AI is not replacing physical models but complementing them to create more robust forecasting systems. For example, AI can learn from high-fidelity simulations and observations to improve turbulence modelling and pollutant dispersion predictions. This allows for faster and more accurate forecasts, enabling cities to respond to air quality challenges in near real-time.