A data-assimilative approach to air quality analytics for policy and emergency planning

Air pollution remains a pressing environmental and public health concern, particularly in urban and industrial regions. This study develops a two-dimensional advection–diffusion model to simulate pollutant dispersion in the atmospheric boundary layer, incorporating both molecular and turbulent diffusivities to capture dispersion processes under varying wind conditions and diffusion parameters. Analytical and numerical approaches are employed to examine spatial and temporal concentration patterns. To enhance predictive accuracy, Kalman filtering (KF) is applied for assimilating sparse and noisy observational data, thereby reducing uncertainty in concentration estimates. Although KF is not a direct control strategy, it strengthens the reliability of model outputs for evaluating emission reduction policies and identifying high-risk zones. Results demonstrate that integrating dispersion modeling with KF significantly improves concentration predictions and provides a robust framework for air quality management. The proposed approach is adaptable to environmental planning, regulatory assessment, and rapid-response applications during emergency pollutant releases, linking modeling, monitoring, and decision-making into a cohesive system.