Quantitative insights into regime-dependent aerosol pH variability in ammonia-rich urban Beijing from explainable machine learning

Aerosol acidity (pH) plays a crucial role in atmospheric chemistry. Meteorological conditions and chemical properties jointly contribute to pH variation, yet their behavior differs across environmental regimes and remains incompletely understood. Here, we integrate machine learning with interpretable model analyses, field observations, and thermodynamic modeling to quantitatively assess the relative contributions and associations of key factors to aerosol pH variability in the ammonia-rich urban atmosphere of Beijing. Temperature exhibits a strong negative association with pH variation, with an average decrease of ∼ 0.6 units per 10 °C increase. Excess ammonia, nitrate-to-sulfate mass ratio (N/S), and PM1 mass loading are positively associated with pH, showing stronger sensitivities at lower values and diminishing responses at higher levels. In contrast, the contribution of relative humidity (RH) depends strongly on its interactions with temperature, aerosol composition, and mass loading, resulting in pronounced regime-dependent reversals. Higher RH is associated with enhanced aerosol acidity under low-temperature (<∼ 15 °C), nitrate-dominant (N/S >∼ 1.25), or high-mass (PM1>∼ 50 µg m−3) conditions, whereas the opposite tendency occurs under warmer, sulfate-dominant, or low-mass regimes. This study provides new quantitative insights into the coupled meteorological and chemical modulation of pH and highlights the importance of multifactor interactions in understanding aerosol acidity variability in real-world atmospheres.