Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning

Ground-level ozone (O₃) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O₃ formation and developing an accurate and efficient method for timely assessing the O₃-VOCs-NOx relationships applicable in different O₃ pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O₃ formation regime and derive O₃ formation sensitivity curves. The algorithm was tested for O₃ events during the COVID-19 lockdown, a sandstorm event, and a heavy O₃ pollution episode (maximum hourly O₃ concentration >200 μg/m³) from 2019 to 2021. We show that increasing O₃ concentrations during the COVID-19 lockdown and the heavy O₃ pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O₃ levels were mainly attributable to weak sunlight and low precursor levels. O₃ formation sensitivity curves demonstrate that O₃ formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O₃ sensitivity curves can also help make hybrid and timely strategies for O₃ abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O₃ formation.