Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

Abstract

Abstract. We compare nine emission inventories of nitrogen oxides including four satellite-derived NO x inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multiresolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellitederived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NO x emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NO x emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation

“…There are large differences between the NO x emission inventories for South Korea (0.30 – 0.43 Tg N) and eastern China (6.2 – 8.3 Tg N), as similarly discussed by Ding et al () for 2005–2015. In South Korea, the top‐down emissions of NO x emissions estimated using QA4ECV is 0.42 Tg N, which is about 40% higher than the KORUS v2 and HTAP v2 inventories but is equivalent to the EDGAR v4.3.2 inventories.…”

“…The simulation did not consider the influence of a rapid NO x emission reduction after 2010 for China that was reported by Miyazaki et al () and Liu et al (). For South Korea, most of the top‐down estimates revealed increases after 2010 (Ding et al, ). The use of the 2010 emissions, along with large uncertainty in emission factors, could explain a part of the overestimation over China and the underestimation over South Korea.…”

“…Miyazaki, Eskes, and Sudo () developed a system to simultaneously optimize concentrations and emissions of various species from assimilation of multiconstituent measurements from multiple satellite sensors. Chemical reanalysis using the ensemble Kalman filter (EnKF) has been used to provide comprehensive information on atmospheric composition variability and to elucidate variations in precursor emissions (Ding et al, ; Jiang et al, ; Miyazaki et al, , ). It has also been used to validate chemistry‐climate model simulations (Miyazaki & Bowman, ).…”

Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0

“…The total amounts of anthropogenic NO x emissions in China in 2008 differ by 27 % between two (highest and lowest) bottom-up inventories: EDGAR4.2 and MEIC (Saikawa et al, 2017). Ding et al (2017a) also discussed large diversity in emission inventories over East Asia. Biomass burning NO x emissions also differ significantly between inventories: for example, the annual mean emission is 2.293 Tg yr −1 in GFASv1.0 in contrast to 2.700 Tg yr −1 in GFEDv3.1 over the SH Africa, as reported by Kaiser et al (2012).…”

Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> over China

“…A negative systematic bias of 10-20 % by season plus a random error of 30 % are generated by model simulation using GEOS-Chem (Martin et al, 2003;Lin and McElroy, 2010;Lin, 2012). As suggested by Ding et al (2017), multiple sensors can give more comprehensive and accurate constraints on the priori spatial and temporal emission estimates, which can be further applied in future work.…”