nitrogen and sulfur deposition modeling for romans with camx

Nitrogen and Sulfur Deposition Modeling for ROMANS with CAMxMike Barna1, Marco Rodriguez2, Kristi Gebhart1, John Vimont1, Bret Schichtel1 and Bill Malm1

1 National Park Service - Air Resources Division2 Cooperative Institute for Research in the Atmosphere – CSU

6-7 February 2007 WRAP Technical Analysis Forum Meeting

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

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motivation

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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)

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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?

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Field Study

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Measurements

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

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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

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deposition modeling in CAMx

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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

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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 ) vd = dry deposition velocity = wet deposition scavenging coefficient

must predict concentrations and vd / correctly to accurately simulate deposition

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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

ra

rb

rs

-1

vd =

relative NH3 deposition downwind of poultry farm (Fowler et al., 1998): deposits quickly

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dry deposition in CAMxresistors 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)

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dry deposition in CAMx take this a step further

by refining the surface resistance to make a ‘big leaf’ model (from Seinfeld & Pandis 1998)

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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

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dry deposition in CAMx deposition enhancement from orography, forest

canopies (from Hicks, 2003)

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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

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wet deposition in CAMx wet scavenging of ambient gases

occurs within and below cloudwithin a cloudy cell, determine aqueous

partitioning with Henry’s Law:

in falling rain drop, can’t assume instantaneous equilibrium, so estimate transfer coef:

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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

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wet deposition in CAMx wet scavenging of gases dissolved in cloud

waterraindrops collect cloud drops via impactionassuming monodisperse rain and cloud drops:

scale c to get fraction in aq. phase:

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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

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wet deposition in CAMx wet scavenging of dry particles

again, use c defined previously

but define new collection efficiency

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wet deposition in CAMx how well do met models simulate clouds

and precip?better during large synoptic forcingconvective cumulus parameterized

BRAVO MM5 GOES-East

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wet deposition in CAMx Example precip estimated at Big Bend

during BRAVO field campaign

(a)

(b)

observed

MM5

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results from 2002 36km CAMx run at RMNP

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emissions ROMANS: which N emission sources are

impacting RMNP?N sources in CO (from WRAP Base02b)

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emissions area source NOx

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emissions area source ammonia

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emissions point source NOx

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CAMx 2002 deposition-NH4

NH4 dry deposition NH4 wet deposition

NO3 dry deposition NO3 wet deposition

SO4 dry deposition SO4 wet deposition

NH4 dry depositionNH4 dry deposition NH4 wet depositionNH4 wet deposition

NO3 dry depositionNO3 dry deposition NO3 wet depositionNO3 wet deposition

SO4 dry depositionSO4 dry deposition SO4 wet depositionSO4 wet deposition

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CAMx 2002 deposition-NO3







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CAMx 2002 deposition-SO4







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N conc and wet dep at RMNP

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S conc and wet dep at RMNP

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MPE at RMNP

Fractional Error - RMNP

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0.2

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castnet castnet castnet castnet castnet nadp nadp nadp

hno3 nh4 no3 so2 so4 nh4 no3 so4

Frac

tiona

l Err

or

Fractional Bias - RMNP

-2

-1.5

-1

-0.5

0

0.5

1

1.5

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castnet castnet castnet castnet castnet nadp nadp nadp

hno3 nh4 no3 so2 so4 nh4 no3 so4Frac

tiona

l RM

NP

deposition significantly underpredicted

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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 )

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MM5 precip: January 2002

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MM5 precip: July 2002

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results of 15-28 April 2007 tracer simulation

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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

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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

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use nested grids

36/12/4 km MM5 domainsFront Range orography

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tracer emissionsExample 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

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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

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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

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CAMx resultsFocus 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

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CAMx resultsAn 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)

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CAMx resultsThree 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 28Examine the time series of impacts at RMNP during this period in terms of NOx tracer and NH3 tracer concentrations

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NOx Tracer at RMNP

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2006 (MST)

NO

x tr

acer

con

cent

ratio

n (p

pbV)

NOx: All SourcesNOx: All Sources - CONOx: CO only

results: NOx tracershaded areas indicate periods when some easterly or southeasterly flow was measured

black = red + blue

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results: NH3 tracer

NH3 Tracer at RMNP

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2006 (MST)

NH

3 tr

acer

con

cent

ratio

n (p

pbV)

NH3: All SourcesNH3: All Sources - CONH3: CO only

shaded areas indicate periods when some easterly or southeasterly flow was measured

black = red + blue

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

NOx tracerAll Sources All Sources - CO CO only

average (ppb): 2.17 1.11 1.06average (%): 51.1% 48.9%

NH3 tracerAll Sources All Sources - CO CO only


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CAMx results: 20 April 2006 (24 hrs)





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





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CAMx results: 28 April 2006(17 hrs)





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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

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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

nitrogen and sulfur deposition modeling for romans with camx

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