Evaluation of the CALPUFF Modelin Predicting Concentration, Visibility andDeposition at Class I Areas in WyomingJoseph Scire, Zhong Wu and Gary MooreEarth Tech, Inc.Concord, MAPresented at theAWMA Specialty ConferenceGuideline on Air Quality Models: The Path ForwardMystic, ConnecticutOctober 22-24, 2003Study Objectives• SW Wyoming Regional Air Quality ModelingProject (Feb, 2001)– Evaluate degree of degradation of air quality,visibility and other AQRVs due to local and distantupwind anthropogenic sources• Bridger and Fitzpatrick Class I areas– Evaluate the performance of the CALPUFFmodeling systemStudy Objectives (Cont.)• Extension of Modeling and AdditionalAnalysis– Evaluate the importance of inflow boundaryconditions and the Ammonia Limiting Method(ALM) on nitrate aerosol concentration predictions– Sensitivity testing with FLAG-recommendedconstant ammonia vs. time-varying ammoniaComponents of the Study• Emission inventory development– major point sources– area sources (urban areas, minor sources)– wild forest fires and prescribed burns– mobile sources (roads and railways)– petroleum/mining sources– biogenic emissions• MM5 meteorological modeling– 1995 simulation, 60 km grid, 20 km nest• CALMET diagnostic meteorological modeling– 4 km grid (116 x 100 cells)– terrain enhanced precipitation– MM5 as initial guess fieldComponents of the Study• CALPUFF modeling– point, area, line, and volume sources– new organic aerosol module– visibility, acid deposition, ambient pollutantconcentrations of SO2, SO4, NO, NO2, HNO3, NO3,NH3, and PM• Evaluation– visibility, PM, wet deposition fluxesCALPUFF Modeling Systemmeteorologicaldatageophysicaldataprogram controldataCALMET meteorological modelemissions datachemical, ozone,deposition dataterrain, receptor,meteorological dataprogram controldataCALPUFF dispersion modelpostprocessing & graphicaldisplay programsPUFF MODELING+RELEASEPOINT...... ....Why Use a Puff Approach forRegulatory Modeling ?Gaussian framework– can reproduce plume result under steady-stateconditions– easy to implement many of the plume-based modules– can provide a measure of consistency with previousregulatory results under appropriate conditionsComplex flows – near-field sources– complex terrain (terrain channeling, slope flows)– inhomogeneity in surface conditions/dispersion rates,urban/rural, land/water, land use variations– inversion breakup, plume fumigation– calm and low wind speed dispersion– pollutant build-up in valleys, stagnation, re-circulationWhy Use a Puff Approach forRegulatory Modeling ?(cont.)Long range transport– spatial variability in meteorological fields– causality effects– chemical transformation, wet deposition, dry deposition– mesoscale circulationsland/lake breeze circulationsup/down valley flowsMultiple source impacts within a spatially-varyingfield– cumulative impact assessments– source contributions– non-steady-state releases-800 -600 -400 -200 0 200 400 600 800East (km)-600-400-2000200400600 

# North (km)MM5 and CALMET DomainsMM5 60kmMM5 20kmCALMET 4km-300 -250 -200 -150 -100 -50 0 50 100X Coordinate (km)-250-200-150-100-50050100 

# Y Coo r dinate (km)Idaho FallsPocatelloRock SpringsEvanstonProvoSalt Lake CityRivertonJacksonPinedaleKemmererLanderThermopolisRawlinsBaggsCraig1200140016001800200022002400260028003000320034003600TerrainElevation(M MSL)8015WyomingColoradoUtahIdahoCALPUFF Domain – Terrain Elevations116 x 100 cells, 4km cells-300 -250 -200 -150 -100 -50 0 50 100East (km)-250-200-150-100-50050100 

