Advancing Sectoral Emission Estimates of NOx, SO2, and CO Using Satellite Observations

Accurate quantification of the magnitude, trend, and national contribution of air pollutant emissions from each human activity is critical for the planning and verification of emission reduction efforts. Top-down estimates with satellite data provide important information on the sources of air pollutants. We apply a newly developed sector-based inversion method to quantify NO_x, SO₂, and CO emissions over 2005–2012 from various activities, including transportation, industry, residential, aviation, shipping, energy, and biomass burning. We incorporate OMI NO₂, OMI SO₂, and MOPITT CO observations and leverage the co-emission of these gases to identify the source sectors. The framework improves emission estimates at the process level by optimizing emission factors and activity rates without relying on explicit knowledge of their values and resolves discrepancies with bottom-up inventories at the sector level. We first applied this approach to estimate sectoral emissions in China and India, where posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)–15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)–9% (NO₂) compared to a species-based inversion. The posterior estimates capture the peak years of Chinese SO₂ (2007) and NO_x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO_x and SO₂ trends mostly to energy and CO trend to residential emissions. We are extending this framework to estimate sectoral emissions in the US and evaluate the posterior estimates with the EPA NEI.