Laboratoire de Mathématiques Raphaël Salem

UMR 6085 CNRS - Université de Rouen Normandie

Urban air quality is a major issue today. Pollutant concentrations, such as NO2, must be monitored to ensure that they do not exceed dangerous thresholds. Two recent techniques help map pollutant concentrations on a small scale. First, deterministic physicochemical models take into account the street network and calculate concentration estimates on a grid, providing a map. On the other hand, the advent of new low-cost technologies allows monitoring organizations to densify measurement networks. However, these devices are less reliable than reference devices and need to be corrected. We propose a new approach to improve maps generated using deterministic models by combining measurements from multiple sensor networks. To do this, we model the bias of deterministic models and estimate it using an MCMC method. This allows us to account for new variables and provide a corrected map in a forecasting context.