Appendix C.1



VISTAS Modeling Protocol

Tennessee Department of Environment and Conservation 

Division of Air Pollution Control 

9th Floor L&C Annex 

401 Church Street 

Nashville, TN 37243

TN Regional Haze SIP Appendix C.1 - 1
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TN Regional Haze SIP Appendix C.1 - 2
 May 2003 

Second Draft Report 

VISTAS Emissions and 

Air Quality Modeling— 

Phase I Task 6 Report: 

Modeling Protocol for the VISTAS Phase II 

Regional Haze Modeling 

Prepared by: 

Ralph E. Morris 

Bonyoung Koo 

ENVIRON International Corporation 

101 Rowland Way 

Novato, California 94945 

T.W. Tesche 

Cyndi Loomis 

Gregory Stella 

Alpine Geophysics LLC 

3479 Reeves Drive 

Ft. Wright, Kentucky 41017 

Gail Tonnesen 

Zion Wang 

UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, California 92507 

May 6, 2004 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 

TN Regional Haze SIP Appendix C.1 - 3
May 2004 

i 

TABLE OF CONTENTS 

Page 

EXECUTIVE SUMMARY ...........................................................................ES-1 

Background...........................................................................................ES-1 

Top-Down Evaluation Methodology .............................................................ES-1 

Results ............................................................................................ES-3 

Conclusions ..........................................................................................ES-6 

Recommendations...................................................................................ES-7 

1. INTRODUCTION ...................................................................................1-1 

2. MODELING METHODOLOGY.................................................................2-1 

Modeling Databases ..................................................................................2-1 

Emission Inventories..................................................................................2-3 

DDM Sensitivity Coefficients.......................................................................2-6 

Ambient Data ........................................................................................ 2-15 

3. RESULTS..............................................................................................3-1 

Aircraft Data...........................................................................................3-1 

Summary of Model Performance for Aircraft Measurements .................................3-4 

Top-Down Evaluation of Emissions by Source Category ......................................3-8 

Model Performance Evaluation with Surface Precursor Data ............................... 3-46 

Top-Down Evaluation of Emissions by Geographic Region ................................. 3-50 

4. VOC RELATIVE REACTIVITY FACTORS.................................................4-1 

5. SUMMARY AND CONCLUSIONS .............................................................5-1 

 Top-Down Evaluation Methodology ...............................................................5-1 

 Results ..............................................................................................5-2 

 Conclusions ............................................................................................5-5 

 Recommendations.....................................................................................5-6 

REFERENCES........................................................................................... R-1 

APPENDICES 

Appendix A: Comparisons of Modeled and Observed Ozone 

 Concentrations for TexAQS Aircraft Flights on August 27-31, 2000 

TN Regional Haze SIP Appendix C.1 - 4
May 2004 

ii 

TABLES 

Tables ES-1. Summary of emissions scenarios....................................................ES-3 

Table 2-1. Summary of MM5 and CAMx layer heights........................................2-1 

Table 2-2. Model-ready emissions (tons/day) for the HGBPA 11 county area. ............2-5 

Table 2-3. Average OH-reactivity (kOH, ppm-1 min-1) for VOC emissions. ..............2-6 

Table 3-1. Regression coefficients from least squares fits to single term models......... 3-15 

Table 3-2. Sensitivity of least squares fits to HCHO errors.................................. 3-16 

Table 3-3. Regression coefficients from least squares fits to combinations 

 of NOx emission categories under the PT_O2N2 emissions scenario......... 3-17 

Table 3-4. Summary of VOC emissions scenarios............................................. 3-18 

Table 3-5a. Summary of model performance statistics for emission 

 scenarios described in the text. .................................................... 3-20 

Table 3-5b. Summary of model performance statistics for emission 

 scenarios described in the text....................................................... 3-21 

Table 3-6a. Summary of model performance statistics for emission 

 scenarios described in the text....................................................... 3-23 

Table 3-6b. Summary of model performance statistics for emission 

 scenarios described in the text....................................................... 3-24 

Table 3-7. Model performance summary statistics based on 

 aircraft observations for O3, NOy, and HCOH .................................. 3-25 

Table 3-8. Regression coefficients from least squares fits to combinations of 

 VOC emission categories under the MABVOC emissions scenario........... 3-38 

Table 3-9. Summary of ozone model performance statistics for the 

 NOxMAP emission scenario relative to the PT_O2N2 scenario............... 3-39 

Table 3-10. Summary of CAMx model performance for formaldehyde 

 at the La Porte and Williams Tower monitoring sites ........................... 3-46 

Table 3-11. Summary of CAMx model performance for NOy at the 47 

Table 3-12. Emission totals (episode average tons/day) for sub-regions 

 identified in Figure 3-21 (“Other” refers to the entire 

 modeling domain outside the six sub-regions). ................................... 3-51 

Table 3-13. Single term least squares fit results based on sub-regional 

 sensitivities (highlighted rows indicates statistically 

 significant fits at 98% confidence level). .......................................... 3-58 

Table 3-14. Least squares fit results based on sub-regional VOC sensitivities 

 for all species (five term model; highlighted rows indicates statistically 

 significant regression coefficients at 98% confidence level).................... 3-58 

Table 3-15. Least squares fit results based on sub-regional VOC sensitivities 

 for ozone and formaldehyde only (five term model; 

 highlighted rows indicates statistically significant regression 

 coefficients at 98% confidence level)............................................... 3-59 

Table 3-16. Least squares fit results based on sub-regional VOC sensitivities 

 for ozone only (five term model; highlighted rows indicates 

 statistically significant regression coefficients at 98% confidence level) ..... 3-59 

TN Regional Haze SIP Appendix C.1 - 5
May 2004 

iii 

Table 3-17. Least squares fit results based on sub-regional NOx sensitivities 

 for all species (five term model; highlighted rows indicates 

 statistically significant regression coefficients at 98% confidence level). .... 3-59 

Table 3-18. Least squares fit results based on sub-regional NOx sensitivities 

 for NOy only (five term model; highlighted rows indicates statistically 

 significant regression coefficients at 98% confidence level).................... 3-60 

Table 3-19. Least squares fit results based on sub-regional NOx sensitivities 

 for ozone only (five term model; highlighted rows indicates statistically 

 significant regression coefficients at 98% confidence level).................... 3-60 

Table 4-1. Reactivities for CB4-VOCs relative to ethylene for the Houston 

 points source emissions and the EKMA box model MIR scenario ..............4-2 

Table 5-1. Summary of emissions scenarios......................................................5-2 

FIGURES 

Figure 2-1. The CAMx modeling domain showing the 

 nested 36, 12 and 4 km grids. .........................................................2-2 

Figure 2-2. Detail of the CAMx 4 km grid and the flexi- 

 nested 1 km grid used on selected days. .............................................2-2 

Figure 2-3. Mask defining 4 km grid cells in the 11 County 

 HGBPA nonattainment area............................................................2-4 

Figure 2-5. Sensitivity of ozone (ppb) to emissions in the 

 11 county HGBPA area for 13:00 on August 29, 2000 under 

 the PT_O2N2 emissions and the 070PBLmeteorology: 

 on-road mobile VOC; b) area and low-level point VOC; 

 c) biogenic VOC; d) elevated point VOC; e) on-road 

 mobile NOx; f) area and low-level point NOx; g) biogenic 

 NOx; h) elevated point NOx......................................................... 2-10 

Figure 2-6. Sensitivity of ozone (ppb) to area and low-level point VOC 

 emissions in the 11 county HGBPA area for 13:00 on 

 August 29, 2000: a) Base4a emissions with standard meteorology; 

 b) Base4a emissions with the 070PBL meteorology; c) PT_O2N2 

 emissions with 070PBL meteorology............................................... 2-12 

Figure 2-7. Comparison of modeled (filled contours) and aircraft observed 

 (filled circles) ozone at 13:00 on August 29, 2000 for: a) Base4a 

 emissions with 070_PBL meteorology, and; b) PT_O2N2 emissions 

 with 070_PBL meteorology. ......................................................... 2-13 

Figure 2-8. Surface ozone monitoring site locations and codes in the 1 km grid. ......... 2-16 

Figure 3-1. Example aircraft/model ozone comparisons for: a) August 27; b) 

 August 28; c) August 29; d) August 30 and e) August 31, 2000. ...............3-2 

Figure 3-2. Scatter plot of aircraft ozone observations and corresponding 

 CAMx predictions under the Base4a emissions scenario (red stars 

 indicate observed/predicted pairs for which observed ozone is greater 

 than 125 ppb).............................................................................3-5 

TN Regional Haze SIP Appendix C.1 - 6
May 2004 

iv 

Figure 3-3. Scatter plot of aircraft ozone observations and corresponding CAMx 

 predictions under the PtO2N2 emissions scenario (red stars indicate 

 observed/predicted pairs for which observed ozone is greater 

 than 125 ppb).............................................................................3-5 

Figure 3-4. Scatter plot of aircraft formaldehyde observations and corresponding 

 CAMx predictions under the Base4a emissions scenario (red stars 

 indicate observed/predicted pairs for which observed ozone is greater 

 than 125 ppb).............................................................................3-6 

Figure 3-5. Scatter plot of aircraft formaldehyde observations and corresponding 

 CAMx predictions under the PtO2N2 emissions scenario (red stars 

 indicate observed/predicted pairs for which observed ozone is greater 

 than 125 ppb).............................................................................3-6 

Figure 3-6. Scatter plot of aircraft NOy observations and corresponding 

 CAMx predictions under the Base4a emissions scenario (red stars 

 indicate observed/predicted pairs for which observed ozone is greater 

 than 125 ppb).............................................................................3-7 

Figure 3-7. Scatter plot of aircraft NOy observations and corresponding CAMx 

 predictions under the PtO2N2 emissions scenario (red stars indicate 

 observed/predicted pairs for which observed ozone is greater than 125 ppb ..3-7 

Figure 3-8. Percentile box plot displays of DDM O3 sensitivity coefficient 

 distributions under the PtO2N2 emissions scenario for low and high 

 O3 aircraft observations (high O3 defined as O3 > 125 ppb). ................ 3-10 

Figure 3-9. Percentile box plot displays of DDM NOy sensitivity coefficient 

 distributions under the PtO2N2 emissions scenario for low and high 

 O3 aircraft observations (high O3 defined as O3 > 125 ppb). ................ 3-11 

Figure 3-10. Percentile box plot displays of DDM HCOH sensitivity coefficient 

 distributions under the PtO2N2 emissions scenario for low and high 12 

Figure 3-11. Scatter plot matrix for DDM sensitivity coefficients under the PtO2N2 

emissions scenario. .................................................................... 3-12 

Figure 3-12. Time series of predicted and observed ozone at surface monitoring 

 sites under three emissions scenarios described in the text. .................... 3-13 

Figure 3-13. Contour map (light gray contours at 20 ppb intervals) of 

 predicted surface layer daily maximum ozone concentrations 

 for 25 August 2000 under the four emissions scenarios described 

 in the text. Predicted maximum shown in red; observed daily 

 maximum ozone concentrations shown in blue at monitor site 

 locations. County boundaries and coast line shown in orange................. 3-26 

Figure 3-14. Contour map (light gray contours at 20 ppb intervals) of 

 predicted surface layer daily maximum ozone concentrations 

 for 29 August 2000 under the four emissions scenarios described 

 in the text. Predicted maximum shown in red; observed daily 

 maximum ozone concentrations shown in blue at monitor site locations. 

 County boundaries and coast line shown in orange .............................. 3-33 

TN Regional Haze SIP Appendix C.1 - 7
May 2004 

v 

Figure 3-15. Contour map (light gray contours at 20 ppb intervals) of 

 predicted surface layer daily maximum ozone concentrations 

 for 30 August 2000 under the four emissions scenarios described 

 in the text. Predicted maximum shown in red; observed daily 

 maximum ozone concentrations shown in blue at monitor site locations. 

 County boundaries and coast line shown in orange. ............................. 3-35 

Figure 3-16. Contour map (light gray contours at 20 ppb intervals) of 

 predicted surface layer daily maximum ozone concentrations 

 for 31 August 2000 under the four emissions scenarios described 

 in the text. Predicted maximum shown in red; observed daily 

 maximum ozone concentrations shown in blue at monitor site 

 locations. County boundaries and coast line shown in orange................. 3-36 

Figure 3-17. Locations of surface (fixed site) ozone monitoring network in Houston...... 3-37 

Figure 3-18. Time series of observed ozone at surface observing sites along 

 corresponding predicted values under the PT_O2N2 and 

 AP scenarios............................................................................ 3-40 

Figure 3-19. Comparison of predicted and observed formaldehyde 

 concentrations at La Porte and Williams Tower monitoring sites 

 under the PT_O2N2 and VOCMAB emission scenarios. ....................... 3-48 

Figure 3-20. Comparison of predicted and observed NOy concentrations 

 at La Porte and Williams Tower monitoring sites under the 

 PT_O2N2 and VOCMAB emission scenarios .................................... 3-49 

Figure 3-21. Spatial distribution of emissions in the Houston modeling 

 domain for 28 August 2000 under the PT_O2N2 scenario: 

 elevated and low point source NOx (top) and on-road mobile 

 VOC (bottom); black boxes indicate sub-region boundaries; 

 sub-regions are numbered 1 to 6 as shown in top panel......................... 3-52 

Figure 3-22. Relationship of low-level to elevated source VOC DDM 

 sensitivities for ozone ................................................................. 3-53 

Figure 3-23. Scatter plot matrix of low-level VOC and elevated point 

 NOx DDM coefficients from the sub-region sensitivity analysis 

 (E0n01VOC and E0n02NOx refer to the low-level VOC and 

 elevated point NOx DDM sensitivities for sub-region 

 1, 2 ... 5 as defined in Figure 3-21) ................................................ 3-54 

Figure 3-24. Percentile box plot displays of sub-region (Reg. 1 – Reg. 5) 

 low plus elevated source O3 sensitivity coefficient distributions 

 low (right panel) and high (left panel) O3 aircraft observations 

 h O3 defined as O3 > 125 ppb)...................................................... 3-55 

Figure 3-25. Percentile box plot displays of sub-region (Reg. 1 – Reg. 5) 

 low plus elevated source NOy sensitivity coefficient distributions 

 low (right panel) and high (left panel) O3 aircraft 

 observations (high O3 defined as O3 > 125 ppb)................................. 3-56 

Figure 3-26. Percentile box plot displays of sub-region (Reg. 1 – Reg. 5) 

 low plus elevated source formaldehyde sensitivity coefficient 

 contributions for low (right panel) and high (left panel) O3 aircraft 

 observations (high O3 defined as O3 > 125 ppb)................................. 3-57 

Figure 3-27. Mean ozone prediction errors (ppb) from aircraft data: PT_O2N2 scenario. 3-61 

Figure 3-28. Mean NOy prediction errors (ppb) from aircraft data: PT_O2N2 scenario .. 3-62 

TN Regional Haze SIP Appendix C.1 - 8
May 2004 

vi 

Figure 3-29. Mean formaldehyde prediction errors (ppb) from 

 aircraft data: PT_O2N2 scenario.................................................... 3-63 

Figure 4-1. Ozone sensitivity to OLE (olefins) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-3 

Figure 4-2. Ozone sensitivity to PAR (paraffins) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-3 

Figure 4-3. Ozone sensitivity to TOL (toluenes) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-4 

Figure 4-4. Ozone sensitivity to XYL (xylenes) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-4 

Figure 4-5. Ozone sensitivity to MEOH (methanol) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-5 

Figure 4-6. Ozone sensitivity to ETOH (ethanol) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-5 

Figure 4-7. Ozone sensitivity to FORM (formaldehyde) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-6 

Figure 4-8. Ozone sensitivity to ALD2 (aldehydes) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-6 

Figure 4-9. Ozone sensitivity to ISOP (isoprene) compared to ETH 

 (ethylene) for O3 > 80ppb and VOC sensitive conditions........................4-7 

Figure 4-10. Ozone sensitivity to PT_O2N2 point source VOC emissions compared 

 to ETH (ethylene) for O3 > 80ppb and VOC sensitive conditions..............4-7 

Figure 4-11. Comparison of relative reactivities for CB4-VOCs from 

 Houston point sources and the EMKA MIR scenario..............................4-8 

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EXECUTIVE SUMMARY 

INTRODUCTION 

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) is a 

collaborative effort of state governments, tribal governments, and various federal agencies 

established to initiate and coordinate activities associated with the management of regional haze, 

visibility and other air quality issues in the Southeastern United States. It is one of five Regional 

Planning Organizations (RPOs) covering the U.S. that are developing a regional approach for 

addressing regional haze, fine particulate matter (PM) and ozone issues. VISTAS is comprised 

of the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South 

Carolina, Tennessee, Virginia, and West Virginia as well as the Eastern Band of Cherokee 

Indians. The agencies participating in VISTAS are committed to a sound and thorough scientific 

analysis of regional haze problems, impacts from natural and man-made pollutants, and potential 

solutions. Stakeholders are encouraged to participate at the workgroup level in order that all 

aspects of the problem and possible strategies may be given consideration. 

The Clean Air Act establishes goals for visibility in many national parks, wilderness 

areas, and international parks as “the prevention of any future, and the remedying of any 

existing, impairment of visibility in mandatory Class I Federal areas which impairment results 

from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards, and Tribes also may opt to assume 

responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation 

Plans (TIPs). The goal of the Regional Haze Rule (RHR) is to achieve natural visibility 

conditions at the Federally mandated Class I areas by 2064. To achieve this goal, the RHR has 

set up several milestone years of 2018, 2028, 2038, 2048, 2058 and 2064 to monitor progress 

toward natural visibility conditions. Section 308 requires the first visibility SIP/TIP be submitted 

to EPA by 2007 - 2008 that demonstrates progress to natural visibility conditions in 2018 using 

the 2000-2004 five-year baseline. 

VISTAS has embarked on a multi-year effort to address the RHR requirements for the 

southeastern U.S. One of the components of VISTAS is the use of meteorological, emissions 

and regional particulate matter (PM)/ ozone models to project visibility at VISTAS Class I areas 

in future-years. The VISTAS modeling effort has been split into two Phases. Under Phase I, 

which was performed primarily in 2003, VISTAS tested and evaluated meteorological, emissions 

and PM/ozone models for three episodes to identify the optimal configuration for regional fine 

particulate, ozone and visibility modeling in the southeastern U.S. In Phase II, which was 

initiated in 2004, VISTAS is setting up and applying the selected models, with the optimal 

configuration identified by Phase I work, for an annual cycle and possibly additional periods. 

The short-term objectives of the Phase II activities are to achieve a satisfactory base year base 

case simulation and model performance evaluation, as well as initial future-year simulations, by 

the end of 2004 so that control strategy evaluation can begin in 2005. This document is the 

second draft Modeling Protocol for the VISTAS Phase II work efforts that has incorporated 

comments from the VISTAS Technical Analysis workgroup that was dated March 12, 2004 

(Morris et. al., 2004). 

 In order to meet the VISTAS Phase II schedule, that VISTAS Phase II emissions and air 

quality modeling is dependent on receiving data from other VISTAS contractors. Figure ES-1 

TN Regional Haze SIP Appendix C.1 - 10
 May 2004 

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-2 

displays key milestone dates of the VISTAS Phase II activities including the emissions and air 

quality modeling work elements that are the shaded boxes in Figure ES-1. The Phase II 

emissions and air quality modeling is dependent upon receiving data in a timely fashion from 

other VISTAS contractors in order to meet the deliverable deadlines. 

Figure ES-1. VISTAS Phase II emissions meteorological and air quality modeling activities to 

be carried out in 2004 and early 2005. 

PRELIMINARY CONCEPTUAL MODEL OF VISIBILITY IMPAIRMENT IN THE 

VISTAS REGION 

VISTAS is currently developing a comprehensive conceptual model of visibility 

impairment in the southeastern US. Brewer, Holman and Hornback (2003) have reported a 

preliminary analysis to characterize the components of PM2.5 and their contributions to visibility 

impairment in the VISTAS region. The analyses was based on monitoring data from the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the period 

1998 to 2001 and from the Southeast Aerosol Research Characterization Study (SEARCH) 

network for the period 1999 to 2001. 

Jan 2004

Revised 2002 

VISTAS Em Inv

Feb 2004

MM5 Met runs

6 mo 2002 

Apr 2004

Draft “2018” 

National Inv 

Sep 2004

Revised 2002

National Inv

Mar-Sep 2004

Annual 2002 

CMAQ model

performance

Dec 04 ?

“2018” Base 

Run 

Apr 2004:

DDM in 

CMAQ

May-Oct 2004

“2018” Emissions 

Sensitivity Runs 

Sep 2004

“Typical” 2002

Modeling Inv

Oct-Dec 2004:

Control Strategy 

Inventories

Jan 2005

Phase II “2018”

Sensitivity Runs 

Jan-Jun 2005

“2018” Control 

Strategy Runs

Apr 2004

CART:select

episodes

July-Dec 2005:

Observations

Conclusions

Recommendations

Jan 2005

Interim Future 

Year Model Runs

Dec 2004

Interim Future 

Year Inventories

Emissions, Meteorological, Air Quality Modeling Deliverables

State Regulatory 

Activities

Jan-Jun 2004

Define BART sources

Jun 2004

Identify BART controls

Draft 1/16/04

EPA- approved

Modeling 

Protocol

June 2005

Economic Analyses

Feb 2004

Em Modeling 

QA + Fill Gaps

Jan 2004

Met modeling

protocol 

Feb 2004

AQ modeling

protocol 

Jan 2004

AQ Phase I 

wrapup 

Jan-Mar 2004

Define inv growth and 

control assumptions 

Dec 2004

Revised 2002

Base Run 

(model 

performance)

Oct 2004:

Revised “2018”

Em Inv

Sept 2004

MM5 Met 

Final Report Dec 2004

“Typical” 

2002 Run 

(compare to 

“2018” runs)

Aug 2004

Natural Background and Reasonable Progress Goals

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Fine particle composition and contribution to light extinction were evaluated for days with the 

20% highest and 20% lowest total light extinction (on annual basis) for 1998 to 2001 in the 

IMPROVE network and for 1999 to 2001 in the SEARCH network. For the IMPROVE sites, on 

the 20% haziest days (highest extinction), light extinction (i.e., visibility impairment) is greater 

at the Southern Appalachian, Mammoth Cave, and Sipsey sites than at the coastal and coastal 

plain sites (Figure ES-2). Visual range on the 20% haziest days varies from 18 -25 km at the 

northern IMPROVE sites and 27-48 km at the coastal and coastal plain sites. 

Figure ES-2. Components of average light extinction (Mm-1) at southeastern US IMPROVE 

monitors for the 20% best visibility days from 1998-2001). 

Light extinction for the 20% haziest days is generally higher at the northern SEARCH 

sites than the coastal SEARCH sites. Visual range on the 20% haziest days at the SEARCH sites 

varies from 15-24 km. For the 20% clearest days (lowest extinction), there is less spatial 

variation in extinction than is observed for the 20% haziest days. Visual range on the 20% 

clearest days varies from 75-131 km at the IMPROVE sites and 57-91 km at the SEARCH sites. Extinction (Mm-1)

 Coarse

Soil

Organics

EC

NH3NO3

(NH4)2SO4

Rayleigh

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Shining Rock, NC

Figure 3. Light Extinction on 20% Poorest Visibility Days - IMPROVE 1998 - 2001

50

100

150

200

250

0

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Figure ES-3. Components of average light extinction (Mm-1) at southeastern US IMPROVE 

monitors for the 20% best visibility days from 1998-2001). 

In both the IMPROVE and SEARCH networks, the 20% haziest days in the year occur 

most frequently in the summer and spring quarters and least frequently in the winter quarters. 

Variation in sulfate concentrations is the greatest contributor to seasonal variation in extinction 

in the VISTAS region. Ammonium sulfate, (NH4)2SO4, fine particles are the largest contributors 

to light extinction for both the 20% clearest and 20% haziest days at all IMPROVE and all 

SEARCH sites in all years and all quarters. (NH4)2SO4 fine particles are also the largest 

contributors to reconstructed fine particle mass at all IMPROVE and SEARCH sites for the 20% 

haziest days and at all IMPROVE sites for the 20% clearest days. On the 20% clearest days at 

the SEARCH sites, organic carbon (OC) mass can be greater than (NH4)2SO4 mass. 

