A new global anthropogenic SO 2 emission inventory for the last decade: A mosaic of satellite-derived and bottom-up emissions

Sulfur dioxide (SO 2 ) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO 2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. OMI-HTAP SO 2 emissions estimates for Persian Gulf, Mexico, and Russia are 59 %, 65 %, and 56 % higher than HTAP estimates, respectively, in year 2010. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO 2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus the validation on year 2010 for which HTAP is most valid and a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the model agreement with observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to −0.03 (OMI-HTAP) for year 2010. Additionally, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO 2 emissions that are observed with OMI. For example, correlation coefficients of the observed and modelled surface SO 2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends.