PNNL

Abstract

While SO2 emissions are an important driver of air pollution and have a significant impact on radiative forcing, not all large sources around the world are included in emission inventories. Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories. We evaluated the uncertainties in anthropogenic sulfur dioxide (SO2) emission estimates from the OMI satellite for the contiguous US using high quality emissions inventory data. Specifically, we evaluated uncertainties in OMI measurements from NASA’s OMI point source catalog for different dimensions including, size of individual sources, aggregate vs individual source errors, and potential bias in in individual source estimates over time. For sources that are detected by the satellite, we find that errors in aggregate (total of all detected sources) are relatively low. Moreover, errors are lowest when looking at comprehensive inventories i.e., inventories that include both powerplant and non-powerplant sources. Errors for individual sources in any given year can be substantial, however, with over or under-estimates ranging from -90% to +500% (roughly 10 - 90th percentile) in an asymmetric distribution with a long tail. We find that these errors are not necessarily random over time and that there can be consistently positive or negative biases over time. We find, as expected, that emission sources not detected by the satellite are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission sizes have been decreasing. This analysis of uncertainties provides information on the error structure of the OMI measurements, which is a useful guide when using this data for research and assessment.