Science Inventory

Data mining approaches to understanding the formation of secondary organic aerosol

Citation:

Olson, D., J. Offenberg, M. Lewandowski, Tad Kleindienst, K. Docherty, M. Jaoui, Jonathan D. Krug, AND T. Riedel. Data mining approaches to understanding the formation of secondary organic aerosol. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 252:118345, (2021). https://doi.org/10.1016/j.atmosenv.2021.118345

Impact/Purpose:

The purpose of this paper is to use data mining techniques to better understand the formation of secondary organic aerosol (SOA).

Description:

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 (N2O5).