1. Applications and Evaluation of USEPA’s Models-3/CMAQ System: From Regional and Urban Air Pollution to Global Climate Change
Carey Jang, Pat Dolwick, Norm Possiel,
Brian Timin, Joe Tikvart
U.S. EPA
Office of Air Quality Planning and Standards (OAQPS)
Research Triangle Park, North Carolina

2. OUTLINE
Overview of USEPA’s “One-Atmosphere” Models-3/CMAQ Modeling System
Applications and Evaluation of Models-3 /CMAQ System
OAQPS Modeling Initiative on Intercontinental Transport and Climatic Effects of Pollutants

3. Third-Generation Air Quality Models: U.S.EPA’s Models-3/CMAQ System
“Open-Access” Community-Based Models :
User-friendly, Modular, Common modeling framework for scientists and policy-makers.
Advanced Computer Technologies :
High performance hardware and software technologies (Cross-platform, GUI, distributed computing, visualization tools, etc.).
“One-Atmosphere” Modeling :
Multi-pollutant (Ozone, PM, visibility, acid deposition, air toxics, etc.), Multi-scale.

4. “One-Atmosphere” Management and Modeling
Mobile
Sources
Ozone
NOx, VOC,
PM, Toxics PM
(Cars, trucks, planes,
boats, etc.)
Industrial
Sources
Acid Rain
Chemistry
Meteorology
NOx, VOC,
SOx, PM,
Toxics
Visibility
(Power plants, refineries/
chemical plants, etc.)
Air Toxics
Atmospheric
Deposition
Area
Sources
Climate Change
NOx, VOC,
PM, Toxics
(Residential, farming
commercial, biogenic, etc.)

5. (Nitrogen deposition, Lake Acidification)
NOx-Related Air Quality Issues
(NO3-, NH4+) PM
(NOx + VOC + hv) -->
Ozone
NOx
Acid Rain
(NO3- deposition)
Visibility
(Fine PM)
Water Quality
(Nitrogen deposition, Lake Acidification)

6. SOx-Related Air Quality Issues
(Fine PM)
Visibility
(SO42-, NH4+) PM
SOx
Acid Rain
(SO42- deposition)
Water Quality
(Lake acidification,
Toxics deposition)

7. .OH role in pollutants formation : One-Atmosphere
PM2.5
VOC + OH --->
Orgainic PM
SOx [or NOx] + NH3 + OH
---> (NH4)2SO4 [or NH4NO3]
One Atmosphere
Ozone
Visibility
Fine PM
(Nitrate, Sulfate,
Organic PM)
One Atmosphere
.OH
NOx + VOC + OH
+ hv ---> O3
Water
Quality
Acid Rain
SO2 + OH ---> H2SO4
NOx + SOx + OH
(Lake Acidification,
Eutrophication)
NO2 + OH ---> HNO3
Air Toxics
OH <---> Air Toxics
(POM, PAH, Hg(II), etc.)

8. Example of “One-Atmosphere” Modeling
Impact of
50 % NOx
Emission Reduction
on PM 2.5

9. Impact of 50% NOx emission reduction
Nitrate PM decrease
Sulfate PM decrease

10. Impact of 50% NOx emission reduction
O3 decrease
HOx decrease

11. Formation of Secondary PM :
Sulfate PM formation:
H2SO4 + 2 NH3 ---> (NH4)2SO4 (s)
Gas Phase: O2,H2O
SO2 + OH ---> H2SO4
Aqueous Phase: H2O
SO2 + H2O2 ---> H2SO4 (Dominate over low pH)
SO2 + O > H2SO4
Nitrate PM formation:
HNO3 + NH3 <---> NH4NO3 (aq,s)
Gas Phase : (daytime)
NO2 + OH ---> HNO3
Gas &Aq Phase : (nighttime)
N2O5 + H2O ---> HNO3
Oraginc PM formation:
Gas Phase :
VOC + OH ---> Organic PM(semi-volatile)
(Long-chain VOCs, Aromatics, Biogenic VOCs)

12. Impact of 50% NOx emission reduction
O3 decrease
HOx decrease

13. Impact of 50% NOx emission reduction
Nitrate PM decrease
Sulfate PM decrease

14. Third-Generation Air Quality Models: U.S.EPA’s Models-3/CMAQ
“Open-Access” Community-Based Models :
User-friendly, Modular, Common modeling framework for scientists and policy-makers.
Advanced Computer Technologies :
High performance hardware and software technologies (Cross-platform, GUI, distributed computing, visualization tools, etc.).
“One-Atmosphere” Modeling :
Multi-pollutant (Ozone, PM, visibility, acid deposition, air toxics, etc.), Multi-scale.

