Developing a method for resolving NOx emission inventory biases using Discrete Kalman Filter inversion, direct sensitivities, and satellite-based NO2 columns

An inverse method was developed to integrate satellite observations of atmospheric pollutant column concentrations with specie concentrations and direct sensitivities predicted by a regional air quality model in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields were analyzed using a "top-down" approach with an inversion performed by Discrete Kalman Filter (DKF) and direct sensitivities calculated using the Decoupled Direct Method in 3D (DDM-3D) embedded in the Community Multiscale Air Quality (CMAQ) model. The system was tested through ail experiment focusing on NO2 concentrations and emissions of NOx in the southeastern United States. The method reproduced the expected NOx emission fields from initially perturbed starting values. Responses to different parameters in the system, including assumptions for uncertainties in the emission fields and satellite observations, were also tested. The method is readily extendable to other Pollutants.