Sector‐Based Top‐Down Estimates of NOx, SO2, and CO Emissions in East Asia. Issue 2 (20th January 2022)

• Record Type: Journal Article
• Authors: Qu, Zhen; Henze, Daven K.; Worden, Helen M.; Jiang, Zhe; Gaubert, Benjamin; Theys, Nicolas; Wang, Wei
• Is Part Of: Geophysical research letters. Volume 49:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-20
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL096009
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: Satellite observations are widely used to estimate air pollutant emissions and evaluate their trends. We design a new method based on Bayesian statistics to estimate emissions of major air pollutants in East Asia according to their sources (e.g., energy, industry, transportation, etc.). Results from this approach show better agreement with independent surface measurements than the previous estimates that use observations to optimize emissions by species and estimates that compile emissions using activity data and emission factors. This method provides a new perspective to analyze the trend of air pollutants by sources and is crucial for countries and regions that lack detailed and timely emission estimates for each source sector. Key Points: A new sector‐based multispecies inversion framework is developed to estimate NO x, SO2, and CO emissions using satellite observations The sector‐based inversion leads to smaller biases and errors in surface NO2 and SO2 simulations than a species‐based inversion The framework provides a new perspective to analyze the trend of emissions by sectors and evaluates bottom‐up estimates