A generative ozone concentration imputation and forecasting method based on a novel conditional diffusion model

Ground-level ozone has emerged as a major pollutant in China, where maintaining data integrity and prediction accuracy is crucial for effective environmental governance and policymaking. Nevertheless, the inevitable missingness in ozone time series data compromises dataset completeness, thereby posing a significant obstacle to downstream prediction and advanced analysis. In this paper, we propose a generative imputation and forecasting method, SSSD-Transformer, that injects empirical knowledge as conditional information and combines structured state space diffusion (SSSD) and Transformer to capture the temporal trends and detailed patterns exhibited by ozone pollution for generating the clean ozone sequences. The experiments and case studies on three different missingness scenarios clearly demonstrate that SSSD-Transformer generates more accurate and robust imputations, exhibiting greater reliability. In forecasting task, the model also demonstrated strong capabilities, with performance remaining relatively stable as the prediction horizon increased. Empowered by the potent empirical knowledge and weighted combination, the presented method successfully achieves excellent performance in ozone concentration imputation and forecasting. The simultaneous achievements provide a new perspective for ensuring data integrity and enhancing prediction credibility, and will make significant contributions to effective air pollution control.