Olson David A, Offenberg John H, Lewandowski Michael, Kleindienst Tadeusz E, Docherty Kenneth S, Jaoui Mohammed, Krug Jonathan, Riedel Theran P
Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
Jacobs Technology, Inc., Research Triangle Park, North Carolina 27709, United States.
Atmos Environ (1994). 2021 May 1;252. doi: 10.1016/j.atmosenv.2021.118345.
This research used data mining approaches to better understand factors affecting the formation of secondary organic aerosol (SOA). Although numerous laboratory and computational studies have been completed on SOA formation, it is still challenging to determine factors that most influence SOA formation. Experimental data were based on previous work described by Offenberg et al. (2017), where volume concentrations of SOA were measured in 139 laboratory experiments involving the oxidation of single hydrocarbons under different operating conditions. Three different data mining methods were used, including nearest neighbor, decision tree, and pattern mining. Both decision tree and pattern mining approaches identified similar chemical and experimental conditions that were important to SOA formation. Among these important factors included the number of methyl groups for the SOA precursor, the number of rings for the SOA precursor, and the presence of dinitrogen pentoxide (NO).
本研究采用数据挖掘方法，以更好地理解影响二次有机气溶胶（SOA）形成的因素。尽管已经针对SOA形成完成了大量的实验室和计算研究，但确定对SOA形成影响最大的因素仍然具有挑战性。实验数据基于Offenberg等人（2017年）描述的先前工作，在139个实验室实验中测量了SOA的体积浓度，这些实验涉及在不同操作条件下单一碳氢化合物的氧化。使用了三种不同的数据挖掘方法，包括最近邻法、决策树法和模式挖掘法。决策树法和模式挖掘法都识别出了对SOA形成很重要的相似化学和实验条件。这些重要因素包括SOA前体的甲基数量、SOA前体的环数量以及五氧化二氮（NO）的存在。
