Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Abstract. The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. Traditional models have difficulty assessing O3 formation sensitivity in a timely manner due to the limitations of flexibility and computational efficiency. 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 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 initial concentrations of VOC species (R2 = 0.87) was better than that using total VOCs (TVOCs) concentrations (R2 = 0.77). Meanwhile, the RIs of VOC species correlated well with their O3 formation potentials (OFPs). O3 formation presented a negative response to NOx, PM2.5 and relative humidity, and a positive response to temperature, solar radiation and VOCs. The O3 isopleth curves calculated by the RF model were 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.