资源环境科技发展态势分析平台

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO_x, SO₂, and CO emissions in East Asia during 2005-2012. 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. This new inversion captures 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.