# Appendix C.1

VISTAS Modeling Protocol

Tennessee Department of Environment and Conservation

Division of Air Pollution Control

9 th 

### Floor L&C Annex

401 Church Street
Nashville, TN 37243

### [This page intentionally left blank.]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 415.899.0700May 2004

i TABLE OF CONTENTS

Page

EXECUTIVE SU MMARY ...........................................................................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 Co efficients.......................................................................2-6

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

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

Aircraft Data...........................................................................................3-1 Summary of Model Performance fo r Aircraft Measurements .................................3-4 Top-Down Evaluation of Emis sions by Source Category ......................................3-8

Model Performance Evaluation wi th Surface Precurs or Data ............................... 3-46 Top-Down Evaluation of Emissi ons by Geographic Region ................................. 3-50

4. VOC RELATIVE REACT IVITY FACTORS.................................................4-1

5. SUMMARY AND CONCLUSIO NS .............................................................5-1

Top-Down Evaluati on Methodology ...............................................................5-1

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

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

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

REFERENC ES........................................................................................... R-1

APPENDICES

Appendix A: Comparisons of Modeled and Observed Ozone Concentrations for TexAQS Aircraft Flights on August 27-31, 2000May 2004

ii TABLES

Tables ES-1. Summary of e missions 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 leas t squares fits to single term models......... 3-15 Table 3-2. Sensitivity of least s quares fits to HC HO 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 perf ormance statistics for emission

scenarios described in the text. .................................................... 3-20 Table 3-5b. Summary of model perf ormance statistics for emission

scenarios described in the text....................................................... 3-21 Table 3-6a. Summary of model perf ormance statistics for emission

scenarios described in the text....................................................... 3-23 Table 3-6b. Summary of model perf ormance 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 mode l 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 NO y 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 square s 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 indi cates 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 m odel; highlighted rows indicates statistically significant regression co efficients at 98% confidence level) ..... 3-59May 2004

iii Table 3-17. Least squares fit results based on sub-regional NO

x sensitivities for all species (five term m odel; highlighted rows indicates statistically significant regression co efficients at 98% confidence level). .... 3-59 Table 3-18. Least squares fit results based on sub-regional NO

x sensitivities

for NO y 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 NO

x sensitivities for ozone only (five term model; hi ghlighted rows indicates statistically significant regression coefficients at 98% confidence level).................... 3-60

Table 4-1. Reactivities for CB4-VOCs re lative 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 mode ling 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 us ed 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 em issions with standard meteorology; b) Base4a emissions with th e 070PBL meteorology; c) PT_O2N2 emissions with 070PBL meteorology............................................... 2-12 Figure 2-7. Comparison of modeled (fill ed contours) and aircraft observed (filled circles) ozone at 13: 00 on August 29, 2000 for: a) Base4a emissions with 070_PBL meteorol ogy, and; b) PT_O2N2 emissions with 070_PBL meteorology. ......................................................... 2-13 Figure 2-8. Surface ozone monitoring site locat ions 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 Base 4a emissions scenario (red stars indicate observed/predicted pair s for which observed ozone is greater than 125 ppb).............................................................................3-5May 2004

iv Figure 3-3. Scatter plot of aircraft oz one observations and corresponding CAMx predictions under the PtO2N2 emissi ons 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 Base 4a emissions scenario (red stars indicate observed/predicted pair s 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 PtO2 N2 emissions scenario (red stars indicate observed/predicted pair s 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 Base 4a emissions scenario (red stars indicate observed/predicted pair s 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 emissi ons 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 max imum 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 max imum shown in red; observed daily

maximum ozone concentrations shown in blue at monitor site locations. County boundaries and coas t line shown in orange .............................. 3-33May 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 max imum 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 max imum 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 VOC MAB emission scenarios. ....................... 3-48 Figure 3-20. Comparison of predicted and observed NO y 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 sour ce 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-le vel to elevated source VOC DDM

sensitivities for ozone ................................................................. 3-53 Figure 3-23. Scatter plot matrix of low-level VOC and elevated point

