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Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMx

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

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Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMx

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  1. Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMx Mike Barna1, Marco Rodriguez2, Kristi Gebhart1, John Vimont1, Bret Schichtel1 and Bill Malm1 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. 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. motivation

  4. motivation • 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. 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. Field Study

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

  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. deposition modeling in CAMx

  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. 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) * (vd or L) • vd = dry deposition velocity • L = wet deposition scavenging coefficient • must predict concentrations and vd / L correctly to accurately simulate deposition

  12. -1 ra rb vd = rs dry deposition in CAMx • to estimate dry deposition velocity, use an electric circuit analogue (e.g, Wesely, 1989) • example vd 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 relative NH3 deposition downwind of poultry farm (Fowler et al., 1998): deposits quickly

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

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

  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. 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. 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 Lg

  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 Lc to get fraction in aq. phase:

  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 Lc defined previously

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

  23. BRAVO MM5 GOES-East wet deposition in CAMx • how well do met models simulate clouds and precip? • better during large synoptic forcing • convective cumulus parameterized

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

  25. results from 2002 36km CAMx run at RMNP

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

  27. emissions • area source NOx

  28. emissions • area source ammonia

  29. emissions • point source NOx

  30. CAMx 2002 deposition-NH4

  31. CAMx 2002 deposition-NO3

  32. CAMx 2002 deposition-SO4

  33. N conc and wet dep at RMNP

  34. S conc and wet dep at RMNP

  35. MPE at RMNP • deposition significantly underpredicted

  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 (101 – 102 m) • Precip figures from Environ (2005 )

  37. MM5 precip: January 2002

  38. MM5 precip: July 2002

  39. results of 15-28 April 2007 tracer simulation

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

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

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

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