Inverse modeling of Texas NO_x emissions using space-based and ground-based NO₂ observations

Abstract. Inverse modeling of nitrogen oxide (NO_x) emissions using satellite-based NO₂ observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO₂ column densities are used to conduct inverse modeling of NO_x emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NO_x emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite-observed NO₂ over rural regions. Satellite-based top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with decoupled direct method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO₂-based DKF inversion. The DKF inversions yield conflicting results: the satellite-based inversion scales up the a priori NO_x emissions in most regions by factors of 1.02 to 1.84, leading to 3–55% increase in modeled NO₂ column densities and 1–7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NO_x emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO₂ or ground-level ozone (O₃) concentrations.