Research Information System
ENVIRONMENTAL MODELLING & SOFTWARE, pp.1-24, 2025 (SCI-Expanded)
Air pollution poses severe threats to global public health and environmental sustainability, with complex spatio-temporal dynamics. Traditional air quality models face limitations in capturing longitudinal dependencies and managing computational costs at scale. To address these challenges, we propose the Hybrid Mixed-Effect Diffusion (H-MED) model, a novel framework integrating mixed-effects regression, Gaussian-Process Regression (GPR), Long Short-Term Memory (LSTM), and diffusion-based learning for enhanced predictive accuracy and uncertainty quantification. Applied to a comprehensive longitudinal dataset spanning 61 countries (2013–2023), H-MED demonstrated superior performance, achieving the lowest prediction errors (MAE:0.1423, RMSE:0.1925), while maintaining computational efficiency (5.69s). The model significantly outperformed state-of-the-art approaches including advanced spatiotemporal models such as HITS, DCRNN, ST-GCN, and DeepAR by 15-20\% across all metrics. By incorporating country-level random effects and providing SHAP-based feature attribution, H-MED delivers interpretable, high-resolution forecasts that capture regional pollution heterogeneity and enable targeted policy interventions, a critical advantage over black-box deep learning models that lack explainability for environmental decision-making.