15. Models-3/CMAQ System Framework
Meteorology
Processor
RAMS or
Emission
Processor
SMOKE
Air Quality
Model
PAVE

16. Models-3/CMAQ Demo & Evaluation:
USEPA/ORD
Domain :
Eastern U.S.A.
Grid Resolution :
36-km/12-km/4-km
(Nested Modeling)
Episode :
July , 1995

17. O3 Episode in the Northeast U.S. (7/12-15, 1995)
Nested 4 km grid domain (144 x 147 cells)

18. Measurement Sites and Terrain Features
(Courtesy of USEPA/ORD, Daewon Byun)

19. Model Predictions vs. Observations

20. Models-3/CMAQ Applications at EPA/OAQPS
Western U.S. Application
Episodic O3, July 96, 36/12 km, Evaluation completed
Annual Nationwide U.S. Application
1-atmosphere, annual 1996, 36-km, evaluation & diagnostics, on-going annual 2000
Eastern U.S. Application
1-atmosphere, July 95, urban applications, 36/12/4-km, emissions control & growth
Intercontinental Transport/Air Quality & Climate Change
Intercontinental transport and climatic effects of air pollutants

21. Models-3/CMAQ Modeling: Domain Maps
36 km eastern US domain
4 km domain
Showed this plot before -- just to reorient.
Western US - 36/12, ozone only, episodic (July 1996)
National US - 36, ozone & PM, entire year of 1996
Eastern US - 36/12/4, ozone & PM, episodic (July 1995)
12 km domain
12 km western US ozone domain
36 km western US ozone domain
36 km Annual National US domain

22. Western U.S. Application
Objectives :
New M3/CMAQ Domain
New Episode (July 1996)
Model Setup :
Episodic O3 modeling
Meteorology : MM5
Emissions : Tier-2 regridded
36km/12km, 12 layers
Compared against UAM-V
177
153

23. Modeled
Observed

25. Process Contribution to O3
Process Analysis : (Los Angeles grid)
Diffusion
Process Contribution to O3
(ppm / hr)
Chem
O3 Conc.
O3 Conc. and Trend
( ppm & ppm / hr)
dO3/dt
Time Step (7/19 - 7/31/96)

26. Western U.S. CMAQ Ozone Modeling

27. BEIS3 Sensitivity Testing -- Western U.S.

28. Annual Nationwide U.S. Application
Features :
Annual CMAQ Run
Nationwide CMAQ Domain
Model Setup :
Annual PM and O3 (1996)
36-km, 8 vertical layers
Meteorology : MM5
Emissions Processing: SMOKE
Model Evaluation: Compared against observed data (IMPROVE & CASTNET) & REMSAD

