Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method
Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method
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使用机器学习方法对大气含氧有机分子进行前体分配
DOI：
10.1039/d2ea00128d
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Wang, Zhe
中科院分区：
文献类型：
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作者：
Qiao, Xiaohui;Li, Xiaoxiao;Yan, Chao;Sarnela, Nina;Yin, Rujing;Guo, Yishuo;Yao, Lei;Nie, Wei;Huang, Dandan;Wang, Zhe
气相氧化有机分子（OOMs）对大气新粒子生长和二次有机气溶胶的形成都有重要作用。大气OOMs的前体解析将它们与挥发性有机化合物（VOCs）联系起来。由于大气OOMs通常是多步反应的高功能化产物，因此揭示OOMs与其前体之间的完整映射关系具有挑战性。在这项研究中，我们证明了机器学习方法在使用几个化学指标（如O/C比和H/C比）将大气OOMs归因于其前体方面是有用的。该模型使用在受控实验室实验中获得的数据进行训练和测试，涵盖四种主要类型的挥发性有机化合物（异戊二烯，单萜烯，脂肪族和芳烃）的氧化产物。然后，将该模型用于分析北京城市和芬兰南部北方森林环境中测量的大气OOMs。结果表明，这两种环境下的大气oom可以合理地分配给它们的前体。北京是一个人为挥发性有机化合物占主导地位的环境，芳香族和脂肪族有机化合物占64%，而其他北方森林地区的单萜有机化合物占76%。这项初步研究表明，机器学习可以成为大气化学中连接各个点的有前途的工具。
Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ∼64% aromatic and aliphatic OOMs, and the other boreal forested area has ∼76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots.