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

Abstract

Abstract. 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 25 bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMIdetected 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 30 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus the validation

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005–2017

“…Still, as of 2017, the annual budget of posterior SO 2 emissions in China is approximately 2.2 times that of India. Annual budgets of posterior SO 2 emissions are mostly lower than bottom-up estimates in China and India, which is consistent with the factor of 6 high bias for power plant SO 2 emissions in bottom-up inventories compared to top-down estimates from Liu et al (2018). These are related to the distribution of SO 2 sources over too few point sources in bottom-up inventories (Liu et al, 2018), lower SO 2 column densities in OMI retrievals compared to simulations, and small sources that are not detectable in OMI retrievals when the signal is comparable to the noise.…”

“…Posterior SO 2 emissions in the United States (bottom right panel of Figure 6) also peak in the summer due to cooling requirements ( emissions are 11%-27% (NASA) and 10%-28% (BIRA) smaller than the prior emissions in summer months. Posterior emissions mainly decrease in the Eastern United States and the annual budget decreases by 11% in both posteriors (Table 2), consistent with the difference between posterior and HTAP SO 2 emissions in Liu et al (2018). The larger HTAP emissions are likely due to omission of updates on fuel quality and technologies, discontinuous monitoring of SO 2 sources, and misplacement of point sources over larger areas (Liu et al, 2018).…”

“…Posterior SO 2 emissions in India have larger seasonal variation than the prior emissions, with a peak in summer, opposite to the peak in the prior inventory (bottom left panel of Figure 6). The differences in magnitude (Table 2) and seasonality between simulations using prior and posterior emissions are related to different seasonalities in the simulated versus measured SCDs in India (as shown in Figure S28) and a less accurate prior inventory due to limited and/or inaccurate statistics, as described in Liu et al (2018). The 2010 SO 2 annual budget in India reduces by 18% in the NASA posterior and by 26% in the BIRA posterior (Table 2).…”

“…Scattering weights below clouds are treated as zero, leading to zero GEOS-Chem SCDs below clouds. Also, both NASA and BIRA algorithms treat aerosol optical effects implicitly as "effective" clouds, leading to larger uncertainty in retrievals in regions with high aerosol loading, that is, China (Liu et al, 2018). The cloud radiance fraction (CRF) is used to weight scattering weights and AMFs between cloudy and clear parts of a pixel, and is calculated differently in two products.…”

“…We therefore only focus on applying the hybrid method to estimate trend of nation-wide SO 2 emissions in China and India in this work. Other emission trend estimates focusing on large point sources instead of nation-wide emissions using a plume model approach (e.g., Fioletov et al, 2016;Fioletov et al, 2017;Liu et al, 2018;;McLinden et al, 2016) take long-term averages of SO 2 observations and are less hindered by negative SO 2 retrievals. Still, SO 2 emissions below 15 kt S/year (determined by the detection limit of OMI SO 2 column densities, same for all methods) could hardly be detected .…”