Articles | Volume 15, issue 5
Atmos. Meas. Tech., 15, 1511–1520, 2022
https://doi.org/10.5194/amt-15-1511-2022
Atmos. Meas. Tech., 15, 1511–1520, 2022
https://doi.org/10.5194/amt-15-1511-2022

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 et al.

Data sets

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species Junlei Zhan, Wei Ma, Yongchun Liu, Xin Zhang, Xuezhong Wang, Fang Bi, Yujie Zhang, Zhenhai Wu, and Hong Li https://doi.org/10.5281/zenodo.6330176

Model code and software

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 https://doi.org/10.5281/zenodo.6327734

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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.