# No r th (km)Idaho FallsPocatelloRock SpringsEvanstonProvoSalt Lake CityRivertonJacksonPinedaleKemmererLanderThermopolisRawlinsBaggsCraig1. Urban2. Agricultural3.Rangeland 4. Forest5. Water6. Wetland7. Barren land8. TundraCALPUFF Domain – Land UseMeteorological Observations• Hourly surface observations– 22 stations• Upper air observations– 5 stations• Precipitation observations– 67 stations230622306624021241271234567891011123017230662401824021240272408924127241567257472576-400 -300 -200 -100 0 100 200 300 400East (km)-400-300-200-1000100200 

# No r th (km)Surface stations (22)Upper air stations (4)12001400160018002000220024002600280030003200340036003800Class 1 Wilderness AreasTerrainElevation(M MSL)Surface and Upper Air Stations-300 -250 -200 -150 -100 -50 0 50 100 150 200 250East(km)-250-200-150-100-50050100150200 

# North (km)12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667120014001600180020002200240026002800300032003400360038004000TerrainElevation(M MSL)Precipitation Stations-180 -160 -140 -120 -100 -80 -60 -40 -20 0East (km)-40-20020406080100120140 

# North (km)Class 1 Wilderness AreasState Boundaries12001400160018002000220024002600280030003200340036003800Terrain(M MSL)10 m/sCALMET Winds - 7/12/95, Hour: 3, 10m-180 -160 -140 -120 -100 -80 -60 -40 -20 0East (km)-40-20020406080100120140 

# North (km)Class 1 Wilderness AreasState Boundaries11112222233333Terrain(M MSL)6 m/sCALMET Winds - 7/17/95, Hour: 13, 10mClimatological Annual Precipitation(PRISM)-300 -250 -200 -150 -100 -50 0 50 100 150 200 250East (km)-250-200-150-100-50050100150200 

# North (km)527229484760343957374658118426944214949535863654318434437303548434336452158455042418417263120333522415340403838Class 1 Wilderness AreasUnits:cm1030507090110130150170190XX = Annual precipitation (cm)= Location of precipitation stationstate boundaries1995 Annual Precipitation - NearestNeighbor Technique – 4km Cells-300 -200 -100 0 100 200East (km)-200-1000100200 

# North (km)Units:cm10305070901101301501701901995 Annual Precipitation – MM5Model – 20km CellsTerrain Enhanced Precipitation Algorithm• Uses three sources of data– Climatological datato determine spatial pattern ofprecipitation distribution– MM5 precipitation datato determine the timing ofprecipitation where observations are not available– Observational precipitation dataused whereavailable-300 -200 -100 0 100 200East (km)-200-1000100200 

# North (km)52294847603439374658118426944214949535863654318434437303548434336452158455042418417263120333522415340403838Units:cm10305070901101301501701901995 Annual Precipitation – CALMET TerrainAdjusted Precipitation – 4 km cellsEmission Inventory• Comprehensive 1995 inventory developedfrom “top-down” and “bottom-up” approaches– TP: GCVTC and 1990 EPA Interim EmissionsInventory– BU: state records, population stats, industrial data,CEM data• Emissions components– Constant point sources– Hourly varying point sources– Constant area sources– Hourly-varying area sourcesEmission Inventory• Emissions components (continued)– CEMS– Monthly-varying area sources– Monthly petroleum field emissions– Mobile sources– Hourly-varying on-road sources– Hourly-varying off-road sources– Constant railroad sources– Hourly-varying biomass fire emissions– Hourly-varying biogenic emissions– Monthly-varying ammonia emissions• 1776 Sources-300 -250 -200 -150 -100 -50 0 50 100East (km)-250-200-150-100-50050100 

# North (km)Class 1 Wilderness AreasState boundaries12001400160018002000220024002600280030003200340036003800Constant point sourcesCEM point sourcesExcess point sourcesTerrainElevation(M MSL)Constant and Varying Point Sources-300 -250 -200 -150 -100 -50 0 50 100East (km)-250-200-150-100-50050100 

