Abstract. In this paper, we propose for the first time to the best of our knowledge, extend the application of a stochastic Eulerian numerical approach based on the Extended Kalman Filter (EKF_E.N.M.) to address the limitations of the Eulerian air pollution model CHIMERE. This approach integrates a comprehensive set of processes, including advection, turbulence, chemical reactions, emissions, and deposition, to model the dynamics of pollutant mass concentration. The EKF technique is employed to transform nonlinear dynamic problems into a succession of locally linearized ones, which are then used to estimate system states and adjust pollutant concentrations based on measured data. This stochastic approach is tested through two scenarios: one without external forces or control terms, and another that incorporates external factors like temperature, wind speed, and nitrogen dioxide as ozone precursors. A comparison of the obtained results with those from the standard CHIMERE model and studies from the literature demonstrates the accuracy and effectiveness of the proposed method.

Key words: Eulerian numerical model; Stochastic model; Extended Kalman filter; atmospheric pollution dynamics; mass conservation equation.