Qu Zhen, Henze Daven K, Worden Helen M, Jiang Zhe, Gaubert Benjamin, Theys Nicolas, Wang Wei
Department of Mechanical Engineering University of Colorado Boulder Boulder CO USA.
School of Engineering and Applied Science Harvard University Cambridge MA USA.
Geophys Res Lett. 2022 Jan 28;49(2):e2021GL096009. doi: 10.1029/2021GL096009. Epub 2022 Jan 20.
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 NO, OMI SO, and MOPITT CO observations to estimate NO , SO, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO)-15% (SO) and normalized mean square error (NMSE) by 8% (SO)-9% (NO) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO (2007) and NO (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 and SO trends mostly to energy and CO trend to residential emissions.
利用卫星数据进行的自上而下估算提供了有关空气污染物来源的重要信息。我们基于GEOS-Chem伴随模型开发了一个基于部门的四维变分框架，以解决共排放和化学相互作用对自上而下排放估算的影响。我们应用OMI NO、OMI SO和MOPITT CO观测数据来估算2005 - 2012年东亚地区的NO 、SO和CO排放量。与基于物种的反演相比，利用地面测量进行的后验评估显示，归一化平均偏差（NMB）降低了7%（NO） - 15%（SO），归一化均方误差（NMSE）降低了8%（SO） - 9%（NO）。这种新的反演捕捉到了中国SO（2007年）和NO （2011年）排放的峰值年份，并将其驱动因素归因于工业和能源活动。2007年中国CO排放峰值是由居民和工业排放驱动的。在印度，反演将NO 和SO趋势主要归因于能源，而将CO趋势归因于居民排放。