Ammonium nitrate, NH4NO3, typically contributes 0-15% of the fine particle mass and 

fine mass light extinction at the SEARCH and IMPROVE sites. NH4NO3 has the highest 

contributions at the urban Birmingham and Atlanta SEARCH sites and less contribution at the 

more rural SEARCH and IMPROVE sites. Available NH4 measurements for the SEARCH and 

IMPROVE sites indicate that SO4 is not fully neutralized, particularly during the summer 

quarter. Modeling analyses suggest that NH4NO3 formation in the southeastern U.S. is limited 

by NH4 availability in the atmosphere 

Organic carbon, OC, has the second largest contribution to fine particle mass and fine 

mass light extinction for the 20% haziest days at all sites (see Figure ES-2). On the 20% clearest 

days at IMPROVE sites, NH4NO3 has an equal or sometimes larger contribution to fine mass 

extinction than OC (Figure ES-3). The contribution of OC to fine mass extinction is relatively 

constant across the quarters of the year. Analysis indicates that OC fine mass in the fall and 

winter periods is predominantly from primary sources (e.g. gasoline, diesel, wood burning) and Extinction (Mm-1)

 Coarse

Soil

Organics

EC

NH3NO3

(NH4)2SO4

Rayleigh

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Shining Rock, NC

Figure 5. Extinction on 20% Best Visibility Days - IMPROVE 1998 - 2001

0

10

20

30

40

50

60

TN Regional Haze SIP Appendix C.1 - 13
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that secondary organic aerosols (e.g. from biogenic emissions) have small contributions to total 

OC fine mass in the fall and winter months and larger contributions in the spring and summer 

months. 

Elemental carbon, EC, typically contributes 3-15% to fine particle mass and fine mass 

light extinction at the IMPROVE and SEARCH sites. The highest levels of EC are at the urban 

Birmingham and Atlanta SEARCH sites. Elevated levels of OC, EC, and non-soil potassium can 

be used as indicators of episodes where emissions from fires impacted the monitoring sites. In 

particular, poor visibility conditions in October 2000 and November 2001 at several sites 

throughout the southeastern U.S. have been attributed to impacts due to emissions from fires. 

Further analyses is needed to separate the relative contributions of different types of combustion 

sources (e.g. forest fire, prescribed forest burning, land clearing, agricultural burning, residential 

wood combustion, or industrial wood combustion) to elevated OC and EC measures. 

Fine Soil generally contributes 0-3 % of the fine mass extinction and 5-20% of the fine 

particle mass at the southeastern U.S. SEARCH and IMPROVE sites. Soil has a higher 

percentage contribution to fine mass and fine mass extinction at Chassahowitzka and Everglades 

IMPROVE monitors in Florida than at the other IMPROVE and SEARCH sites in the 

southeastern U.S. Coarse mass generally contributes less than 5% to total extinction at the 

IMPROVE sites and at those SEARCH sites where coarse mass is measured. There is 

considerable inter-annual variability in all of the above measures of fine particle mass and 

extinction. 

Fine particle mass and composition are also measured by the state agencies at several 

sites throughout the VISTAS region. The national ambient air quality standard for PM2.5 for an 

annual average (15 μg/m3

) is measured to be exceeded in several urban areas in the southeastern. 

SUMMARY OF PHASE I FINDINGS 

The Phase I program entailed systematic meteorological, emissions, and air quality model 

testing and evaluation to identify the most reliable, scientifically valid, and operational efficient 

modeling configuration for visibility assessment in the Southeastern U.S. VISTAS selected three 

episodes to serve as the foundation for model testing and evaluation: (a) 1-20 January 2002, (b) 

13-27 July 2001, and (c) 13-21 July 1999. A brief summary of the Phase I findings is given 

below. 

Meteorological Modeling Findings 

The VISTAS meteorological modeling contractor, Baron Advanced Meteorological 

Systems (BAMS), applied the PSU/NCAR Mesoscale Model (MM5) to the three Phase I 

episodes in multiple configurations and evaluated its performance against surface and aloft 

meteorological observations (see: http://www.baronams.com/projects/VISTAS/). Several 

different Land Surface Model (LSM) and Planetary Boundary Layer (PBL) configurations were 

evaluated (LSM_PBL): 

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> px_acm: Pleim-Xiu land surface model, asymmetric convective mixing 

(ACM) Planetary Boundary Layer (PBL), interppx to link LSM 

run segments. 

> px_acm2: Similar to px_acm, except no iterppx linkage across run 

segments of LSM data. 

> noah_mrf: Noah land surface scheme with the medium range forecast 

(MRF) PBL. 

> multi_blkdr: Multi-layer soil scheme with Blackadar PBL and Zilitinkevich 

thermal roughness length. 

> noah_eta-my: Noah land-surface scheme with the ETA Mellor-Yamada PBL 

(IMDVDIF = 0). 

One of the key findings from the Phase I MM5 meteorological modeling was that the 

px_acm2 configuration was judged “best” overall, in part because it did not exhibit significant 

instances of very poor statistical performance for any quantity. Thus, a variant the px_acm2 

configuration (px_acm8) was selected for the Phase II modeling (further details can be found at 

the BAMS VISTAS website listed above). 

Emissions Modeling Findings

The emissions modeling under VISTAS Phase I was performed using the Sparse Matrix 

Operator Kernal Emissions (SMOKE) modeling system. Key findings in the Phase I emissions 

processing with the SMOKE system include: 

3⁄4 Although the SMOKE computational methodology provides quick executions 

times, the large number and sizes of input, output and intermediate data files 

require substantial time and disk space for file management activities. 

3⁄4 Data input and output review, process monitoring and data management, and QA 

reporting and evaluation activities substantially increase the calendar time 

required for each production episode. 

Computer runtimes for the SMOKE emissions processing were significant, 

notwithstanding the computational efficiencies inherent in the sparse matrix solution technique. 

Based on the SMOKE processing time requirements encountered with all three Phase I episodes, 

we project that the processing time for a full 365 day annual 36 km and 12 km grid will require 

approximately 20 days of dedicated CPU time and 60 calendar days to complete. This amount of 

processing time would limit the number of emission scenarios that could be examined potentially 

compromising the Phase II schedule. Accordingly, the VISTAS modeling team has developed 

an emissions processing approach that models a representative week (on-road mobile sources) or 

days for each month of the year in order to make the SMOKE processing time more manageable 

and consistent with the Phase II schedule. Day and hourly specific emissions for the base year 

base case are still modeled for biogenic, Electrical Generating Unit (EGU) point and fire sources. 

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Air Quality Modeling Findings

One of the main objectives of the Phase I modeling was the identification of the optimal 

model configuration(s) for simulating fine particulate and visibility in the Southeastern U.S. 

Details of the emissions and air quality modeling activities, publications, and modeling results 

under Phase I may be found at: http://pah.cert.ucr.edu/vistas/. The identification of the optimal 

Phase II model configuration considered three main, and often competing, issues: 

(1) Science: Preferable to use better science options in the Phase II model configuration; 

 

(2) Model Performance: Preferable to use model options that produce better model 

performance. 

(3) Run Time: Faster run times are preferable. 

Key findings from the Phase I air quality model testing include: 

3⁄4 Duration of CMAQ Spin-up Initialization Period: A 10-15 day spin up period 

is needed to initialize the model for modeling domains the size of the continental 

US. For the VISTAS Phase II annual modeling, the 2002 annual run will be 

performed in four quarters. The first quarter (January – March, 2002) will be 

initialized with ~13 spin up days (starting 00Z on December 18, 2001) with the 

other three quarters initialized with 15 spin up days.

3⁄4 Number of Vertical Layers: Comparisons between the CMAQ modeling results 

for the base configuration of 19 vertical layers with 34 vertical layers that exactly 

matched the MM5 layer structure revealed small changes in modeled 

concentrations, but essentially no change in the basic attributes of model 

performance. The 34 layer version takes almost twice the CPU time of the 19 

layer version. Thus, the 19 vertical layer model configuration is adopted for the 

Phase II modeling. 

3⁄4 36 km vs. 12 km Horizontal Grid Size: Similar overall model performance was 

exhibited by CMAQ using a 36 km versus a 12 km grid. However, at specific 

times and locations there could be substantial differences in estimated 

concentrations due to different precipitation and transport patterns. Although the 

12 km runs require significantly more computer resources, they do represent 

better science for photochemical and PM grid modeling so will be retained in 

Phase II. However, more sensitivity tests will be performed using the 36 km 

model configuration than with the 12 km configuration. 

3⁄4 Minimum Vertical Kz: The VISTAS Phase I modeling tested minimum vertical 

turbulent exchange coefficients (Kz) of 1.0 m2

/s and 0.1 m2/s. Model 

performance using the two minimum Kz values was mixed, with no one 

configuration consistently producing superior model performance over the other 

across all species, locations and periods when analyzing the 24-hour fine particle 

measurements. However, when analyzing the hourly carbon measurements the 

0.1 m2

/s Kz_min sensitivity test exhibited spikes in the diurnal variations that was 

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not present in the observations and use a 1.0 M2/s Kz_min reduced this 

undersizable attribute. Thus, a 1.0 Kz_min value was selected for the initial Phase 

II model configuration. 

3⁄4 Fine Dust Transportable Fraction: For the Phase I modeling, the application of 

a fugitive dust transport fraction of 0.25 was recommended by EPA and 

minimized the bias for the Coarse Matter (CM) species over the other factors 

studied (1.00 and 0.05). For the Phase II modeling, county-specific fugitive dust 

emission adjustment factors, based on land-use categories, are applied to the 

fugitive dust emissions within the base inventories prior to SMOKE emissions 

modeling, so no additional adjustment will be made in the emissions modeling. 

3⁄4 Adjustment to NH3 Mass Emissions Estimates: Ammonia emissions were 

reduced 50% for the January 2002 Phase I episode and no adjustments were made 

for the two summer Phase I episodes (July 1999 and July 2001). For the Phase II 

modeling, the ammonia emissions inventory has been updated using the CMU 

ammonia emissions model and state submissions so additional adjustments may 

not be needed. Thus, the initial Phase II simulations would be performed without 

any additional ammonia emission adjustments. However, the Phase I sensitivity 

analysis indicated that the fine particulate and visibility estimates were very 

sensitive to ammonia emissions so they will likely be a subject of sensitivity tests 

in Phase II. 

3⁄4 Adjustment to NH3 Emissions Temporal Profile: An ammonia emissions 

temporal adjustment sensitivity test was performed that reduced ammonia 

emissions more at night. Although some marginal improvements in model 

performance were seen, the new diurnal profile could not be justified so that 

Phase II modeling will use the current EPA recommended diurnal profile for 

ammonia emissions. 

3⁄4 Significance of Mexico and Canadian Emissions: CMAQ PM estimates in the 

VISTAS states appear to be not substantially affect by emissions from Canada 

and Mexico for the three Phase I episodes. Use of Canadian and Mexican 

emissions is more technically justifiable, so they will be included in the Phase II 

modeling. 

3⁄4 Minimum PBL Depth: There are some concerns that MM5 px_acm may 

sometimes estimate a too low a PBL depth under some conditions (e.g., clouds). 

In Phase I, minimum PBL depth sensitivity tests were performed, but they did not 

substantially improve model performance and were difficult to technically justify. 

Thus, for Phase II the MM5 PBL depth estimates will be used as is. 

3⁄4 Use of Global Chemistry Transport Model Output for Boundary Conditions: 

Boundary condition (BC) sensitivity tests were conducted using CMAQ default 

and seasonal, monthly and 3-hourly values from a 2001 simulation of the GEOSCHEM global model. Use of the GEOS-CHEM results produced more realist 

spatial and temporal changes in BCs so will be used in Phase II. 

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3⁄4 Wesely Dry Deposition Scheme: Comparison of CMAQ model performance 

using MM5 Pleim-Xiu (PX) and Wesely algorithms saw that the PX dry 

deposition scheme produced better model performance and represents scientific 

improvements over the Wesely scheme so will be used in Phase II. 

3⁄4 Eta-Mellor-Yamada MM5 PBL Scheme: The MM5 simulation using the EtaMY PBL scheme sometimes produced negative PBL heights and when used with 

CMAQ produced degraded PM model performance. Thus, based on better 

science and improved model performance, the MM5 using the px_acm LSM_PBL 

configuration was selected for VISTAS Phase II modeling. 

3⁄4 Aerosol Mass Conservation Patch: A mass conservation patch that was 

developed by the Georgia Institute of Technology was added to CMAQ to assure 

the conservation of sulfur, reactive nitrogen and reduced nitrogen in the CMAQ 

aerosol modules. Model performance with and without the mass conservation 

patch was essentially identical. Therefore, based on better science, the mass 

conservation patch was adopted for the Phase II modeling. 

3⁄4 CB4-2002 Chemistry: Comparisons of CMAQ model performance using the 

CB4 and CB4-2002 revealed almost identical, or slightly degraded (nitrate for 

CB4-2002) model performance. Although the CB4-2002 chemical mechanism is 

more scientifically correct than CB4, it runs substantially slower since it is only 

implemented in the slower SMVGEAR solver. Given the much slower run times 

and little change or degraded model performance, CB4-2002 was not selected for 

the VISTAS Phase II modeling. This decision will be reconsidered if CB4-2002 

is implemented in the faster EBI chemistry solver. 

3⁄4 SAPRC99 Chemistry: The CMAQ simulations using the CB4 and SAPRC99 

chemical mechanisms produced different model performance, but both exhibited 

similar skill such that no one simulation was clearly performing better than the 

other across all species, locations and periods. Not tested, however, was whether 

the chemical mechanisms responded differently in response to emission 

reductions. SAPRC99 chemistry represents more current science than CB4 

chemistry. However, CMAQ using SAPRC99 chemistry takes twice the CPU 

time as CMAQ/CB4 and also requires more memory and disk space. Thus, for 

Phase II modeling it was decided to use the CB4 chemistry for the initial CMAQ 

2002 base case simulations and evaluate the sensitivity of SAPRC99 and CB4 to 

emissions reductions using the Phase I databases. Depending on the results of the 

Phase II emission reduction sensitivity tests, the SAPRC99 chemistry may be 

used in the revised 2002 CMAQ base case simulation starting in September 2004. 

3⁄4 AIM Aerosol Chemistry: The AIM sectional aerosol module did not produce 

any improvements in model performance over the standard CMAQ modal AE3 

approach and required substantially more computing time. Thus, the standard 

CMAQ model was adopted for Phase II modeling. 

3⁄4 Alternative Model: The CAMx model was exercised for the July 1999 and July 

2001 Phase I episodes. In general model performance was similar, but there were 

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some differences. CMAQ performed better for sulfate, whereas CAMx 

performed better for carbon (EC and OC). Both models performed poorly for 

nitrate. As neither model performed significantly better than the other across all 

species and periods, the CMAQ model was retained as the lead model for the 

Phase II modeling with CAMx an optional corroborative model.

Note that there are two parameters in the Phase I CMAQ base configuration that differ 

from the default recommendations: (1) use of a 0.1 m2

/s over a 1.0 m2/s minimum Kz; and (2) 

use of the mass conservation patch. Regarding the 0.1 m2

/s minimum Kz, the VISTAS science 

team believes it is more technically correct and produces better nighttime ozone performance, 

whereas PM performance was mixed. The conservation of sulfur and nitrogen is a fundamental 

property of the real atmosphere so we believe that justifies the use of the mass conservation 

patch. 

PHASE II MODELING APPROACH 

The Phase II modeling will include annual PM/regional haze simulations for an annual 

period plus additional shorter duration episodes. 

Overview of Approach 

The Phase II modeling will perform a detailed performance testing of the modeling 

system followed by exercising the model for a variety of emissions control scenarios aimed at 

enabling VISTAS to assess the effects of future year emission control strategies on visibility and 

other air quality issues. The modeling system will also allow VISTAS to track reasonable 

progress toward regional haze goals. More specifically, the VISTAS Phase II program will focus 

on the use of the CMAQ modeling system for calendar year 2002 over the same 36/12 km 

horizontal grid system used in Phase I. In addition, the modeling of episodes from 2003 or other 

non-2002 years is being considered to capture conditions not present in the b2002 year. A 

potentially large number of annual (and episodic) model simulations will be performed, the list 

below reflects current short-term (i.e., 2004) plans: 

3⁄4 2002 Annual Run. The initial annual model simulations and performance 

evaluations will be performed in early 2004 using the initial 2002 inventory for 

VISTAS and non-VISTAS states, Canada and Mexico. Multiple iterations of the 

2002 annual simulation will be required to confirm the appropriateness of the 

model science configuration(s) recommended by the Phase I work, to evaluate 

updates to the model and model inputs, and to refine model performance. 

3⁄4 2002 Revised Annual Run. A subsequent annual 2002 simulation using revised 

2002 modeling inventory for VISTAS and non-VISTAS states, Canada and 

Mexico would be performed in Fall 2004. The primary objective of this run is 

model performance demonstration using updated emissions inventories and best 

science model configurations. Additional model sensitivity simulations would be 

carried out using the revised 2002 emissions. 

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3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 

simulation representing the 2000-2004 baseline period for EGU and fire 

emissions and using 2002 revised inventory for all other source sectors. The 

primary objective of this inventory is to provide the base line modeled air quality 

condition against which future year modeling runs will be compared to develop 

relative reduction factors for each pollutant species. 

3⁄4 Future Year Annual Runs. Future year simulations involving a base case 

inventory of typical EGU and fire emissions for VISTAS-selected future period. 

Additional future year inventories may also be modeled. The objective of these 

future year model runs is to establish the modeled air quality basis against which 

the effectiveness of emissions control strategies will be evaluated. 

3⁄4 Future year emission control strategies. Prescription of the future year 

emissions control strategies to be performed later in Phase II (e.g., 2005) and will 

be defined after the foregoing simulations and analyses have been completed. A 

combination of annual and episodic runs is anticipated. 

Closely integrated with the annual (and possibly episodic) meteorological, emissions and 

air quality modeling will be ongoing project management, technical review, and quality 

assurance activities performed under the guidance of the VISTAS Contracting Officer and the 

VISTAS Technical Analysis Workgroup (TAWG). The modeling team members will participate 

with VISTAS management in regular monthly conference calls, as well as ad hoc topical 

conference calls as needed, and will attend periodic meetings with the TAWG members 

throughout Phase II. 

Episode Selection 

EPA’s current draft guidance on PM2.5/Regional Haze modeling (EPA, 2001) identifies 

specific goals to consider when selecting one or more episodes for use in demonstrating 

reasonable progress in attaining the regional haze NAAQS. EPA recommends that episode 

selection derive from three principal criteria: 

3⁄4 A variety of meteorological conditions should be covered; 

 

3⁄4 To the extend possible, the modeling data base should include days for which 

extensive data bases (i.e. beyond routine aerometric and emissions monitoring) 

are available; and 

3⁄4 Sufficient days should be available such that relative reduction factors (RRFs) can 

be based on several (i.e., > 15) days. 

 

For regional haze modeling, the guidance goes further by suggesting that the preferred 

approach is to model a full, representative year. Moreover, the required RRF values should be 

based on model results averaged over the 20% worst and 20% best visibility days determined for 

each Class I are based on monitoring data from the 2000 – 2004 baseline period. More recent 

EPA guidance (Timin, 2002) suggests that states should model at least 10 worst and 10 best 

visibility days at each Class 1 area. EPA also lists several ‘other considerations’ to bear in mind 

when choosing potential PM/regional haze episodes including: (a) choose periods which have 

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already been modeled, (b) choose periods which are drawn from the years upon which the 

current design values are based, (c) include weekend days among those chosen, and (d) choose 

modeling periods that meet as many episode selection criteria as possible in the maximum 

number of nonattainment or Class I areas as possible. 

 VISTAS adopted a logical, stepwise approach in implementing the EPA guidance in 

order to identify the most preferable, representative year for PM/regional haze modeling. These 

steps include the following: 

Representativeness of Meteorological Conditions: The VISTAS meteorological 

contractor (BAMS) identified important meteorological characteristics and data sets in 

the VISTAS region directly relevant to the evaluation of candidate annual modeling 

episodes. 

Initial Episode Typing: A separate detailed aerometric analysis and pattern recognition 

study was carried out by ICF Consulting to characterize the extent to which days from 

January-March 2002 and January-March 2003 represent the type of meteorological 

conditions that are most frequently associated with high and low values of a haze index 

and PM2.5 concentrations using the standard Classification and Regression Tree (CART) 

analysis software. 

Data Availability: In parallel with the CART analysis, episode characterization analyses, 

collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, FL DEP) 

intensively studied the availability of PM2.5, meteorological, and emissions data and 

representativeness of alternative Baseline modeling periods from a regulatory standpoint. 

Years to be used by Other RPOs: VISTAS also considered what years other RPO would 

be modeling, several had already chosen CY-2002 as the modeling year. 

After a lengthy process of integrated studies, the episode selection process culminated in 

the selection of calendar year 2002 (1 January through 31 December) as the most current, 

representative, and pragmatic choice for VISTAS Phase II modeling. All of the EPA criteria for 

PM/regional haze episode selection were directly considered in this process together with many 

other pragmatic considerations (e.g., timing of new emissions or aerometric data deliveries by 

EPA or the states to the modeling teams). In addition to selecting CY-2002 as the base year, the 

merits of selecting one or more other shorter duration episode periods (say 2-4 weeks or more in 

duration) was thoroughly considered. Such additional episodes were believed to add potentially 

valuable insights into the various source-receptor relationships influencing PM2.5 and regional 

haze air quality at Class I sites and other locations of interest and have the potential to capture 

important meteorological conditions not covered in the CY-2002 period. Work on identifying 

potentially attractive candidate episodes to include with the annual 2002 modeling is ongoing 

and will be summarize in a subsequent updates to this protocol. In particular, ICF Consulting 

has been funded to determine whether any key meteorological conditions are not captured by the 

2002 model year and identify additional modeling episodes. Additional modeling periods from 

2003 would be ideal due to the presence of continuous PM monitoring data at the FOCUS sites 

that were not in operation during CY-2002. 

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Model Selection 

Based on the Phase I findings, VISTAS selected the following models for use in 

modeling particulate matter (PM) and regional haze in the Southeast under Phase II: 

3⁄4 MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, 

prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate, and regional haze regulatory modeling studies. 

 

3⁄4 SMOKE: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system 

is an emissions modeling system that generates hourly gridded speciated emission 

inputs of mobile, nonroad, area, point, fire and biogenic emission sources for 

photochemical grid models. 

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling 

system is a ‘One-Atmosphere’ photochemical grid model capable of addressing 

ozone, particulate matter (PM), visibility and acid deposition at regional scale for 

periods up to one year. 

In addition, EPA’s fine particulate and regional haze modeling guidance encourages the 

use of alternative models to corroborate the findings of the primary model. The VISTAS Phase II 

modeling currently has an optional task to apply alternative models. The following are the 

alternative models identified in the Phase I work effort. 

3⁄4 CAMx: ENVIRON’s Comprehensive Air Quality Model with Extensions (CAMx) 

modeling system is also a state-of-science ‘One-Atmosphere’ photochemical grid 

model capable of addressing ozone, particulate matter (PM), visibility and acid 

deposition at regional scales for periods up to one year. 

3⁄4 CMAQ – AIM or CMAQ-MADRID: These two models are variations of CMAQ 

that utilize a sectional treatment of PM size distribution and have alternative science 

options for some aerosol processes. They could also be used in Phase II as 

corroborative diagnostic tools. 

MM5 Configuration for Phase II Modeling

Based on the extensive sensitivity testing carried out under VISTAS Phase I, the MM5 

(Version 3.6, MMP) configuration to be used the Phase II modeling will consist of the following: 

3⁄4 Nested 108/36/12 km grids, with 34 vertical layers; 

3⁄4 Two way nesting, no feedback; 

3⁄4 Initialization and boundary conditions from Eta analysis fields; 

3⁄4 Pleim-Xiu (P-X) soil model; 

3⁄4 Asymmetric Convective Mixing (ACM) PBL model; 

3⁄4 Kain-Fritsch 2 cumulus parameterization; 

3⁄4 Mixed phase (Reisner 1) cloud microphysics; 

3⁄4 Raptid Radiative Transfer Model (RRTM) radiation; 

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3⁄4 Snow effect turned on; 

3⁄4 ETA model sea surface temperature; 

3⁄4 24-category USGS vegetation data sets; 

3⁄4 Thermal roughness by the Garratt method; and 

3⁄4 Standard FDDA analysis nudging on 36 km and 12 km grid nests. 

MM5 will be operated in 5 day segments starting at 12Z on December 17, 2001. Each 

segment would overlap by 12 hours the previous segment and would be initialized off of the 

input analysis fields (i.e., no linking of the LSM or PBL variables between run segments). 