32. NOx Emissions
SO2 Emissions
July 1, 1996

33. Models-3/CMAQ Simulation: Annual Average
PM 2.5
Sulfate PM
Nitrate PM
Organic PM

34. Models-3/CMAQ : Monthly Average (July)
Sulfate PM
PM 2.5
Nitrate PM
Organic PM

35. Models-3/CMAQ : Monthly Average (January)
Sulfate PM
PM 2.5
Nitrate PM
Organic PM

36. Despite the relative lack of ambient data in this case, it is still challenging to condense the evaluation analyses into a handful of plots. Intent of the next series of plots is to provide a general overview of model performance, while highlighting specific performance problems.
Describe metric: Annual Average PM 2.5
Explain plot:
x-axis: observations (ug/m3)
y-axis: model predictions, paired in space/time (ug/m3)
therefore, each point represents an IMPROVE site/model pair
warn audience that scales will change over the next series of comparable plots
dashed lines indicate 25% error bounds
Note:
1) Generally, the model performance for annual average PM 2.5 is reasonable. The majority of the sites fall w/in 25% of the observed values. Coincidentally, the average annual average in the obs is 6.3; in the model it is 6.4
2) Note overpredictions can be critical near annual standard of 15: (model 6, observed 2)
3) Note six or so underpredicted sites are varied (Everglades, Sequoia, Guadeloupe, Pac NW)
4) Model performance for annual average PM 2.5 can be misleading if the PM components are not being accurately predicted.

37. Now looking at 3 key PM components
Describe metric: Annual Average sulfate ion, nitrate ion, and organic carbons
Explain plot:
same format as before (smaller scales)
red = sulfate ion; blue = nitrate ion; organic carbon = maroon
Note:
1) Can immediately see that the relatively accurate PM2.5 predictions resulted from compensating errors -- overestimated nitrates and underestimated organic carbons
2) Sulfates are generally accurate, but the highest values are overestimated by about 15-30%
3) What could cause overestimation of nitrates?
emissions, especially ammonia emissions -- will discuss later
meteorology - cloud fractions -- crude sensitivity test showed can have impact
chemistry calculations w/in the model
4) What could cause underestimation of organics?
emissions, especially biomass burning (remember no wild fires & prescribed burning numbers are highly uncertain) and biogenics (lg. source of OC PM)
meteorology/chemistry: SOA module? (aerosols added); clouds/precipitation issues

38. Same as before but focusing on three key PM species in the summer: East vs. West
Describe metric: Summer Average sulfate ion, nitrate ion, and organic carbons
Explain plot:
same format as before (smaller scale)
squares are east; black-outlined circles are west
red = sulfate ion; blue = nitrate ion; organic carbon = maroon
Note:
1) Have identified summer issues in the western US - sulfates and organic carbons appear to be largely underestimated (esp. OC). Not shown here but Soil is an issue in the west in the summer.
2) Eastern U.S summer - mild underestimation, apparently driven by organics

39. Same as before but focusing on three key PM species in the winter: East vs. West
Describe metric: Winter Average sulfate ion, nitrate ion, and organic carbons
Explain plot:
same format as before (smaller scale)
squares are east; black-outlined circles are west
red = sulfate ion; blue = nitrate ion; organic carbon = maroon
Note:
1) Have identified winter performance problem in the eastern US - - nitrate overestimation appears to be exclusively the problem
2) Western U.S. winter also features model over predictions (on a percentage basis may even be larger than eastern U.S. due to small observed values) -- again nitrate calculations are the culprit.

40. National 1996 CMAQ Modeling: CB4 vs. RADM2
Ammonium tied to NH3 emissions as well.
Other Fine PM = crustal (note EI issues)
Nitrate PM (Jan. 1996)

41. Formation of Secondary PM :
Sulfate PM formation:
H2SO4 + 2 NH3 ---> (NH4)2SO4 (s)
Gas Phase: O2,H2O
SO2 + OH ---> H2SO4
Aqueous Phase: H2O
SO2 + H2O2 ---> H2SO4 (Dominate over low pH)
SO2 + O > H2SO4
Nitrate PM formation:
HNO3 + NH3 <---> NH4NO3 (aq,s)
Gas Phase : (daytime)
NO2 + OH ---> HNO3
Gas &Aq Phase : (nighttime)
N2O5 + H2O ---> HNO3
Oraginc PM formation:
Gas Phase :
VOC + OH ---> Organic PM(semi-volatile)
(Long-chain VOCs, Aromatics, Biogenic VOCs)

42. NH3 Sensitivity Modeling
Nitrate PM : (January Avg.)
Base % NH3 reduction
0.62
0.70
0.09
0.08
0.53
1.69
1.16
1.24

43. Same as before but focusing on three key PM species in the winter: East vs. West
Describe metric: Winter Average sulfate ion, nitrate ion, and organic carbons
Explain plot:
same format as before (smaller scale)
squares are east; black-outlined circles are west
red = sulfate ion; blue = nitrate ion; organic carbon = maroon
Note:
1) Have identified winter performance problem in the eastern US - - nitrate overestimation appears to be exclusively the problem
2) Western U.S. winter also features model over predictions (on a percentage basis may even be larger than eastern U.S. due to small observed values) -- again nitrate calculations are the culprit.

