Constraining NOx and VOC Emissions with GOME, SCIAMACHY, and OMI Data for the Study of Long-Term Air Quality in Los Angeles

It is imperative to have accurate knowledge of changes in atmospheric composition for air quality and climate studies. Atmospheric chemistry-transport models are a useful tool for understanding and predicting atmospheric composition, but the reliability of the model results heavily depends on the accuracy of emission inventories that the model uses. Satellite retrievals of tropospheric NO₂ and HCHO during past decades may help to constrain NO_x and VOC emissions in urban areas. In this study, we choose the Los Angeles Basin in the US as a test site to quantify its long-term urban emissions using GOME, SCIAMACHY, and OMI data. There have been large decreases in ozone and its precursors in Los Angeles before and since the beginning of the satellite observational period. By comparing satellite columns with the multi-year model simulations utilizing optimized emission inventories, we examine the accuracy of satellite NO₂ and HCHO retrievals and explore the possibility of using multi-satellite data to improve our knowledge of long-term emission trends.