SMOKE Configuration

SMOKE will be run on the same grid and configured in the same manner as was 

performed in the Phase I modeling, only SMOKE Version 2.0 will be used instead of Version 

1.5beta that was used in Phase I. As an emissions processor, there are far less science options in 

SMOKE than in MM5 or CMAQ. The number of days SMOKE will be used to process 

emissions for the annual modeling period has been optimized to maximize the temporal 

variations in emissions while minimize the number of days to be processed. For many source 

categories (i.e., area, non-road and non-EGU point sources) temporal adjustments of the annual 

emissions are made by month and by day of week, Thursday, Friday, Saturday, Sunday and 

Monday, with Tuesday and Wednesday assumed to be the same as Thursday (i.e., 5 days). For 

biogenic, fire and EGU point sources, day-specific hourly emissions will be used for the whole 

year. The most computationally limiting step in emissions modeling is the generation of on-road 

mobile sources. Accordingly, on-road mobile sources will be generated for one week (7 days) 

per month to obtain monthly and day-of-week variations in the mobile sources emissions. This 

results in the following number of days SMOKE will be applied for the annual simulation: 

3⁄4 365 biogenic days; 

3⁄4 365 CEM days for EGU point sources; 

3⁄4 All days with fires (365 maximum); 

3⁄4 5 x 12 = 60 “other” point days; 

3⁄4 5 x 12 = 60 area source days; 

3⁄4 5 x 12 = 60 nonroad mobile sources days; and 

3⁄4 7 x 12 = 84 on-road mobile source days. 

CMAQ Configuration

The CMAQ modeling system would be set up and exercised on the same nested 36/12 

km grid domain used in Phase I, employing one-way grid nesting. A total of 19 vertical layers 

would be implemented, extending up to a region top of 100 mb (approximately 15 km AGL). 

The PPM advection solver would be used along with the spatially varying (Smagorinsky) 

horizontal diffusion approach and K-theory for vertical diffusion. MM5 meteorological output 

based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer 

(PBL) scheme will be used and the recently updated CMAQ Meteorological-Chemistry Interface 

Processor (MCIP2.2) would process the MM5 data using the “pass through” option. The CB4 

gas-phase, RADM aqueous-phase, and AERO3/ISORROPIA aerosol chemistry schemes will be 

used in the initial CMAQ 2002 modeling. Treatment of reversible secondary organic aerosols 

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would be simulated by the SORGAM implementation in CMAQ (ver 4.3). Under the Phase I 

modeling, VISTAS evaluated three photochemical mechanisms: CB4, CB4-2002 and SAPRC99. 

CB4-2002 produced nearly identical results as CB4 but took much longer to run since it is only 

implemented in the slower SMVGEAR chemistry solver. Thus, CB4-2002 was dropped from 

consideration in Phase II. The Phase I comparisons of CB4 and SAPRC99 found they produced 

somewhat similar but different model performance. However, one mechanism was not 

performing better than the other across all species, sites and periods. The Phase I testing only 

evaluated the mechanism’s base case performance, not their response to emission reductions. 

Given that CB4 runs twice as fast as SAPRC99, for Phase II it was decided to perform initial 

2002 CMAQ modeling using CB4 and evaluate CB4 and SAPRC99 responses to emission 

reductions using the Phase I episodes. If SAPRC99 is deemed to be performing better than CB4 

it would be used for the revised 2002 CMAQ base case simulation in September 2004. 

Quality Assurance 

 Effective and extensive Quality Assurance (QA) and Quality Control (QC) are main 

components of the VISTAS Phase II modeling activities. In December 2002, the USEPA 

publish extensive guidance on developing a Quality Assurance Project Plan (QAPP) for 

modeling studies (EPA, 2002). The objective of a QAPP is to ensure that a modeling study is 

scientifically sound, robust, and defensible. The new EPA guidance suggests that a QAPP should 

include the following elements: 

• a systematic planning process including identification of assessments and related 

performance criteria; 

• peer reviewed theory and equations; 

• a carefully designed life-cycle development process that minimizes errors; 

• clear documentation of assumptions, theory, and parameterization that is detailed enough 

so others can fully understand the model output; 

• input data and parameters that are accurate and appropriate for the problem; 

• output data that can be used to help inform decision making. 

• documentation of any changes from the original quality assurance plan; 

 Moreover, the EPA guidance specifies that different levels of QAPP may be required 

depending on the intended application of the model, with a modeling study designed for 

regulatory purposes requiring the highest level of quality assurance. The QAPP also provides a 

valuable resource for project management. It can be used to document data sources and 

assumptions used in the modeling study, and it can be used to guide project personnel through 

the data processing and model application process to ensure that choices are consistent with the 

project objectives. The modeling team has already developed QA documents and procedures in 

the Phase I effort. For the Phase II effort these will be enhanced and modified to assure that it 

conforms to the EPA QAPP guidance. 

Complementing the data acquisition, modeling input development activities, and project 

management activities, four other Phase II activities will be performed, consistent with the 

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Quality Assurance (QA) plan: 

Data Gatekeepers

The VISTAS Phase II emissions and air quality modeling team are receiving emissions, 

meteorological and air quality data from other VISTAS contractors or other sources and as a first 

line of QA, we have defined a Gatekeeper function to assure the data have been received 

correctly, evaluate the quality of the data, and document the data received. Separate air quality, 

meteorological and emissions Gatekeepers have been identified whose roles are defined below. 

In addition, a Data Management Gatekeeper has been defined who will post data, reports and 

results to the project website and archive all key data generated in the project. 

3⁄4 Air Quality Data Gatekeeper. Obtain air quality data as appropriate for 

model input development and model performance evaluation and assure 

quality of all air quality data obtained, consistent with approved QA plan. 

This gatekeeper will also provide documentation of evaluation and generate 

IC/BC inputs for CMAQ for all modeling runs. 

3⁄4 Meteorological Gatekeeper. Obtain meteorological data, as MM5 or MCIP 

files, as appropriate for annual 2002 modeling runs and other episode periods 

and perform data quality checks as approved in QA plan together with 

appropriate documentation of model performance evaluation activities. 

3⁄4 Emissions Gatekeeper. Obtain emissions inventory data necessary to 

support annual 2002 and future year modeling and recommend source of 

emissions data to be used for Canada and Mexico. Assure quality of all 

emissions data received, consistent with approved QA plan and develop all 

emissions modeling files to support modeling runs for 2002. This gatekeeper 

will also develop the chemical speciation files and temporal and spatial 

allocation files necessary to convert annual inventories into hourly and daily 

emissions modeling files, as appropriate. The Emissions Gatekeeper will also 

develop all emissions modeling files for non-VISTAS states to support 

modeling runs for future year base case and emissions strategies as defined by 

VISTAS. 

3⁄4 Data Management Gatekeeper: This gatekeeper will maintain the VISTAS 

Modeling Website including posting modeling input and output files, reports, 

interpretation of results, and other documents as requested by VISTAS to 

support all Phase II tasks. This includes, for example, the storage of model 

inputs and outputs for annual (and episodic) runs and the transfer (via fire 

wire or alternative media) of electronic files to VISTAS states, other regional 

planning organizations, EPA, other contractors, and stakeholders. 

Emissions QA/QC

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical 

steps in performing air quality modeling studies. Because emissions processing is tedious, time 

consuming and involves complex manipulation of many different types of large data sets, errors 

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are frequently made in emissions processing and, if rigorous QA measures are not in place, these 

errors may remain undetected. In Phase II we will continue with multistep emissions the QA/QC 

approach applied in the Phase I modeling. This includes the initial emissions QA/QC by the 

Emissions Gatekeeper described above, as well as QA/AC by the Emissions Modeler during the 

processing of emissions and then additional QA/QC by the air quality modeler of the processed 

model ready emission files. This multistep process with three separate groups involved in the 

QA/QC of the emissions is much more likely to catch any errors prior to the air quality model 

simulations. 

Emissions QA/QC that would be performed as part of the emissions modeling will include: 

EMS and EPA Input Screening Error Checking Algorithms: Although the SMOKE 

emissions model will be used for emissions processing, some of the more advanced EMS 

input error checking algorithms will be used to screen the data and identify potential 

emission input errors. Additionally, EPA has issued a revised stack QA and augmentation 

procedures memorandum that will be used to identify and augment any outlying stacks. 

SMOKE error messages: SMOKE provides various cautionary or warning messages 

during the emissions processing. We will redirect the SMOKE output to log files and 

review the log files for serious error messages. An archive of the log files will be 

maintained so that the error messages can be reviewed at a later date if necessary. 

SMOKE emissions summaries: We will use QA functions built into the SMOKE 

processing system to provide summaries of processed emissions as daily totals according 

to species, source category and county and state boundaries. These summaries will then 

be compared with summary data prepared for the pre-processed emissions, e.g., state and 

county totals for emissions from the augmented emissions data. 

Once the CMAQ-ready emission inputs have been prepared, we will perform additional 

emissions QA/QC as follows: 

Spatial Summary: We will sum the emissions for all layers and for all 24 hours that is 

used to prepare a PAVE plot showing the daily total emissions spatial distribution. For a 

20 day simulation this produces approximately 20 days x 20 species x 5 emissions 

categories = 2,000 plots. In our base case simulations these plots will be presented as tons 

per day. The objective of this step is to identify errors in spatial distribution of emissions. 

Vertical Profile: For point sources the emissions total for each layer will be summed and 

plotted to show the vertical distribution of emissions. These plots show the emissions on 

the x-axis for each model layer on the y-axis. The objective of this step is to identify 

possible errors in vertical distribution of emissions. 

Short Term Temporal Summary: The total domain emissions for each hour will be 

accumulated and time series plots prepared that display the diurnal variation in total 

hourly emissions. The objective of this step is to identify errors in temporal profiles. 

Long Term Temporal Summary: The total domain emissions for each day will be 

accumulated and displayed as time series plots that show the daily total emissions across 

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the domain as a function of time. The objective of this step is to identify particular days 

for which emissions appear to be inconsistent with other days for no reason (e.g., not a 

weekend) and compare against the general trend. 

Control Strategy Spatial Displays: Spatial summary plots of the daily total emissions 

differences between a control strategy and base case emissions scenarios will be 

generated. These plots can be used to immediately identify a problem in a control 

strategy. For example, if a VISTAS states SO2 control strategy is being analyzed and 

there are changes in emissions for other pollutants or for SO2 outside of the VISTAS 

states problems in emissions processing can be identified prior to the air quality model 

simulation. 

Meteorology QA/QC

The VISTAS meteorological modeling contractor (BAMS) will have primary 

responsibility in the QA/QC of the MM5 meteorological fields. However, the emissions and air 

quality modeling team will also perform some QA/QC of the meteorological data to assure that it 

has transferred correctly, to obtain an assessment of the quality of the data and to assist in the 

interpretation of the air quality modeling results. 

The VISTAS Phase II Meteorological Gatekeeper will perform the following: 

3⁄4 Analyze the MM5 data to assure it has been transferred correctly; 

 

3⁄4 Evaluate the MM5 using METSTAT and the surface meteorological network; 

3⁄4 Evaluate upper-air MM5 meteorological estimates by comparison them to upper-air 

observations and satellite images; and 

3⁄4 Compare the VISTAS 2002 MM5 simulation with the one generated by WRAP. 

3⁄4 Generate the CMAQ-ready meteorological inputs using the MCIP2.2 processor. 

Air Quality Modeling QA/QC

Key aspects of QA for the CMAQ input and output data include the following: 

• Verification that correct configuration and science options are used in compiling and 

running each model in the CMAQ modeling system, where these include the MCIP, 

JPROC, ICON, BCOM and the CCTM. 

• Verification that correct input data sets are used when running each model. 

• Evaluation of CCTM results to verify that model output is reasonable and consistent with 

general expectations. 

• Processing of ambient monitoring data for use in the model performance evaluation. 

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• Evaluation of the CCTM results against concurrent observations. 

• Backup and archiving of critical model input data. 

 The most critical element in the QA plan for CMAQ simulations is the QA/QC of the 

meteorological and emissions input files, which is discussed above. The major QA issue 

specifically associated with the air quality model simulations is verification that the correct 

science options were specified in the model itself and that the correct input files were used when 

running the model. For the CMAQ model we employ a system of naming conventions using 

environment variables in the compile and run scripts that guarantee that correct inputs and 

science options are used. We also employ a redundant naming system so that the name of key 

science options or inputs are included in the name of CMAQ executable program, in the name of 

the CMAQ output files, and in the name of the directory in which the files are located. This is 

accomplished by using the environment variables in the scripts to specify the names and 

locations of key input files. 

 A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the 

original runs scripts and directory structure that were used in performing a model simulation. 

 We will also perform a post-processing QA of the CMAQ output files similar to that 

described for the emissions processing. We will generate animated gif files using PAVE that can 

be viewed to search for unexpected patterns in the CMAQ output files. In the case of model 

sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case 

minus the base case. Often, errors in the emissions inputs can be discovered by viewing the 

animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ 

simulations. This provides a summary that can be useful for more quickly comparing various 

model simulations. 

 Another important component of the air quality modeling QA/QC is the model 

performance evaluation, which is discussed next. 

Model Performance Evaluation 

The model performance evaluation methodology to be used is an extension of that used in 

Phase I and is fully consistent with current EPA guidance and science community consensus on 

how Eulerian fine particulate/regional haze and ozone models should be evaluated with 

contemporary surface and aloft monitoring databases. Key to this evaluation is that the model is 

evaluated separately for each component of particulate matter (PM), including sulfate, nitrate, 

elemental carbon, organic carbon, other fine particulate and coarse particulate, as well as 

important gaseous species (e.g., ozone, NOx, CO, ammonia and nitric acid). 

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and 

Regional Haze” (EPA, 2001) recommends a comprehensive, multi-layered approach to model 

performance testing should be performed, consisting of the following four components: 

> Operational Evaluation: Tests the ability of the model to estimate PM 

concentrations (both fine and coarse) and the components at PM10 and PM2.5

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including the quantities used to characterize visibility (i.e., sulfate, nitrate, 

ammonium, organic carbon, elemental carbon, PM2.5, and PM10); 

> Diagnostic Evaluation: Tests the ability of the model to predict visibility and 

extinction, PM chemical composition including PM precursors (e.g., SOx, NOx, and 

NH3) and associated oxidants (e.g., ozone and nitric acid); PM size distribution; 

temporal variation; spatial variation; mass fluxes; and components of light extinction 

(i.e., scattering and absorption); 

> Mechanistic Evaluation: Tests the ability of the model to predict the response of PM 

and visibility to changes in variables such as emissions and meteorology; and 

> Probabilistic Evaluation: Takes into account the uncertainties associated with the 

model predictions and observations of PM and visibility. 

Given the scope of the VISTAS Phase II effort and with consideration of the type and 

amount of measurement data available to support the various evaluations, the CMAQ model 

evaluation will included elements of each of these components. Obviously, the operational and 

diagnostic evaluations will receive the greatest attention, mechanistic tests (with probing tools 

such as DDM) will also be considered along with potentially probabilistic evaluations, through 

the use of traditional sensitivity and uncertainty simulations as well as through the use of an 

alternative science platforms such as CAMx (optional). 

An important consideration is that different PM monitoring networks may use different 

measurement approaches that “measure” different amounts of the same species that are also 

different from the modeled species. For example, the IMPROVE network only speciates PM2.5

so any sulfate or nitrate in the coarse mode (PM2.5-10) is included in the CM species. Like was 

done in Phase I, the model will be evaluated separately for each network in addition to a 

composite evaluation using all data. The mapping of the modeled species to the monitored data 

will also have to be performed in a consistent fashion. 

We will compare the model outputs to observational data obtained from the IMPROVE, 

SEARCH, CASTNet, EPA-FRM, EPA-STN and other monitoring networks. These monitoring 

data will be obtained from AIRS, VIEWS, and other appropriate organizations. These 

comparisons will be performed as follows: 

3⁄4 Compare both daily averages and longer-term (e.g., monthly, seasonal and annual) 

averages for SO2, SO4, NO3, EC, OC, PM2.5, and PM10, taking care to exclude 

periods of sampling interference in the observational data. We will look for 

systematic biases between the model results and IMPROVE observations, and if 

biases are found, identify possible sources of error in the model inputs. 

 

3⁄4 Compare hourly/high resolution PM species and gaseous species concentrations at 

sites where available (e.g., SEARCH, AIRS and EPA-Supersites). 

3⁄4 At sites with contrasting aerosol mass loadings (i.e. differing major components of 

fine particulate), we will analyze the temporal behavior of the major scattering and 

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absorbing aerosol constituents along with the visibility trends, to establish 

correlations. 

The model evaluation will also include the following analyses: 

3⁄4 Evaluate seasonal trends in observations of organic and inorganic aerosol precursors 

and their effects on PM composition and visibility, and evaluate the ability of the 

model to capture these seasonal trends. 

3⁄4 Evaluate how well the model predicts the relative roles and budgets of various 

processes by (1) examining observed and modeled correlations between various 

species pairs, and (2) comparing model-predicted ratios of various species (individual 

or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total 

nitrate, SO4/SOx). 

3⁄4 Investigate the performance of the model at selected observational sites characterized 

by different chemical regimes that may be encountered either spatially or during 

different seasons to help identify any inadequacies in the model and to provide a 

better understanding of conditions under which model inferences may be weak. 

3⁄4 Compare hourly 24-hour average, and episode averages of the PM constituents for the 

2002 Phase I VISTAS annual modeling period as well as the 2003 episodes across all 

sites. 

3⁄4 Create scatter plots of modeled vs. observed data and hourly and 24-hour averages by 

site and subregion to help identify any site-specific biases. 

3⁄4 Create time series plots of predicted and observed concentrations as appropriate. 

3⁄4 Evaluate for total sulfur (SO2 + SO4), nitrate (HNO3 + NO3) and ammonia (NH3 + 

NH4). 

3⁄4 Compare observed versus modeled mass fractions of PM constituents at various sites 

that are characterized by their proximity or remoteness relative to sources, or by 

specific meteorological conditions (e.g., frontal passage, stagnation, precipitation); 

these will enable identification of trends in the model of over- or under-prediction of 

specific PM constituents under these conditions. 

3⁄4 Pay particular close attention to the model performance at the Class I areas for SO4

NO3 EC OC and CM on the 20% best and 20% worst days to document whether 

certain of these days should be eliminated from the visibility projections due to 

inadequate model performance. 

A suite of statistical metrics and graphical tools will be employed to identify performance 

problems with the CMAQ simulations should they exist and to and highlight differences between 

model runs. Experience in ozone/PM modeling is the best basis upon which to identify 

obviously flawed simulation results. Efforts to improve the CMAQ model’s base case 

performance will be made if needed (i.e., to reduce the discrepancies between model estimates 

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and observations), and consistent with the project resources and schedule; however, these model 

performance improvements efforts must be based on sound scientific principles. “Curve-fitting” 

exercises will be avoided. 

Current draft EPA’s guidance suggests an interim set of fine particulate modeling 

performance goals for aggregated mean normalized gross error and mean normalized bias as 

follows: 

Pollutant Gross Error Normalized Bias 

PM2.5 ~30-50% ~"10% 

Sulfate ~30-50% ~"20-30% 

Nitrate ~20-70% ~"15-50% 

EC ~15-60% NA 

OC ~40-50% ~"38% 

Because regional-scale fine particulate and regional haze modeling is an evolving science, 

these performance goals are not fully established and certainly not universally endorsed by the 

scientific community. Indeed, EPA recognizes this and sponsored a 2-day national model 

performance evaluation workshop in February 2004 to address current science in PM model 

performance evaluation. 

Model sensitivity experiments are useful in three distinct phases or ‘levels’ of an air quality 

modeling study and all will be used as appropriate in the VISTAS Phase II modeling with 

CMAQ. These levels are 

 Level I. Model algorithm evaluation and configuration testing; 

Level II. Model performance testing, uncertainty analysis and compensatory error 

diagnosis, and 

Level III. Investigation of model output response (e.g., ozone, aerosol, deposition) to 

changes in precursors as part of emissions control scenario analyses. 

Many of the Level I sensitivity tests with CMAQ have already been completed in the 

Phase I configuration and diagnostic analyses. However, given that open community nature of 

CMAQ and the frequent science updates to the model and supporting data bases, it is possible 

that some additional configuration sensitivity testing will be necessary in Phase II. 

 

 Level II sensitivity analyses are an integral part of the Phase II CMAQ model 

performance evaluation activity. Level II sensitivity analyses will be performed, as necessary, to 

accomplish the following objectives: 

3⁄4 To reveal internal inconsistencies in the model; 

3⁄4 To provide a basis for compensatory error analysis; 

3⁄4 To reveal the parameters (or inputs) that dominate (or do not dominate) the model’s 

operation; 

3⁄4 To reveal propagation of errors through the model; and 

3⁄4 To provide guidance for model refinement and data collection programs. 

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 At this time, it is not possible to compile a specific listing of the types of Level II 

sensitivity runs that might be needed to establish a reliable annual 2002 CMAQ base case. 

Noteworthy in EPA’s new ozone, PM, and regional haze guidance documents is the 

encouragement of the use of alternative modeling methods to corroborate the performance 

findings and control strategy response of the primary air quality simulation model. Mindful of 

EPA’s endorsement of corroborative modeling methods and the rigorous use of ‘weight of 

evidence’ investigations, we recommend that the most recent version of CAMx be used in 

supportive, corroborative role to CMAQ in Phase II. Among other things, this will permit us to 

more explicitly identify the expected range of model uncertainty and to corroborate the general 

effectiveness of the Phase II CMAQ regional haze control strategies. Other corroborative 

modeling methods such as the CMAQ-AIM and CMAQ-MADRID should also be considered. 

However, as these models are derivatives of CMAQ they would not provide as robust 

independent corroboration as CAMx. Although at this time VISTAS does not plan to use 

alternative models to corroborate the CMAQ findings, this option is available to VISTAS at a 

later date if desired. 

Visibility Assessment and Comparisons 

One of the primary purposes of the VISTAS Phase II work effort is to develop the 

modeling tools necessary to project future-year visibility estimates at Class I areas to 

demonstrate progress toward clean natural background visibility levels in 2064. The Regional 

Haze Rule (RHR) requires that progress toward natural background visibility be demonstrated 

every 10 years. The 2000-2004 five-year period is used to define baseline conditions and the 

first future progress period is 2018, that has been interpreted as either the 2013-2017 or 2014-

2018 five year periods. A State is required to set progress goals for each Class I area in the State 

for two visibility metrics: 

• Provide for an improvement in visibility for the most impaired (i.e., 20% worst) 

visibility days; and 

 

• Ensure no degradation in visibility for the least impaired (i.e., 20% best) visibility 

days. 

EPA guidance recommends using the model in a relative sense to project future-year 

visibility conditions. This is done through the use of Relative Reduction Factors (RRFs) that are 

defined as the ratio of the future-year to the current-year modeling results. The RRFs are applied 

to the baseline visibility conditions to project future-year visibility. The major features of EPA’s 

recommended visibility projections are as follows: 

• Monitored data should be used to define current air quality. 

• Monitored concentrations of PM10 are divided into six major components, the first 

five of which are assumed to be PM2.5 and the sixth is PM2.5-10. 

3⁄4 SO4 (sulfate); 

3⁄4 NO3 (particulate nitrate); 

3⁄4 OC (organic carbon); 

3⁄4 EC (elemental carbon); 

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3⁄4 OF (other fine particulate); and 

3⁄4 CM (coarse matter). 

• Models are used in a relative sense to develop relative reduction factors (RRFs) 

between future and current predicted concentrations of each component. 

• Component-specific relative reduction factors are multiplied by current monitored 

values to estimate future component concentrations. 

• Estimates of future component concentrations are consolidated to provide an 

estimate of future air quality, which can be related to a goal for regional haze. 

• Future estimated air quality is compared with the goal to see if the simulated 

control strategy results in the goal being met. 

• It is acceptable to assume that all measured sulfate is in the form of ammonium 

sulfate [(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate 

[NH4NO3]. 

Associated with each PM species is an extinction coefficient that converts concentrations (in 

g/m3

) to light extinction (in inverse mega meters, Mm-1). Sulfate and nitrate are hygroscopic so 

relative humidity adjustment factors [f(RH)] are used to modify the extinction coefficients that 

increase the particle’s extinction efficiency with increasing RH to account for the particles taking 

on water and having higher light scattering properties. EPA has defined site-specific monthly 

average f(RH) values that will be used in the visibility projections. Note that some Organic 

Matter (OM) compounds may also have hygroscopic properties, but the IMPROVE 

reconstructed mass extinction equations assume OM is non-hygroscopic. 

bSulfate = 3 x f(RH) x [SO4] 

bNitrate = 3 x f(RH) x [NO3] 

bEC = 10 x [EC] 

bOM = 4 x [OM] 

bSoil = 1 x [Soil] 

bCM = 0.6 x [CM] 

The total light extinction (bext) is assumed to be the sum of the light extinction due to the six PM 

species listed above plus Rayleigh (blue sky) background (bRay) that is assumed to be 10 Mm-1. 

 bext = bRay + bSulfate + bNitrate + bEC +bOM + bSoil + bCM

The total light extinction (bext) in Mm-1 is related to visual range (VR) in km using the following 

relationship: 

 VR = 3912 / bext

The Regional Haze Rule requires that visibility be expressed in terms of deciview (dv) 

that uses natural logarithms of the extinction as follows: 

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 dv = 10 ln(bext/10) 

Modeling results are used in a relative fashion to project future-year visibility through 

relative reduction factors (RRFs). RRFs are expressed as the ratio of the modeling results for the 

future-year to the results of the base year and are Class I area and PM species specific. RRFs are 

applied to the Baseline Conditions observed PM species to project future-year PM levels from 

which visibility can be assessed using the IMPROVE RCM extinction equations listed above. 