NO

x DDM coefficients from the su b-region sensitivity analysis (E0n01VOC and E0n02NOx refer to the low-level VOC and elevated point NO

x DDM sensitivities for sub-region 1, 2 ... 5 as defi ned in Figure 3-21) ................................................ 3-54 Figure 3-24. Percentile box plot displa ys of sub-region (Reg. 1 – Reg. 5) low plus elevated source O 3 sensitivity coefficient distributions low (right panel) and high (left panel) O 3 aircraft observations

h O 3 defined as O 3 > 125 ppb)...................................................... 3-55 Figure 3-25. Percentile box plot displa ys of sub-region (Reg. 1 – Reg. 5) low plus elevated source NO y sensitivity coefficient distributions low (right panel) and high (left panel) O 3 aircraft observations (high O 3 defined as O 3 > 125 ppb)................................. 3-56 Figure 3-26. Percentile box plot displa ys of sub-region (Reg. 1 – Reg. 5) low plus elevated source fo rmaldehyde sensitivity coefficient contributions for low (right panel) and high (left panel) O 3 aircraft observations (high O 3 defined as O 3 > 125 ppb)................................. 3-57 Figure 3-27. Mean ozone prediction errors (ppb) from aircraft data: PT_O2N2 scenario. 3-61 Figure 3-28. Mean NO y prediction errors (ppb) from aircraft data: PT_O2N2 scenario .. 3-62May 2004

vi Figure 3-29. Mean formaldehyde pr ediction errors (ppb) from

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

Figure 4-1. Ozone sensitivity to OLE (olefins) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-3 Figure 4-2. Ozone sensitivity to PA R (paraffins) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-3 Figure 4-3. Ozone sensitivity to TOL (toluenes) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-4 Figure 4-4. Ozone sensitivity to XYL (xylenes) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-4 Figure 4-5. Ozone sensitivity to MEOH (methanol) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-5 Figure 4-6. Ozone sensitivity to ET OH (ethanol) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-5 Figure 4-7. Ozone sensitivity to FO RM (formaldehyde) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-6 Figure 4-8. Ozone sensitivity to ALD2 (aldehydes) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-6 Figure 4-9. Ozone sensitivity to ISOP (isoprene) compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions........................4-7 Figure 4-10. Ozone sensitivity to PT_O2N 2 point source VOC emissions compared to ETH (ethylene) for O 3 > 80ppb and VOC sensitive conditions..............4-7 Figure 4-11. Comparison of relative reactivities for CB4-VOCs from Houston point sources a nd the EMKA MIR scenario..............................4-8May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-1 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 as sociated with the management of regional haze, visibility and other air quality issu es in the Southeastern United Stat es. It is one of five Regional Planning Organizations (RPOs) c overing the U.S. that are devel oping a regional approach for addressing regional haze, fine particulate matter (PM) and ozone issues. VISTAS is comprised of the states of Alabama, Florida, Georgi a, 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 problem s, impacts from natural and man- made pollutants, and potential solutions. Stakeholders are encouraged to partic ipate at the workgroup level in order that all aspects of the problem and possible st rategies may be gi ven consideration.

The Clean Air Act establishes goals for visibility in many national parks, wilderness areas, and international parks as “the preven tion 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 al so may opt to assume responsibility for visibility programs under 40 CF R Part 49 by developing Tribal Implementation Plans (TIPs). The goal of the Regional Haze Ru le (RHR) is to achieve natural visibility conditions at the Federally manda ted Class I areas by 2064. To ach ieve 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 re quires 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 VI STAS is the use of me teorological, emissions and regional particulate matter (PM)/ ozone models to project visibility at VISTAS Class I areas in future-years. The VISTAS modeling effort ha s been split into two Phases. Under Phase I, which was performed primarily in 2003, VISTAS te sted and evaluated meteorological, emissions and PM/ozone models for three episodes to iden tify 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 se tting up and applying the select ed 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 evaluati on 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 Analys is workgroup that was dated March 12, 2004 (Morris et. al., 2004).

In order to meet the VISTAS Phase II sche dule, that VISTAS Phase II emissions and air quality modeling is dependent on receiving data fr om other VISTAS contra ctors. Figure ES-1May 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 th e 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 meteorologica l and air quality modeling activities to be carried out in 2004 and early 2005.

