Accurate reconstruction of historical photolysis frequencies using a machine learning approach enables long-term...

The photolysis frequency (J value) is a critical parameter in atmospheric photochemical simulations. However, routine observations of J values remain scarce, introducing substantial uncertainties in ozone (O₃) and photochemical analysis. In this study, significantly lower J values were observed at a typical urban site in the Fenwei Plain (FWP) region of China. J values simulated by the tropospheric ultraviolet and visible (TUV) radiative model were also significantly overestimated. We developed a machine learning model using routine air pollutant, meteorological, and reanalysis data aiming to accurately reconstruct long-term J values to constrain an observation-based model (OBM). The model showed great J value predictive performance (R² = 0.92-0.93). The reconstructed historical J values exhibited interannual variations consistent with those of O₃. Multi-year OBM simulations indicated that sustained emission reductions of nitrogen oxides (NO_X) in recent years have driven a gradual shift in the O₃ formation regime toward NO_X-limited conditions, while pronounced seasonal variability has persisted. The primary production of the peroxyl radicals, rather than radical chain length (ChL), governed the monthly variation in O₃ production. Alkenes, aromatics, and OVOCs were consistently the most sensitive VOC species for O₃ formation, while the importance of CO increased markedly during autumn. Industrial and transportation sectors are major emission sources of O₃ precursors. To shift to the NO_X-sensitive regime in September and October, more than ∼54% and ∼75% reductions in NO_X reactivity are needed. Overall, this study provides an efficient machine learning approach for reconstructing historical J values using routine observations, thereby facilitating long-term photochemical assessments and supporting air quality management in regions lacking routine J value observations.