Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman USEPA/OAQPS October 22, 2002

Introduction USEPA has performed an annual simulation of CMAQ and REMSAD for a 1996 base year An operational evaluation has been completed for both models Model performance is difficult to summarize due to the lack of ambient PM2.5 data (from 1996) Performance varies by season and by PM2.5 component

1996 National CMAQ and REMSAD- Model Setup CMAQ- May 2001 release w/MEBI solver REMSAD- Version 7.01 Model Setup: – Domain: CMAQ and REMSAD: 36km, 12 layers, ~38 m surface layer – Emissions: CMAQ and REMSAD: 1996 NEI w/adjustments, processed via SMOKE – Meteorology: 1996 MM5 – Chemistry: CMAQ: CB-IV chemical mechanism w/ fast solver (MEBI) REMSAD: micro-CB-IV chemical mechanism

CMAQ Modeling Domain Nationwide Modeling Domains REMSAD Modeling Domain CMAQ National domain is a Lambert conformal projection from 100°W, 40°N REMSAD uses a lat-long projection

Notes on Emission Inventory Base Year 1996 NEI w/adjustments Removal of wildfires, wind blown dust, and residential on-site incineration Removal of commercial wood-fired combustion – Maryland and Maine PM Transport Factor – 75% reduction in fugitive dust sources Adjusted CA NOx and VOC (non-EGU)

Notes on Emission Inventory (con’t) Revised Temporal Data – Prescribed burning – Animal husbandry Used results from ORD inverse modeling (monthly reductions of 20-60%) Annual NH3 inventory reduced by ~30% – Crop fertilization / agricultural burning CMU NH3 inventory USDA Crop Calendar Biogenic Emissions – BEIS 3.09

Model Performance- Ambient Data Issues PM2.5 data is collected from a variety of networks with different measurement protocols and analysis techniques – FRM PM2.5 – IMPROVE – Urban speciation sites – CASTNET dry deposition network – CASTNET visibility network – Continuous PM2.5 and speciation monitors – NADP wet deposition network Certain measurements are highly uncertain It is a challenge to determine how to match model output to ambient data – “Draft” data mapping will be provided

CMAQ and REMSAD Model Performance Completed statistical comparison against observations for 12 layer REMSAD and CMAQ Data sources: IMPROVE network; CASTNET dry dep. Network; NADP wet deposition network; CASTNET visibility network All comparisons paired in time/space Statistics and scatterplots for seasonal and annual averages – Calculated performance statistics by year and season for each monitoring site Thousands of individual numbers; only presenting gross summary Limited data base (in 1996) makes conclusive statements re: model performance difficult

Annual Average PM2.5

IMPROVE Annual Average Performance Statistics- REMSAD -Modeled PM2.5 is compared to measured PM2.5 fine mass -Organic aerosols includes a 1.4 multiplication factor -East/West is defined by 100th meridian -Annual mean predicted/annual mean observed -Negative numbers are underpredictions

IMPROVE Annual Average Performance Statistics- CMAQ -Modeled PM2.5 is compared to measured PM2.5 fine mass -Organic aerosols includes a 1.4 multiplication factor -Annual mean predicted/annual mean observed -Negative numbers are underpredictions

Seasonal Average Sulfate Performance

July Average Sulfate

Seasonal Average Particulate Nitrate Performance

Seasonal Average Total Nitrate Performance

January Average Particulate Nitrate

Differences in Winter Nitrate Much of the difference in winter nitrate predictions between CMAQ and REMSAD can be traced to different implementations of the dry deposition routines Nitrate concentrations were found to be sensitive to dry deposition of NH3, HNO3, and NO2 Improvements and adjustments are needed in both CMAQ and REMSAD, particularly in the areas of: – Treatment of snowcover and freezing temperatures – Specification of land use and surface roughness – Treatment of soluble species when canopies are wet January nitrate concentrations agreed to within ~25% after the dry deposition routines were made more similar to each other through a series of sensitivity runs (with REMSAD)

January Nitrate Comparison After Dry Deposition Sensitivities

Seasonal Average Organic Aerosols Performance

July Average Organic Aerosols

Seasonal Average Elemental Carbon Performance

January Average Elemental Carbon

Seasonal Average Crustal/Other PM2.5 Performance

July Average Crustal/Other PM2.5

Winter Average Nitrate CMAQ 1996 vs. Observed (IMPROVE and Urban Speciation) Qualitative comparison of spatial patterns with more recent urban speciation data

Model Performance- Summary of Individual Species CMAQ tends to predict higher concentrations than REMSAD; especially in the West REMSAD slightly underpredicts sulfate in the East; CMAQ slightly overpredicts sulfate Nitrate is overpredicted in the East – Total nitrate (particulate + nitric acid) is overpredicted in all seasons Indicates an overestimation of nitric acid REMSAD underpredicts organic carbon; CMAQ is relatively unbiased – Large uncertainty in the primary organic inventory (no wildfires), the organic measurements, and the secondary organic chemistry – CMAQ is predicting much more biogenic SOA; but it is using an aerosol yield approach (AE2) Much of the biogenic SOA in REMSAD is being partitioned into the gas phase

Model Performance- Individual Species Elemental carbon is generally unbiased – Large uncertainty in measurement of elemental carbon (EC/OC split) IMPROVE sites have very low EC concentrations Soil/other concentrations are overpredicted – Inventory issues Fugitive dust, unspeciated emissions from construction, paved roads, etc. in urban areas NADP wet concentration comparisons – Sulfate CMAQ overpredicts in the East; REMSAD underpredicts – Nitrate Both models overpredict in the East; REMSAD underpredicts in the West – Ammonium REMSAD underpredicts; CMAQ slightly overpredicts in the East

Next Steps Additional evaluation techniques can be applied – Further comparisons to more recent urban speciation data – Closer look at individual sites, days, seasons, regions Time series plots 20% best/worst days for visibility Plan to model 2001 base year – Significantly more ambient data available Continue to look at PM monitoring issues and how they affect model performance evaluation – Uncertainty in nitrate observed data – EC/OC split – Monitoring network protocol differences