PRELIMINARY CONCEPTUAL MODEL OF VI SIBILITY IMPAIRMENT IN THE

VISTAS REGION

VISTAS is currently devel oping a comprehensive conceptual model of visibility impairment in the southeastern US. Brewer , Holman and Hornback (2003) have reported a preliminary analysis to char acterize the components of PM 2.5 and their contributi ons to visibility impairment in the VISTAS region. The anal yses was based on monitoring data from the Interagency Monitoring of Protect ed Visual Environments (IMP ROVE) network for the period

1998 to 2001 and from the Southeast Aerosol Re search Characteriza tion 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 GoalsMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-3 Fine particle composition and contribution to light extinction were evaluated for days with the 20% highest and 20% lowest total light extin ction (on annual basis) for 1998 to 2001 in the

IMPROVE network and for 1999 to 2001 in the SEAR CH network. For the IMPROVE sites, on the 20% haziest days (highest ex tinction), 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). Visu al 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 vi sibility days from 1998-2001).

Light extinction for the 20% ha ziest days is generally hi gher at the northern SEARCH

sites than the coastal SEARCH s ites. Visual range on the 20% h aziest 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 th e 20% haziest days. Vi sual range on the 20% clearest days varies from 75-131 km at the IM PROVE sites and 57-91 km at the SEARCH sites.Extinction (Mm-1) Coarse

Soil

Organics

EC

NH 3 NO 3 (NH 4 ) 2 SO 4 RayleighDolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FLShining Rock, NC Figure 3. Light Extinction on 20% Poorest Visibility Days - IMPROVE 1998 - 2001

50 100 150 200 250

0May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-4 Figure ES-3. Components of average light extinction (Mm -1 ) at southeastern US IMPROVE

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

In both the IMPROVE and SEARCH networks, the 20% haziest days in the year occur most frequently in the summer a nd spring quarters and least freque ntly in the winter quarters. Variation in sulfate concentrations is the greates t contributor to seasonal variation in extinction in the VISTAS region. Ammonium sulfate, (NH 4 ) 2 SO 4 , fine particles are th e 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. (NH 4 ) 2 SO 4 fine particles are also the largest

contributors to reconstructed fi ne 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 (O C) mass can be greater than (NH 4 ) 2 SO 4 mass.

Ammonium nitrate, NH 4 NO 3 , typically contributes 0-15% of the fine particle mass and fine mass light extinction at th e SEARCH and IMPROVE sites. NH 4 NO 3 has the highest contributions at the urban Birmingham and Atla nta SEARCH sites and le ss contribution at the

more rural SEARCH and IMPROVE sites. Available NH 4 measurements for the SEARCH and

IMPROVE sites indicate that SO 4 is not fully neutralized, particularly during the summer quarter. Modeling analyses suggest that NH 4 NO 3 formation in the southeastern U.S. is limited by NH 4 availability in the atmosphere

Organic carbon, OC, has the second largest co ntribution to fine particle mass and fine mass light extinction for the 20% haziest days at a ll sites (see Figure ES-2). On the 20% clearest days at IMPROVE sites, NH 4 NO 3 has an equal or sometimes la rger contribution to fine mass extinction than OC (Figure ES-3). The contributi on of OC to fine mass extinction is relatively constant across the quarters of th e year. Analysis indicates that OC fine mass in the fall and winter periods is predominantly from primary sources (e.g. gasoline, di esel, wood burning) andExtinction (Mm-1) Coarse

Soil

Organics

EC

NH 3 NO 3 (NH 4 ) 2 SO 4 RayleighDolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FLShining Rock, NC Figure 5. Extinction on 20% Best Visibility Days - IMPROVE 1998 - 2001

0 10 20 30 40 50 60May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-5 that secondary organic aerosols (e.g. from biogenic emissions) ha ve small contributions to total

OC fine mass in the fall and wi nter months and larger contribu tions 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. ha ve been attributed to impacts due to emi ssions from fires. Further analyses is needed to separate the relati ve contributions of diff erent types of combustion sources (e.g. forest fire, prescr ibed forest burning, land clearing, agricultural burning, residential wood combustion, or industrial wood combusti on) 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. SEA RCH 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 th e other IMPROVE and SEARCH sites in the southeastern U.S. Coarse mass generally contri butes 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 variabil ity in all of the above measur es of fine particle mass and

extinction.

