1. Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMx Mike Barna 1, Marco Rodriguez 2, Kristi Gebhart 1, John Vimont 1, Bret Schichtel 1 and Bill Malm 1 1 National Park Service - Air Resources Division 2 Cooperative Institute for Research in the Atmosphere – CSU 6-7 February 2007 WRAP Technical Analysis Forum Meeting

2. 2 outline motivation dry and wet deposition in CAMx results from 2002 36km CAMx run at RMNP results of 15-28 April 2006 tracer simulation

3. 3 motivation

4. 4 Nitrogen deposition has exceeded a ‘critical load’ of 1.5 kg ha -1 yr -1 at Rocky Mountain NP  N acts as a fertilizer → ecosystem change (e.g., wildflowers to sedges, C.L. based on aquatic changes)  changes may be hard to reverse  most deposition occurs as wet dep (~2/3)

5. 5 motivation ROMANS – Rocky Mountain Atmospheric Nitrogen and Sulfur Study Field study and analysis Use an air quality model as part of source attribution analysis Where is extra N coming from?  NOx emissions decreasing from mobile sources and EGU’s  NH3 from agriculture/feedlots?

6. 6 Field Study

7. 7 Measurements URG annular denuder/filter-pack samplers Ionic composition of daily wet deposition PILS MOUDI Profiler Surface Met IMPROVE/CASTNet at core

8. 8 General Observations Gas phase higher concentrations E & W of park than at park Seasonal difference at park, not so much near source areas Particle phase similar both seasons

9. 9 deposition modeling in CAMx

10. 10 CAMx overview CAMx: ‘comprehensive air quality model with extentions’ One of two (the other being CMAQ) models being ‘widely’ used for simulating regional air quality  ozone  visibility (SO4, NO3, EC, OC, coarse PM)  not very often: mercury, toxics, deposition

11. 11 deposition modeling in CAMx relative importance of wet vs. dry deposition depends on  gas or particle  water solubility of species  clouds  amount of precipitation  orographic effects  land cover deposition flux = (concentration) * (v d or  )  v d = dry deposition velocity   = wet deposition scavenging coefficient must predict concentrations and v d /  correctly to accurately simulate deposition

12. 12 dry deposition in CAMx to estimate dry deposition velocity, use an electric circuit analogue (e.g, Wesely, 1989) example v d over land  NO = 0.016 cm s -1  NO2 = 0.1 cm s -1  HNO3 = 4 cm s -1  NH3 = 3.2 cm s -1 rara rbrb rsrs v d = relative NH3 deposition downwind of poultry farm (Fowler et al., 1998): deposits quickly

13. 13 dry deposition in CAMx  resistors correspond to the three phases of dry deposition r a = turbulent diffusion from the bulk flow to near the surface: r b = molecular (gases) or brownian (particles) diffusion across a viscous quasi-laminar sublayer: r s = uptake at the surface (complicated)

14. 14 dry deposition in CAMx take this a step further by refining the surface resistance to make a ‘big leaf’ model (from Seinfeld & Pandis 1998)

15. 15 dry deposition in CAMx things not considered in current dry deposition schemes  no transient wetted surfaces - effective for removing soluble gases (e.g., SO2, NH3)  enhanced turbulence from terrain gradients (‘flat earth’ assumption is bad); not described by surface roughness length  filtering by leading edges of forest canopies other models out there  NOAA’s multi-layer model (MLM)  more complicated, but not necessarily better

16. 16 dry deposition in CAMx deposition enhancement from orography, forest canopies (from Hicks, 2003)

17. 17 wet deposition in CAMx make some assumptions about scavenging:  only cloud water and precip are effective scavengers  rain drops and cloud drops are only one size  equilibrium between ambient concentration and cloud droplet  acidity of cloud water doesn’t change (pH ~ 5)  ideal gas  PM is hygroscopic and internally mixed  no ‘dry’ aerosols in interstitial air between cloud drops  no sub-grid clouds

18. 18 wet deposition in CAMx wet scavenging of ambient gases  occurs within and below cloud  within a cloudy cell, determine aqueous partitioning with Henry’s Law:  in falling rain drop, can’t assume instantaneous equilibrium, so estimate transfer coef:

19. 19 wet deposition in CAMx wet scavenging of ambient gases  specify drop diameter based on rainfall rate (provided by met model), and estimate speed:  multiply mass collected by number density (not shown) and divide by total concentration and ‘drop sweep time’ to get  g

20. 20 wet deposition in CAMx wet scavenging of gases dissolved in cloud water  raindrops collect cloud drops via impaction  assuming monodisperse rain and cloud drops:  scale  c to get fraction in aq. phase:

21. 21 wet deposition in CAMx wet scavenging of in-cloud aerosols  in cloudy grid cells, all aerosols are assumed to be in cloud liquid water  therefore, can use  c defined previously

22. 22 wet deposition in CAMx wet scavenging of dry particles  again, use  c defined previously  but define new collection efficiency

23. 23 wet deposition in CAMx how well do met models simulate clouds and precip?  better during large synoptic forcing  convective cumulus parameterized BRAVO MM5GOES-East