# North (km)Idaho FallsPocatelloRock SpringsEvanstonProvoSalt Lake CityRivertonJacksonPinedaleKemmererLanderThermopolisRawlinsBaggsCraigClass 1 Wilderness AreasState boundaries12001400160018002000220024002600280030003200340036003800County area sourcesCity sourcesTerrainElevation(M MSL)City and County Area SourcesClass 1 Wilderness AreasState boundaries12001400160018002000220024002600280030003200340036003800PAW sourcesTerrainElevation(M MSL)-300 -250 -200 -150 -100 -50 0 50 100East (km)-250-200-150-100-50050100 

# North (km)Idaho FallsPocatelloRock SpringsEvanstonProvoSalt Lake CityRivertonJacksonPinedaleKemmererLanderThermopolisRawlinsBaggsCraigPetroleum Sources-300 -250 -200 -150 -100 -50 0 50 100East (km)-250-200-150-100-50050100 

# No r th (km)Idaho FallsPocatelloRock SpringsEvanstonProvoSalt Lake CityRivertonJacksonPinedaleKemmererLanderThermopolisRawlinsBaggsCraigClass 1 Wilderness AreasState boundaries12001400160018002000220024002600280030003200340036003800RailroadHighwayUnpaved roadTerrainElevation(M MSL)Mobile Sources – Roads and Railroads-120 -110 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10X(km)-100102030405060708090100 

# Y (KM)Gysum CreekPinedaleSouth PassSinks CanyonRoss LakeHobbs LakeBlack Joe LakeDeep LakeGannett PeakMt. BonnevilleWind River PeakWest Atlantic PeakGreen River LakeW of New Fork LakeNear V LakeNear Soda Lake (Boulder)Cottonwood CreekSweetwater NeedlesBridger IMPROVELower Saddlebag LakeReceptor LocationsModeling ResultsBase Simulation00.20.40.60.811.21.41.61.820 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Bridger IMPROVE [SO4]. Units: ug/m**3 of SO4 

# CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4CALPUFF Performance Predicting SO4W/ BCs and ALM (Bridger IMPROVE site)00.10.20.30.40.50.60 0.1 0.2 0.3 0.4 0.5 0.6Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3 

# CALPUFF Predicted [NO3]. Units: ug/m**3 of NO 3CALPUFF Performance Predicting NO3W/ BCs and ALM (Bridger IMPROVE site)00.20.40.60.811.21.41.61.820 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2Pinedale IMPROVE [SO2]. Units: ug/m**3 of SO2 

# CALPUFF Predicted [SO2]. Units: ug/m**3 of SO2CALPUFF Performance Predicting SO2W/ BCs and ALM (CASTNet Pinedale site)01020304050607080901001 31 61 91 121 151 181 211 241 271 301 331 361Julian Day in 1995 

# Bext (1/Mm)CALPUFF RPT 247 (Method 2 - MM5 RH)Bridger Valid Transmis.24-hr CALPUFF Bext (Method 2) vs.Bridger IMPROVE Transmissometer Data01020304050607080901001 31 61 91 121 151 181 211 241 271 301 331 361Julian Day in 1995 

# Bext (1/Mm)CALPUFF RPT247 (Method 6 - FLAG RH)Bridger Valid Transmis.24-hr CALPUFF Bext (Method 6) vs.Bridger IMPROVE Transmissometer DataObserved and Predicted SO4,NO3and SO2(ug/m3)90%.207.172.585.366SO278%.081.082.0922.0978NO394%.248.286.492.453SO4Within2xPredSDObsSDPredMeanObsMeanSpeciesObserved and Predicted Bext(Mm-1)Bridger Transmissometer Site--7.25.54.6StandardDeviation-0.030-0.01724.722.523.2MeanFractionalBiasMethod 6FractionalBiasMethod 2ObsCALPUFFMethod 6CALPUFFMethod 2Variable00.20.40.60.811.21.41.61.820 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2IMPROVE Observation (ug/m**3) 

