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

Abstract

The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing, China, from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using concentrations of measured/initial VOC species (R2Combining double low line0.82/0.81) was better than that using total VOC (TVOC) concentrations (R2Combining double low line0.77). Meanwhile, the RIs of initial VOC species correlated well with their O3 formation potentials (OFPs), which indicate that the model results can be partially explained by the maximum incremental reactivity (MIR) method. O3 formation presented a negative response to nitrogen oxides (NOx) and relative humidity (RH), and a positive response to temperature (T), solar radiation (SR), and VOCs. The O3 isopleth calculated by the RF model was generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis.