Modeling the coupled PM_2.5-Ozone system in China: chemical mechanisms, persistent model biases and future...

China's air quality governance has transitioned from separate particulate matter and ozone controls into a compound pollution era where particulate matter (PM₂.₅) and ozone (O₃) are linked through chemical interactions and increasingly co-occur during pollution episode. This review synthesizes the historical evolution, spatiotemporal characteristics, process-level mechanics, and modeling challenges of this coupled system. The mechanistic core relies on a shared precursor pool of volatile organic compounds (VOCs), nitrogen oxides (NO_x), sulfur dioxide, and ammonia, driving a non-linear "pollution seesaw" where single-pollutant reductions can paradoxically exacerbate the co-pollutant. This coupling is driven by hydrogen oxide radical (HOx) cycling, where missing OH reactivity and underestimated daytime nitrous acid (HONO) sources contaminate model predictions. Furthermore, aerosol-photochemistry feedbacks amplify these interactions, shifting chemical regimes as particle concentrations decline. Current chemical transport models exhibit systematic biases, including an underestimate of secondary organic aerosols of 40% that varies substantially across models, seasons, and regions, uncertainties in ammonia and episodic agricultural burning inventories, and inadequate representation of nocturnal boundary layer mixing. These simulation errors limit the accuracy of diagnostic frameworks, data assimilation, and machine learning applications. This review highlights that the co-occurrence of PM₂.₅ and O₃ induces synergistic human mortality risks and crop yield losses, making integrated governance an ecological and public health imperative. Finally, we outline a roadmap to close these mechanistic gaps, calling for mechanism-faithful, regionally resolved, and climate-aware prediction systems. This synthesis provides a foundation for optimizing multi-pollutant control strategies in China and developing nations undergoing similar atmospheric transitions.