Decadal changes in global surface NO<sub><i>x</i></sub> emissions from multi-constituent satellite data assimilation

Abstract

Abstract. Global surface emissions of nitrogen oxides (NOx) over a 10-year period (2005–2014) are estimated from an assimilation of multiple satellite data sets: tropospheric NO2 columns from Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), O3 profiles from Tropospheric Emission Spectrometer (TES), CO profiles from Measurement of Pollution in the Troposphere (MOPITT), and O3 and HNO3 profiles from Microwave Limb Sounder (MLS) using an ensemble Kalman filter technique. Chemical concentrations of various species and emission sources of several precursors are simultaneously optimized. This is expected to improve the emission inversion because the emission estimates are influenced by biases in the modelled tropospheric chemistry, which can be partly corrected by also optimizing the concentrations. We present detailed distributions of the estimated emission distributions for all major regions, the diurnal and seasonal variability, and the evolution of these emissions over the 10-year period. The estimated regional total emissions show a strong positive trend over India (+29 % decade−1), China (+26 % decade−1), and the Middle East (+20 % decade−1), and a negative trend over the USA (−38 % decade−1), southern Africa (−8.2 % decade−1), and western Europe (−8.8 % decade−1). The negative trends in the USA and western Europe are larger during 2005–2010 relative to 2011–2014, whereas the trend in China becomes negative after 2011. The data assimilation also suggests a large uncertainty in anthropogenic and fire-related emission factors and an important underestimation of soil NOx sources in the emission inventories. Despite the large trends observed for individual regions, the global total emission is almost constant between 2005 (47.9 Tg N yr−1) and 2014 (47.5 Tg N yr−1).

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

“…The annual growth rate of China's emissions of NO x is highly consistently estimated by ECLIPSE, MIX and satellite-based inventories, around 4.0 % annual growth in the period of 2005 to 2010 (see Table 5). The results are comparable with previous work using various inversion methodologies, satellite sensors or CTMs (Gu et al, 2013;Krotkov et al, 2016;Miyazaki et al, 2017). Figure 11 shows the gridded emission changes from 2005 to 2010 among different inventories for NO x .…”

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

“…The results are comparable with previous work using various inversion methodology, satellite sensor or CTMs (Gu et al, 2013;Krotkov et al, 2016;Miyazaki et al, 2017). Significantly larger growth is observed in northern China's emissions from top-down inventories, than in estimates of ECLIPSE and MIX.…”

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

“…High-resolution simulations are thus important for regions with complex and strong local sources. At the same time, the remaining negative bias at 0.56 • suggests that power plant and industrial emissions are underestimated, as suggested by Miyazaki et al (2017), or that a model resolution higher than 0.56 • is essential. Figure 8 compares simulated high NO 2 concentrations with satellite retrievals at selected megacities.…”

“…2.1). The optimized NO x emissions from an assimilation of multiple species satellite measurements (Miyazaki et al, 2017) suggest that surface NO x emissions over the DMA in July-August increased by 7 % from 2010 to 2014. The temporal variation, together with large uncertainties in the emission inventories, could explain part of the negative biases of NO and NO 2 at 800 hPa, which also affects OH, HO 2 , and O 3 through nonlinear chemistry processes.…”

“…Global chemical data assimilation (e.g., Inness et al, 2015;Miyazaki et al, 2015) and emission inversion (e.g., Stavrakou et al, 2013;Miyazaki et al, 2017) would also benefit from high-resolution global CTMs through improvements in model performance (e.g., Arellano Jr. et al, 2007) and reduced spatial representation gaps between observed and simulated fields. Several previous studies (Mijling and van der A, 2012;Ding et al, 2017b;Liu et al, 2017) demonstrated the importance of high-resolution modeling in detecting small-scale NO x emission sources such as urban, new power plant, and ship emissions.…”