# BC Source Used in CALPUFF (ug/m**3)Q-Q Plot of 24-hr SO4Concentrations at theBridger IMPROVE Site w/o Chemistry andDeposition Applied to BCs00.10.20.30.40.50.60 0.1 0.2 0.3 0.4 0.5 0.6IMPROVE Observation (ug/m**3) 

# BC Source Used in CALPUFF (ug/m**3)Q-Q Plot of 24-hr NO3Concentrations at theBridger IMPROVE Site w/o Chemistry andDeposition Applied to BCsComparison of CALPUFF and NADP Wet Deposition at Sinks Canyon0.0000.0500.1000.1500.2000.2500.3001 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet S Deposition (kg/ha)CALPUFF SulfurNADP SulfurComparison of CALPUFF and NADP Wet Deposition at Sinks Canyon0.0000.0500.1000.1500.2000.2500.3000.3500.4001 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet N Deposition (kg/ha)CALPUFF NitrogenNADP NitrogenComparison of CALPUFF and NADP Wet Deposition at South Pass0.0000.0500.1000.1500.2000.2501 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet S Deposition (kg/ha)CALPUFF SulfurNADP SulfurComparison of CALPUFF and NADP Wet Deposition at South Pass0.0000.0500.1000.1500.2000.2500.3000.3500.4001 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet N Deposition (kg/ha)CALPUFF NitrogenNADP NitrogenComparison of CALPUFF and NADP Wet Deposition at Gypsum Creek0.0000.0200.0400.0600.0800.1000.1200.1400.1601 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet S Deposition (kg/ha)CALPUFF SulfurNADP SulfurComparison of CALPUFF and NADP Wet Deposition at Gypsum Creek0.0000.0500.1000.1500.2000.2500.3001 2 3 4 5 6 7 8 9 10 11 12Month 

# Wet N Deposition (kg/ha)CALPUFF NitrogenNADP NitrogenObserved and Predicted Annual Wet S andN Deposition (kg/ha/yr) at NADP Sites0.720.73GypsumS1.241.18GypsumN0.790.97South PassS1.281.43South PassN0.831.11Sinks CanyonS1.361.34Sinks CanyonN0.690.75PinedaleS1.170.99PinedaleNPredictedWet FluxObservedWet FluxSiteSpeciesModeling ResultsTesting of ALM/BC vs FLAGConstant AmmoniaAssumptionCALPUFF Chemistry - ALM• HNO3/NO3equilibrium based on T and RH• Formation of NH4NO3may be limited by theavailability of NH3• NH3-limiting condition shifts split of TNO3toward HNO3• Sulfate preferentially scavenges NH3overNO3• Background SO4and NO3/HNO3,affectavailability of NH3and the formation of NO3PUFF MODELING+RELEASEPOINT...... ....Boundary Condition Module• CALPUFF BC module allows user to specifytime-varying inflow boundary concentrationsof modeled pollutants– Seasonal values of BC defined based onobservational data• Different boundary types defined by the user(e.g., east, west, north, south)• Background concentrations based on bestavailable measurements in region(IMPROVE, CASTNet and AIRS data)Boundary Condition Module• CALPUFF chemistry and deposition appliedto BC air parcels– HNO3/NO3repartitioned based on spatiallyvarying T and RH derived from CALMETmeteorological fields– Chemical transformation of BC air(chemically ages)– Wet deposition and dry deposition modeledin BC air-300 -250 -200 -150 -100 -50 0 50 100E AS T (k m )-250-200-150-100-5005 0100 

# NO RTH (km )[0 .4 5 ][0 .6 1 ][0.87 ][1 .0 9 ]0 .000 .0 10 .400 .901 .301 .70Julian D ay: 15 7H o ur: 00SO4( ug / m3)Boundary Condition – SO4,Hr: 0Boundary Condition – SO4,Hr: 5-300 -250 -200 -150 -100 -50 0 50 100EAST (k m )-250-200-150-100-5005 0100 

