A neural operator for forecasting carbon monoxide evolution in cities

Abstract Real-time forecasting of carbon monoxide (CO) concentrations is essential for enabling timely interventions to improve urban air quality. Conventional air quality models often require extensive computational resources for accurate, multi-scale predictions, limiting their practicality for rapid, real-time application. To address this challenge, we introduce the Complex Neural Operator for Air Quality (CoNOAir), a machine learning model that forecast CO concentrations efficiently. CoNOAir demonstrates superior performance over state-of-the-art models, such as the Fourier Neural Operator (FNO), in both short-term (hourly) and extended (72-h) forecasts at a national scale. It excels in capturing extreme pollution events and performs consistently across multiple Indian cities, achieving an R 2 above 0.95 for hourly CO predictions across all evaluated locations. CoNOAir equips authorities with an effective tool for issuing early warnings and designing targeted intervention strategies. This work marks a step forward in achieving dependable, real-time CO pollution predictions for densely populated urban centres.