Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Data mining approaches to understanding the formation of secondary organic aerosol
https://orcid.org/0000-0002-0213-4024
https://orcid.org/0000-0002-7045-7650
Abstract 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).
Highlights Volume concentrations from 139 chamber experiments on SOA formation were analyzed. Three different data mining methods were used: nearest neighbor, decision tree, and pattern mining. The number of methyl groups, the number of rings and the presence of dinitrogen pentoxide (N2O5) were important factors.
Data mining approaches to understanding the formation of secondary organic aerosol
https://orcid.org/0000-0002-0213-4024
https://orcid.org/0000-0002-7045-7650
Abstract 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).
Highlights Volume concentrations from 139 chamber experiments on SOA formation were analyzed. Three different data mining methods were used: nearest neighbor, decision tree, and pattern mining. The number of methyl groups, the number of rings and the presence of dinitrogen pentoxide (N2O5) were important factors.
Data mining approaches to understanding the formation of secondary organic aerosol
https://orcid.org/0000-0002-0213-4024
https://orcid.org/0000-0002-7045-7650
Abstract 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).
Highlights Volume concentrations from 139 chamber experiments on SOA formation were analyzed. Three different data mining methods were used: nearest neighbor, decision tree, and pattern mining. The number of methyl groups, the number of rings and the presence of dinitrogen pentoxide (N2O5) were important factors.
Data mining approaches to understanding the formation of secondary organic aerosol
Olson, David A. (Autor:in) / Offenberg, John H. (Autor:in) / Lewandowski, Michael (Autor:in) / Kleindienst, Tadeusz E. (Autor:in) / Docherty, Kenneth S. (Autor:in) / Jaoui, Mohammed (Autor:in) / Krug, Jonathan (Autor:in) / Riedel, Theran P. (Autor:in)
Atmospheric Environment ; 252
12.03.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Secondary organic aerosol formation from the photo-oxidation of benzene
| Elsevier | 2011