Exploring the impact of nocturnal boundary layer stability on wintertime air pollution in a highly polluted basin city using unsupervised learning classification

This study utilizes ten years of wintertime boundary layer meteorological and surface air quality observations to characterize the nocturnal boundary layer (NBL) stability and assess its relationship with air pollution in Taiyuan, a highly polluted basin city in China. An unsupervised learning feature extraction technique known as the self-organizing map (SOM) is applied to objectively classify nocturnal virtual potential temperature (VPT) profiles. The SOM-based classification scheme allows the representation of wintertime day-to-day NBL evolutions by just nine regimes. Special attention is given to four dominant regimes: weak to moderate stability regime (NBL1), cloudy moderate stability regime (NBL3), windy moderate stability regime (NBL7), and strong stability regime (NBL9). These dominant regimes have relatively higher occurrence frequencies (>10%), with the highest frequency associated with the strong stability regime (NBL9) at 25.2%. The diurnal cycles of selected pollutants (CO, NO₂, SO₂, and PM_2.5) exhibit significant distinctions among the different NBL regimes. For instance, in the strong stability regime, CO, NO₂, and PM_2.5 show explosive growth in the evening due to the accumulation of primary pollutants. However, in the cloudy moderate stability regime, PM_2.5 exhibits persistent slow growth throughout the day, likely due to secondary particle formation under high humidity and high SO₂ conditions. These findings enhance our understanding of NBL meteorological impacts on surface air pollution in basin cities.