化学 燃烧 有机化学 挥发性有机化合物 气相色谱法 气溶胶 色谱法 哲学 神学 生物质燃烧
作者
Kai Song,Rongzhi Tang,Ang Li,Zichao Wan,Yuan Zhang,Yuanzheng Gong,Daqi Lv,Sihua Lü,Yu Tan,Shuyuan Yan,Shichao Yan,Jingshun Zhang,Bo Fan,Chak K. Chan,Song Guo
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:897: 165319-165319 被引量:6
标识
DOI:10.1016/j.scitotenv.2023.165319
摘要

Incense burning is a common practice in Asian cultures, releasing hazardous particulate organics. Inhaling incense smoke can result in adverse health effects, yet the molecular compositions of incense-burning organics have not been well investigated due to the lack of measurement of intermediate-volatility and semi-volatile organic compounds (I/SVOCs). To elucidate the detailed emission profile of incense-burning particles, we conducted a non-target measurement of organics emitted from incense combustion. Quartz filters were utilized to trap particles, and organics were analyzed by a comprehensive two-dimensional gas chromatography-mass spectrometer (GC × GC-MS) coupled with a thermal desorption system (TDS). To deal with the complex data obtained by GC × GC-MS, homologs are identified mainly by the combination of selected ion chromatograms (SICs) and retention indexes. SICs of 58, 60, 74, 91, and 97 were utilized to identify 2-ketones, acids, fatty acid methyl esters, fatty acid phenylmethyl esters, and alcohols, respectively. Phenolic compounds contribute the most to emission factors (EFs) among all chemical classes, taking up 24.5 % ± 6.5 % of the total EF (96.1 ± 43.1 μg g-1). These compounds are largely derived from the thermal degradation of lignin. Biomarkers like sugars (mainly levoglucosan), hopanes, and sterols are extensively detected in incense combustion fumes. Incense materials play a more important role in shaping emission profiles than incense forms. Our study provides a detailed emission profile of particulate organics emitted from incense burning across the full-volatility range, which can be used in the health risk assessments. The data processing procedure in this work could also benefit those with less experience in non-target analysis, especially GC × GC-MS data processing.
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