EPA has identified the following six steps to project future-year visibility for the 20% best and 

20% worst days: 

1. For each Class I area visibility is ranked using IMPROVE reconstructed PM mass (RCM) 

extinction equation for each year that comprises the five-year Baseline Conditions (i.e., 

2000-2004). 

2. Calculate the arithmetic average of the 20% best and 20% worst visibility days, in 

deciview (dv), for each year of the five-year baseline period and then calculate the fiveyear average of the 20% best and 20% worst days. Document which days during the 

five-year baseline period comprise the 20% best and 20% worst observed visibility days. 

3. Use an air quality model to simulate the baseline period emissions and future-year 

emissions. Extract 24-hour average PM species concentrations “near” each Class I area. 

Calculate the average PM species concentration estimates in the current and future-year 

simulation across the 20% best and 20% worst observed visibility days for each year in 

the five-year baseline period. Average across the five-years the average PM species 

concentrations for the 20% best and 20% worst days and for each year in the five-year 

baseline period. Calculate Class I area and best/worst 20% days RRFs for each of the six 

PM components as the ratio of the five-year average estimated PM species concentration 

in the future-year to current year. 

4. Multiple PM species dependent RRFs for the 20% best/worst days by the observed 24-

hour PM species concentrations for each day from the 20% best/worst days to obtain 

future-year daily average PM species concentrations for each day from the 20% best and 

20% worst visibility days from the five-year baseline period. 

5. Using the future-year estimated 24-hour PM species concentrations for the best/worst 

20% observed days, calculate extinction using the IMPROVE reconstructed mass 

extinction equation and daily deciview, perform annual averaging of the visibility (dv) 

estimates for the 20% best/worst days for each year and then obtain the five-year average 

visibility (dv) for the future-year. 

When selecting model estimated PM species concentrations “near” the monitor, EPA 

recommends taking a spatial average of PM concentrations across a grid cell resolution 

dependent NX by NY array of cells centered on the grid containing the monitor. For the 

VISTAS Phase II 36 km modeling, just the model estimates for the grid cell containing the 

monitor will be used (i.e., NX=NY=1). For the VISTAS Phase II 12 km modeling, EPA 

recommends that NX=NY=3, that is the model estimated PM species concentrations are 

averaged across 9 grid cells centered on the IMPROVE monitor (EPA, 2001). 

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VISTAS Phase II Future-Year Modeling

The VISTAS Phase II future-year modeling will use the 2002 MM5 meteorological 

conditions. That is, we will assume that the meteorological conditions for the future-year are the 

same as for 2002. This will allow for the comparison of the changes in visibility at Class I areas 

from the current (2002) to future-year due to changes in air emissions. This means that the 

effects of climate change and climatic variations will not be accounted for. 

Future-Year Emissions

The VISTAS emissions projection contractor will provide future-year emissions to the 

emissions and air quality modeling team. They would be processed into the gridded speciated 

hourly three-dimensional emissions inputs for photochemical grid modeling using the SMOKE 

emissions model. The same biogenic emissions as used in the 2002 Base Case modeling will be 

used for the future-year modeling. This assume that the same land use and biomass distribution 

as used in the 2002 Base Case emissions would exit in the future-year emission scenarios. The 

same Typical Year fire emissions that were used in the 2002 Typical Year emissions scenario 

would be used in the future-year. Future-year Typical Year emissions for Electrical Generating 

Units (EGUs) would also be used for the future-year scenarios, however for EGUs these would 

not be identical to the 2002 Typical Year EGU emissions. 

Visibility Projections

 The CMAQ modeling system would be used to estimate 2002 and future-year PM 

estimates for Base Case emission scenarios that include “typical” fire and EGU emissions. 

Future-year visibility estimates for the best 20% and worst 20% days would be projected using 

the latest observed current conditions and the model derived RRFs. The future-year visibility 

estimates for the worst 20% days would be compared against the visibility goal at each Class I 

area and the projected visibility for the best 20% days would be compared to the current 

conditions to assure there is no worsening of air quality. As an initial future-year visibility target 

or goal we will assume a linear glide path slope (in dV) from the current conditions to the EPA 

default natural conditions for each Class I area. Refinements to this approach are likely (e.g., 

updated natural conditions). These calculations are repeated for each future-year emissions 

scenario. 

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1.0 INTRODUCTION 

This report constitutes the first draft of the Air Quality Modeling Protocol and Quality 

Assurance (QA) plan for the VISTAS Phase II emissions and air quality modeling activities to be 

performed by the contractor team of ENVIRON International Corp, the University of California 

at Riverside, and Alpine Geophysics, LLC. Development of this first draft protocol governing 

the VISTAS Phase II emissions and air quality modeling and model evaluation was performed as 

Task 6 in the Phase I study. 

1.1 Background 

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) is a 

collaborative effort of state governments, tribal governments, and various federal agencies 

established to initiate and coordinate activities associated with the management of regional haze, 

visibility and other air quality issues in the Southeastern United States. Figure 1-1 identifies the 

various Regional Planning Organizations in the U.S, including VISTAS. Figure 1-2 depicts the 

key Class I areas within the study domain. As shown in these figures, VISTAS is comprised of 

the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

Tennessee, Virginia, and West Virginia as well as the Eastern Band of Cherokee Indians. The 

agencies participating in VISTAS are committed to a sound and thorough scientific analysis of 

regional haze problems, impacts from natural and man-made pollutants, and potential solutions. 

Stakeholders are encouraged to participate at the workgroup level in order that all aspects of the 

problem and possible strategies may be given consideration. 

The Clean Air Act establishes special goals for visibility in many national parks, 

wilderness areas, and international parks. Through the 1977 amendments to the Clean Air Act, 

Congress set a national goal for visibility as “the prevention of any future, and the remedying of 

any existing, impairment of visibility in mandatory Class I Federal areas which impairment 

results from manmade air pollution” (40 CFR 51.300). States are required to develop State 

Implementation Plans (SIPs) to attain visibility standards, and Tribes also may opt to assume 

responsibility for visibility programs under 40 CFR Part 49 by developing Tribal Implementation 

Plans (TIPs). The goal of the Regional Haze Rule (RHR) is to achieve natural visibility 

conditions at the Federally mandated Class I areas by 2064. To achieve this goal, the RHR has 

set up several milestone years of 2018, 2028, 2038, 2048, 2058 and 2064 to monitor progress 

toward natural visibility conditions. Section 308 requires the first visibility SIP/TIP be submitted 

to EPA by 2007 - 2008 to demonstrate progress toward natural visibility conditions in 2018 

using the 2000-2004 five-year baseline. The VISTAS “game plan” is to have an evaluation of 

initial strategies by 2005 so that States and Tribes can start their planning process for submitting 

their plans to EPA that were originally due by 2008. 

Regional haze is linked to fine particulate (PM2.5) for which EPA has a new standard. 

PM2.5 SIPS are to be submitted 3 years after designation of PM2.5 nonattainment areas. EPA 

intends to perform the final designations of the PM2.5 nonattainment areas by December 2004, 

which would make the PM2.5 SIP due by December 2007. As regional haze is intricately linked 

to PM2.5, the PM2.5 and regional haze SIPs are in the process of being aligned so they would both 

be due by December 2007, if the PM2.5 final designations occur as planned. EPA also intends to 

designate 8-hour nonattainment areas by April 2004 that would make 8-hour ozone SIPs also due 

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Figure 1-1. Regional Planning Organizations Engaged in Regional Haze Modeling.

in 2007. States are considering whether to use the integrated one-atmosphere VISTAS base year 

modeling for ozone modeling in addition to PM2.5 and regional haze. Those decisions will be 

based on schedule constraints, model performance, appropriateness of the episodes and 

feasibility. These regulatory drivers emphasize the importance of completing the VISTAS Phase 

I modeling to test and evaluate model configurations in 2003 and starting the annual modeling in 

2004. These issues also emphasize the importance in testing the models for ozone as well as 

PM2.5 and visibility. 

VISTAS is one of five Regional Planning Organizations (RPOs) in the United States that 

are implementing the requirements of the RHR. The purposes of VISTAS are: 

> To promote the coordinated efforts of State, Local, and Tribal air agencies to meet 

federal regional haze and visibility requirements; 

> To identify regional or common air management issues; 

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Figure 1-2. VISTAS Study Region Showing the Class 1 Areas.

> To conduct research and undertake other activities to provide the membership 

with scientific and technological information which may be of value in the 

development of agency strategies; and 

> To develop strategies on regional haze for use by member states and tribes, where 

appropriate, in developing their implementation programs. 

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The Southeastern States Air Resource Managers (SESARM) has been designated by the 

US Environmental Protection Agency as the entity responsible for coordinating and 

implementing regional planning for the states in the VISTAS region. 

1.2 VISTAS Two-Phased Approach 

1.2.1 Phase I 

The objective of VISTAS Phase I was to determine the optimal modeling configuration 

for use in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a 

comprehensive literature review of recent relevant visibility studies using various 

photochemical/aerosol modeling platforms in order to assess and identify appropriate model 

configurations, data bases, and model testing methodologies that were appropriate for use in 

conducting the VISTAS Phase I emissions and PM modeling assessment. Key elements of 

Phase I included: 

3⁄4 Review all relevant air quality model simulations that have been completed related to 

regional haze and PM2.5 modeling and document the relevant sensitivity analyses, model 

configuration testing, and performance evaluations that have been performed 

(ENVIRON, 2003d); 

 

3⁄4 Review the current science in regional emissions modeling (e.g., EPS, EMS and 

SMOKE) and PM air quality modeling (e.g., CMAQ, CMAQ-MADRID, CMAQ-AIM, 

REMSAD, UAM-V/PM, CAMx4 and PMCAMx) to determine the most appropriate 

model(s) for use by VISTAS (ENVIRON, 2003b); 

3⁄4 Review available ambient data for evaluating one-atmosphere PM/ozone models 

(ENVIRON, 2003c);

3⁄4 Develop and implement a plan for testing and evaluating alternative science 

configurations of the recommended Phase I model(s) and document the results 

(ENVIRON, 2003d); 

3⁄4 Prepare a Task 6 report prescribing the model set-up, data base development, 

performance testing, and control strategy evaluation procedures to be implemented in 

VISTAS Phase II (this protocol document). 

VISTAS formed three standing workgroups to plan and direct. These included (a) the 

Technical Analysis (emissions and modeling) Workgroup; (b) the Data (monitoring) Workgroup; 

and (c) the Planning Workgroup. Under Phase I, the VISTAS Technical Analysis Workgroup 

(TAWG) managed the comprehensive model configuration testing program aimed, as noted 

above, at evaluating the capabilities of current state-of-science regional emissions, prognostic 

meteorological and PM/visibility models. The resultant modeling system (models and databases) 

identified and tested in Phase I were intended to be applied in Phase II (discussed below) 

following the procedures set forth in this protocol. 

For the meteorological component of the Phase I modeling, SESARM contracted with 

Baron Advanced Meteorological Systems (BAMS) to apply the PSU/NCAR Mesoscale Model 

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(MM5) in multiple configurations and to evaluate its performance against surface and aloft 

meteorological observations. The emissions modeling component of VISTAS Phase I was 

carried out by the research team of ENVIRON/UC/Alpine with staff at Alpine Geophysics taking 

the lead role in setting up, testing, and applying the emissions modeling system. The air quality 

modeling component was performed by the team at the ENVIRON/UC/Alpine modeling centers. 

A dominant theme during Phase I was the exchange of modeling codes, databases, and 

evaluation software between the three modeling centers as the air quality modeling was carried 

out. 

1.2.2 Phase II 

The Phase II modeling will include annual PM/regional haze simulations plus additional 

shorter duration episodes. After detailed performance testing, the modeling system will then be 

exercised with a variety of emissions control scenarios aimed at enabling VISTAS to assess the 

effects of future year emission control strategies on visibility and other air quality issues. The 

modeling system will also allow VISTAS to track reasonable progress toward regional haze 

goals. More specifically, the VISTAS Phase II program will focus on the use of the CMAQ 

modeling system for calendar year 2002 over the same 36/12 km horizontal grid system used in 

Phase I. A potentially large number of annual (and episodic) model simulations will be 

performed; the list below reflects current plans: 

3⁄4 2002 Initial Annual Run. The initial annual model simulations and performance 

evaluations using the 2002 inventory for VISTAS and non-VISTAS states, Canada and 

Mexico. Multiple iterations of the 2002 annual simulation will be required to confirm the 

appropriateness of the model science configuration(s) recommended by the Phase I work, 

to evaluate updates to the model and model inputs and to refine model performance. 

 

3⁄4 2002 Revised Annual Run. A subsequent annual 2002 simulation using revised 2002 

modeling inventory for VISTAS and non-VISTAS states, Canada and Mexico. The 

primary objective of this run is model performance demonstration using updated 

emissions inventories and best science model configurations. Additional sensitivity tests 

will be conducted using the revised base case year annual run. 

3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 

simulation representing the 2000-2004 baseline period for EGU and fire emissions and 

using 2002 revised inventory for all other source sectors. The primary objective of this 

inventory is to provide the base line modeled air quality condition against which future 

year modeling runs will be compared to develop relative reduction factors for each 

pollutant species. 

3⁄4 Future Year Annual Runs. Future year simulations involving a base case inventory of 

typical EGU and fire emissions for VISTAS-selected future period. Additional future 

year inventories may also be modeled. The objective of these future year model runs is 

to establish the modeled air quality basis against which the effectiveness of emissions 

control strategies will be evaluated. 

3⁄4 Future year emission control strategies. Prescription of the future year emissions 

control strategies to be performed later in Phase II (e.g., 2005) will be defined after the 

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foregoing simulations and analyses have been completed. A combination of annual and 

episodic runs is anticipated. 

Closely integrated with the annual (and possibly episodic) meteorological, emissions and 

air quality modeling will be ongoing project management, technical review, and quality 

assurance activities performed under the guidance of the VISTAS Contracting Officer and the 

TAWG. The modeling team members will participate with VISTAS management in regular 

monthly conference calls, as well as ad hoc topical conference calls as needed, and will attend 

periodic meetings with the TAWG members throughout Phase II. 

Complementing the data acquisition, modeling input development activities, and project 

management activities, four other Phase II activities will be performed, consistent with the 

Quality Assurance (QA) plan: 

Data Gatekeepers

The VISTAS Phase II emissions and air quality modeling team are receiving emissions, 

meteorological and air quality data from other VISTAS contractors or other sources and as a first 

line of QA, we have defined a Gatekeeper function to assure the data have been received 

correctly, evaluate the quality of the data, and document the data received. Separate air quality, 

meteorological and emissions Gatekeepers have been identified whose roles are defined below. 

In addition, a Data Management Gatekeeper has been defined who will post data, reports and 

results to the project website and archive all key data generated in the project. 

3⁄4 Air Quality Data Gatekeeper. Obtain air quality data as appropriate for 

model input development and model performance evaluation and assure 

quality of all air quality data obtained, consistent with approved QA plan. 

This gatekeeper will also provide documentation of evaluation and generate 

IC/BC inputs for CMAQ for all modeling runs. 

3⁄4 Meteorological Gatekeeper. Obtain meteorological data, as MM5 or MCIP 

files, as appropriate for annual 2002 modeling runs and other episode periods 

and perform data quality checks as approved in QA plan together with 

appropriate documentation of model performance evaluation activities. 

3⁄4 Emissions Gatekeeper. Obtain emissions inventory data necessary to 

support annual 2002 and future year modeling and recommend source of 

emissions data to be used for Canada and Mexico. Assure quality of all 

emissions data received, consistent with approved QA plan and develop all 

emissions modeling files to support modeling runs for 2002. This gatekeeper 

will also develop the chemical speciation files and temporal and spatial 

allocation files necessary to convert annual inventories into hourly and daily 

emissions modeling files, as appropriate. The Emissions Gatekeeper will also 

develop all emissions modeling files for non-VISTAS states to support 

modeling runs for future year base case and emissions strategies as defined by 

VISTAS. 

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3⁄4 Data Management Gatekeeper: This gatekeeper will maintain the VISTAS 

Modeling Website including posting modeling input and output files, reports, 

interpretation of results, and other documents as requested by VISTAS to 

support all Phase II tasks. This includes, for example, the storage of model 

inputs and outputs for annual (and episodic) runs and the transfer (via fire 

wire or alternative media) of electronic files to VISTAS states, other regional 

planning organizations, EPA, other contractors, and stakeholders. 

Emissions QA/QC 

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical 

steps in performing air quality modeling studies. Because emissions processing is tedious, time 

consuming and involves complex manipulation of many different types of large data sets, errors 

are frequently made in emissions processing and, if rigorous QA measures are not in place, these 

errors may remain undetected. In Phase II we will continue with multistep emissions the QA/QC 

approach applied in the Phase I modeling. This includes the initial emissions QA/QC by the 

Emissions Gatekeeper described above, as well as QA/AC by the Emissions Modeler during the 

processing of emissions and then additional QA/QC by the air quality modeler of the processed 

model ready emission files. This multistep process with three separate groups involved in the 

QA/QC of the emissions is much more likely to catch any errors prior to the air quality model 

simulations. 

Emissions QA/QC that would be performed as part of the emissions modeling will include: 

EMS and EPA Input Screening Error Checking Algorithms: Although the SMOKE 

emissions model will be used for emissions processing, some of the more advanced EMS 

input error checking algorithms will be used to screen the data and identify potential 

emission input errors. Additionally, EPA has issued a revised stack QA and augmentation 

procedures memorandum that will be used to identify and augment any outlying stacks. 

SMOKE error messages: SMOKE provides various cautionary or warning messages 

during the emissions processing. We will redirect the SMOKE output to log files and 

review the log files for serious error messages. An archive of the log files will be 

maintained so that the error messages can be reviewed at a later date if necessary. 

SMOKE emissions summaries: We will use QA functions built into the SMOKE 

processing system to provide summaries of processed emissions as daily totals according 

to species, source category and county and state boundaries. These summaries will then 

be compared with summary data prepared for the pre-processed emissions, e.g., state and 

county totals for emissions from the augmented emissions data. 

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Once the CMAQ-ready emission inputs have been prepared, we will perform additional 

emissions QA/QC as follows: 

Spatial Summary: We will sum the emissions for all layers and for all 24 hours that is 

used to prepare a PAVE plot showing the daily total emissions spatial distribution. For a 

20 day simulation this produces approximately 20 days x 20 species x 5 emissions 

categories = 2,000 plots. In our base case simulations these plots will be presented as tons 

per day. The objective of this step is to identify errors in spatial distribution of emissions. 

Vertical Profile: For point sources the emissions total for each layer will be summed and 

plotted to show the vertical distribution of emissions. These plots show the emissions on 

the x-axis for each model layer on the y-axis. The objective of this step is to identify 

possible errors in vertical distribution of emissions. 

Short Term Temporal Summary: The total domain emissions for each hour will be 

accumulated and time series plots prepared that display the diurnal variation in total 

hourly emissions. The objective of this step is to identify errors in temporal profiles. 

Long Term Temporal Summary: The total domain emissions for each day will be 

accumulated and displayed as time series plots that show the daily total emissions across 

the domain as a function of time. The objective of this step is to identify particular days 

for which emissions appear to be inconsistent with other days for no reason (e.g., not a 

weekend) and compare against the general trend. 

Control Strategy Spatial Displays: Spatial summary plots of the daily total emissions 

differences between a control strategy and base case emissions scenarios will be 

generated. These plots can be used to immediately identify a problem in a control 

strategy. For example, if a VISTAS states SO2 control strategy is being analyzed and 

there are changes in emissions for other pollutants or for SO2 outside of the VISTAS 

states problems in emissions processing can be identified prior to the air quality model 

simulation. 

Meteorology QA/QC 

The VISTAS meteorological modeling contractor (BAMS) will have primary 

responsibility in the QA/QC of the MM5 meteorological fields. However, the emissions and air 

quality modeling team will also perform some QA/QC of the meteorological data to assure that it 

has transferred correctly, to obtain an assessment of the quality of the data and to assist in the 

interpretation of the air quality modeling results. 

The VISTAS Phase II Meteorological Gatekeeper will perform the following: 

3⁄4 Analyze the MM5 data to assure it has been transferred correctly; 

 

3⁄4 Evaluate the MM5 using METSTAT and the surface meteorological network; 

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3⁄4 Evaluate upper-air MM5 meteorological estimates by comparison them to upper-air 

observations and satellite images; and 

3⁄4 Compare the VISTAS 2002 MM5 simulation with the one generated by WRAP. 

3⁄4 Generate the CMAQ-ready meteorological inputs using the MCIP2.2 processor. 

Air Quality Modeling QA/QC 

Key aspects of QA for the CMAQ input and output data include the following: 

• Verification that correct configuration and science options are used in compiling and 

running each model of the in the CMAQ modeling system, where these include the 

MCIP, JPROC, ICON, BCOM and the CCTM. 

• Verification that correct input data sets are used when running each model. 

• Evaluation of CCTM results to verify that model output is reasonable and consistent with 

general expectations. 

• Processing of ambient monitoring data for use in the model performance evaluation. 

• Evaluation of the CCTM results against concurrent observations. 

• Backup and archiving of critical model input data. 

 The most critical element in the QA plan for CMAQ simulations is the QA/QC of the 

meteorological and emissions input files, which is discussed above. The major QA issue 

specifically associated with the air quality model simulations is verification that the correct 

science options were specified in the model itself and that the correct input files were used when 

running the model. For the CMAQ model we employ a system of naming conventions using 

environment variables in the compile and run scripts that guarantee that correct inputs and 

science options are used. We also employ a redundant naming system so that the name of key 

science options or inputs are included in the name of CMAQ executable program, in the name of 

the CMAQ output files, and in the name of the directory in which the files are located. This is 

accomplished by using the environment variables in the scripts to specify the names and 

locations of key input files. 

 A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the 

original runs scripts and directory structure that were used in performing a model simulation. 

 We will also perform a post-processing QA of the CMAQ output files similar to that 

described for the emissions processing. We will generate animated gif files using PAVE that can 

be viewed to search for unexpected patterns in the CMAQ output files. In the case of model 

sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case 

minus the base case. Often, errors in the emissions inputs can be discovered by viewing the 

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animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ 

simulations. This provides a summary that can be useful for more quickly comparing various 

model simulations. 

1.3 Preliminary Conceptual Model of Visibility Impairment in the VISTAS Region 

VISTAS is currently developing a comprehensive conceptual model of visibility impairment in 

the southeastern US. Brewer, Holman and Hornback (2003) have reported a preliminary 

analysis to characterize the components of PM2.5 and their contributions to visibility impairment 

in the VISTAS region. The analyses was based on monitoring data from the Interagency 

Monitoring of Protected Visual Environments (IMPROVE) network (Malm et. al., 2003) for the 

period 1998 to 2001 and from the Southeast Aerosol Research Characterization Study 

(SEARCH) network (Hansen et al., 2004)for the period 1999 to 2001. IMPROVE monitors are 

located in 15 of the 18 Class I areas (national parks greater than 6,000 acres and wilderness areas 

greater than 5,000 acres) in the in the VISTAS region (Figure 1-3). These areas represent a 

broad geographic range from the Atlantic and Gulf Coasts and coastal plains to the Southern 

Appalachian Mountains and western Kentucky. The SEARCH network consists of four urban 

sites (Atlanta, GA, Birmingham, AL, Gulfport, MS, and Pensacola, FL) paired with a nearby 

rural site (or suburban in the case of Pensacola) for a total of 8 sites (Figure 1-3). 

Figure 1-3. Locations of IMPROVE and SEARCH fine particulate monitoring sites used to 

characterize visibility impairment in the southeastern U.S. 

Similar multi-filter systems and analytical methods are used in the two networks. The 

IMPROVE network collects 24-hour samples every third day. The SEARCH network collects 

24-hour samples either daily or every third day. Light extinction was calculated as described in 

SEARCH (urban)

SEARCH (rural)

IMPROVE (limited data)

IMPROVE (5+ years)

Sipsey

Chassahowitzka

Okefenokee

Mammoth Cave

Shining Rock

Great Smoky Mtns.

