Key chemical NOx sink uncertainties and how they influence top-down emissions of nitrogen oxides

Abstract

Triggered by recent developments from laboratory and field studies regarding major NO_x sink pathways in the troposphere, this study evaluates the influence of chemical uncertainties in NO_x sinks for global NO_x distributions calculated by the IMAGESv2 chemistry-transport model, and quantifies their significance for top-down NO_x emission estimates. Our study focuses on five key chemical parameters believed to be of primary importance, more specifically, the rate of the reaction of NO₂ with OH radicals, the newly identified HNO₃-forming channel in the reaction of NO with HO₂, the reactive uptake of N₂O₅ and HO₂ by aerosols, and the regeneration of OH in the oxidation of isoprene. Sensitivity simulations are performed to estimate the impact of each source of uncertainty. The model calculations show that, although the NO₂+OH reaction is the largest NO_x sink globally accounting for ca. 60% of the total sink, the reactions contributing the most to the overall uncertainty are the formation of HNO₃ in NO+HO₂, leading to NO_x column changes exceeding a factor of two over tropical regions, and the uptake of HO₂ by aqueous aerosols, in particular over East and South Asia. Emission inversion experiments are carried out using model settings which either minimise (MINLOSS) or maximise (MAXLOSS) the total NO_x sink, both constrained by one year of OMI NO₂ column data from the DOMINO v2 KNMI algorithm. The choice of the model setup is found to have a major impact on the top-down flux estimates, with 75% higher emissions for MAXLOSS compared to the MINLOSS inversion globally. Even larger departures are found for soil NO (factor of 2) and lightning (1.8). The global anthropogenic source is better constrained (factor of 1.57) than the natural sources, except over South Asia where the combined uncertainty primarily associated to the NO+HO₂ reaction in summer and HO₂ uptake by aerosol in winter lead to top-down emission differences exceeding a factor of 2. Evaluation of the emission optimisation is performed against independent satellite observations from the SCIAMACHY sensor, with airborne NO₂ measurements of the INTEX-A and INTEX-B campaigns, as well as with two new bottom-up inventories of anthropogenic emissions in Asia (REASv2) and China (MEIC). Neither the MINLOSS nor the MAXLOSS setup succeeds in providing the best possible match with all independent datasets. Whereas the minimum sink assumption leads to better agreement with aircraft NO₂ profile measurements, consistent with the results of a previous analysis (Henderson et al., 2012), the same assumption leads to unrealistic features in the inferred distribution of emissions over China. Clearly, although our study addresses an important issue which was largely overlooked in previous inversion exercises, and demonstrates the strong influence of NO_x loss uncertainties on top-down emission fluxes, additional processes need to be considered which could also influence the inferred source.