24. 24 wet deposition in CAMx Example precip estimated at Big Bend during BRAVO field campaign observed MM5

25. 25 results from 2002 36km CAMx run at RMNP

26. 26 emissions ROMANS: which N emission sources are impacting RMNP?  N sources in CO (from WRAP Base02b)

27. 27 emissions area source NOx

28. 28 emissions area source ammonia

29. 29 emissions point source NOx

30. 30 CAMx 2002 deposition-NH4

31. 31 CAMx 2002 deposition-NO3

32. 32 CAMx 2002 deposition-SO4

33. 33 N conc and wet dep at RMNP

34. 34 S conc and wet dep at RMNP

35. 35 MPE at RMNP deposition significantly underpredicted

36. 36 MM5 precip estimates compare precip: MM5 vs. NOAA CPC relative influence of synoptic vs. convective rain have more confidence in synoptic (stratus) rain convective rain depends on parameterization  Kain-Fritsch – more widespread, less intense  Betts-Miller – less widespread, more intense to explicitly resolve convection requires very small grids (10 1 – 10 2 m) Precip figures from Environ (2005 )

37. 37 MM5 precip: January 2002

38. 38 MM5 precip: July 2002

39. 39 results of 15-28 April 2007 tracer simulation

40. 40 ROMANS tracer runs CAMx was used to estimate the maximum potential contribution of nitrogen species to RMNP during the last two weeks of the spring ROMANS field campaign The results that follow represent maxima since there is no loss through:  - chemical transformation  - wet or dry deposition

41. 41 ROMANS tracer runs (cont’d) Two tracers, scaled to match the ‘real emission rates’ of NOx and NH3, were evaluated Two scenarios were considered:  - simulate all tracer sources  - simulate all tracer sources minus Colorado The difference between these two scenarios represents CO’s contribution relative to all other sources

42. 42 use nested grids 36/12/4 km MM5 domains Front Range orography

43. 43 tracer emissions Example emissions for the two tracer runs: - ‘all emissions’ on the left - ‘no Colorado’ on the right - do this for the NOx and NH3 tracers, and then run CAMx

44. 44 tracer emissions (cont’d) Tracer emissions behave just like ‘real’ emissions:  area sources are released in the surface layer  point sources have attendant stack characteristics, such as stack height, temperature, etc., so that CAMx can calculate the plume rise  forest fire NOx and NH3 treated as an ‘effective plume height’, estimated by fire emissions forum

45. 45 tracer emissions (cont’d) Caveats:  these aren’t really 2006 emissions, but rather 2002 (from the WRAP inventory)  expect substantial day-to-day variability for some source categories (like ammonia from ag and feedlots, and NOx from mobile and point)  since we don’t have 12km and 4km inventories, CAMx is interpolating the existing 36km inventory to these finer scales  none of the above are too dire for the purposes of this tracer run, and will be addressed once the ROMANS inventory is available

46. 46 CAMx results Focus on the last two weeks of the Spring 2006 ROMANS field campaign (15 – 28 April 2006) To address complex terrain, use nested grids (36/12/4km) Use two-way nesting (fine grids inform coarse grids) Examine results at the RMNP IMPROVE monitor for NOx and NH3 tracer for the ‘all sources’ run and the ‘no CO’ run; again, the difference between these two represents CO’s impact relative to all other sources within the domain

47. 47 CAMx results An example of separating ‘CO vs. rest of the world’:  - left: NOx tracer from all sources  - middle: NOx tracer from all sources except CO  - right: NOx tracer from CO sources only (the difference between the previous two frames)

48. 48 CAMx results Three periods were identified as having easterly or southeasterly winds during the last two weeks of the Spring ROMANS field campaign: April 20, April 23-25, April 28 Examine the time series of impacts at RMNP during this period in terms of NOx tracer and NH3 tracer concentrations

49. 49 results: NOx tracer shaded areas indicate periods when some easterly or southeasterly flow was measured black = red + blue

50. 50 results: NH3 tracer shaded areas indicate periods when some easterly or southeasterly flow was measured black = red + blue

51. 51 CAMx results: 15-28 April 2006 (all data)

52. 52 CAMx results: 20 April 2006 (24 hrs)

53. 53 CAMx results: 23-25 April 2006 (72 hrs)

54. 54 CAMx results: 28 April 2006 (17 hrs)

55. 55 Summary CAMx is being used to estimate a nitrogen and sulfur source apportionment as part of the ROMANS study Deposition fluxes significantly underestimated during the 2002 WRAP 36km simulation  36km domain too coarse for complex terrain of Rockies  precipitation estimates suspect, especially in terms of parameterized convective precip A conserved tracer simulation corresponding to the latter part of the ROMANS spring field campaign indicates that both Colorado sources and sources outside of Colorado significantly contribute to estimated nitrogen at RMNP

56. 56 Summary (cont’d) Updates for ROMANS  use 36/12/4km nested domains  update 2002 emission inventories to 2006 ammonia from fertilizer and feedlots importance of soil ammonia? new CEM data for large point sources updated Front Range mobile emissions  update N and S source apportionment to account for deposition  define boundary conditions from global model MOZART, GEOS-CHEM, GOCART