Fine particle mass and composition are also m easured by the state agencies at several sites throughout the VISTAS region. The natio nal ambient air quality standard for PM 2.5 for an annual average (15 μ g/m 3 ) is measured to be exceeded in se veral urban areas in the southeastern.

SUMMARY OF PHASE I FINDINGS

The Phase I program entailed sy stematic meteorological, emi ssions, and air quality model testing and evaluation to identify the most reliable , scientifically valid, a nd operational efficient modeling configuration for visibil ity assessment in the Southeastern U.S. VISTAS selected three episodes to serve as the foundation for model te sting 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 contra ctor, Baron Advanced Meteorological Systems (BAMS), applied the PSU/NCAR Meso scale Model (MM5) to the three Phase I episodes in multiple configurations and evaluated its performance against surface and aloft meteorological obse rvations (see: http://www.baronams.com/projects/VISTAS/ ). Several different Land Surface Model (LSM) and Planetar y Boundary Layer (PBL) configurations were evaluated (LSM_PBL):May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-6 > 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 performan ce for any quantity. Thus, a variant the px_acm2 configuration (px_acm8) was selected for the Ph ase 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 sy stem. Key findings in the Phase I emissions processing with the SMOKE system include:

3⁄4 Although the SMOKE computational me thodology provides quick executions times, the large number and sizes of input , output and intermediate data files require substantial time and disk sp ace 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 em issions processing were significant, notwithstanding the computa tional 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 36 5 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 emissi on scenarios that could be examined potentially compromising the Phase II sche dule. Accordingly, the VISTAS modeling team has developed an emissions processing approach that models a re presentative week (on-road mobile sources) or days for each month of the year in order to ma ke 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 bi ogenic, Electrical Ge nerating Unit (EGU) point and fire sources.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-7 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 particul ate and visibility in th e Southeastern U.S. Details of the emissions and air quality modeli ng 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 opti ons in the Phase II model configuration;

(2) Model Performance : Preferable to use model op tions 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 mode ling 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 (s tarting 00Z on December 18, 2001) with the other three quarters initiali zed 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 essentia lly 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 laye r 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 precipita tion and transport patterns. Although the 12 km runs require significantly more computer resources, they do represent better science for photochemical and PM gr id modeling so will be retained in Phase II. However, more sensitivity te sts 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 m 2 /s and 0.1 m 2 /s. Model performance using the two minimum Kz values was mixed, with no one configuration consistently producing supe rior model performance over the other across all species, locations and periods wh en analyzing the 24-hour fine particle measurements. However, when analyz ing the hourly carbon measurements the

0.1 m 2 /s Kz_min sensitivity test exhibited spikes in the diurnal variations that wasMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-8 not present in the observations and use a 1.0 M 2 /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 la nd-use categories, are applied to the fugitive dust emissions within the base inventories prior to SMOKE emissions modeling, so no additional adjustment w ill 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 (Jul y 1999 and July 2001). For the Phase II modeling, the ammonia emissions invent ory has been updated using the CMU

ammonia emissions model and state subm issions 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 visibi lity estimates were very sensitive to ammonia emissions so they w ill likely be a subject of sensitivity tests

in Phase II.

3⁄4 Adjustment to NH3 Emissi ons 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 pr ofile 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 a nd Canadian Emissions: CMAQ PM estimates in the VISTAS states appear to be not substa ntially affect by emissions from Canada and Mexico for the three Phase I epis odes. Use of Cana dian 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 difficu lt to technically justify. Thus, for Phase II the MM5 PBL depth estimates will be used as is.

3⁄4 Use of Global Chemistry Transport Mo del Output for Boundary Conditions: Boundary condition (BC) sensitivity tests were conducted using CMAQ default and seasonal, monthly and 3-hourly valu es from a 2001 simulation of the GEOSCHEM global model. Use of the GE OS-CHEM results produced more realist spatial and temporal changes in BCs so will be used in Phase II.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-9 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 repr esents 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 negati ve PBL heights and when used with CMAQ produced degraded PM model pe rformance. Thus, based on better science and improved model performan ce, 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 nitrog en and reduced nitrogen in the CMAQ aerosol modules. Model performance with and without the mass conservation patch was essentially identic al. 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 r uns substantially slow er since it is only implemented in the slower SMVGEAR solv er. Given the much slower run times and little change or degraded model pe rformance, CB4-2002 was not selected for the VISTAS Phase II modeling. This de cision will be reconsidered if CB4-2002 is implemented in the faster EBI chemistry solver.

