Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning

Abstract. Ozone (O3) pollution is posing significant challenges to urban air quality improvement in China. The formation of surface O3 is intricately linked to chemical reactions which are influenced by both meteorological conditions and local emissions of precursors (i.e., NOx and volatile organic compounds, VOCs). When meteorological conditions deteriorate, the atmosphere's capacity to cleanse pollutants decreases, leading to the accumulation of air pollutants. Although a series of emission reduction measures have been implemented in urban areas, the effectiveness of O3 pollution control proves inadequate. Primarily due to adverse changes in meteorological conditions, the effects of emission reduction are masked. In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. We found that transport and dispersion impact the distribution of O3 concentration. During the warm season, positive contributions of dispersion and transport to O3 concentration ranged from 12.9 % to 24.0 %. After meteorological normalization, the sensitivity of O3 formation and the source apportionment of VOCs changed. The sensitivity of O3 formation shifted towards the transition regime between VOC- and NOx-limited regimes during the O3 pollution event. Vehicle exhaust became the primary source of VOC emissions after “removing” the effect of dispersion, contributing 41.8 % to VOCs during the pollution periods. On the contrary, the contribution of combustion to VOCs decreased from 33.7 % to 25.1 %. Our results provided new recommendations and insights for implementing O3 pollution control measures and evaluating the effectiveness of emission reduction in urban areas.