Articles | Volume 15, issue 5
Research article
16 Mar 2022
Research article |  | 16 Mar 2022

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Junlei Zhan, Yongchun Liu, Wei Ma, Xin Zhang, Xuezhong Wang, Fang Bi, Yujie Zhang, Zhenhai Wu, and Hong Li

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Cited articles

Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,, 2001. 
Carter, W.: Updated maximum incremental reactivity scale and hydrocarbon bin reactivities for regulatory applications, California Air Resources Board Contract 07-339, 2010. 
Copernicus:, last access: 4 March 2022. 
di Carlo, P., Brune, W. H., Martinez, M., Harder, H., Lesher, R., Ren, X., Thornberry, T., Carroll, M. A., Young, V., Shepson, P. B., Riemer, D., Apel, E., and Campbell, C.: Missing OH Reactivity in a Forest: Evidence for Unknown Reactive Biogenic VOCs, Science, 304, 722–725,, 2004. 
Short summary
Our study investigated the O3 formation sensitivity in Beijing using a random forest model coupled with the reactivity of volatile organic compound (VOC) species. Results found that random forest accurately predicted O3 concentration when initial VOCs were considered, and relative importance correlated well with O3 formation potential. The O3 isopleth curves calculated by the random forest model were generally comparable with those calculated by the box model.