3⁄4 SAPRC99 Chemistry: The CMAQ simulations us ing 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 pe riods. Not tested, however, was whether the chemical mechanisms responded differently in response to emission reductions. SAPRC99 chemistry represen ts more current science than CB4 chemistry. However, CMAQ using SAP RC99 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 us e 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 data bases. Depending on the results of the Phase II emission reduction sensitivity tests, the SAPRC99 chemistry may be used in the revised 2002 CMAQ base cas e simulation starting in September 2004.

3⁄4 AIM Aerosol Chemistry: The AIM sectional aerosol module did not produce any improvements in model performan ce over the standard CMAQ modal AE3 approach and required subs tantially 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 pe rformance was similar, but there wereMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-10 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 bette r 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 m 2 /s over a 1.0 m 2 /s minimum Kz; and (2)

use of the mass conservation patch. Regarding the 0.1 m 2 /s minimum Kz, the VISTAS science team believes it is more technically correct and produces better nightt ime ozone performance, whereas PM performance was mixed. The conserva tion 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/r egional haze simulations for an annual period plus additional shor ter duration episodes.

Overview of Approach

The Phase II modeling will perform a detail ed performance testing of the modeling system followed by exercising the model for a va riety of emissions cont rol scenarios aimed at enabling VISTAS to assess the effect s of future year emission cont rol strategies on visibility and other air quality issues. The modeling system will also allow VISTAS to track reasonable progress toward regional haze goals. More spec ifically, 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 ad dition, 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 si mulations and performance evaluations will be performed in earl y 2004 using the initial 2002 inventory for VISTAS and non-VISTAS states, Canada an d Mexico. Multiple iterations of the 2002 annual simulation will be required to confirm the appropriateness of the model science configuration(s) recomme nded by the Phase I wo rk, 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 a nd 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-11 3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 simulation representing the 2000-2004 ba seline period for EGU and fire emissions and using 2002 revised inventor y 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 mode ling 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 a nd fire emissions for VISTAS-selected future period. Additional future year inventories may al so 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 perfor med 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 episodi c runs is anticipated.

Closely integrated with the annual (and possi bly episodic) meteorological, emissions and air quality modeling will be ongoing project management, technical review, and quality assurance activities performed under the guidan ce of the VISTAS Contra cting Officer and the VISTAS Technical Analysis Workgroup (TAWG). The modeling team me mbers 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/Regi onal Haze modeling (EPA, 2001) identifies specific goals to consider when selecting one or more epis odes for use in demonstrating reasonable progress in attaining the regional haze NAAQS. EPA recommends that episode

selection derive from th ree 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 fr om 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 lis ts several ‘other considerations’ to bear in mind when choosing potential PM/regional haze episode s including: (a) choose periods which haveMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-12 already been modeled, (b) choose periods whic h are drawn from the years upon which the current design values are based, (c) include we ekend days among those chosen, and (d) choose modeling periods that meet as many episode se lection criteria as possible in the maximum

number of nonattainment or Class I areas as possible.

VISTAS adopted a logical, stepwise appr oach in implementing the EPA guidance in order to identify the most prefer able, 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 char acterize the extent to which days from January-March 2002 and January-March 2003 represent the type of meteorological conditions that are most frequently associat ed with high and low values of a haze index

and PM 2.5 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 st ates (e.g., NCDAQ, Georgia DNR, FL DEP) intensively studied the availability of PM 2.5 , 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 y ear 2002 (1 January through 31 December) as the most current, representative, and pragmatic choi ce for VISTAS Phase II modeli ng. 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 du ration episode periods (say 2-4 weeks or more in duration) was thoroughly considered. Such additio nal episodes were believed to add potentially valuable insights into the various source-recepto r relationships influencing PM2.5 and regional haze air quality at Class I sites a nd other locations of interest and have the potential to capture important meteorological conditi ons not covered in the CY-2002 period. Work on identifying potentially attractive candidate episodes to include with th e 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 me teorological conditions are not captured by the 2002 model year and identify addi tional modeling episodes. Additional modeling periods from 2003 would be ideal due to the pr esence of continuous PM monito ring data at the FOCUS sites that were not in operation during CY-2002.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-13 Model Selection

Based on the Phase I findings, VISTAS sel ected the following models for use in modeling particulate matter (PM) and regi onal haze in the Sout heast under Phase II:

3⁄4 MM5: The Pennsylvania State University/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorologica l 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 ge nerates hourly gridded speciated emission inputs of mobile, nonroad, area, point, fi re 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’ photochemi cal grid model capable of addressing ozone, particulate matter (PM), visibility a nd acid deposition at regional scale for periods up to one year.