44. National 1996 CMAQ Modeling: O3 (July Max in ppb)56
149 99 62
134 76
132
139
137
107
126
119
131
215
Next series of plots are a small sample of some of the output from the annual CMAQ simulation.
Overlay values from AIRS data
Comment on:
1) clearly identify metric: maximum hourly surface O3 in July 1996
2) broad area of ozone in eastern U.S.
3) relative absence of ozone in CA
4) Pacific Northwest is actually accurate (Portland, Seattle)
5) correlation of high ozone w/ shoreline areas
6) 36km is certainly insufficient coarse for urban ozone assessments
127
144
113
194

45. National 1996 CMAQ Modeling: Visibility (January Average in Deciview)18 16 11 19 14 22 9 26 16 26 24 10 9 26 24 15 22
Highlight one-atmosphere capabilities of CMAQ
This is annual average reconstructed visibility (ignore erroneous subtitle)
of course, higher deciviews = poorer visibility
as expected, matches annual PM2.5 patterns
The Great Smoky Mountains NP is the most affected Class 1 area
Overlaid numbers represent 3-year averages ( ) of deciview values from IMPROVE & CASTNET networks.
Any visibility skill may be incidental, mostly a function of sulfate concentrations 12 24 14 23

46. National 1996 CMAQ Modeling (January average)
Sulfur Wet Deposition
Nitrogen Wet Deposition
Ammonium tied to NH3 emissions as well.
Other Fine PM = crustal (note EI issues)

47. OAQPS Modeling Initiative on Intercontinental Transport and Climatic Effects of Air Pollution

48. Air Pollution/Climate Change Modeling Initiative
Background :
O3 and PM are not only key air pollutants, but also major climate-forcing substances;
Reduction of non-CO2 substances (e.g., O3 and PM, especially black carbon) could be a viable alternative to CO2 reduction to curb global warming. A key strategy suggested was to focus on air pollution to benefit regional and local air quality and global climate simultaneously (Hansen et al., PNAS, 2000);
Black carbon could be the second largest heating component after CO2 contributing to global warming; Control of fossil-fuel black carbon could be the most effective method of slowing glabal warming (Jacobson, Nature, 2001);

49. Climatic Effects of Air Pollution
Black (0.8)
Carbon
(Hansen et al., PNAS, 2001)

51. Air Pollution/Climate Change Modeling Initiative
Background (continued):
There is also mounting evidence that criteria pollutants originating from some developing countries, especially those in Asia such as China and India, could impact U.S. domestic air quality as well as contribute to the global background of climate-forcing substances. This intercontinental transport issue is expected to worsen with the rapid growth in emissions in these regions.
For example, recent modeling studies showed that by 2020 Asian emissions could contribute as much as 2 ~ 6 ppb of O3 in the western U.S., offsetting the Clean Air Act efforts up to 25% in that region (Jacob et al., Geophys. Res. Letts., 1999) and increase global mean O3 level up to 10% (Collins et al., Atmos. Env., 2000); Asian and Sahara dust could contribute a significant amount of PM in the western and southeastern U.S. (Husar, !

52. Asian Dust Storm Event: April 2001 (NASA/TOMS)
(4/7)
(4/9)
(4/11)
(4/12)
(4/13)
(4/14)

53. Transport of CO : March 2000 (NASA/MOPITT)
(3/10)
(3/12)
(3/15)
(3/13)
(Byun and Uno, 2000)

54. Air Pollution/Climate Change Modeling Initiative
Objectives :
To assess available approaches for evaluating the linkage of air pollution to climate change and enhancing modeling capacity within EPA to address these linkage issues.
To explore the impacts of intercontinental transport of O3 and PM as well as their implications for US domestic and regional air quality and global climate change
To design integrated emissions control strategies to benefit global climate and regional and local air quality simultaneously

55. Air Pollution/Climate Change Modeling Initiative
Work Plan :
Phase I : Short-Term (~6 months)
Establish a better scientific foundation to address the issues related to intercontinental transport and climatic effects of air pollutants by leveraging current studies
1. Global Modeling of O3 and PM
2. Global Radiative Forcing of Aerosols
3. Emission Inventories for Climate-Forcing Pollutants
Hold a “Workshop on Air Quality and Climate Change” and establish an expert advisory panel to provide guidance in developing a conceptual model and modeling protocol for Phase II work.

56. Air Pollution/Climate Change Modeling Initiative
Work Plan : Phase II - Long-Term (~2 years)
Based on Phase I effort, a series of activities will be conducted. These efforts may include, but not limited to:
1. Support continued development of global and regional modeling capabilities for studying “policy-relevant” climatic effects of air pollution and the impacts of intercontinental transport
2. Improve global and regional emission inventories for global and regional modeling of O3 and PM
3. Develop nesting capability between global chemistry/climate models and regional air quality models
4. Simulate hemispheric or regional air quality under a variety of current and future global and regional emission scenarios
5. Evaluate global and regional air quality models using a diverse set of observational data sets, including data from satellites, surface networks, intensive field studies, etc
6. Assessment of the potential radiative forcing and climate benefits resulting from planned and alternative non-CO2 control strategies

57. Air Pollution/Climate Change Modeling Initiative
Trans-Pacific O3 simulation
3D Tracer from Gobi Desert
(Byun and Uno, 2000)
(MCNC, 2000)

58. Charge Questions
1. Support continued development of global and regional modeling capabilities for studying “policy-relevant” climatic effects of air pollution and the impacts of intercontinental transport
Are there existing global/regional models that can be practically used for the assessment of these two issues simultaneously?
If not, which models are better for addressing the climatic effects of air pollution (global climate/chemistry model)? Which are better for addressing the impacts of intercontinental transport (global chemistry/regional air quality models)?
2. Improve global and regional emission inventories for global and regional modeling of O3 and PM
Are current global and regional EI sufficient for O3 and PM modeling?
If not, what are the weaknesses of exising regional/global EI?
O3 and PM precursors (NOx, SOx, VOC, NH3, etc.), black carbon?
Geographic distribution (Asia, America, Europe?) and resolution (global, regional, and urban)?
Source categories (biomass burning, biogenic emissions, domestic, mobile/point/area sources, etc.)?
EI modeling tools to convert EI to data needed for modeling?

59. Charge Questions
3. Develop nesting capability between global chemistry/climate models and regional air quality models
Are the chemical boundary conditions sufficiently represented in regional air quality models?
Do global models have sufficient resolution to address regional impacts?
Is grid-nesting between global and regional models a good approach to bridge these gaps?
4. Simulate hemispheric or regional air quality under a variety of current and future global and regional emission scenarios
What current and future scenarios are to be simulated?
Emissions sensitivity scenarios (NOx, SOx, VOC, BC, CH4, etc.)?
Source categories sensitivity scenarios (fossil fuel, transportation, biomass burning, etc.)?
IPCC & LRTAP emission scenarios?
Climate change scenarios? Energy use scenarios?

60. Charge Questions
5. Evaluate global and regional air quality models using a diverse set of observational data sets
What data sets are available for evaluating the model results?
Satellite and spacecraft? Surface network? Remote/Sentinel monitoring stations? Special field studies?
How to effectively use the observed data to evaluate against the model results?
6. Assessment of the potential radiative forcing and climate benefits resulting from planned and alternative non-CO2 control strategies
Can global climate models be used to estimate the climate-forcing effects of air pollutants?
How to account for the photochemistry of O3 and PM?
Can regional air quality models be used to estimate the climate-forcing effects
How to translate changes in pollutant concentrations to climatic-forcing? Can we assume changes in pollutant concentration is linear to changes in radiative properties?
How to extrapolate from regional-/hemispheric-scale modeling results to global-scale climate change?