Improving NO<sub>2</sub> and ozone simulations through global NO<sub><i>x</i></sub> emission inversions

Abstract

Abstract. Tropospheric ozone simulations have large uncertainties, but their biases, seasonality and trends can be improved with more accurate estimates of precursor gas emissions. We perform global top-down estimates of monthly NOx emissions using two OMI NO2 retrievals (NASAv3 and DOMINOv2) from 2005 to 2016 through a hybrid 4D-Var/mass balance inversion. The 12-year averages of regional NOx budgets from the NASA posterior emissions are 37 % to 53 % smaller than the DOMINO posterior. Compared to surface NO2 measurements, GEOS-Chem adjoint NO2 simulations using the DOMINO posterior NOx emissions have smaller biases in China (by 15 %) and the US (by 22 %), but NO2 trends have more consistent decreases (by 26 %) with the measurements (by 32 %) in the US from 2006 to 2016 when using the NASA posterior. The two posterior NOx emissions datasets have different strengths with respect to simulation of ozone concentrations. Simulations using NASA-based emissions provide better agreement for polluted conditions. Ozone from these shows the highest correlation (R2 = 0.88) with annual MDA8 ozone trends and alleviates the double peak in the prior simulation of global ozone seasonality. The DOMINO-based emissions, on the other hand, are better for simulating ozone at remote sites, making them well-suited to generation of boundary conditions for regional models, and in capturing the interannual variability of daytime-average (R2 = 0.72–0.81) and 24-hour average (R2 = 0.88–0.96) surface ozone. We recommend using NOx emission datasets that have the best performance in the corresponding spatial domain and temporal focus to improve ozone simulations.

Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA‐Supported Science‐Stakeholder Collaborative

“…The Lancet Countdown is also incorporating our wildfire and green space estimates in future iterations. The project helped facilitate the development of new methods for estimating pollen exposure worldwide, dust storm frequency, and global ozone concentrations (Chang et al, 2019;Qu et al, 2020). In the coming years, we anticipate seeing ripple effects of these outputs in both end-user efforts and scientific studies.…”

“…Satellite data can mitigate shortcomings in model ozone estimates, which may lack up-to-date emissions information in all areas of the world, and help fill in gaps in ozone monitoring data, especially outside of the United States, Europe, and China. A global atmospheric chemical transport model (GEOS-Chem) was merged with satellite NO 2 observations (including both NASA v3 and DOMINOv2 products) to constrain emissions of NO x , a key precursor of ozone from 2005 to 2016 (Qu et al, 2020). The 4D-Var approach was used with the GEOS-Chem adjoint model to make adjustments to monthly NO x emissions in each 2°× 2.5°grid cell globally.…”

Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions From Bottom‐Up Inventories, Top‐Down Estimates, and Emission Scenarios

“…The two estimates from Qu are derived using identical methods, except the emissions are constrained using different OMI satellite retrievals (Qu-BIRA and Qu-NASA;Qu, Henze, Li, et al, 2019) The impact of the two different retrievals on the magnitude of emissions is more significant in China where the average difference in annual SO 2 emission estimates is about 20%. In India, this difference is around 6%, a consequence of the fact that differences in the satellite retrievals are not the same in all regions as highlighted in Qu et al (2020). Both estimates indicate relatively similar trends in China and India between the two OMI SO 2 products.…”

“…The other two estimates from Qu et al (2020), Qu-NASA and Qu-DOMINO, allow us to assess to the impact of different satellite products on the top-down NO x emission estimates (see section 2 for details). An intercomparison of the magnitudes of NO 2 vertical column densities from these two products, as well as from the QA4ECV product used to constrain the TCR-2 emissions, is given in the supporting information of Qu et al (2020). They reported that the DOMINO NO 2 columns are considerably higher than in the other 10.1029/2020EF001520…”