Isomeric Identification of Particle-Phase Organic Nitrates through Gas Chromatography and Time-of-Flight Mass Spectrometry Coupled with an Electron Capture Negative Ionization Source

Organic
nitrates (ONs) are an important component of secondary
organic aerosols that play significant roles in atmospheric chemical
processes such as ozone formation and as a reservoir of nitrogen oxides
(NOx). However, hindered by the availability
of analytical techniques, characteristics of ON molecules remain unclear
in regions influenced by anthropogenic volatile organic compounds
(VOCs) and pollution. In this study, we achieved isomeric identification
of particle-phase ONs in such regions. Using gas chromatography and
time-of-flight mass spectrometry with an electron capture negative
ionization source, we established a systematic procedure for screening
unknown ONs in fine particulate matter (PM) collected in Beijing based
primarily on the characteristic fragment ions of NO2– and [M–NO2]−/[M–NO2–H2]−. We found 78 ON
candidates, 12 of which were confirmed using synthesized standards.
Seventy-three of these detected ONs might originate from anthropogenic
VOC precursors especially alkenes. Significantly, we observed two
isomers generated from straight-chain 1-alkenes, namely, 2-hydroxy-1-nitrate
and 1-hydroxy-2-nitrate. The signal ratios of the two isomers suggested
that these hydroxy nitrates are mainly produced photochemically rather
than through nighttime reactions. This study provides a promising
method for identifying ONs in atmospheric PM and elucidating their
formation pathways.

有机硝酸盐（Organic nitrates, ONs）是二次有机气溶胶的重要组成部分，在臭氧形成等大气化学过程中发挥关键作用，同时也是氮氧化物（NOx）的储存库。然而，受限于分析技术的可得性，在受人为源挥发性有机物（VOCs）及污染影响的区域，有机硝酸盐分子的特征仍未明晰。本研究针对此类区域内的颗粒相有机硝酸盐实现了异构体识别。研究采用搭载电子捕获负电离源的气相色谱-飞行时间质谱，建立了一套以NO₂⁻及[M–NO₂]⁻/[M–NO₂–H₂]⁻特征碎片离子为核心的系统流程，用于筛查北京地区采集的细颗粒物（PM）中的未知有机硝酸盐。本研究共检出78种有机硝酸盐候选物，其中12种通过合成标准品得以确认。所检出的有机硝酸盐中，73种可能源自人为源挥发性有机物前体物，尤其是烯烃。值得关注的是，本研究观察到两种由直链1-烯烃生成的异构体，即2-羟基-1-硝酸酯与1-羟基-2-硝酸酯。两种异构体的信号比值表明，此类羟基硝酸酯主要通过光化学途径生成，而非夜间反应。本研究为大气细颗粒物中有机硝酸盐的识别及其生成路径的阐明提供了极具应用前景的方法。