Shenandoah

James River Face

Dolly Sods

Cape Romain

Jefferson St 

(Atlanta)

Birmingham

Pensacola

Old Fort

Gulfport

Yorkville

Centerville

Oak Grove

Saint Marks

Cohutta

Linville Gorge

Swan Quarter

Everglades

Figure 2. IMPROVE and SEARCH Sites in VISTAS region

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the draft Guidance for Tracking Progress under the Regional Haze Rule (EPA, 2001), except 

that relative humidity was not allowed to exceed.95%. Coarse mass measurements were not 

available for the SEARCH sites in Gulfport and Oak Grove, Mississippi. 

EPA’s guidance (EPA, 2001) calculates visibility impairment through fine mass light extinction 

that is attributable to light scattering due to air molecules (Rayleigh scattering), scattering by 

ammonium sulfate, (NH4)2SO4; ammonium nitrate, NH4NO3; organic carbon (OC), and fine Soil, 

and absorption by elemental carbon, EC. Coarse mass (fine particles with diameters between 2.5 

and 10 microns) also contributes to total light extinction. Fine particle composition and 

contribution to light extinction were evaluated for days with the 20% highest and 20% lowest 

total light extinction (on annual basis) for 1998 to 2001 in the IMPROVE network and for 1999 

to 2001 in the SEARCH network (ARS, 2002). For the IMPROVE sites, on the 20% haziest 

days (highest extinction), light extinction (i.e., visibility impairment) is greater at the Southern 

Appalachian, Mammoth Cave, and Sipsey sites than at the coastal and coastal plain sites (Figure 

1-4). Visual range on the 20% haziest days varies from 18 -25 km at the northern IMPROVE 

sites and 27-48 km at the coastal and coastal plain sites. 

Light extinction for the 20% haziest days is generally higher at the northern SEARCH 

sites than the coastal SEARCH sites (Figure 1-5). Visual range on the 20% haziest days at the 

SEARCH sites varies from 15-24 km. For the 20% clearest days (lowest extinction), there is less 

spatial variation in extinction than is observed for the 20% haziest days (see Figures 1-4 and 1-

5). Visual range on the 20% clearest days varies from 75-131 km at the IMPROVE sites and 57-

91 km at the SEARCH sites. 

Figure 1-4. Components of average light extinction (Mm-1) at southeastern US IMPROVE 

monitors for the 20% worst visibility days from 1998-2001. Extinction (Mm-1)

 Coarse

Soil

Organics

EC

NH3NO3

(NH4)2SO4

Rayleigh

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Shining Rock, NC

Figure 3. Light Extinction on 20% Poorest Visibility Days - IMPROVE 1998 - 2001

50

100

150

200

250

0

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Figure 1-5. Components of average light extinction (Mm-1) at southeastern US 

SEARCH monitors for the 20% worst visibility days from 1999-2001. 

Figure 1-6. Components of average light extinction (Mm-1) at southeastern US 

IMPROVE monitors for the 20% best visibility days from 1998-2001). Atlanta, GA Yorkville, GA Birmingham, AL Centerville, AL Gulfport, MS* Oak Grove, MS* Pensacola, FL Old Fort, FL

50

100

150

200

250

300

Extinction (Mm-1)

 Coarse*

 Soil

 Organics

 EC

 NH4NO3

 (NH4)2 SO4

 Rayleigh

Figure 4. Light Extinction on 20% Poorest Visibility Days - SEARCH 1999 - 2001

*coarse not measured at 2 MS sites 

Extinction (Mm-1)

 Coarse

Soil

Organics

EC

NH3NO3

(NH4)2SO4

Rayleigh

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Shining Rock, NC

Figure 5. Extinction on 20% Best Visibility Days - IMPROVE 1998 - 2001

0

10

20

30

40

50

60

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Figure 1-7. Components of average light extinction (Mm-1) at southeastern US SEARCH 

monitors for the 20% best visibility days from 1999-2001. 

In both the IMPROVE and SEARCH networks, the 20% haziest days in the year occur 

most frequently in the summer and spring quarters and least frequently in the winter quarters. 

Variation in sulfate concentrations is the greatest contributor to seasonal variation in extinction 

in the VISTAS region. 

Ammonium sulfate, (NH4)2SO4, fine particles are the largest contributors to light 

extinction for both the 20% clearest and 20% haziest days at all IMPROVE and all SEARCH 

sites in all years and all quarters. (NH4)2SO4 fine particles are also the largest contributors to 

reconstructed fine particle mass at all IMPROVE and SEARCH sites for the 20% haziest days 

and at all IMPROVE sites for the 20% clearest days. On the 20% clearest days at the SEARCH 

sites, organic carbon (OC) mass can be greater than (NH4)2SO4 mass. 

Ammonium nitrate, NH4NO3, typically contributes 0-15% of the fine particle mass and 

fine mass light extinction at the SEARCH and IMPROVE sites. NH4NO3 has the highest 

contributions at the urban Birmingham and Atlanta SEARCH sites and less contribution at the 

more rural SEARCH and IMPROVE sites. Available NH4 measurements for the SEARCH and 

IMPROVE sites indicate that SO4 is not fully neutralized, particularly during the summer quarter 

(ARS, 2002b). NH4 will preferentially neutralize SO4 particles before reacting with nitric acid to 

form NH4NO3. Modeling analyses suggest that NH4NO3 formation in the southeastern U.S. is 

limited by NH4 availability in the atmosphere (Odman et al., 2002). NH4NO3 volatizes at higher 

temperatures, and therefore NH4NO3 particle concentrations are higher in the fall and winter 

months than in the warmer months. On a daily basis, NH4NO3 is most likely to be formed early Atlanta, GA Yorkville, GA Birmingham, AL Centerville, AL Gulfport, MS* Oak Grove, MS* Pensacola, FL Old Fort, FL Extinction (Mm-1)

 Coarse*

 Soil

 Organics

 EC

 NH4NO3

 (NH4)2 SO4

 Rayleigh

Figure 6. Light Extinction on 20% Best Visibility Days - SEARCH 1999 - 2001

*coarse not measured at 2 MS sites 

10

20

30

40

50

60

70

0

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in the morning when temperatures are generally cooler and relative humidity higher than later in 

the day when conditions are hotter and drier. 

Organic carbon, OC, has the second largest contribution to fine particle mass and fine 

mass light extinction for the 20% haziest days at all sites (see Figures 1-4 and 1-5). On the 20% 

clearest days at IMPROVE sites, NH4NO3 has an equal or sometimes larger contribution to fine 

mass extinction than OC (Figures 1-6 and 1-7). The contribution of OC to fine mass extinction is 

relatively constant across the quarters of the year. The IMPROVE and SEARCH 24-hr samples 

do not allow separation of primary OC from secondary OC sources nor anthropogenic sources 

from biogenic sources. However, chemical mass balance receptor model analysis (Zheng et al., 

2002) and carbon-14 analyses of OC samples for the SEARCH sites (Edgerton, 2003) suggest 

that OC fine mass in the fall and winter is predominantly from primary sources (e.g. gasoline, 

diesel, wood burning). These analyses suggest that secondary organic aerosols (e.g. from 

biogenic emissions) have small contributions to total OC fine mass in the fall and winter months 

and larger contributions in the spring and summer months. 

Elemental carbon, EC, typically contributes 3-15% to fine particle mass and fine mass 

light extinction at the IMPROVE and SEARCH sites. The highest levels of EC are at the urban 

Birmingham and Atlanta SEARCH sites. Elevated levels of OC, EC, and non-soil potassium can 

be used as indicators of episodes where emissions from fires impacted the monitoring sites 

(ARS, 2002c). In particular, poor visibility conditions in October 2000 and November 2001 at 

several sites throughout the southeastern U.S. have been attributed to impacts due to emissions 

from fires. Further analyses is needed to separate the relative contributions of different types of 

combustion sources (e.g. forest fire, prescribed forest burning, land clearing, agricultural 

burning, residential wood combustion, or industrial wood combustion) to elevated OC and EC 

measures. 

Fine Soil generally contributes 0-3 % of the fine mass extinction and 5-20% of the fine 

particle mass at the southeastern U.S. SEARCH and IMPROVE sites. Soil has a higher 

percentage contribution to fine mass and fine mass extinction at Chassahowitzka and Everglades 

IMPROVE monitors in Florida than at the other IMPROVE and SEARCH sites in the 

southeastern U.S. Coarse mass generally contributes less than 5% to total extinction at the 

IMPROVE sites and at those SEARCH sites where coarse mass is measured. There is 

considerable inter-annual variability in all of the above measures of fine particle mass and 

extinction. 

Fine particle mass and composition are also measured by the state agencies at several 

sites throughout the VISTAS region. The national ambient air quality standard for PM2.5 for an 

annual average (15 μg/m3

) is measured to be exceeded in several urban areas in the southeastern 

states (see Figure 1-8). The annual PM2.5 standard is not exceeded at any of the IMPROVE sites. 

No exceedances of the 24-hour standard (65 μg/m3) have been reported through 2002, except 

those related to forest fire events. 

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Figure 1-8. Counties in the southeastern U.S. with 1999-2001 annual PM2.5 Design Values that 

exceed the fine particulate annual standard. 

1.4 Summary of Phase I Findings 

The Phase I program entailed systematic meteorological, emissions, and air quality model 

testing and evaluation to identify the most reliable, scientifically valid, and operational efficient 

modeling configuration for visibility assessment in the Southeastern U.S. Collaboratively, the 

VISTAS TAWG and modeling contractors selected three episodes to serve as the foundation for 

model testing and evaluation: (a) 1-20 January 2002, (b) 13-27 July 2001, and (c) 13-21 July 

1999. A brief summary of the Phase I findings is given below. 

1.4.1 Meteorological Modeling Findings 

As noted above, SESARM contracted with Baron Advanced Meteorological Systems 

(BAMS) to apply the PSU/NCAR Mesoscale Model (MM5) in multiple configurations and to 

evaluate its performance against surface and aloft meteorological observations. Moreover, 

BAMS scientists participated with the emissions and air quality modeling team in assessing how 

the air quality modeling system responded to sensitivity features of the meteorological model. 

Significant findings of the Phase I MM5 meteorological modeling are presented in a series of 

reports (Olerud, 2003a,b; Olerud and Sims, 2003) and meeting presentations (e.g., Olerud, 

2003c; Abraczinskas, 2003; Appel, 2003). Further details and a compendium of meteorological 

modeling results may be found at the following website: 

http://www.baronams.com/projects/VISTAS/. 

Figure 7. Counties with 1999-01 design values above PM2.5 annual standard

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 Following the Phase I MM5 modeling protocol (Olerud, 2003a), BAMS generated a 

prodigious array of results that helped elucidate several key questions, namely: 

3⁄4 Are the synoptic patterns captured? 

 

3⁄4 Are the modeled meteorological fields reasonable? 

3⁄4 Does the model properly show diurnal evolution in key state fields including mixing 

heights, temperatures, and winds? 

3⁄4 Are cloud cover and precipitation fields unbiased and reasonably accurate? 

3⁄4 Do the fields lead to acceptable air quality results? 

The MM5 (Version 3.6; Grell et al., 1994, MPP version) was exercised for all three 

VISTAS episodes. The specific MM5 modeling periods were: (a) 00Z 26 Dec ’01 to 12Z 21 Jan 

’02, (b) 00Z 6 Jul to 12Z 28 Jul ’01, and (c) 00Z 9 Jul to 12Z 22 Jul ’99. The MM5 sensitivity 

matrix focused on five model configurations: 

px_acm: Pleim-Xiu land surface model (LSM), asymmetric convective 

mixing (ACM) Planetary Boundary Layer (PBL) (Xiu and Pleim, 

2000), interppx to link LSM run segments. 

px_acm2: Similar to px_acm, except no iterppx linkage across run segments 

of LSM data. 

noah_mrf: Noah land surface scheme (Chen and Dudhia, 2001) with the 

medium range forecast (MRF) PBL (Hong and Pan, 1996). 

multi_blkdr: Multi-layer soil scheme with Blackadar PBL and Zilitinkevich 

thermal roughness length. 

noah_eta-my: Noah land-surface scheme with the Eta Mellor-Yamada PBL 

(IMDVDIF = 0). 

Common MM5 physics options underlying the Phase I sensitivity tests included: (a) 

Kain-Fritsch 2 cumulus parameterization, (b) mixed phase (Reisner 1) microphysics, (c) Rapid 

Radiative Transfer Model (RRTM) radiation, (d) snow effects turned on, and (e) analysis 

nudging of surface and aloft winds, temperature, and moisture on the 36 km and 12 km grids. A 

variety of statistical time series plots, statistical tables, and spatial plots were generated to 

quantify and display the modeling results, both at the ground and aloft. 

Key findings distilled from the work of Olerud (2003d,e) included: 

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o The MM5 (ver 3.6) is most sensitive to the choice of planetary boundary layer 

(PBL) and soil (LSM) schemes1

; 

 

o There was no ‘definite winner’ among the five model configurations tested; 

o The noah_mrf and noah_eta-my exhibit markedly different PBL behavior; 

o The noah_mrf wind performance was poorer than the other configurations, while 

the temperature and precipitation of the noah_eta-my was also poorer than the 

others; 

o Negative PBL heights were generated by the noah_eta-my scheme under some 

situations; 

o The px_acm2 was judged “probably best overall” in part because it did not 

exhibit significant instances of very poor statistical performance for any quantity; 

 

o The PBL behavior produced by both px_acm and px_acm2 was a concern in some 

situations; and 

o There was some residual uncertainty as to whether the px_acm or px_acm2 

scheme was preferable, based on the available results. 

Further details and interpretation of the MM5 configuration analyses are described in the 

citations mentioned above and are also available on the BAMS project website. Reflecting 

Olerud’s findings and the collective judgments of the Emissions and Air Quality Modeling Team 

and the VISTAS TAWG, a slight modification of the px_acm2 scheme (pc_acm8) was chosen as 

the final MM5 configuration for the Phase II modeling. The pc_acm8 MM5 configuration 

differs from pc_acm2 in that the MM5 program little_r is used instead of regrid to generate the 

analysis fields used in the MM5 data assimilation. 

1.4.2 Emissions Modeling Findings 

The emissions and air quality modeling activities of VISTAS Phase I were carried out as 

a joint effort by the research team of ENVIRON/UC/Alpine. For the emissions processing with 

SMOKE, day-to-day emissions modeling tasks were performed principally by staff at Alpine, 

although all team members participated in emissions-related tasks in a number of ways, 

including quality assurance (QA). Key findings in the Phase I emissions processing with the 

SMOKE system include: 

 

1

 This judgment, proffered by Olerud (2003d; page 1) in the VISTAS Task2e meteorological 

modeling review and assessment report, was not derived from the Phase I meteorological 

modeling sensitivity analyses since the latter were not designed to test model physics options 

beyond PBL and LSM schemes. Nonetheless, we agree with Olerud’s assessment based on the 

Emissions and Air Quality Modeling teams experience in literally dozens of MM5 modeling 

studies throughout the U.S. 

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3⁄4 Although the SMOKE computational methodology provides quick executions 

times, the large number and sizes of input, output and intermediate data files 

require substantial time and disk space for file management activities. 

3⁄4 Data input and output review, process monitoring and data management, and QA 

reporting and evaluation activities substantially increase the calendar time 

required for each production episode. 

Computer runtimes for the SMOKE emissions processing were significant, 

notwithstanding the computational efficiencies inherent in the sparse matrix solution technique. 

After the initial system configuration and setup process, the following runtimes were required for 

a production run of the 2-20 January 2002 episode for the 12K and 36K domains: 

 30 CPU hours 

 4 calendar days 

 40.3 Gigabytes (Gb) output file storage

These times were produced on a dual Athlon 2600+ system utilizing a local Ultra 320 RAID 5 

disk array. 

Based on the SMOKE processing time requirements encountered with all three Phase I 

episodes, we project that the processing time for an annual (365 days) 36 km and 12 km will be 

approximately: 

 19 CPU days of dedicated machine time 

 60 calendar days, and 

1,398 Gb (1.4 Terabytes, Tb) output file storage space.

SMOKE processing also requires approximately 3 times the output file storage space for 

intermediate file handling. These extreme file size requirements will significantly affect the 

amount of time required for emissions generation, file handling and data transfers. 

1.4.3 Air Quality Modeling Findings 

The air quality modeling component was also performed as a joint effort by the 

ENVIRON/UC/Alpine research team. Details of the emissions and air quality modeling 

activities, publications, and modeling results under Phase I may be found at: 

http://pah.cert.ucr.edu/vistas/. The Final Report on the Phase I modeling, including an 

evaluation of CMAQ and CAMx model performance together with results of pertinent modeling 

sensitivity experiments is expected to be delivered to VISTAS in early May 2004. 

The identification of the Phase II model configuration considered three main, and often 

competing, issues: 

(1) Science: Preferable to use better science options in the Phase II model configuration; 

 

(2) Model Performance: Preferable to use model options that produce better model 

performance. 

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(3) Run Time: Faster run times are preferable. 

Key findings from the Phase I CMAQ configuration testing include: 

3⁄4 Duration of CMAQ Spin-up Initialization Period: Our experience with the 

VISTAS Phase I and Western Regional Air Partnership (WRAP) regional 

modeling was that a minimum 10-15 day spin up period is needed to initialize the 

model for modeling domains the size of the continental US. Even with a 10-15 

day spin up, small differences in concentrations are seen versus a continuous run. 

However, these differences are small enough that they do not affect model 

performance. In general, longer spin up periods are needed for summer than 

winter periods due to more stagnation, higher photochemical oxidant levels and 

weaker synoptic forcing. Just as important as the length of the spin up period is 

the need to operate the model in a consistent fashion across scenarios. For the 

VISTAS Phase II annual modeling, the 2002 annual run will be performed in four 

quarters. The first quarter (January – March, 2002) will be initialized with ~13 

spin up days (starting 00Z on December 18, 2001) with the other three quarters 

initialized with 15 spin up days.

3⁄4 Number of Vertical Layers: Comparisons between the CMAQ modeling results 

for the base configuration of 19 vertical layers with 34 vertical layers that exactly 

matched the MM5 layer structure revealed small changes in modeled 

concentrations, but essentially no change in the basic attributes of model 

performance. The concentration changes were greater for those species that are 

emitted mainly aloft (e.g., SO2 and sulfate), whereas the estimated concentrations 

for primarily surface emitted species (e.g., EC) were essentially identical using 

the two vertical layer structures. Model run time is almost linearly proportional to 

the number of vertical layers, thus the 34 layer CMAQ run requires approximately 

80% more CPU time than the 19 layer run. Based on these results, the 19 vertical 

layer model configuration with a 100 mb (~15 km) region top is adopted for the 

Phase II modeling. 

3⁄4 36 km vs. 12 km Horizontal Grid Size: Similar overall model performance was 

exhibited by CMAQ using a 36 km versus a 12 km grid. However, at specific 

times and locations there could be substantial differences in estimated 

concentrations due to different precipitation and transport patterns. With some 

exceptions, the general trend was a slight degradation in the model performance 

measures using a 12km verses 36km grid. However, the basic model performance 

features remained unchanged using the two grid resolutions. The 12 km CMAQ 

runs required approximately 2 times more CPU time than the 36 km runs. 

Although the 12 km runs require significantly more computer resources, they do 

represent better science for photochemical and PM grid modeling so will be 

retained in Phase II. However, more sensitivity tests can be performed using the 

36 km model configuration. 

3⁄4 Minimum Vertical Kz: The VISTAS Phase I modeling tested minimum vertical 

turbulent exchange coefficients (Kz) of 1.0 m2

/s and 0.1 m2/s. Model 

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performance using the two minimum Kz values was mixed, with no one 

configuration consistently producing superior model performance over the other 

across all species, locations and periods. Although EPA recommends using a 1.0 

m2

/s minimum Kz, the consensus of the VISTAS modeling group was that a 0.1 

m

2

/s was more technically correct because use of a 1 m2/s minimum Kz would 

result in complete mixing between layers 1 and 2 so could not simulate a stable 

inversion layer near the surface that traps pollutants. Thus, a 0.1 m2

/s minimum 

Kz values was selected for the Phase II modeling. 

3⁄4 Fine Dust Transportable Fraction: For the Phase I modeling, the application of 

a fugitive dust transport fraction of 0.25 was recommended by EPA and 

minimized the bias for the Coarse Matter (CM) species over the other factors 

studied (1.00 and 0.05). For the Phase II modeling, county-specific fugitive dust 

emission adjustment factors based on land-use categories are applied to the 

fugitive dust emissions within the base inventories prior to SMOKE emissions 

modeling (Pace, 2003). Thus, no additional fugitive dust transport fraction factor 

will be applied in the emissions modeling step. 

3⁄4 Adjustment to NH3 Mass Emissions Estimates: Current ammonia emission 

inventories (e.g., NEI99) are believed to overstate ammonia emissions during 

colder months. EPA has performed inverse modeling to estimate the level of 

ammonia emissions needed to match measurements and developed monthly 

ammonia emission adjustment factors that resulted in an approximately 50% 

reduction in ammonia emissions for the January 2002 and no adjustments for the 

July 1999 and July 2001 Phase I episodes. The VISTAS Phase I modeling 

produced much better model performance for the January 2002 episode using the 

50% ammonia emissions adjustment factor. For the Phase II modeling, the 

ammonia emissions inventory has been updated using the CMU ammonia model 

and state submissions so additional adjustments may not be needed. 

3⁄4 Adjustment to NH3 Emissions Temporal Profile: An ammonia emissions 

temporal adjustment sensitivity test was performed that reduced ammonia 

emissions by 50% with more reductions occurring at night and less during the 

day. Model performance was marginally improved over the across-the-board 

50% adjustment. However, as we could not justify the arbitrarily selected 

ammonia emissions diurnal profile, Phase II modeling will use the default diurnal 

profile for ammonia emissions. 

3⁄4 Significance of Mexico and Canadian Emissions: CMAQ runs with and without 

emissions from Canada and Mexico produced nearly identical model performance 

in the VISTAS Southeast US region. Use of Canadian and Mexican emissions is 

more technically justifiable, so they will be included in the Phase II modeling. 

3⁄4 Minimum PBL Depth: There are some concerns that MM5 may sometimes 

estimate a PBL depth that is too low under some conditions (e.g., clouds). In 

Phase I, minimum PBL depth sensitivity tests were performed, but they did not 

substantially improve model performance and were difficult to technically justify. 

Thus, for Phase II the MM5 PBL depth estimates will be used as is. 

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3⁄4 Use of Global Chemistry Transport Model Output for Boundary Conditions: 

Three boundary condition (BC) sensitivity tests were carried out using the July 

2001 VISTAS Phase I episode: EPA CMAQ default BCs; monthly average July 

2001 GEOS-CHEM values; and 3-hour specific GEOS-CHEM results. Although 

model performance in the VISTAS domain was only marginally affected by the 

different BCs for the July 2001 episode, BCs could have more effect under 

cleaner conditions and for other episodes with higher wind speeds. VISTAS is 

currently evaluating whether a 2002 GEOS-CHEM simulation should be 

performed to provide BCs for the 2002 CMAQ run, although it would not be 

ready until later in 2004. Thus, in the mean time the initial 2002 CMAQ 

modeling would use monthly average GEOS-CHEM BCs based on the 2001 

GEOS-CHEM values. 

3⁄4 Wesely Dry Deposition Scheme: CMAQ has two dry deposition options, one 

that uses data from the MM5 Pleim-Xiu (PX) LSM model (M3Dry), and one 

based on the Wesely scheme from RADM. In order to evaluate alternative MM5 

simulations that cannot use the PX dry deposition scheme, a dry deposition 

sensitivity test was performed using the Wesely dry deposition scheme. The 

Wesely dry deposition sensitivity test produced degraded model performance 

compared to when the PX scheme was used. Thus, based on better science and 

better model performance, the PX dry deposition scheme was selected for Phase 

II CMAQ modeling. 

3⁄4 Eta-Mellor-Yamada MM5 Scheme: The MM5 simulation using the Eta-MY 

PBL scheme produced some negative PBL heights. Thus, a patch was added to 

the CMAQ MCIP processor to correct the negative PBL heights. CMAQ model 

performance using the MM5_Eta-MY PBL scheme (with the MCIP PBL patch) 

was not as good as the performance using the MM5 with the Pleim-Xiu LSM and 

ACM PBL (PX_ACM) configuration (no MCIP PBL patch needed). Thus, based 

on better science and improved model performance, the MM5 using the PX_ACM 

LSM_PBL configuration was selected for VISTAS Phase II modeling. 

3⁄4 Aerosol Mass Conservation Patch: Georgia Institute of Technology noted that 

sulfur and nitrogen mass was not being conserved on calls to the CMAQ aerosol 

algorithms subroutines. Thus a mass conservation patch was added to CMAQ 

that assured the conservation of sulfur, reactive nitrogen and reduced nitrogen. 

