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.

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Cited articles

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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.