Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMx

Transcription

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 outline motivation dry and wet deposition in CAMx results from km CAMx run at RMNP results of April 2006 tracer simulation 2

3 motivation 3

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) 4

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? 5

6 Field Study 6

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

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 8

9 deposition modeling in CAMx 9

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 10

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 Λ) v d = dry deposition velocity Λ = wet deposition scavenging coefficient must predict concentrations and v d / Λ correctly to accurately simulate deposition 11

12 dry deposition in CAMx to estimate dry deposition velocity, use an electric circuit analogue (e.g, Wesely, 1989) v d = r a r b r s -1 example v d over land NO = 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 12

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) 13

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) 14

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 15

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

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 17

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: 18

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 Λ g 19

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 Λ c to get fraction in aq. phase: 20

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 Λ c defined previously 21

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

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

24 wet deposition in CAMx Example precip estimated at Big Bend during BRAVO field campaign observed (a) MM5 (b) 24

25 results from km CAMx run at RMNP 25

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

27 emissions area source NOx 27

28 emissions area source ammonia 28

29 emissions point source NOx 29

30 CAMx 2002 deposition-nh4 NH4 dry deposition NH4 wet deposition 30

31 CAMx 2002 deposition-no3 NO3 dry deposition NO3 wet deposition 31

32 CAMx 2002 deposition-so4 SO4 dry deposition SO4 wet deposition 32

33 N conc and wet dep at RMNP 33

34 S conc and wet dep at RMNP 34

35 MPE at RMNP deposition significantly underpredicted Fractional Error - RMNP Fractional Bias - RMNP Fractional Error Fractional RMNP castnet castnet castnet castnet castnet nadp nadp nadp hno3 nh4 no3 so2 so4 nh4 no3 so4 0 castnet castnet castnet castnet castnet nadp nadp nadp -1.5 hno3 nh4 no3 so2 so4 nh4 no3 so4-2 35

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 ( m) Precip figures from Environ (2005 ) 36

37 MM5 precip: January

38 MM5 precip: July

39 results of April 2007 tracer simulation 39

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 40

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 41

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

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 43

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 44

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 45

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 46

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) 47

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 48

49 results: NOx tracer shaded areas indicate periods when some easterly or southeasterly flow was measured black = red + blue NOx Tracer at RMNP NOx tracer concentration (ppbv) NOx: All Sources NOx: All Sources - CO NOx: CO only 0 4/29 4/28 4/27 4/26 4/25 4/24 4/23 4/22 4/21 4/20 4/19 4/18 4/17 4/16 4/15 4/ (MST) 49

50 results: NH3 tracer shaded areas indicate periods when some easterly or southeasterly flow was measured black = red + blue NH3 Tracer at RMNP NH3 tracer concentration (ppbv) NH3: All Sources NH3: All Sources - CO NH3: CO only 0 4/29 4/28 4/27 4/26 4/25 4/24 4/23 4/22 4/21 4/20 4/19 4/18 4/17 4/16 4/15 4/ (MST) 50

51 CAMx results: April 2006 (all data) NOx tracer All Sources All Sources - CO CO only average (ppb): average (%): 51.1% 48.9% NH3 tracer All Sources All Sources - CO CO only average (ppb): average (%): 65.6% 34.4% 51

52 CAMx results: 20 April 2006 (24 hrs) NOx tracer All Sources All Sources - CO CO only average (ppb): average (%): 74.2% 25.8% NH3 tracer All Sources All Sources - CO CO only average (ppb): average (%): 85.4% 14.6% 52

53 CAMx results: April 2006 (72 hrs) NOx tracer All Sources All Sources - CO CO only average (ppb): average (%): 48.4% 51.6% NH3 tracer All Sources All Sources - CO CO only average (ppb): average (%): 66.0% 34.0% 53

54 CAMx results: 28 April 2006 (17 hrs) NOx tracer All Sources All Sources - CO CO only average (ppb): average (%): 77.9% 22.1% NH3 tracer All Sources All Sources - CO CO only average (ppb): average (%): 72.9% 27.1% 54

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 55

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 56