1. ROMANS Nitrogen Source Sensitivity Analysis Mike Barna 1 Marco Rodriguez 2 Kristi Gebhart 1 Bill Malm 1 Bret Schichtel 1 Jenny Hand 2 1 ARD-NPS, Fort Collins, CO 2 CIRA, Fort Collins, CO WRAP Workshop on Regional Emissions & Air Quality Modeling Denver, CO 29-30 July 2008 National Park Service U.S. Department of the Interior Cooperative Institute for Research in the Atmosphere

2. 2 N deposition increasing in the Rocky Mountains Alpine ecosystems are susceptible to extra N –N acts as a fertilizer → ecosystem change –changes may be hard to reverse –most deposition occurs as wet dep (~2/3) critical load: 1.5 kg/ha/yr Nitrogen deposition at RMNP

3. 3 Complex (small scale) diurnal and seasonal mountain circulation patterns. Vertical de-coupling due to inversions and stagnation in valleys. Orographic precipitation & isolated convective storms Lack of observations in remote mountainous areas. We still want accurate modeled winds, moisture, temperature, precip for CAMx, trajectories. Rocky Mountains = magnificent views, fragile ecosystem, complex met

4. 4 Two field campaigns conducted during spring (April) and summer (July – Aug) of 2006 Measure concentration and wet dep of important N and S species: NH4, NO3, NH3, NOx, SO4 ROMANS: Rocky Mountain Atmospheric Nitrogen & Sulfur Study

5. 5 Where is the nitrogen coming from? National Park Service U.S. Department of the Interior Cooperative Institute for Research in the Atmosphere local v. regional v. distant? oxidized or reduced?

6. 6 Modeling & data analysis ROMANS Back trajectories Airmass conditional probability Dry deposition of ‘missing’ nitrogen Tracer simulations -> EOF analysis (Bill Malm) ‘Lagrangian process analysis’ Base case simulation Source apportionment of N and S with PSAT ‘Hybrid modeling’ (Bret Schichtel) ….at the end, need to reconcile results from these different analyses

7. 7 Domain 1 36 Km 165 x 129 Domain 2 12 Km 103 x 115 Domain 3 4 Km 163 x 118 35 layers – 34 from WRAP, plus a 10-m layer Applying CAMx in ROMANS 36/12/4 km domains Met from obs-nudged MM5 Emissions based on updated 2002 WRAP inventory

8. 8 Distribution of NH3, NO3 NH3: Rapidly dry deposits Emissions very uncertain Strong spatial gradients NO3: Longer lifetime Particle or gas phase

9. 9 NH4+ NO3 SO4= NH3 HNO3 SO2 Beaver Meadows (RMNP)Grant, Nebraska April 2006 ROMANS base case

10. 10 ‘Lagrangian process analysis’ What processes influence the concentration within an airmass during its trajectory to RMNP? Wet and dry deposition Emissions Gas and aerosol chemistry NH3 NH4+

11. 11 –Sulfur species SO2 i Primary SO 2 emissions PS4 i Particulate sulfate ion from primary emissions plus secondarily formed sulfate –Nitrogen species RGN i Reactive gaseous nitrogen including primary NOx (NO + NO 2 ) emissions plus nitrate radical (NO 3 ), nitrous acid (HONO) and dinitrogen pentoxide (N 2 O 5 ). TPN i Gaseous peroxyl acetyl nitrate (PAN) plus peroxy nitric acid (PNA) NTR i Organic nitrates (RNO 3 ) HN3 i Gaseous nitric acid (HNO 3 ) PN3 i Particulate nitrate ion from primary emissions plus secondarily formed nitrate –Ammonia/ammonium NH3 i Gaseous ammonia (NH 3 ) PN4 i Particulate ammonium (NH 4 ) CAMx PSAT source apportionment

12. 12 CAMx tracer simulations ~100 source regions Tracers for NH3, NOx, SO2 Conserved, dry dep, wet dep, total dep Use with EOF’s

13. 13 ‘Missing nitrogen’ at RMNP N dry deposition at RMNP based on CASTNet Only three N species are typically ‘measured’ for dry deposition: NH 4 +, NO 3 - and HNO 3 What happens when we consider the dry deposition of total N at RMNP? Oxidized N (the NO y budget): NO x, HNO 3, NO 3 -, PAN + other organic nitrates, HONO, nitrate radical + N 2 O 5 Reduced N: NH 3, NH 4 + Simulate this ‘missing N’ with CAMx

14. 14 Annual average modeled nitrogen concentration from CAMx for 2002 CASTNet species: example ‘missing N’ species:

15. 15 What happens to emitted NOx & NH3 NH 3 : rapid deposition, NH 3  NH 4 +, no gas- phase oxidation NO x : complicated photochemistry, HNO 3  NO 3 -, some species rapidly deposit (HNO 3, NO. ) NH3NOx

16. 16 CAMx total N vs CASTNet N at RMNP Total Reduced N (NH 3 + NH 4 +):Total Oxidized N (NO y ): Conc: Dry Dep:

17. 17 Modeled dry deposition at RMNP

18. 18 Modeled dry deposition at RMNP

19. 19 CASTNet v. CAMx dry dep velocities CASTNet [cm/s] CAMx [cm/s]Reference Values HNO 3 1.86.27.5 1 (conifer forest) 3-6 2 1-5 3 NH 3 N/A7.50.6-3 (low vegetation) 4 2-20 (high vegetation) 4 NO 2 N/A0.110.1 3 0.1-0.5 5 OrgNN/A0.18 NxOyNxOy N/A5.6 PANN/A0.08 PM0.200.03<0.5 3 0.1 – 0.5 2 0.01 – 0.2 6 (grassland) 0.1–1 6 (forest) 1 Pryor et al., 2004 2 Duyzer et al., 1992 3 Finlayson-Pitts and Pitts, 1999 4 Asman, 2004 5 Seinfeld and Pandis, 2006 6 Pryor et al., 2008

20. 20 Yearly CAMx and CASTNet estimates of dry deposited N at RMNP for 2002

21. 21 Summary Nitrogen deposition is increasing at RMNP –> ROMANS Numerous approaches applied to N source apportionment at RMNP Receptor models Deterministic models EOF analysis Hybrid approach No single technique will provide the entire answer – need to reconcile

22. 22 Summary (cont’d) Can’t get enough simulated N to RMNP Nitric acid estimates not bad PM N (NH4 and NO3) underestimated Not capturing the late spring upslope event, although tracer transport ok Use ‘lagrangian process analysis’ to investigate this – chemistry, deposition or emissions?

23. 23 Summary (cont’d) Accounting for ‘missing’ nitrogen can almost double the estimated dry deposition at RMNP for 2002 (1.2 vs 2.2 kg/ha/yr). SpeciesN-flux [kg/ha yr]contribution HNO 3 1.1653% NH 3 0.6028% NxOyNxOy 0.2210% PAN + Org N0.115% Other N species0.126%

24. 24

25. 25 CAMx bias relative to CASTNet: HNO 3

26. 26 CAMx bias relative to CASTNet: NH 4 +

27. 27 CAMx bias relative to CASTNet: NO 3 +