Model performance with and without the mass conservation patch was essentially 

identical. Therefore, based on better science, CMAQ using the mass conservation 

patch was adopted for the Phase II modeling. 

3⁄4 CB4-2002 Chemistry: The CB4-2002 chemical mechanism was implemented in 

the SMVGEAR chemistry solver in CMAQ. The CB4 and SAPRC99 chemical 

mechanisms are implemented in the much faster EBI chemistry solver that 

requires some hard coding. For most species, the CMAQ model performance 

using CB4 and CB4-2002 are nearly identical. For nitrate, CB4-2002 produced 

degraded model performance for the January 2002 episode. Given the much 

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slower run times and little changed or degraded model performance, CB4-2002 

was not selected for the VISTAS Phase II modeling. 

3⁄4 SAPRC99 Chemistry: The CMAQ simulations using the CB4 and SAPRC99 

chemistries produced different model performance, but both exhibited similar 

skill so that one simulation was not clearly performing better than the other across 

all species, locations and periods. Not tested, however, was whether one chemical 

mechanism behaved differently in response to emission reductions over another. 

SAPRC99 chemistry represents more current science than CB4 chemistry. 

However, CMAQ using SAPRC99 chemistry takes twice the CPU time as 

CMAQ/CB4 and requires more memory and disk space also. Thus, for Phase II 

modeling it was decided to use the CB4 chemistry for the initial CMAQ 2002 

base case simulations and evaluate the sensitivity of SAPRC99 and CB4 to 

emissions reductions using the January 2002 and July 2001 Phase I databases. 

Depending on the results of the Phase II emission reduction sensitivity tests, the 

SAPRC99 chemistry may be used in the revised 2002 CMAQ base case 

simulation starting in September 2004. 

3⁄4 AIM Aerosol Chemistry: The AIM sectional aerosol module did not produce 

any improvements in model performance over the standard CMAQ modal AE3 

approach and required significantly more computer time and disk storage. Thus, 

the standard CMAQ model was adopted for Phase II modeling. 

3⁄4 Alternative Model: The CAMx model was exercised for the July 1999 and July 

2001 Phase I episodes. In general model performance between the two models 

was similar, but there were some differences. CMAQ performed better for 

sulfate, whereas CAMx performed better for carbon (EC and OC). Both models 

performed poorly for nitrate. As neither model performed significantly better 

than the other across all species and periods, the CMAQ model was retained as 

the lead model for the Phase II modeling with CAMx an optional corroborative 

model.

Note that there are two parameters in the Phase I CMAQ base configuration that differ 

from the default recommendations: (1) use of a 0.1 m2

/s over a 1.0 m2/s minimum vertical 

diffusion coefficient (Kz); and (2) use of the mass conservation patch. Regarding the 0.1 m2

/s 

minimum Kz, the VISTAS science team believes it is more technically correct and, as described 

by Morris and co-workers (2004), it produced better nighttime ozone performance, whereas PM 

performance was mixed. The conservation of sulfur and nitrogen is a fundamental property of 

the real atmosphere so we believe that justifies the use of the mass conservation patch. 

 The CMAQ run times are highly dependent on the number of CPUs used and the network 

loading as well as vary for CB4 vs. SAPRC99 chemistry and 12 km vs. 36 km grid. Using 6 

Athlon 2600+ CPUs, the following are the elapsed times for several CMAQ configurations: 

 CB4 36 km – 1.9 hrs/day 

 CB4 12 km – 3.9 hrs/day 

 SAPRC99 36 km – 2.8 hrs/day 

 SAPRC 12 km – 8.0 hrs/day 

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Thus an annual CMAQ/CB4 run on the 36 km and 12 km grid using 6 Athlon 2600+ 

processors would require approximately 30 and 60 days to complete if run sequentially from 

January to December. 

1.5 Modeling Protocol 

Based on the model configuration decisions made in Phase I, the Phase II emissions and 

air quality modeling protocol is intended to specify the procedures for performing the base year 

modeling and the evaluation of emission control strategies for future years. The protocol will 

prescribe model set up methods, procedures for performance evaluation, sensitivity analysis, 

uncertainty examination, and the various statistical metrics and graphical tools to be used in the 

interpretation of base case and future year control simulations. Included as part of the Phase II 

protocol is the Quality Assurance Plan. The QA plan defined how quality assurance tasks will 

be performed and sets forth guidelines for accountability in correcting errors identified through 

the various review activities. Although completed at the outset of Phase II, the modeling 

protocol is expected to evolve as the project proceeds and will likely be revised as the knowledge 

base expands. 

As discussed later in this report, particular emphasis in the Modeling Protocol has been 

given to: (a) general and specific emissions and air quality model configurations; (b) 

configurations of all key interface and processor programs; (c) emissions processing 

methodologies and data sources to be used; (d) air quality model input preparation procedures 

and data sources to be used; (e) application procedures for the historical year base case, typical 

base year, future year, and strategies; (f) procedures for model evaluation, sensitivity analyses, 

and uncertainty examination; (g) documentation; (h), data transfer, backup and archiving; (i) 

design and maintenance of project website, (j) quality assurance plans for each element of the 

Phase II modeling including accountability for correcting errors found through review and 

quality assurance activities and, (k) schedule and deliverables. 

1.6 Phase II Study Participants and Responsibilities 

The VISTAS Phase II Emissions and Air Quality Modeling Study is being carried out by 

ENVIRON, Alpine and UCR under contract to SESARM. Table 1-1 lists the contact 

information of the key participants. Their roles in the work are as follows: 

3⁄4 Pat Brewer, VISTAS Technical Coordinator is the Contracting Officer for the 

study; 

 

3⁄4 James Boylan of the Georgia Environmental Protection Division is the Technical 

Contact for the VISTAS Emissions and Air Quality Modeling; 

3⁄4 Michael Abraczinskas of the North Carolina Department of Environment and 

Natural Resources is the Technical Contact for the VISTAS MM5 Meteorological 

Modeling; 

3⁄4 Laura Boothe of the North Carolina Department of Environmental and Natural 

Resources is the Technical contact for the VISTAS Emissions Inventory; 

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3⁄4 Sheila Holman of the North Carolina Department of Environment and Natural 

Resources is the chair of the VISTAS Technical Analysis Work Group (TAWG); 

3⁄4 Ralph Morris of ENVIRON is the Project Manager for the VISTAS Emissions 

and Air Quality Modeling ENVIRON/Alpine/UCR/UCD Team; 

3⁄4 Ralph Morris of ENVIRON, T.W. Tesche of Alpine, and Gail Tonnesen of UCR 

will lead the efforts of their respective institutions; 

3⁄4 Cyndi Loomis, Greg Stella and Jim Wilkinson of Alpine are leading the emissions 

modeling efforts; 

3⁄4 Dennis McNally of Alpine, Chris Emery of ENVIRON and Zion Wang of UCR 

will lead the meteorological data processing; and 

1.7 Communications and Issue Resolution 

The ENVIRON/UC/Alpine Team will maintain regular contact with the VISTAS 

Contracting Office, Technical Contacts, key members of the TAWG and other VISTAS 

Contractors through regular conference calls and emails. Four list servers have been set up to 

facilitate communications among the groups to address specific air quality, emissions, and 

meteorologist issues as well as all issues as follows: 

Vistas-modeling@cert.ucr.edu

Vistas-emissions@cert.ucr.edu

Vistas-met@cert.ucr.edu

Vistas-all@cert.ucr.edu

The modeling team will take direction from the VISTAS TAWG through the VISTAS 

Contracting Officer and the VISTAS Technical Contact for the Emissions and Air Quality 

Modeling Study. Issues that need to be resolved among the TAWG will follow VISTAS 

resolution principles. 

1.8 VISTAS Phase II SOW 

A preliminary Scope of Work (SOW) for the VISTAS Phase II emissions and air quality 

modeling has been developed and is provided in Appendix A. The Phase II SOW delineates the 

tasks, approach and schedule for carrying out the work identified in this modeling protocol. The 

SOW is broken down into a base effort that focuses on annual modeling of 2002 and several 

optional tasks. The ultimate objective of the Phase II work effort in 2004 is to obtain a fully 

evaluated and operational emissions and air quality modeling system for the 2002 and 2018 

annual calendar years so that control strategy evaluation can begin in 2005. 

1.9 Organization of Phase II Emissions and Air Quality Modeling Protocol 

Following this introduction, Chapters 2 and 3 identify the models and modeling period(s) 

selected by the VISTAS TAWG for use in Phase II. Chapter 2 also identifies the Phase I 

recommendations concerning the optimal configurations and science options to be used in the 

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SMOKE emissions and CMAQ air quality models. Details of the modeling domain specification 

for annual modeling and the data sets to be used in developing model inputs and in support of the 

model performance evaluation and strategy application are presented in Chapter 4. Subsequent, 

in Chapter 5, the procedures for preparing emissions and air quality inputs are specified. Chapter 

6 discusses the Phase II Quality Assurance Plan while Chapters 7 and 8 address the model 

performance evaluation and model application activities, respectively. In Chapter 9, we outline 

the procedures to be used in utilizing the annual (and potentially episodic) air quality modeling 

results to develop future year visibility estimates and to make pertinent comparisons with the 

reasonable progress goals. Issues related to data management and security (Chapter 10), 

documentation (Chapter 11), and the Phase II schedule (Chapter 12) are discussed in turn. The 

VISTAS Phase II SOW is provided in Appendix A. The core procedures to be used in 

conducting the operational and scientific performance evaluation of the air quality model for key 

gas-phase and primary and secondary aerosol species is discussed in the VISTAS Phase I Task 2 

Modeling Protocol (Morris et al. 2003) and is not repeated here. 

Table 1-1. Summary of Key Participants and Contact Information for the VISTAS Phase II 

Emissions and Air Quality Modeling Study.

Person & Role Affiliation/Address Contact Information 

Patricia Brewer 

(Contracting Officer) 

VISTAS Technical Coordinator 

59 Woodfin Place 

Asheville, NC 28801 

(828) 251-6208 

(Fax) (828) 251-6452 

pat.brewer@ncmail.net

James Boylan 

(Technical Contact 

for Emissions and 

Air Quality 

Modeling) 

Georgia DNR 

Air Protection Branch 

4244 International Pkwy, Suite 

120 

Atlanta, GA 30354-3906 

(404) 362-4851 

(Fax) (404) 363-7100 

James_Boylan@mail.dnr.state.ga.us

Michael 

Abraczinskas 

(Technical Contact 

for MM5 Modeling) 

North Carolina DENR 

1641 Mail Service Center 

Raleigh, NC 27699-1641

(919) 715-3743 

Michael.Abraczinskas@ncmail.net

Ralph Morris 

(Project Manager 

and Co-Principal 

Investigator) 

ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0708 

(Fax) (415) 899-0707 

rmorris@environcorp.com

T.W Tesche 

(Co-Principal 

Investigator) 

Alpine Geophysics, LLC 

3479 Reeves Drive 

Ft. Wright, KY 41017 

(859) 341-7502 

(Fax) (859) 341-7502 

twt@iac.net

Gail Tonnesen 

(Co-Principal 

Investigator) 

UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5676 

(Fax) (909) 781-5790 

tonnesen@cert.ucr.edu

Key ENVIRON Participants 

Greg Yarwood ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0704 

gyarwood@environcorp.com

Chris Emery ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0740 

cemery@environcorp.com

Bonyoung Koo ENVIRON 

101 Rowland Way 

(415) 899-0727 

bkoo@environcorp.com

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Person & Role Affiliation/Address Contact Information 

Novato, CA 94945 

Edward Tai ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0725 

etai@environcorp.com

Steven Lau ENVIRON 

101 Rowland Way 

Novato, CA 94945 

(415) 899-0739 

slau@environcorp.com

Key Alpine Geophysics Participants 

Dennis McNally Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

(303) 421-2211 

(Fax) (303) 421-9553 

dem@alpinegeophysics.com

Cyndi Loomis Alpine Geophysics, LLC 

7341 Poppy Way 

Arvada, CO 80007 

(303) 421-2211 

(Fax) (303) 421-9553 

cfl@alpinegeophysics.com

Greg Stella Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 

(828) 675-9045 

gms@alpinegeophysics.com

UCR CE-CERT Participants 

Zion Wang UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5655 

zsw@cert.ucr.edu

Bill Carter UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5797 

carter@cert.ucr.edu

Bo Wang UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5680 

bwang@cert.ucr.edu

Chao-Jung Chien UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5666 

chien@cert.ucr.edu

Glen Kaukola UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, CA 92507 

(909) 781-5630 

glen@cert.ucr.edu

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2.0 MODEL SELECTION 

This chapter introduces the regional meteorological, emissions and air quality models to 

be used in the annual PM/regional haze modeling for VISTAS Phase II. The specific science 

configurations for each modeling system are identified and discussed briefly, where necessary. 

Although the configurations of each modeling system have been selected as the culmination of 

the Phase I modeling, there remains the possibility that certain algorithms and parameter settings 

may still be updated in the establishment of the final annual 2002 base case simulation and 

model performance testing—the first major modeling activity in Phase II to be completed in 

early 2004. The Phase II modeling team will remain alert to progressive model code 

improvements, data base refinements, and emergent analysis procedures throughout the entire 

activity. For example a major update in the CMAQ code is expected to be released around June 

2004 that would require a rerunning of the initial CMAQ 2002 base case and reanalysis of the 

model performance evaluation. Notable limitations of the models relevant to their intended 

purpose in VISTAS are identified. We conclude with a general overview of the input 

requirements for each system with more details provided in chapter 5. 

2.1 Recommended Models 

Based on the Phase I findings, VISTAS selected the following models for use in 

modeling particulate matter (PM) and regional haze in the Southeast under Phase II: 

3⁄4 MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, 

prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate, and regional haze regulatory modeling studies. 

 

3⁄4 SMOKE: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system 

is an emissions modeling system that generates hourly gridded speciated emission 

inputs of mobile, nonroad, area, point, fire and biogenic emission sources for 

photochemical grid models. 

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling 

system is a ‘One-Atmosphere’ photochemical grid model capable of addressing 

ozone, particulate matter (PM), visibility and acid deposition at regional scale for 

periods up to one year. 

In addition, alternative models may also be applied as part of Phase II modeling to corroborate 

the CMAQ analyses such as: 

3⁄4 CAMx: ENVIRON’s Comprehensive Air Quality Model with Extensions (CAMx) 

modeling system is also a state-of-science ‘One-Atmosphere’ photochemical grid 

model capable of addressing ozone, particulate matter (PM), visibility and acid 

deposition at regional scale for periods up to one year. Its use in Phase II will likely 

be as a corroborative, diagnostic tool and is currently an optional task (see Appendix 

A). 

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3⁄4 CMAQ – AIM or CMAQ-MADRID: These two models are variations of CMAQ 

that utilize a sectional treatment of PM size distribution and have alternative science 

options for some aerosol processes. They could also be used in Phase II as 

corroborative diagnostic tools. 

Application of the MM5 in Phase II is being performed by BAMS under contract to SESARM. 

Details of the model application and evaluation procedures being carried out by BAMS may be 

found at http://www.baronams.com/projects/VISTAS/. For completeness, in this chapter we 

describe all four regional modeling systems and their intended use in the Phase II annual 

modeling. 

2.2 MM5 Mesoscale Prognostic Model 

Over the past decade, researchers at the Pennsylvania State University (PSU) and the 

National Center for Atmospheric Research (PSU/NCAR) have collaborated in the refinement 

and extension of the PSU Mesoscale Meteorological Model leading to the current version of the 

system, MM5 (ver 3.6, MPP). Originally developed in the 1970s at PSU and first documented 

by Anthes and Warner (1978), the MM5 modeling system maintains its status as a state-of-thescience model through enhancements provided by a broad user community (e.g., Chen and 

Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 2000). The MM5 modeling 

system is routinely employed in forecasting projects as well as refined investigations of severe 

weather. Utilization of MM5 within air quality applications is also a common practice. In recent 

years, the MM5 modeling system has been successfully applied in continental scale annual 

simulations for the years 1996 (Olerud et al., 2000), 2001 (McNally and Tesche, 2003), and 2002 

(Johnson, 2003). Due to its ongoing scientific development worldwide, extensive historical 

applications, broad user community support, public availability, and established performance 

record compared with other applications-oriented prognostic models, VISTAS selected the MM5 

as the preferred meteorological model. This section provides an overview of the MM5 and its 

data input requirements. 

2.2.1 MM5 Overview 

The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a threedimensional, limited-area, primitive equation, prognostic model that has been used widely in 

regional air quality model applications (Seaman, 2000). The basic model has been under 

continuous development, improvement, testing and open peer-review for more than 20 years 

(Anthes and Warner, 1978; Anthes et al., 1987) and has been used world-wide by hundreds of 

scientists for a variety of mesoscale studies, including cyclogenesis, polar lows, cold-air 

damming, coastal fronts, severe thunderstorms, tropical storms, subtropical easterly jets, 

mesoscale convective complexes, desert mixed layers, urban-scale modeling, air quality studies, 

frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and operational 

mesoscale forecasting. 

MM5 is based on the prognostic equations for three-dimensional wind components (u, v, 

and w), temperature (T), water vapor mixing ratio (qv), and the perturbation pressure (p'). Use of 

a constant reference-state pressure increases the accuracy of the calculations in the vicinity of 

steep terrain. The model uses an efficient semi-implicit temporal integration scheme and has a 

nested-grid capability that can use up to ten different domains of arbitrary horizontal and vertical 

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resolution. The interfaces of the nested grids can be either one-way or two-way interactive. The 

model is also capable of using a hydrostatic option, if desired, for coarse-grid applications. 

 

MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical 

coordinate similar to that used in many operational and research models. In the non-hydrostatic 

MM5 (Dudhia, 1993), the sigma levels are defined according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded 

meteorological fields produced by MM5 are directly compatible with the input requirements of 

‘one atmosphere’ air-quality models using this coordinate (e.g., CMAQ). MM5 fields can be 

easily used in other regional air quality models with different coordinate systems (e.g., CAMx) 

by performing a vertical interpolation, followed by a mass-conservation re-adjustment. 

Distinct planetary boundary layer (PBL) parameterizations are available for air-quality 

applications, both of which represent sub-grid-scale turbulent fluxes of heat, moisture and 

momentum. These parameterizations employ various surface energy budget equations to 

estimate ground temperature (Tg), based on the insolation, atmospheric path length, water vapor, 

cloud cover and longwave radiation. The surface physical properties of albedo, roughness 

length, moisture availability, emissivity and thermal inertia are defined as functions of land-use 

for numerous categories via a look-up table. One scheme uses a first-order eddy diffusivity 

formulation for stable and neutral environments and a modified first-order scheme for unstable 

regimes. The other uses a prognostic equation for the second-order turbulent kinetic energy, 

while diagnosing the other key boundary layer terms. 

Initial and lateral boundary conditions are specified from mesoscale three-dimensional 

analyses performed at 12-hour intervals on the outermost grid mesh selected by the user. 

Additional surface fields are analyzed at three-hour intervals. A Cressman-based technique is 

used to analyze standard surface and radiosonde observations, using the National Meteorological 

Center's (NMC) spectral analysis as a first guess. The lateral boundary data are introduced into 

MM5 using a relaxation technique applied in the outermost five rows and columns of the most 

coarse grid domain. 

A major feature of the MM5 is its use of state-of-science methods for Four Dimensional 

Data Assimilation (FDDA). The theory underlying this approach and details on how it has been 

applied in a variety of applications throughout the country are described in depth elsewhere 

(Stauffer and Seaman, 1990, 1991; Seaman et al., 1992, 1997). 

 Results of detailed performance evaluations of the MM5 modeling system in regulatory 

air quality application studies have been widely reported in the literature (e.g., Emery et al., 

1999; Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic 

models such as RAMS and SAIMM. The MM5 enjoys a far richer application history in 

regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations 

of these models in over 60 recent regional scale air quality application studies since 1995, we 

have generally found that MM5 model tends to produce somewhat better photochemical model 

inputs than alternative models. For these and other reasons set forth in the MM5 modeling 

protocol developed by BAMS (Olerud and Sims, 2003), MM5 was selected as the 

meteorological modeling system for the VISTAS study. 

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2.2.2 MM5 Configuration for Phase II Modeling 

 Based on the extensive sensitivity testing carried out by Olerud and Sims (2003), the 

MM5 (ver 3.6, MMP) configuration to be used by BAMS modelers in the Phase II modeling will 

consist of the following (see Table 2-1 for more details): 

 > Nested 36/12 km grids, with 34 vertical layers; 

 > Two way nesting, no feedback; 

 > Initialization and boundary conditions from Eta analysis fields; 

 > Pleim-Xiu (P-X) soil model; 

 > Asymmetric Convective Mixing (ACM) PBL model; 

 > Kain-Fritsch 2 cumulus parameterization; 

 > Mixed phase (Reisner 1) cloud microphysics; 

 > Raptid Radiative Transfer Model (RRTM) radiation; 

 > Snow effect turned on; 

> ETA model sea surface temperature; 

 > 24-category USGS vegetation data sets; 

 > Thermal roughness by the Garratt method; and 

 > Standard FDDA analysis nudging on 36 km and 12 km grid nests. 

2.3 SMOKE Emissions Modeling System 

2.3.1 SMOKE Overview 

The Sparse Matrix Operator Kernel Emissions (SMOKE) Emissions Processing System 

Prototype was originally developed at MCNC (Coats, 1995; Houyoux and Vukovich, 1999). As 

with most ‘emissions models’, SMOKE is principally an emission processing system and not a 

true emissions modeling system in which emissions estimates are simulated from ‘first 

principles’. This means that, with the exception of mobile and biogenic sources, its purpose is to 

provide an efficient, modern tool for converting emissions inventory data into the formatted 

emission files required by an air quality simulation model. For mobile sources, SMOKE actually 

simulates emissions rates based on input mobile-source activity data, emission factors and 

outputs from transportation travel-demand models. 

SMOKE was originally designed to allow emissions data processing methods to utilize 

emergent high-performance-computing (HPC) as applied to sparse-matrix algorithms. Indeed, 

SMOKE is the fastest emissions processing tool currently available to the air quality modeling 

community. The sparse matrix approach utilized throughout SMOKE permits both rapid and 

flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series 

of matrix calculations instead of less efficient algorithms used in previous systems. The 

processing is flexible because the processing steps of temporal projection, controls, chemical 

speciation, temporal allocation, and spatial allocation have been separated into independent 

operations wherever possible. The results from these steps are merged together at a final stage of 

processing. 

SMOKE supports area, mobile, fire and point source emission processing and also 

includes biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory 

System, version 3 (BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis). SMOKE 

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has been available since 1996, and it has been used for emissions processing in a number of 

regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigned and 

improved with the support of the U.S. Environmental Protection Agency (EPA), for use with 

EPA's Models-3/CMAQ (http://www.epa.gov/asmdnerl/models3). The primary purposes of the 

SMOKE redesign were support of: (a) emissions processing with user-selected chemical 

mechanisms and (b) emissions processing for reactivity assessments. 

SMOKE contains a number of major features that make it an attractive component of the 

VISTAS modeling system (Seppanen, 2003). The model supports a variety of input formats from 

other emissions processing systems and models including the Inventory Data Analyzer (IDA), 

Emissions Modeling System—2003 (EMS-2003), and the Emissions Preprocessor System 2.x 

(EPS2.x). It supports both gridded and county total land use scheme for biogenic emissions 

modeling. Although not necessary in VISTAS, SMOKE can accommodate emissions files from 

up to 10 countries and any pollutant can be processed by the system. 

Recent computational improvements to SMOKE include: (a) enhanced disk space 

requirements compared with other emissions processing software, (b) run-time memory 

allocation, eliminating any need to recompile the programs for different inventories, grids, or 

chemical mechanisms, and (c) updated I/O API libraries. A number of science features have 

been incorporated into the latest version of SMOKE (ver. 2.0) including: (a) any chemical 

mechanism can be used to partition pollutants to model species, as long as the appropriate input 

data are supplied, (b) integration with the MOBILE6.2 on-road mobile source emissions model 

including link based processing, (c) support of plume-in-grid (PiG) processing, (d) integration of 

the BEIS3 emissions factors in SMOKE. 

