Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China

This study investigates the seasonal variations in O₃ levels in Qingdao, a typical coastal city, and quantifies the effects of key photolysis rate constants (J[O¹D] and J[NO₂]), meteorological parameters (RH, TEMP, and SF), and pollutants (ΔCO, PM_2.5, and NO₂) on O₃ levels across different seasons using machine learning. Additionally, the summer months, when photochemical reactions are most active, were analyzed in detail. The results indicate that the factors contributing to summer O₃ levels in order of importance, were RH, ΔCO, SF, PM_2.5, J[O¹D], NO₂, TEMP, WS, and J[NO₂]. RH was the most significant factor, with high humidity levels (>75%) inhibiting O₃ formation. ΔCO, representing regional transport, was the second most influential, suggesting that direct O₃ transport and the delivery of high concentrations of precursors significantly promoted local O₃ production and accumulation. While J[O¹D] and J[NO₂] had different roles in O₃ promotion and depletion, J[O¹D] had a greater impact overall. The temperature in the range of 26 °C–32 °C inhibits O₃ production, When RH exceeded 90%, J[O¹D] accelerates while other photolysis rate constants decline, further suppressing the production of O₃. For comparison, multiple linear regression models were used to develop empirical equations for calculating hourly O₃ concentrations across the four seasons. The results showed that these factors explained 50%, 64%, 61%, and 63% of the O₃ sources in Qingdao for spring, summer, autumn, and winter, respectively. Sensitivity tests on factors influencing summer O₃ concentrations found that MLR could not quantify their contributions to O₃ levels.