Automated compound speciation, cluster analysis, and quantification of organic vapours and aerosols using comprehensive two-dimensional gas chromatography and mass spectrometry

IF 5.2 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Xiao He, Xuan Zheng, Shuwen Guo, Lewei Zeng, Ting Chen, Bohan Yang, Shupei Xiao, Qiongqiong Wang, Zhiyuan Li, Yan You, Shaojun Zhang, Ye Wu
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引用次数: 0

Abstract

Abstract. The advancement of analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS), enables the efficient separation of complex organic matrix. Developing innovative methods for data processing and analysis is crucial to unlock the full potential of GC×GC-MS in understanding intricate chemical mixtures. In this study, we proposed an innovative method for the semi-automated identification and quantification of complex organic mixtures using GC×GC-MS. The method was formulated based on self-constructed mass spectrum patterns and the traversal algorithms and was applied to organic vapor and aerosol samples collected from tailpipe emissions of heavy-duty diesel vehicles and the ambient atmosphere. Thousands of compounds were filtered, speciated, and clustered into 26 categories, including aliphatic and cyclic hydrocarbons, aromatic hydrocarbons, aliphatic oxygenated species, phenols and alkyl-phenols, and heteroatom containing species. The identified species accounted for over 80 % of all the eluted chromatographic peaks at the molecular level. A comprehensive analysis of quantification uncertainty was undertaken. Using representative compounds, quantification uncertainties were found to be less than 37.67 %, 22.54 %, and 12.74 % for alkanes, polycyclic aromatic hydrocarbons (PAHs), and alkyl-substituted benzenes, respectively, across the GC×GC space, excluding the first and the last time intervals. From source apportionment perspective, adamantane was clearly isolated as a potential tracer for heavy-duty diesel vehicles (HDDVs) emission. The systematic distribution of N-containing compounds in oxidized and reduced valences was discussed and many of them served as critical tracers for secondary nitrate formation processes. The results highlighted the benefits of developing self-constructed model for the enhanced peak identification, automated cluster analysis, robust uncertainty estimation, and source apportionment and achieving the full potential of GC×GC-MS in atmospheric chemistry.
利用综合二维气相色谱和质谱法自动进行有机蒸气和气溶胶的化合物标样、聚类分析和定量分析
摘要综合二维气相色谱-质谱联用技术(GC×GC-MS)等分析技术的发展使复杂有机基质的高效分离成为可能。要充分发挥 GC×GC-MS 在理解复杂化学混合物方面的潜力,开发创新的数据处理和分析方法至关重要。在本研究中,我们提出了一种利用 GC×GC-MS 对复杂有机混合物进行半自动鉴定和定量的创新方法。该方法以自建质谱模式和遍历算法为基础,应用于从重型柴油车尾气排放和环境大气中采集的有机蒸气和气溶胶样本。数以千计的化合物经过过滤、规格化后被归类为 26 个类别,包括脂肪族和环状碳氢化合物、芳香族碳氢化合物、脂肪族含氧物种、苯酚和烷基苯酚以及含杂原子的物种。在分子水平上,已确定的种类占所有洗脱色谱峰的 80% 以上。对定量不确定性进行了全面分析。通过使用代表性化合物,发现在整个 GC×GC 空间(不包括第一个和最后一个时间间隔)内,烷烃、多环芳烃和烷基取代苯的定量不确定性分别小于 37.67%、22.54% 和 12.74%。从源分配的角度来看,金刚烷被明确地分离出来,成为重型柴油车(HDDVs)排放的潜在示踪剂。研究还讨论了氧化价和还原价中含氮化合物的系统分布,其中许多化合物是二次硝酸盐形成过程的关键示踪剂。研究结果凸显了开发自建模型的益处,该模型可用于增强峰值识别、自动聚类分析、稳健的不确定性估计和来源分配,并充分发挥 GC×GC-MS 在大气化学中的潜力。
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来源期刊
Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics 地学-气象与大气科学
CiteScore
10.70
自引率
20.60%
发文量
702
审稿时长
6 months
期刊介绍: Atmospheric Chemistry and Physics (ACP) is a not-for-profit international scientific journal dedicated to the publication and public discussion of high-quality studies investigating the Earth''s atmosphere and the underlying chemical and physical processes. It covers the altitude range from the land and ocean surface up to the turbopause, including the troposphere, stratosphere, and mesosphere. The main subject areas comprise atmospheric modelling, field measurements, remote sensing, and laboratory studies of gases, aerosols, clouds and precipitation, isotopes, radiation, dynamics, biosphere interactions, and hydrosphere interactions. The journal scope is focused on studies with general implications for atmospheric science rather than investigations that are primarily of local or technical interest.
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