Using daily satellite observations to estimate emissions of short‐lived air pollutants on a mesoscopic scale

Abstract

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Using satellite observations for emission estimates has important advantages over bottom‐up emission inventories: they are spatially consistent, have high temporal resolution, and enable updates shortly after the satellite data become available. We present a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short‐lived atmospheric constituents on a mesoscopic scale (∼25 × 25 km 2). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NO x emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO 2 column retrievals of the OMI and GOME‐2 satellite instruments. Closed loop tests show that the algorithm is capable of reproducing new emission scenarios. Applied with real satellite data, the algorithm is able to detect emerging sources (e.g., new power plants), and improves emission information for areas where proxy data are not or badly known (e.g., shipping emissions). Chemical transport model runs with the daily updated emission estimates provide better spatial and temporal agreement between observed and simulated concentrations, facilitating improved air quality forecasts.