In addition, EPA’s fine particulate and re gional 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 ap ply alternative models. The following are the

alternative models identified in the Phase I work effort.

3⁄4 CAMx: ENVIRON’s Comprehensive Air Qua lity Model with Extensions (CAMx) modeling system is also a state-of-sci ence ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, part iculate 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 si ze 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;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-14 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 st arting at 12Z on December 17, 2001. Each segment would overlap by 12 hours the previous se gment and would be in itialized off of the input analysis fields (i.e., no linking of the LSM or PBL vari ables 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, ther e 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 numbe r of days to be processed. For many source categories (i.e., area, non-road and non-EGU poin t 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 s ources, day-specific hourly emissions will be used for the whole year. The most computationally limiting step in emissions modeling is th e generation of on-road mobile sources. Accordingly, on-r oad mobile sources will be ge nerated for one week (7 days) per month to obtain monthly and day-of-week varia tions 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-w ay 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 al ong 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 upd ated CMAQ Meteorological-Chemistry Interface Processor (MCIP2.2) would process the MM5 da ta using the “pass through” option. The CB4 gas-phase, RADM aqueous-phase, and AERO3/IS ORROPIA aerosol chemistry schemes will be used in the initial CMAQ 2002 modeling. Treatmen t of reversible secondary organic aerosolsMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-15 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 chemis try solver. Thus, CB4 -2002 was dropped from consideration in Phase II. The Phase I comp arisons of CB4 and SAPRC99 found they produced somewhat similar but different model perfor mance. However, one mechanism was not performing better than the other acr oss all species, sites and periods. The Phase I testing only evaluated the mechanism’s base case performan ce, not their response to emission reductions.

Given that CB4 runs twice as fa st 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 SAPRC 99 is deemed to be performing better than CB4

it would be used for the revised 2002 CM AQ 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 modeli ng activities. In December 2002, the USEPA publish extensive guidance on developing a Qua lity Assurance Project Plan (QAPP) for modeling studies (EPA, 2002). Th e objective of a QAPP is to en sure 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 id entification of assessments and related performance criteria;

• peer reviewed theory and equations;

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

• clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can fully unde rstand 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 th e 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 pr oject personnel through the data processing and model appl ication process to ensure that choices are consistent with the project objectives. The modeli ng team has already developed QA documents and procedures in

the Phase I effort. For the Phase II effort these w ill be enhanced and modified to assure that it conforms to the EPA QAPP guidance.

Complementing the data acquisi tion, modeling input developm ent activities, and project management activities, four other Phase II activi ties will be performed, consistent with theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-16 Quality Assurance (QA) plan:

Data Gatekeepers

The VISTAS Phase II emissions and air quali ty modeling team are receiving emissions, meteorological and air quality data from other VIST AS contractors or other sources and as a first line of QA, we have defined a Gatekeeper func tion 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 w ho will post data, reports and results to the project website and archiv e all key data generated in the project.