# NO RTH (km )[0 .4 5 ][0 .6 1 ][0 .8 7 ][1 .0 9 ]0 .000 .0 10 .400 .901 .301 .70Ju lian D ay: 1 5 7H o u r: 05SO4( u g / m3)Boundary Condition – SO4,Hr: 17-300 -250 -200 -150 -100 -50 0 50 100EAST (km)-250-200-150-100-5005 0100 

# NO RTH (km )[0 .4 5 ][0.61 ][0 .8 7 ][1 .0 9 ]0.000.010.400.901.301.70Ju lian D ay : 1 5 7H o u r: 1 7SO 4( ug/m3)CALPUFF Model Performance – NO3w/ BCs/ALM - Bridger IMPROVE Site00.10.20.30.40.50.60 0.1 0.2 0.3 0.4 0.5 0.6B r i dg e r IMP ROVE [ NO3 ] . Uni t s : ug / m**3 o f NO3NO3w/ Constant 0.5 ppb NH3- FLAG(2000) - Bridger IMPROVE Site( p p y)00.10.20.30.40.50.60.70.80 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8B r i d g e r IMP R OVE [ N O3 ] . U n i t s : u g / m **3 o f N O3NO3w/ Constant 0.5, 1.0 ppb NH3andtime-varying NH3-Bridger IMPROVESite00.20.40.60.811.20 0.2 0.4 0.6 0.8 1 1.2B ri d g e r IMP R OVE [ N O3 ] . U n i t s : u g / m **3 o f N O3Fact or 1Factor 2Fact or 3Fact or 4Fact or 2Fact or 3Fact or 4CALP UFF S ummed Re part it ion (S e asona l NH3)CALP UFF S e pa rat e d Repa rt it ion (NH3=0.5ppb)CALP UFF S e pa rat e d Repa rt it ion (NH3=1.0ppb)Conclusions – Model Enhancements• New developments improved modelperformance– Boundary condition module– Terrain-enhanced precipitation– ALM (POSTUTIL processor)Conclusions – Distant vs. Local Sources• Substantial fraction of sulfate and nitrateobserved at Bridger is predicted tocome from distant sources outside themodeling domain (~90%)• Primary pollutant impacts (e.g., SO2)are primarily from local sources (~70%)• Approximately 80-90% of wet S and Ndeposition is due to distant sourcesConclusions – Model Performance• Model performance is excellent whenusing BC module and ALM– 24-hr SO4and NO3distributions at BridgerIMPROVE site are mostly with ~10% ofobservations– SO2conservatively predicted at PinedaleCASTNet site (within 1.6 factor)– Wet annual N and S fluxes averages arewithin 9% and 13% of observations atNADP sitesConclusions – Visibility• Twenty-four hour Bextseasonal andrange of magnitudes relate well totransmissometer results• Average predicted Bextwithin 10% ofobservations• Method 2 (hourly RH) and Method 6(monthly averaged RH) producesresults that are similar, but Method 2outperforms Method 6 somewhatConclusions – Model Performance• CALPUFF model shows substantial skillin predicting parameters required inClass I impact assessments– Secondary particulate matterconcentrations– Primary pollutant concentrations– S and N deposition– Visibility - (Bext)Conclusions – Factors inPerformance• High quality emissions inventory• Good resolution prognosticmeteorological dataset to supplementobservations• Treatment of BCs and ammonia limitingeffectsConclusions – Constant NH3• Use of constant NH3, even at the lowestvalues allowed by FLAG (0.5 and 1.0 ppb)results in substantial overprediction of NO3(factors of 2-4 times)• Consideration of distant sources (BCs) andtime- space-varying ammonia is essential togood model performance for NO3• It is recommended that use of ALM/BCs beallowed for refined AQRV analysis