A. INTRODUCTION

1. Implementing sub-grid plume chemistry in GEOS-CHEM for interpretation of OMI NO2 in shipping lanes G.C.M. (Geert) Vinken1 , K.F. (Folkert) Boersma2, E.W. (Ernst) Meijer3, D.J. (Daniel) Jacob4 1 Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands 2 KNMI, PO Box 201, 3730 AE De Bilt, The Netherlands 3 TNO, PO Box 342,7300 AH Apeldoorn, The Netherlands 4 Harvard University, Pierce Hall, 29 Oxford St., Cambridge MA 02138, USA B. APPROACH C. RESULTS & OUTLOOK A. INTRODUCTION Goal of the project Due to instant dilution of ship emissions in grid cells and neglecting small-scale in-plume chemistry, current Chemistry Transport Models (CTMs) overpredict NOx concentrations resulting from these emissions, and hence their ozone production. We adapted an existing Gaussian plume model with chemistry (PARANOX) to parameterize the sub-grid scale processes shortening the lifetime of NOx in the plume. Using this updated CTM we intent to simulate NO2 columns and compare these with OMI (and GOME-2) measured columns to provide constraints on NOx emissions in the ship tracks. Ship tracks in OMI measured NO2 columns By comparing figures 1 and 2 it can be seen that OMI can only detect the most frequenlty used ship tracks. • Parameterizing ship emissions • PARANOX, originally developed by Meijer et al. [1997] , simulates the chemical evolution of atmospheric trace gas concentrations resulting from aircraft emissions in 10 rings in time, as a cross-section of the plume, perpendicular to the wind direction. • The updated model contains: • - chemistry within and outside the plume • dilution of emitted species inside the plume • expansion of the plume using latest plume dispersion techniques • entrainment of ambient air in the plume • updated emissions • updated chemistry rate constants (IUPAC, 2010) • photolysis values for the boundary layer • Using this adapted PARANOX, we were able to successfully simulate observations of a ship plume around noon near the Californian coast in May 2002, reported by Chen et al. [2005]. • The flight path for these measurements can be seen in figure 3. • The comparison of the O3 , NOx and OH concentrations can be seen in figures 4 till 6. The enhanced OH levels lead to a lower NOx lifetime in the plume (around 2-3 hours) compared to the background lifetime (of around 4-6 hours). By instantly diluting the emissions in the CTM , this stage would be missing and leading to higher NOx lifetimes and thus overpredicting NOx concentrations. Look up table To account for the non-linear chemistry in the shipping plume,we use the concept of ‘fraction of NOx remaining’. We use PARANOX to simulate how much NOx is still withing the exhaust plume 5 hours after emission. This fraction is then applied on the actual emissions from the inventory. Running PARANOX online in GEOS-Chem or TM5 for all ship emissions would be computationally expensive, so we build a look up table. In this table we store the fraction of NOx remaining and the integrated Ozone Production Efficiency (OPE) as a function of environmental parameters. After performing a sensitivity analysis we found the seven important variables to be Temperature, [O3], [CO], J(NO2), J(O1D)/(J(NO2), solar zenith angle (at t=0 and t=5). The dependencies of the Fraction of NOxremaning on temperature and [O3] are given in figures 8 and 9. Instant dilution Figure 7: Figure illustrating the preprocessing of the emissions before inserting them in the CTM. The dashed arrow represents the classical approach in most CTM’s. Figure 3: Flight path showing the transects of the ship plume, with the letters A-H corresponding to plume ages of 30 minutes (A) up to 3 hours (H). (source: Chen et al., [2005]) Emissions 30 g/s Emissions 15 g/s More OH More H2O Emissions 60 g/s Temperature (K) O3 (ppbv) Implementation into GEOS-Chem We are currently implementing the look up table in GEOS-Chem. After this we intent to compare the new simulated NO2 and O3 concentrations with available measurements from campaigns or cruises (e.g. QUANTIFY). Figure 9: Dependency of the Fraction of NOx remaining on the [O3]. Figure 8: Dependency of the Fraction of NOx remaining on the temperature for different initial emissions. Figure 1: OMI tropsopehric NO2 column averaged over winter 2004-2008 showing 6 ship tracks. The inset shows that both OMI and SCIAMACHY observe the indicated ship tracks, showing the diurnal cycle in the ship track. [2005] [2005] production recovery titration [2005] Figure 4: O3 concentrations in the plume as function of time. The solid line indicates the PARANOX. The dashed line represents the background concentrations and the squares are dierence between the observations and the observed background given by Chen et al. [2005] added to our background, with the bars indicating the errors in these observations. Figure 5: NOx concentrations in the plume as function of time. The solid line indicates the PARANOX simulation. The dashed line represents the background concentrations and the squares are the observation given by Chen et al. [2005] with the bars indicating the errors in these observations. Figure 6: OH concentrations in the plume as function of time. The solid line indicates the PARANOX simulation. The dashed line represents the background concentrations and the squares are the observation given by Chen et al. [2005] with the bars indicating the errors in these observations. D. REFERENCES [Chen et al., 2005] An investigation of the chemistry of ship emissions plumes during ITCT 2002, Journal of Geophisical Research, 110(D10S90). [Meijer et al., 1997] The effects of the conversion of nitrogen oxides in aircraft exhaust plumes in global models. Geophysical Research Letters, 24(23):3013-3016. [Vinken, 2010] Representing sub-grid scale plume chemistry from shipping emissions in Global Chemistry Transport Models. MSc. Thesis, R-1765-S. Eindhoven University of Technology. Figure 2: Map of shipping emissions based on AMVER-ICOADS inventory in mg [N] m-2 year-1.