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Monthly top-down NOx emissions for China (2005–2012): A hybrid inversion...

Qu, Z., D. Henze, S. L. Capps, J. Wang, X. Xu, J. Wang, and M. Keller (2017), Monthly top-down NOx emissions for China (2005–2012): A hybrid inversion method and trend analysis, J. Geophys. Res., 122, 4600-4625, doi:10.1002/2016JD025852.
Abstract: 

We develop an approach combining mass balance and four-dimensional variational (4D-Var) methods to facilitate inversion of decadal-scale total nitrogen oxides (NOx = NO + NO2 ) emissions. In 7 year pseudo-observation tests, hybrid posterior emissions have smaller normalized mean square error (NMSE) than that of mass balance when compared to true emissions in most cases and perform slightly better in detecting NOx emission magnitudes and trends. Using this hybrid method, OMI NO2 satellite observations and the GEOS-Chem chemical transport model, we find more than 30% increases of emissions over most of East China at the 0.5∘ × 0.667∘ grid cell level, leading to a 16% growth of emissions over all of China from 2005 to 2012, whereas emissions in several urban centers have decreased by 10–26% in the same period. From 2010 to 2012, a decline is found in the North China Plain, Hubei Province, and Pearl River Delta area, coinciding with China’s enforcement of its twelfth “Five Year Plan.” Changes in individual grid cell may be different from changes over the entire city or province, as exemplified by opposite trends in Beijing versus the Mentougou district of Beijing from 2005 to 2012. Also, NO2 columns do not necessarily have the same trend as NOx emissions due to their nonlinear response to emissions and the influence of meteorology, the latter alone which can cause up to 30% interannual changes in NO2 columns. Compared to recent bottom-up inventories, hybrid posterior emissions have the same seasonality, smaller emissions, and emission growth rate at the national scale.

PDF of Publication: 
Download from publisher's website.
Research Program: 
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Mission: 
Aura- OMI
A-Train