High-Resolution Spatial Forecasting of Hourly Air Quality: A Fast Method for a Better Representation of Industrial Plumes and Traffic Emissions Contributions

Efficiently mapping hourly air quality at a fine scale (25 m) remains a computational challenge. This difficulty is heightened when aiming to accurately capture industrial plumes and time-varying traffic emissions. This paper presents a method for generating hourly pollutant concentration maps across an entire region for operational applications. Our approach assumes that concentration maps can be decomposed into three components: traffic concentrations, industrial concentrations and a residual “background” concentrations component. The background concentration is estimated using established fine-scale mapping methods involving ADMS-Urban dispersion simulations. Meanwhile, the traffic and industrial layers are derived using a KNN-based approach applied to a sample of hourly ADMS-Urban simulations. This method enhances the representation of industrial plumes and the temporal variability in traffic emissions while maintaining computational efficiency, making it suitable for the operational production of hourly air quality maps in the Hauts-de-France region (France).