Notable features of SMOKE from an applications standpoint include: (a) improved 

control strategy input formats and designs, (b) control strategies can include changes in the 

reactivity of emitted pollutants, a useful capability, for example, when a solvent is changed in an 

industrial process, (c) no third party software is required to run SMOKE, although some input 

file preparation may require other software, (d) fewer SMOKE programs than the SMOKE 

prototype because programs were combined where possible to be used for multiple source 

categories, (e) integration with Models-3 file formats and settings, (f) improved data file formats, 

(g) support of various air quality model emissions input formats (e.g., CMAQ, MAQSIP, UAMIV, UAM-V, REMSAD and CAMx), (h) enhanced quality assurance pre- and post-processing, 

(h) fully integrated with Models-3, which will provide the SMOKE Tool for SMOKE input file 

preparation, (i) enhanced treatment of growth and control factors, (j) improved emissions 

reporting and QA capabilities, and (k) improved temporal allocation. 

Continuing model development activities with SMOKE now occur out of the University 

of North Carolina (UNC) Carolina Environmental Program (CEP). SMOKE beta Version 1.5b 

was released 17 March 2003 and this is the version employed in Phase I. Several patches to the 

model were provided during the summer of 2003, and SMOKE ver2.0 was released on 30 Sept 

’03. Barring unanticipated difficulties with this release, the Phase II modeling will employ the 

SMOKE Ver 2.0. The SMOKE executables, scripts and databases may be downloaded through 

the Community Modeling and Analysis (CMAS) center’s Model Clearinghouse at 

http://www.cmascenter.org/modelclear.shtml. The SMOKE user’s guide is available online at 

the main SMOKE website, http://www.cep.unc.edu/empd/products/smoke. 

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2.3.2 SMOKE Configuration for Phase II Modeling 

As an emissions processing system, SMOKE has far fewer ‘science configuration’ 

options compared with the MM5 and CMAQ models. For a thorough characterization of the 

methods that will be used to exercise the SMOKE system for the annual 2002 emissions 

processing, see section 5.2, “Development of Emissions Model Inputs and Resultant 

Inventories”. Table 2-1 summarizes the version of the SMOKE system to be used and the 

sources of data to be employed in constructing the required modeling inventories. 

2.4 CMAQ Modeling System 

2.4.1 CMAQ Overview 

For more than a decade, EPA has been developing the Models-3 Community Multiscale 

Air Quality (CMAQ) modeling system with the overarching aim of producing a ‘OneAtmosphere’ air quality modeling system capable of addressing ozone, particulate matter (PM), 

visibility and acid deposition within a common platform (Dennis, et al., 1996; Byun et al., 1998a; 

Byun and Ching, 1999, Pleim et al., 2003). The original justification for the Models-3 

development emerged from the challenges posed by the 1990 Clean Air Act Amendments and 

EPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling 

utilizing state-of-science representations of atmospheric processes in a high performance 

computing environment (Ching, et al., 1998). EPA completed the initial stage of development 

with Models-3 and released the Community Multi-Scale Air Quality model (CMAQ) in mid1999 as the initial operating science model under the Models-3 framework (Byun et al., 1998b). 

The most recent rendition is CMAQ version 4.3, publicly released late-summer 2003 and is the 

version used in the VISTAS Phase I modeling (Although the initial Phase I modeling runs used 

CMAQ version 4.3beta). A major update to CMAQ is expected around summer of 2004. 

CMAQ consists of a core Chemical Transport Model (CTM) and several pre-processors 

including the Meteorological-Chemistry Interface Processor (MCIP), initial and boundary 

conditions processors (ICON and BCON) and a photolysis rates processor (JPROC). EPA is 

continuing to improve and develop new modules for the CMAQ model and typically provides a 

new release each year. In the past EPA has also provides patches for CMAQ as errors are 

discovered and corrected. More recently EPA has funded the Community Modeling and 

Analysis Systems (CMAS) center to support the coordination, update and distribution of the 

Models-3 system. 

A number of features in CMAQ’s theoretical formulation and technical implementation 

make the model well-suited for annual PM modeling. In CMAQ, the modal approach has been 

adapted to dynamically represent the PM size distribution using three log-normal modes (2 fine 

and 1 coarse). Transfer of mass between the aerosol and gas phases is assumed to be in 

equilibrium and all secondary aerosol (sulfate, nitrate, SOA) is assumed to be in the fine modes. 

The thermodynamics of inorganic aerosol composition are treated using the ISORROPIA 

module. Aerosol composition is coupled to mass transfer between the aerosol and gas phases. 

For aqueous phase chemistry, the RADM model is currently employed. This scheme includes 

oxidation of SO2 to sulfate by ozone, hydrogen peroxide, oxygen catalyzed by metals and 

radicals. The impact of clouds on the PM size distribution is treated empirically. For wet 

deposition processes, CMAQ uses the RADM/RPM approach. Particle dry deposition is 

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included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), 

latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 

to be an reversible semi-volatile scheme whereby VOCs can be converted to condensable gases 

that can then form SOA and then evaporate back into condensable gases depending on 

atmospheric conditions. 

 Pleim et al., (2003) describe the newest features implemented in CMAQ ver 4.3 (released 

August 2003). Many of these features are mentioned above; others pertain to details in the 

model’s chemistry, transport, computer implementation, and model operation. Table 2-3 

highlights the major options in CMAQ (ver 4.3) for different processes and compares them with 

the recently released CAMx (ver 4.3) model in Table 2-4, which is discussed later in this chapter. 

2.4.2 CMAQ Configuration for Phase II Modeling 

 One of the principal accomplishments in the Phase I modeling was the identification and 

justification of the CMAQ science options recommended for Phase II modeling. We 

summarized the Phase I findings briefly in the introduction. In this section we identify the main 

science options we recommend for annual PM modeling with CMAQ. In particular, we propose 

to run CMAQ (ver 4.3) with the base configuration as shown in Table 2-3. The model would be 

set up and exercised on the same nested 36/12 km grid domain used in Phase I, employing oneway grid nesting. That is, boundary conditions for the 12 km grid simulation are extracted from 

the 36 km run using the CMAQ BCON processor. A total of 19 vertical layers would be 

implemented, extending up to a region top of 100 mb (approximately 15 km AGL). 

The PPM advection solver would be used along with the spatially varying (Smagorinsky) 

horizontal diffusion approach and K-theory for vertical diffusion. MM5 meteorological output 

based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer 

(PBL) scheme will be used (see Table 2-1) and the recently updated CMAQ MeteorologicalChemistry Interface Processor (MCIP2.2) would process the MM5 data using the “pass through” 

option. The CB4 gas-phase, RADM aqueous-phase, and AERO3/ISORROPIA aerosol 

chemistry schemes are recommended for use in the initial CMAQ 2002 modeling. Treatment of 

reversible secondary organic aerosols would be simulated by the SORGAM implementation in 

CMAQ (ver 4.3). Under the Phase I modeling, VISTAS evaluated three photochemical 

mechanisms: CB4, CB4-2002 and SAPRC99. CB4-2002 produced nearly identical results as 

CB4 but took much longer to run since it is only implemented in the slower SMVGEAR 

chemistry solver, compared to CB4 that is also implemented in the faster EBI chemistry solver. 

Thus, CB4-2002 was dropped from consideration in Phase II. The Phase I comparisons of CB4 

and SAPRC99 found they produced mostly similar but different model performance. However, 

one mechanism was not performing better than the other across all species, sites and periods. 

The Phase I testing only evaluated the mechanism’s base case performance, not their response to 

emission reductions. Given that CB4 runs twice as fast as SAPRC99, for Phase II it was decided 

to perform initial 2002 CMAQ modeling using CB4 and evaluate CB4 and SAPRC99 responses 

to emission reductions using the Phase I episodes. If SAPRC99 is deemed to be performing 

better than CB4 it would be used for the revised 2002 CMAQ base case simulation in September 

2004. 

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2.5 Alternative Models 

 EPA’s guidance on model selection for PM2.5 SIPs and Regional Haze “reasonable 

progress demonstrations” do not identify a preferred photochemical grid modeling system, 

recognizing that at present there is “no single model which has been extensively tested and 

shown to be clearly superior or easier to use than several alternatives” (EPA, 2001, pg. 169.) 

The agency recommends that models used for PM2.5 SIPS or RH reasonable progress 

requirements should meet the requirements for alternative models. The CMAQ, CMAQ-AIM 

and CAMx modeling systems all meet these requirements. 

 We believe that there is potentially significant value in including an alternative regional 

modeling system or multiple systems as an adjunct to the SMOKE/MM5/CMAQ system 

proposed as the mainstay of the Phase II analyses. Our testing of the CAMx model in Phase I 

and other recent PM2.5/regional haze applications demonstrates that the model is capable of 

producing results of comparable accuracy and reliability as CMAQ, given similar effort in 

preparing model inputs, diagnosing and improving model performance and in conducting 

weight-of-evidence investigations. The Phase I testing of CMAQ-AIM model raised questions 

on whether it was performing correctly, the differences with CMAQ could not be explained 

based on their differences in formulation with CMAQ-AIM performing poorer than CMAQ. 

Accordingly, at this time we cannot recommend adopting CMAQ-AIM in Phase II and 

recommend VISTAS consider the parallel use of CAMx in the Phase II program for seven 

specific purposes: 

3⁄4 Diagnosis: To serve as an efficient diagnostic tool addressing model performance 

issues that may arise in the establishment of the CMAQ annual 2002 and episodic 

base cases. CAMx’s suite of diagnostic probing tools plus it’s flexi-nesting 

algorithms make it an attractive tool for assisting in the diagnosis of CMAQ 

performance should unexpected situation arise; 

3⁄4 Model Evaluation Corroboration: To provide corroboration of the base case model 

performance evaluation exercises to be performed with CMAQ and help identify any 

compensatory errors in the MM5/SMOKE/CMAQ modeling system; 

3⁄4 Emissions Control Response Corroboration: To provide corroboration of the 

response of the CMAQ modeling system to generic and specific future year emissions 

changes on modeled gas-phase and particulate aerosol concentrations and resultant 

regional haze impacts; 

3⁄4 Quantification of Model Uncertainty: To provide one estimate of the range of 

uncertainty that attends statements of CMAQ model performance in the annual and 

episodic base case simulations, and in the estimate of PM2.5 and visibility reductions 

associated with future emissions change scenarios; 

3⁄4 Alternative Science: CAMx contains alternative science algorithms that may 

elucidate model performance issues with CMAQ or provide an alternative approach 

for simulating aerosols. 

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3⁄4 Consistency with Other RPOs: The Midwest RPO (MWRPO) may end up using 

CAMx for their regional haze modeling. As sources in the MWRPO likely influence 

visibility at Class I areas in VISTAS and vice versa, having results from a common 

model would be useful for reconciling any differences. 

3⁄4 Backup Contingency: To provide a ‘backstop’ model to CMAQ in the event that 

unforeseen difficulties with the primary model make it necessary to switch to an 

alternative ‘One-Atmosphere’ model at some point during Phase II. 

The benefits of employing a pair of complimentary state-of-science air quality models are thus 

quite significant and well worth considering. Especially considering that the same MM5 output 

(through MM5CAMx) and SMOKE output and CMAQ IC/BC files (through CMAQ-to-CAMx 

emissions and IC/BC converters) can be used to operate CAMx without performing any 

additional meteorological or emissions modeling. Accordingly, below we provide an overview 

of the CAMx regional photochemical/PM modeling system and the science options 

recommended for use in Phase II. 

2.5.1 CAMx Overview 

The Comprehensive Model with Extensions (CAMx) modeling system is a publicly 

available (www.camx.com) three-dimensional multi-scale photochemical/aerosol grid modeling 

system that is developed and maintained by ENVIRON International Corporation. CAMx was 

developed with all new code during the late 1990s using modern and modular coding practices. 

This has made the model an ideal platform for the extension to treat a variety of air quality issues 

including ozone, particulate matter (PM), visibility, acid deposition, and air toxics. The flexible 

CAMx framework has also made it a convenient and robust host model for the implementation 

of a variety of mass balance and sensitivity analysis techniques including Process Analysis (IRR 

and IPR), Decoupled Direct Method (DDM), and the Ozone Source Apportionment Technology 

(OSAT). Designed originally to address multiscale ozone issues from the urban- to regionalscale, CAMx has been widely used in recent years by a variety regulatory agencies for 1-hr and 

8-hr ozone SIP modeling studies. Key attributes of the CAMx model for simulating gas-phase 

chemistry include the following: 

3⁄4 Two-way grid nesting that supports multi-levels of fully interactive grid nesting (e.g., 

36/12/4/1.33 km); 

 

3⁄4 CB4 or SAPRC99 Chemical Mechanisms; 

3⁄4 Two chemical solvers, the CAMx Chemical Mechanism Compiler (CMC) Fast 

Solver or the highly accurate Implicit Explicit Hybrid (IEH) solver; 

3⁄4 Multiple numerical algorithms for horizontal transport including the Piecewise 

Parabolic Method (PPM), Bott, and Smolarkiewicz advection solvers; 

3⁄4 Subgrid-scale Plume-in-Grid (PiG) algorithm to treat the near-source plume dynamics 

and chemistry from large NOx point source plumes; 

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3⁄4 Ability to interface with a variety of meteorological models including the MM5 and 

RAMS prognostic hydrostatic meteorological models and the CALMET diagnostic 

meteorological model (others also compatible); 

3⁄4 The Ozone Source Apportionment Technology (OSAT) ozone apportionment 

technique that identifies the ozone contribution due to geographic source regions and 

source categories (e.g., mobile, point, biogenic, etc.); and 

3⁄4 The Decoupled Direct Method (DDM) sensitivity method is implemented for 

emissions and IC/BC to obtain first-order sensitivity coefficients for all gas-phase 

species. 

3⁄4 Treatment of particulate matter (PM) using an empirical aerosol thermodynamics 

algorithm. 

Culminating extensive model development efforts at ENVIRON and other participating groups, 

the CAMx (ver 4.3+) code was released in the autumn of 2003 as a truly “One-Atmosphere’ 

models that rigorously integrates the gas-phase ozone chemistry with the simulation of primary 

and secondary fine and course particulate aerosols. This extension of CAMx to treat PM 

involved the addition of several science modules to represent important physical processes for 

aerosols. Noteworthy among these are: 

3⁄4 Two separate treatments of particulate matter (PM), Mechanism 4 (M4) “oneatmosphere” treatment uses two size sections and science modules comparable to 

CMAQ (e.g., RADM aqueous-phase chemistry and ISORROPIA equilibrium) and a 

multi-section “full-science” approach using aerosol modules developed at Carnegie 

Mellon University (CMU). 

 

3⁄4 Size distribution is represented using the Multi-component Aerosol Dynamics Model 

(MADM), which uses a sectional approach to represent the aerosol particle size 

distribution (Pilinis et al., 2000). MADM treats the effects of 

condensation/evaporation, coagulation and nucleation upon the particle size 

distribution. 

3⁄4 Inorganic aerosol thermodynamics can be represented using ISORROPIA (Nenes et 

al, 1998; 1999) equilibrium approach within MADM, or a fully dynamic or hybrid 

approach can also be used. 

3⁄4 Secondary organic aerosol thermodynamics are represented using the semi-volatile 

scheme of Strader and co-workers (1999). 

3⁄4 Aqueous-phase chemical reactions are modeled either using the RADM module (like 

CMAQ) or the Variable Size-Resolution Model (VRSM) of Fahey and Pandis (2001), 

which automatically determine whether water droplets can be represented by a single 

‘bulk’ droplet-size mode or whether it is necessary to use fine and coarse droplet-size 

modes to account for the different pH effects on sulfate formation. 

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CAMx (ver 4.3+) provides two key options to users interested in simulating PM. For CPUefficient annual PM modeling applications, CAMx may be run using Mechanism 4 (M4) with 

only two size sections (fine and coarse) and the efficient RADM bulk aqueous-phase module (as 

used in CMAQ). Alternatively, more rigorous aerosol simulations (perhaps for shorter episode) 

may be addressed using the version that treats N-size sections (N is typically 10) and the 

rigorous, but computationally-extensive CMU multi-section aqueous-phase chemistry module. 

2.5.2 CAMx Configuration for Phase II Modeling 

We recommend exercising CAMx (ver 4.3) as a companion model to CMAQ using as 

many similar science options and input data sets as possible. However, in some instances, the 

CMAQ and CAMx model development teams chose different options for characterizing physical 

and chemical processes, or for implementing the governing equations on modern parallel 

computers. In these cases, we will utilize the science configurations embodied in the current 

release of CAMx. At this time VISTAS has not budgeted any resources in Phase II for CAMx 

modeling, however it remains an option if desired at a later date. 

Table 2-4 lists the main CAMx configurations recommended for Phase II. The latest 

version of CAMx (ver 4.3) will be employed and the model will be set up and exercised on the 

same 36/12 km grid as CMAQ. However, CAMx would be run using two-way grid nesting. The 

base configuration of CAMx would use 19 vertical layers that exactly match those used by 

CMAQ. The PPM advection solver would be used along with the spatially varying 

(Smagorinsky) horizontal diffusion approach. Vertical diffusion in CAMx would be modeled by 

K-theory. The MM5 simulation using the Pleim-Xiu Land-Surface Model (LSM) and the ACM 

Planetary Boundary Layer (PBL) scheme would be used in the CAMx base configuration using 

the MM5CAMx processor that is similar to the CMAQ MCIP2.2 “pass through” option of the 

MM5 data invoked. CAMx would be exercised with the CB4 gas-phase, RADM aqueous-phase, 

and CMU/ISORROPIA aerosol chemistry schemes. The SOAP secondary organic aerosol 

scheme would be used for the base configuration in CAMx. 

Note that it may be desirable to exercise CAMx using its “full-science” configuration for 

selected periods to investigative scientific issues that may be of interest that can not be simulated 

by CMAQ, such as: 

3⁄4 The full sectional approach could be used to determine whether allowing secondary 

PM to grow into the coarse mode affects the model estimates; 

 

3⁄4 Model could be exercised with chemical active Sea Salt emissions, this could be 

important for fine particulate and visibility at key coastal sites in the VISTAS 

domain, especially when looking at clean days or natural background; and 

3⁄4 The full sectional aqueous-phase chemistry module may be important for sulfate 

formation. 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-12 

2.6 Model Limitations 

All mathematical models possess inherent limitations owing to the necessary 

simplifications and approximations made in formulating the governing equations, implementing 

them for numerical solution on fast computers, and in supplying them with input data sets and 

parameters that are themselves approximations of the full state of the atmosphere and emissions 

processes. Below, we list the more important limitations of the various modeling systems to be 

employed in VISTAS Phase II 

2.6.1 MM5 

In Phase I, four different configurations of the MM5 Land Soil Model (LSM) and 

Planetary Boundary Layer (PBL) were evaluated. Depending on the meteorological variable 

(e.g., winds, temperature, moisture) and location (e.g., mountains, coastal, east, west) different 

LSM_PBL configurations performed better. The PX_ACM LSM_PBL configuration was 

selected because it consistency was near the top performing configuration in the VISTAS region 

across variables and locations and was never the worst performing configuration. However, 

there are numerous limitations in the MM5 with the LSM and PBL treatment being some of the 

most important. The MM5 PX_ACM frequently predicts very low PBL heights that can appear 

as “holes” in the spatial distribution of PBL heights that don’t appear physically realistic and 

may affect air quality modeling. Although the MM5 P_ACM configuration model performance 

in the VISTAS region mostly met performance benchmarks, the performance was much worst in 

the western U.S. In addition, there is a stochastic component of real world meteorology that is 

not captured by MM5. For example, for some ozone episodes stagnation is an important 

attribute that MM5 fails to simulate well as it tries to organize the flow fields. The MM5 model 

represents approximately 20 years of development by various researchers and is showing its age. 

The many limitations in MM5 have spawned the development of a new meteorological model, 

the Weather Research Forecast (WRF) model. However, the WRF model will not be used or 

tested in the VISTAS Phase II modeling. 

2.6.2 SMOKE 

In Phase I a number of undocumented features of the SMOKE 1.5b version necessitated 

re-runs of the emissions processing software to overcome errors and/or ambiguities in source 

documentation and QA reporting. It is unclear whether similar conditions will be encountered 

with the SMOKE 2.0 release. As a full software release, rather than a “beta” version, we expect 

the SMOKE 2.0 to be more robust and fully documented than the SMOKE 1.5b release. 

However, as a newly released software system, there is the potential for errors and/or ambiguities 

to affect the emissions modeling schedule. Should problems arise or issues be encountered 

which would require additional SMOKE runs or potential SMOKE modifications or alternate 

modeling methods, we will immediately notify VISTAS and make recommendations for 

resolving the issues. Upon receipt of technical direction from VISTAS, appropriate corrective 

action will be taken. 

Features are continuing to be developed in the SMOKE emissions model. As it is not as mature 

as some other emission models (e.g., EMS, EPS, etc.) it does not include as many features. We 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-13 

will keep abreast of SMOKE development activities to identify new features that will assist in 

the VISTAS emissions modeling.

2.6.3 CMAQ 

 

Like all air quality models, a major limitation of CMAQ is the emissions, meteorological 

and IC/BC inputs. Key science limitations in the model itself include the nitrate formation 

chemistry. The Phase I testing found the CMAQ nitrate performance suspect with winter 

overestimations and summer underestimations. Other science limitations in the current version 

of CMAQ include inadequate treatment of sea salt and the assumption that all secondary PM is 

in the fine mode. Lack of any two-way grid nesting limits the ability of the model to properly 

resolve point source plumes or urban photochemistry and their effects on more distant Class I 

areas without a prohibitive number of grid cells. Another limitation of CMAQ is the 

computational requirements, including the need of excessive disk space. 

2.6.4 CAMx 

The model inputs are also a major limitation in CAMx. Nitrate formation chemistry is 

also a major limitation, as evident by the Phase I performance. Although CAMx has some more 

advanced science modules available, such as the VSRM aqueous-phase and MADM dynamic 

aerosol modules, for annual modeling these modules are too computationally expensive to use. 

Treatment of clouds and wet deposition is an area of current research that needs to be updated. 

2.7 Model Input Requirements 

Each of the VISTAS modeling system components have significant data base 

requirements. These data needs fall into two categories: those required for model setup and 

operation, and those required for model evaluation testing. Below, we identify the main input 

data base requirements for the meteorological, emissions, and air quality models. 

2.7.1 MM5 

 The databases required to set up, exercise, and evaluate the MM5 model for the annual 

2002 episode consist of various fixed and variable inputs. 

3⁄4 Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived 

from the Geophysical Data Center global data sets from the National Center for 

Atmospheric Research (NCAR) terrain databases are available for prescribing terrain 

elevations throughout the 36 km and 12 km grid domain. 

3⁄4 

> Vegetation Type and Land Use: Vegetation type and land use information on the 36 km 

grid may be developed using the NCAR/PSU 10 min. (~18.5 km) databases while for the 

12 km grids, the United States Geological Survey (USGS) data are available. 

> Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from 

operationally analyzed fields derived from the National Center for Environmental 

Predictions (NCEP) ETA (40 km resolution) following the procedures outlined by 

Stauffer and Seaman (1990). These 3-hr synoptic-scale initialization data the horizontal 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-14 

wind components (u and v), temperature (T), and relative humidity (RH) at the standard 

pressure levels, plus sea-level pressure (SLP) and ground temperature (Tg). Here, Tg 

represents surface temperature over land and sea-surface temperature over water. 

> Water Temperature: Water temperatures required on both 36 km and 12 km grids can be 

derived from the ETA skin temperature variable. These temperatures are bi-linearly 

interpolated to each model domain and, where necessary, filtered to smooth out 

irregularities. 

> Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and 

precipitation directly through explicit resolved-scale and parameterized sub-grid scale 

processes, the model does not require precipitation or cloud input. The potential for 

precipitation and cloud formation enters through the thermodynamic and cloud processes 

formulations in the model. The only precipitation-related input required is the initial 

mixing ratio field that is developed from the NWS and NMC data sets previously 

discussed. 

> Multi-Scale FDDA: The standard “multi-scale” data assimilation strategy to be used on 

the 36 km and 12 km grids will objectively analyzed three-dimensional fields produced 

every 3-hr from the NWS rawinsonde wind, temperature, and mixing ratio data, and 

similar analyses generated every three hours from the available NWS surface data. 

2.7.2 SMOKE

 

The databases required to set up and operate SMOKE for the 2002 annual simulation are as 

follows: 

 Area Source emissions in IDA format 

 NonRoad source emissions in IDA format 

 Stationary Point Source emissions in IDA format 

 CEM emissions, day specific for 2002 

 Wildfire emissions, day specific for 2002 

 On-road Motor Vehicle VMT and activity data 

 MOBILE6.2 input parameters 

 

Also required for annual modeling are data files specific for: 

 Temporal allocation 

 Spatial allocation 

 Speciation 

Chapter 5 discusses the data input requirements and data sources in detail.

