Understanding the correlation between PM_2.5 and O₃ is critical for complex air pollution control. This study comprehensively analyzed PM_2.5 and O₃ pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, a typical petrochemical city in China’s Bohai Bay region. Results showed that PM_2.5–O₃ correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM_2.5 and O₃ showed strong positive correlations in summer, negative in winter, and weak positive in spring/autumn, with strongest links in western areas. The strongest positive PM_2.5–O₃ correlation occurred in summer when PM_2.5 ≤ 35 μg·m⁻³ and O₃ >160 μg·m⁻³, while the strongest negative correlation was exhibited in winter with PM_2.5 > 75 μg·m⁻³ and O₃ ≤ 100 μg·m⁻³. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, O_x > 125 μg·m⁻³) and non-sea-breeze periods enhanced the PM_2.5–O₃ positive correlation. During the four typical pollution episodes, the positive PM_2.5–O₃ correlation in summer was propelled by synchronous increases in O₃ and secondary components via shared precursors. In autumn, strong positivity resulted from secondary component–O₃ correlations (r > 0.7) and dominance of secondary formation in PM_2.5. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that O_x, RH, and T drove positive PM_2.5–O₃ correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM_2.5 and O₃ control in the petrochemical cities of the Bay region.

Atmospheric intermediate volatile organic compounds (IVOCs) are important precursors of secondary organic aerosols (SOAs), and in-depth research on them is crucial for atmospheric pollution control. This review systematically synthesizes global advancements in understanding IVOC sources, emissions characterization, compositional characteristics, ambient concentrations, SOA contributions, and health risk assessments. IVOCs include long-chain alkanes (C₁₂~C₂₂), sesquiterpenes, polycyclic aromatic hydrocarbons, monocyclic aromatic hydrocarbons, phenolic compounds, ketones, esters, organic acids, and heterocyclic compounds, which originate from primary emissions and secondary formation. Primary emissions include direct emissions from anthropogenic and biogenic sources, while secondary formation mainly results from radical reactions or particulate surface reactions. Recently, the total IVOC emissions have decreased in some countries, while emissions from certain sources, such as volatile chemical products, have increased. Ambient IVOC concentrations are generally higher in urban rather than in rural areas, higher indoors than outdoors, and on land rather than over oceans. IVOCs primarily generate SOAs via oxidation reactions with hydroxyl radicals, nitrate radicals, the ozone, and chlorine atoms, which contribute more to SOAs than traditional VOCs, with higher SOA yields. SOA tracers for IVOC species like naphthalene and β-caryophyllene have been identified. Integrating IVOC emissions into regional air quality models could significantly improve SOA simulation accuracy. The carcinogenic risk posed by naphthalene should be prioritized, while benzo[a]pyrene requires a combined risk assessment and hierarchical management. Future research should focus on developing high-resolution online detection technologies for IVOCs, clarifying the multiphase reaction mechanisms involved and SOA tracers, and conducting comprehensive human health risk assessments.