Abstract: The concentration of surface ozone (O₃) remains high for a long time and its duration continues to extend. Accurately assessing crop yield losses caused by ozone pollution is of great significance for maintaining regio-nal and global food security. We systematically elaborated on the damage mechanism of O₃ pollution on crop yield and the detoxification mechanism of crops, analyzed the limitations of crop yield assessment methods such as O₃ concentration response, dose response, and flux response. By focusing on innovative practices of mechanism model improvement and multi method coupling, we comprehensively introduced the research progress of embedding O₃ damage and detoxification modules in crop models, proposed the research direction of crop model machine learning hybrid framework in O₃ damage assessment. Moreover, we provided a new research paradigm that combined ratio-nality and practicality for quantifying the composite effects of O₃ pollution and extreme climate events. This review could provide reference for the continuous promotion of research on the impact of O₃ pollution on crop yield losses under the background of climate change.

Key words: ozone, crop yield, assessment method, detoxification mechanism, crop model, machine learning