2.7.3 CMAQ 

 

As described in more detail in Chapter 5, the CMAQ Chemical Transport Model (CTM) requires 

the following inputs: 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-15 

3⁄4 Three-dimensional hourly meteorological fields that will be generated by the CMAQ 

MCIP2.2 processing of the BAMS MM5 output; 

3⁄4 Three-dimensional hourly emissions generated by SMOKE; 

3⁄4 Initial conditions and boundary conditions (IC/BC); 

3⁄4 Topographic information; 

3⁄4 Land use categories; and 

3⁄4 Photolysis rates generated by the CMAQ JPROC processor. 

2.7.4 CAMx 

CAMx model inputs include: 

3⁄4 Three-dimensional hourly meteorological fields generated by MM5CAMx processing 

of the BAMS MM5 output; 

3⁄4 Two-dimensional low-level (surface layer) emissions and elevated point source 

emissions generated by the CMAQ-to-CAMx emissions processor. 

3⁄4 IC/BC inputs generated by the CMAQ-to-CAMx IC/BC processors; 

3⁄4 Photolysis rates look up table; 

3⁄4 Albedo/Haze/Ozone Column input file; 

3⁄4 Land use and topography 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-16 Table 2-1. MM5 Meteorological Model Configuration for VISTAS Phase II 

Science Options Configuration Details/Comments 

Model Code MM5 version 3.6 (MPP) Grell et al., 1994 Horizontal Grid Mesh 36/12 km  36 km grid 164 x 128 cells  12 km grid 180 x 189 cells Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coord. Grid Interaction No Feedback IFEED=0 Initialization Eta first guess fields/LittleR Boundary Conditions Eta first guess fields/LittleR Microphysics Reisner I Mixed Ice Look up table Cumulus Scheme Kain-Fritsch 2 On 36/12 Grids Planetary Boundary Layer ACM PBL Radiation RRTM Vegetation Data USGS 24 Category Scheme Land Surface Model P-X Land Surface Model (LSM) Shallow Convection None Sea Surface Temperature Eta Skin Spatially varying Thermal Roughness Garratt Snow Cover Effects None 4D Data Assimilation Analysis Nudging on 36/12 Integration Time Step 90 seconds Simulation Periods Annual 2002 Other episodic periods to be defined Platform Linux Cluster Done at BAMS Run-Time (expected) 10% Real Time Platform Dependent 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-17 Table 2-2. SMOKE Emissions Model Configuration for VISTAS Phase II 

Emissions Component Configuration Details/Comments 

Emissions Model SMOKE ver 2.0 Oct '03 release w/ latest updates Horizontal Grid Mesh 36/12 km  36 km grid 148 x 112 cells RPO Unified Grid  12 km grid 168 x 177 cells Area Source Emissions VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

On-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Point Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states and stakeholders 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Off-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Biogenic Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Temporal Adjustments Seasonal, day, hour Based on latest collected information and CEM-based profiles Chemical Speciation Revised CB4 Chemical Speciation Expected EPA release in Jan '04 Gridding Revised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/ Growth and Controls VISTAS Special Interest Team Generated Data Base Cases defined by VISTAS Planning Workgroup Quality Assurance QA Tools in SMOKE 2.0 Independent QA with AG's Established Protocol Simulation Periods Annual 2002 Other episodic periods to be defined Platform Dual Athelon 2600 \+ Local 1.8 terabyte Ultra 320 RAID 5 system processing and storage 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-18 Table 2-3. CMAQ Air Quality Model Configuration for VISTAS Phase II 

Science Options Configuration Details/Comments 

Model Code CMAQ (ver 4.3) Pleim et al., (2003) Horizontal Grid Mesh 36/12 km 

36 km covering cont. U.S; 12 km covering all VISTAS states 

 36 km grid 148 x 112 cells RPO National Grid  12 km grid 168 x 177 cells Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5 Grid Interaction One-way nesting Initial Conditions 15 days full spin-up Separately run 4 quarters of 2002 Boundary Conditions GEOS-CHEM annual run 

 Initially use 2001 GEOS-CHEM seasonal values; Will be replaced with 2002 GEOSCHEM run. 

Emissions  Baseline Emissions Processing See SMOKE (Ver 2.0) model configuration MM5 Meteorology input to SMOKE, CMAQ  Dust Transport Fraction Applied in emissions before SMOKE Tom Pace updates  NH3 Inventory Adjustment Applied in emissions before SMOKE  Sub-grid-scale Plumes No Plume-in-Grid (PinG) PinG for PM not available yet Chemistry  Gas Phase Chemistry CBM-IV SAPRC99 may be considered in Fall 2004  Aerosol Chemistry AE3/ISORROPIA  Secondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) Schell et al., (2001)  Aerosol Mass Conservation Patch Yes  Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processes N2O5 Reaction Probability 0.01 – 0.001 Meteorological Processor MCIP ver 2.2 

Includes dry deposition and snow cover updates 

Horizontal Transport  Eddy Diffusivity Scheme K-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approach Vertical Transport  Eddy Diffusivity Scheme K-theory  Diffusivity Lower Limit Kzmin = 1.0 Planetary Boundary Layer No Patch Deposition Scheme M3dry Directly linked to Pleim-Xiu Land Surface Model 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-19 Table 2-3. CMAQ Air Quality Model Configuration for VISTAS Phase II 

Science Options Configuration Details/Comments 

parameters 

Numerics  Gas Phase Chemistry Solver Euler Backward Iterative (EBI) solver 

Hertel et al (1993) EBI solver ~ 2x faster than MEBI 

 Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 Other episodic periods to be defined Integration Time Step TBD 15 minute coupling time step Platform Athlon MP 2600+ Run-Time (expected) 30-60 days Platform Dependent Table 2-4 CAMx Air Quality Model Configuration for VISTAS Phase II 

Science Options Configuration Details 

Model Code CAMx (ver 4.3+) ENVIRON (2003) Horizontal Grid Mesh 36/12 km 

36 km covering cont. U.S; 12 km covering all VISTAS states 

 36 km grid 148 x 112 cells  12 km grid 168 x 177 cells Vertical Grid Mesh 19 Layers 17 Layers sync'd w/ MM5 Grid Interaction Two-way nesting Initial Conditions 15 days full spin-up Separately run 4 quarters of 2002 Boundary Conditions GEOS-CHEM annual run 

 Initially use 2001 GEOS-CHEM seasonal values; revised run will use 2002 GEOS-CHEM run. 

Emissions  Baseline Emissions Processing See SMOKE (Ver 2.0) model configuraton MM5 Meteorology input to SMOKE, CAMx  Dust Transport Fraction Applied in emissions before SMOKE  NH3 Inventory Adjustment Applied in emissions before SMOKE  Sub-grid-scale Plumes No Plume-in-Grid (PinG) PinG for PM not available yet Chemistry  Gas Phase Chemistry CBM-IV with Isoprene updates  Aerosol Chemistry ISORROPIA equilbrium Dynamic and hybrid also available 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-20 Table 2-4 CAMx Air Quality Model Configuration for VISTAS Phase II 

Science Options Configuration Details 

 Secondary Organic Aerosols SOAP  Aerosol Mass Conservation Patch Not identified as needed  Cloud Chemistry RADM-type aqueous chemistry 

Alternative is CMU multi-section aqueous chemistry 

N2O5 Reaction Probability None Meteorological Processor MM5CAMx Horizontal Transport  Eddy Diffusivity Scheme K-theory with Kh grid size dependence Vertical Transport  Eddy Diffusivity Scheme K-Theory  Diffusivity Lower Limit Kzmin = 1.0 Planetary Boundary Layer No Patch Deposition Scheme Wesely Numerics  Gas Phase Chemistry Solver CMC Fast Solver  Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 Other episodic periods to be defined Integration Time Step Wind speed dependent Platform Athlon MP 2600+ processors Run-Time (expected) 30-60 days Dual Processor 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 3.doc 3-1 

3.0 EPISODE SELECTION 

This chapter provides a brief overview of the process followed by the VISTAS Technical 

Analysis Workgroup (TAWG) and contractors (including the ENVIRON/UC/Alpine team) in 

determining the most appropriate modeling periods to be used in the Phase II regional haze modeling 

with CMAQ. While the TAWG plans to prepare a formal report documenting these activities, this 

work was not available at the time of this writing. However, much of the technical work 

underpinning the VISTAS episode selection process has been published and peer-reviewed over the 

later stages of Phase I and it is this body of information that we have distilled in preparing the brief 

episode selection summary that follows. 

3.1 Overview of EPA Guidance 

EPA’s current draft guidance on PM2.5/Regional Haze modeling (EPA, 2001) identifies 

specific goals to consider when selecting one or more episodes for use in demonstrating reasonable 

progress in attaining the regional haze NAAQS. However, since there is much in common with the 

goals for selecting episodes for annual and episodic PM2.5 attainment demonstrations as well as 

regional haze, EPA’s guidance addresses all three in a common document. More recently, EPA has 

published an updated summary of PM2.5 and Regional Haze Modeling Guidance (Timin, 2002) that 

serves, in some respects, as in interim placeholder until the final guidance is issued as part of the 

PM2.5/regional haze NAAQS implementation process that is expected during 2004. 

 EPA recommends that episode selection derive from three principal criteria: 

 > A variety of meteorological conditions should be covered; 

> To the extend possible, the modeling data base should include days for which 

extensive data bases (i.e. beyond routine aerometric and emissions monitoring) are 

available; and 

> Sufficient days should be available such that relative reduction factors (RRFs) can be 

based on several (i.e., > 15) days. 

 

For regional haze modeling, the guidance goes further by suggesting that the preferred 

approach is to model a full, representative year (EPA, 2001, pg. 188). Moreover, the required RRF 

values should be based on model results averaged over the 20% worst and 20% best visibility days 

determined for each Class I are based on monitoring data from the 2000 – 2004 baseline period. 

More recent EPA guidance (Timin, 2002) suggests that states should model at least 10 worst and 10 

best visibility days at each Class 1 area. EPA also lists several ‘other considerations’ to bear in 

mind when choosing potential PM/regional haze episodes including: (a) choose periods which have 

already been modeled, (b) choose periods which are drawn from the years upon which the current 

design values are based, (c) include weekend days among those chosen, and (d) choose modeling 

periods that meet as many episode selection criteria as possible in the maximum number of 

nonattainment or Class I areas as possible. 

 VISTAS adopted a logical, stepwise approach in implementing the EPA guidance in order to 

identify the most preferable, representative year for PM/regional haze modeling. These steps are 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 3.doc 3-2 

summarized briefly in this chapter. 

3.2 Episode Selection Methodology 

 The episode selection methodology entailed coordinated investigations by VISTAS 

contractors and members of the Technical Analysis Workgroup (TAWG). To begin, Olerud (2003b) 

identified important meteorological characteristics and data sets in the VISTAS region directly 

relevant to the evaluation of candidate annual modeling episodes. A separate detailed aerometric 

analysis and pattern recognition study was carried out by ICF (Douglas et al., 2003) to characterize 

the extent to which days from January-March 2002 and January-March 2003 represent the type of 

meteorological conditions that are most frequently associated with high and low values of a haze 

index and PM2.5 concentrations. Using the standard Classification and Regression Tree (CART) 

analysis software, these researchers characterized each of the days within these two three-month 

periods relative to haze and PM2.5 observations as well as other relevant meteorological factors. 

This work was based on previous episode characterization work carried out in support of the 

SEARCH and MARAMA research projects (Douglas et al., 2003). The analysis was also supported 

by climate summaries provided by the North Carolina Division of Air Quality (NCDAQ). 

 In parallel with the CART episode characterization analyses, collaborative investigations by 

VISTAS TAWG participants (e.g., NCDAQ, Georgia DNR, FL DEP) intensively studied the 

availability of PM2.5, meteorological, and emissions data and representativeness of alternative 

Baseline modeling periods from a regulatory standpoint (Boylan et al., Brewer et al., 2003). Daily 

average speciated PM 2.5 monitoring data in the VISTAS region were review intensively, by site 

and by monitoring network (e.g., IMPROVE, SEARCH, STN). In addition to analyzing data 

representativeness, consideration was given to the timeliness with which new data could be obtained 

for Phase II modeling. Also, data availability from parallel meteorological and emissions database 

acquisition efforts were considered both for the VISTAS states as well as for other states and 

countries in the 36 km domain. 

 To assess the representativeness of the regional haze rule five year baseline 2000-2004 

period, temperature and precipitation records were examined over the 108 yr period of record and 

additional high-resolution meteorological analyses were considered (e.g., CART analyses for 

SEARCH and MARAMA sites). For each PM2.5 monitoring site in the VISTAS domain, and for 

each component of PM 2.5, monthly means and deviations (from the monthly mean) were calculated 

for the months within the 2002-2003 period of record. Daily, monthly, and annual trends of PM 2.5 

concentrations across the three year period were subsequently analyzed (Boylan et al., 2003). 

Equally as important, the methodologies and decisions underpinning the episode selection processes 

carried out by other RPOs were also considered (several had already chosen CY-2002 as the 

modeling year). 

3.3 Selection of CY 2002 For VISTAS Phase II 

After a lengthy process of integrated studies, the episode selection process culminated in the 

selection of calendar year (CY) 2002 (1 January through 31 December) as the most current, 

representative, and pragmatic choice for VISTAS Phase II modeling. All of the EPA criteria for 

PM/regional haze episode selection were directly considered in this process together with many 

other pragmatic considerations (e.g., timing of new emissions or aerometric data deliveries by EPA 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 3.doc 3-3 

or the states to the modeling teams). In addition to selecting CY-2002 as the base year, the merits of 

selecting one or more other shorter duration episode periods (say 2-4 weeks or more in duration) was 

thoroughly considered. Such additional episodes were believed to add potentially valuable insights 

into the various source-receptor relationships influencing PM2.5 and regional haze air quality at 

Class I sites and other locations of interest. No specific decision was made regarding the exact 

number of shorter term episodes (falling outside CY 2002) nor on the duration or specific periods to 

be considered. Work on identifying potentially attractive candidate episodes to include with the 

annual 2002 modeling is ongoing and will be summarize in a subsequent updates to this protocol. In 

particular, ICF has been funded to determine whether any key meteorological conditions are not 

captured by the 2002 model year and if so identify additional modeling episodes outside of 2002 that 

would capture them. Additional modeling periods from 2003 would be ideal due to the presence of 

continuous PM monitoring data at the FOCUS sites (Millbrook, NC; Cape Romain, SC; and GRSM, 

TN) not available in previous years. 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-1 

4.0 MODELING DOMAINS AND DATA AVAILABILITY 

This chapter summarizes the model domain definitions including the model domain, 

resolution, map projections and nesting schemes for high resolution sub-domains. 

4.1 Horizontal Modeling Domain 

The horizontal domains for each of the models will be identical to those used in the 

Phase I modeling. As in Phase I, a coarse grid continental United States (US) domain with a 36 

km horizontal grid resolution will be used. The CMAQ domain is nested in the MM5 domain. 

The selection of the MM5 domain is described in the VISTAS MM5 modeling protocol (Olerud, 

2003). Figure 4-1 shows the MM5 horizontal domain as the outer most, blue grid. Also shown in 

Figure 4-1 is the CMAQ 36 km domain nested in the MM5 domain. To achieve finer spatial 

resolution in the VISTAS states we will also use a one-way nested high resolution grid with a 12 

km grid resolution. Figure 4-2 shows the 36 km CMAQ continental grid and the high resolution, 

nested 12-km grid in the VISTAS states. Figure 4-3 shows in more detail the 12 km grid for the 

VISTAS region. 

Both MM5 and CMAQ employ the Regional Planning Organization (RPO) unified grid 

definition for the 36 km continental domain. The RPO unified grid consists of a LambertConformal map projection using the map projections parameters listed in Table 4-1. 

Table 4-1. RPO Unified Grid Definition. 

PARAMETER VALUE 

projection Lambert-conformal 

alpha 33 degrees 

beta 45 degrees 

x center 97 degrees 

y center 40 degrees 

The MM5 36 km grid include 164 cells in the east-west dimension and by 128 cells in the 

north-south dimension. The CMAQ 36 km grid include 148 cells in the east-west dimension and 

112 cells in the north-south dimension. Because the MM5 model is also nested in the Eta model, 

there is a possibility of boundary effects near the MM5 boundary that occur as the Eta 

meteorological variables are being simulated by MM5 and must come into dynamic balance with 

MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid 

cells around each boundary of the CMAQ 36 km domain. This is designed to eliminate any 

errors in the meteorology from boundary effects in the MM5 simulation at the interface of the 

MM5 and Eta models. The buffer region used here exceeds the EPA suggestion of at least 5 grid 

cell buffer at each boundary. 

Table 4-2 lists the number of rows and columns and the definition of the X and Y origin 

(i.e., the southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that 

the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are 

reversed. In Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells while 

“cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot 

mesh is equal to the cross mesh plus one. Finally, we note that the grid definition for the CMAQ 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-2 

Meteorology Chemistry Interface Processor (MCIP) and CMAQ Chemical Transport Model 

(CCTM) are identical. 

Table 4-2. Grid Definitions For MM5 and CMAQ. 

MODEL COLUMNS 

DOT(CROSS) 

ROWS 

DOT(CROSS)

XORIGIN YORIGIN 

MM5 36km 129 (128) 165 (164) -2952000 -2304000 

CMAQ 36km 149 (148) 113 (112) -2736000 -2088000 

MM5 12km 190 (189) 181 (180) 7200 -1656000 

CMAQ 12km 169 (168) 178 (177) 108000 -1620000 

4.2 Vertical Modeling Domain 

The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 

modeling. The MM5 model employed a terrain following coordinate system defined by pressure, 

using 34 layers that extend from the surface to the 100 mb. Table 4-3 list the layer definitions for 

both MM5 and for CMAQ. As in Phase 1, a layer averaging scheme is adopted for CMAQ to 

reduce the computational cost of the CMAQ simulations. The effects of layer averaging were 

evaluated in the Phase 1 modeling effort and found to have a relatively minor effect on the model 

performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to 

ambient monitoring data. 

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Figure 4-1. Nesting of 36-km CMAQ Grid in the MM5 36-km Grid. 

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Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid. 

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Figure 4-3. Domain Definition for High Resolution 12-km Grid.

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Table 4-3. Vertical Layer Definition For MM5 Simulations (Left Most Columns), And 

Approach For Reducing CMAQ Layers By Collapsing Multiple MM5 Layers (Right Columns). 

MM5 CMAQ 19L

Layer Sigma Pres(mb) Height(m Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m)

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0

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4.3 Data Availability 

The CMAQ modeling system requires emissions, meteorological, initial and boundary 

condition (IC/BC) and ozone column data for defining the inputs to operate the CMAQ Chemical 

Transport Model and air quality data with which to evaluate the CMAQ CTM concentrations and 

deposition estimates. 

4.3.1 Emissions Data 

The base year emissions inventory for Phase II of the VISTAS modeling will be founded 

on revised 2002 emissions developed by VISTAS emission inventory contractors in NEI Input 

Format (NIF) 3.0. These revised emissions were reviewed by VISTAS stakeholders and 

considered complete in January of 2004. Non-VISTAS state emissions will be based on 

inventories obtained by the Study Team and determined to be representative of the 2002 episode 

year. Mexican and Canadian emissions will be based on the latest available inventories 

obtainable by the Study Team in formats lending themselves to emissions modeling. For 

purposes of air quality model validation, actual 2002 calendar year emissions for EGU and fire 

activity will be used. Whereas for strategy and future year emission runs, “typical year” 

emissions for these categories will be processed for 2002 and the future years. 

A final revised 2002 VISTAS state emission inventory is expected by September 2004 

and will be used in the final model performance demonstration and configuration expected to 

begin in October 2004. Non-VISTAS state emissions are expected to be based on State 

submittals to EPA under the CERR and will be augmented with additional data provided by 

RPO, State, and international sources. As in the initial revised modeling, actual 2002 calendar 

year emissions will be modeled for EGU and fires for base case model performance evaluation, 

while “typical year” emissions for these categories for 2002 and the future years will be 

processed during the strategy runs. 

All emissions will be converted to Inventory Data Analyzer (IDA) formatted versions and 

the data will be processed for air quality modeling using Version 2.0 of the Sparse Matrix 

Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal and 

speciation profiles and cross-reference data provided with the version 2.0 release of the model 

augmented with any recommended and approved emission profile data provided by the 

emissions inventory contractor, obtained from EPA, or prepared by the Study Team prior to 

initial emissions modeling. Spatial allocation of the emissions will be based on profiles and 

spatial allocation factors developed for the National RPO grid. Additional description of 

emissions processing is described in Chapter 5 and emissions QA is described in Chapter 6. 

4.3.2 Air Quality 

Data from ambient monitoring networks for both gas and aerosol species are used in the 

model performance evaluation. Ambient monitoring data are described in detail in the Phase I 

Task 3 Report: “Review and Assessment of Available Ambient Air Quality Data to Support 

Modeling and Modeling Performance Evaluation for the Three VISTAS Phase I Episodes” 

(ENVIRON, UCR and Alpine 2003) so are not repeated here. Table 4-4 summarizes ambient 

monitoring networks. Data have been compiled for all networks except the PAMS and PM 

Supersites. 

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If optional modeling of episode periods from 2003 is performed under VISTAS Phase II, 

then data from the new FOCUS sites will be available. As the FOCUS sites were not in 

operation during the 1999-2002 period that included the three Phase I episodes, they are not 

described in the Phase I Task 3 report so are discussed here (the reader can refer to the Phase I 

Task 3 report for information on the other networks). There are four focus sites in the VISTAS 

region: 

• Look Rock south of the Great Smoky Mountains National Park; 

• Raleigh, North Carolina; 

• Cape Romain, South Carolina; and 

• Atlanta , Georgia. 

Each Focus sites collects continuous PM mass, speciated PM (sulfate, nitrate and carbon) 

and gaseous pollutant concentrations (SO2, NOy, CO and O3). Each site also collects visibility 

(nephelometers), surface meteorology and athalometers (light absorption). 

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G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-9 Table 4-4. Overview of Ambient Data Monitoring Networks. 

Monitoring Network Chemical Species Measured Sampling Period Data Availability/Source The Interagency Monitoring of Protected Visual Environments (IMPROVE) 

Speciated PM25 and PM10 (see species mappings) 

1 in 3 days; 24 hr average 

http://vista.cira.colostate.edu/improve/Data/IMPRO

VE/improve_data.htm

Clean Air Status and Trends Network (CASTNET) 

Speciated PM25, Ozone (see species mappings) 

Approximately 1-week average 

http://www.epa.gov/castnet/data.html

National Atmospheric Deposition Program (NADP) 

Wet deposition (hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium)), Mercury 

1-week average http://nadp.sws.uiuc.edu/

Air Quality System (AQS) Aka 

Aerometric Information Retrieval System (AIRS) 

CO, NO2, O3, SO2, PM25, PM10, Pb 

Typically hourly average 

http://www.epa.gov/air/data/

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/amtic/amticpm.html Southeastern Aerosol Research and Characterization (SEARCH) 

24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); and Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) 

Hourly or 24-hour average, depending on parameter. 

Electric Power Research Institute (EPRI), Southern Company, and other companies. http://www.atmospheric-research.com

EPA Particulate Matter Supersites Speciated PM25 http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical Assessment Monitoring Stations (PAMS) 

Varies for each of 4 station types. http://www.epa.gov/ttn/amtic/pamsmain.html

National Park Service Gaseous Pollutant Monitoring Network 

Acid deposition (Dry; SO4, NO3, HNO3, NH4, SO2), O3, meteorological data 

Hourly http://www2.nature.nps.gov/ard/gas/netdata1.htm

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4.3.3 Ozone Column Data 

Additional data used in the air quality modeling include the Total Ozone Mapping 

Spectrometer (TOMS). TOMS data is available for 24-hour average and is obtained from 

http://toms.gsfc.nasa.gov/eptoms/ep.html. The TOMS data is used in the CMAQ radiation 

model (JPROC) to calculate photolysis rates. 

4.3.4 Meteorological Data 

Meteorological data are being generated using the MM5 prognostic meteorological model 

by Baron Advanced Meteorological Systems (BAMS). BAMS is operating the MM5 at 5-day 

increments for 2002 on the 36 km and 12 km grid with a 14 day spin up period for the end of 

December 2001. Details on the VISTAS Phase II 2002 MM5 modeling can be found at the 

BAMS VISTAS website: http://www.baronams.com/projects/VISTAS/. 

4.3.5 Initial and Boundary Conditions Data 

The CMAQ default Initial Concentrations (ICs) will be used along with a ~15 day spin 

up period to eliminate any significant influence of the ICs. 

The CMAQ Boundary Conditions (BCs) for the initial simulations will be based on 

seasonal averages of 3-hour 2001 GEOS-CHEM global simulation model output. VISTAS and 

other RPOs are finding a 2002 GEOS-CHEM simulation that would be used to define days 

specific high time resolved (e.g., 3-hourly) CMAQ BCs. 

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