Data-driven global air quality model

Operational global air quality forecasting often faces a critical trade-off between computational efficiency and the high spatial resolution required for effective pollution governance. Conventional numerical models are computationally expensive when resolving sub-grid processes, while standard data-driven approaches often struggle to capture global long-range dependencies effectively. In this work, we present a purely data-driven yet geometry-aware framework designed to predict and downscale global atmospheric composition fields. The framework operates in two stages to balance global dynamics with local fidelity. The first stage employs a Spherical Fourier Neural Operator (SFNO), trained on two decades of reanalysis data, meteorological fields, and emission fields. This model learns to evolve global concentrations of seven key pollutants (including PM_2.5, PM₁₀, O₃, CO, NO₂, SO₂, and NO) at a 0.75° resolution. To provide finer spatial detail in regions of interest, the coarse-resolution predictions are downscaled to 0.1° × 0.1° using a Schrödinger Bridge–based stochastic super-resolution approach, while maintaining statistical consistency between the original and refined fields. This two-stage framework allows efficient generation of high-resolution global and regional air quality fields, while reducing the computational demands compared to conventional chemical transport models. The resulting model provides a practical tool for investigating pollutant transport and for supporting the evaluation of emission control strategies across multiple spatial scales.