3⁄4 Air Quality Data Gatekeeper . Obtain air quality da ta as appropriate for model input development and model performance evaluation and assure quality of all air quality data obtaine d, consistent with approved QA plan. This gatekeeper will also provide doc umentation 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 m odeling 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 w ith approved QA plan and develop all emissions modeling files to support mode ling runs for 2002. This gatekeeper will also develop the chemical speciation files and temporal and spatial allocation files necessary to convert a nnual 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 modeli ng 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 electroni c files to VISTAS st ates, 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 proc essing is tedious, time consuming and involves complex manipulation of ma ny different types of large data sets, errorsMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-17 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. Th is 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 Screenin g 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 th e 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 cau tionary or warning messages during the emissions processing. We will redi rect the SMOKE output to log files and review the log files for serious error message s. 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 pr ocessed emissions as daily totals according to species, source category and county a nd state boundaries. These summaries will then be compared with summary data prepared fo r 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 da ily total emissions spa tial distribution. For a 20 day simulation this produces approximate ly 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simula tions 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 tota l 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 th at display the diurna l 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 acrossMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-18 the domain as a function of time. The objective of this step is to identify particular days for which emissions appear to be inconsiste nt with other days fo r no reason (e.g., not a weekend) and compare ag ainst 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 im mediately identify a problem in a control strategy. For example, if a VISTAS states SO 2 control strategy is being analyzed and there are changes in emissions for other pollutants or for SO 2 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 cont ractor (BAMS) will have primary responsibility in the QA/QC of the MM5 meteorological fields. However, the emissions and air quality modeling team will also pe rform some QA/QC of the meteorol ogical 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 Gate keeper 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 es timates by comparison them to upper-air observations and satellite images; and

3⁄4 Compare the VISTAS 2002 MM5 simulati on 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 a nd output data include the following:

• Verification that correct conf iguration 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 i nput data sets are used when running each model.

• Evaluation of CCTM results to verify that m odel output is reasonable and consistent with general expectations.

• Processing of ambient monitoring data for use in the model performance evaluation.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-19 • Evaluation of the CCTM results ag ainst concurrent observations.

• Backup and archiving of cr itical 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, wh ich is discussed above. The major QA issue specifically associated with the air quality mode l simulations is verification that the correct science options were specified in the model itself and that the corre ct input files were used when running the model. For the CMAQ model we em ploy a system of naming conventions using environment variables in the compile and run sc ripts that guarantee th at correct inputs and science options are used. We also employ a redunda nt naming system so that the name of key science options or inputs are included in the na me 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 direct ory 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 gene rate 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 prep ared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions 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 ai r 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 partic ulate matter (PM), including sulfate, nitrate, elemental carbon, organic carbon, other fine part iculate 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 PM 2.5 and Regional Haze” (EPA, 2001) recommends a comprehensive, multi-layered approach to model performance testing should be performed, c onsisting of the following four components:

> Operational Evaluation : Tests the ability of th e model to estimate PM concentrations (both fine and coarse) and the components at PM 10 and PM 2.5May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-20 including the quantities used to characterize visibility (i.e., sulfate, nitrate, ammonium, organic carbon, elemental carbon, PM 2.5 , and PM 10 );

> Diagnostic Evaluation: Tests the ability of the mode l to predict visibility and extinction, PM chemical composition in cluding PM precursors (e.g., SOx, NOx, and

NH 3 ) and associated oxidants (e.g., ozone an d nitric acid); PM size distribution; temporal variation; spatial variation; mass fluxes; and co mponents 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 uncerta inties associated with the model predictions and observat ions of PM and visibility.

Given the scope of the VISTAS Phase II effo rt and with consideration of the type and

amount of measurement data available to su pport the various eval uations, the CMAQ model

evaluation will included elements of each of these components. Obviously, the operational and diagnostic evaluations will receiv e the greatest attention, mechan istic 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 monitori ng networks may use different measurement approaches that “measure” different amounts of the same species that are also different from the modeled species. For ex ample, the IMPROVE network only speciates PM 2.5 so any sulfate or nitrate in the coarse mode (PM 2.5-10 ) is included in the CM species. Like was done in Phase I, the model will be evaluated se parately 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 obser vational data obtained from the IMPROVE, SEARCH, CASTNet, EPA-FRM, EPA-STN and ot her 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-t erm (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 th e observational data. We will look for systematic biases between the model re sults and IMPROVE observations, and if biases are found, identify possible sour ces of error in the model inputs.

3⁄4 Compare hourly/high resolution PM species and gaseous species concentrations at sites where available (e.g., SEA RCH, AIRS and EPA-Supersites).

3⁄4 At sites with contrasting aerosol mass lo adings (i.e. differing major components of fine particulate), we will analyze the tem poral behavior of th e major scattering andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-21 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 i norganic aerosol precursors and their effects on PM composition and visi bility, and evaluate the ability of the model to capture th ese seasonal trends.

3⁄4 Evaluate how well the model predicts th e relative roles and budgets of various processes by (1) examining observed and modeled correlations between various species pairs, and (2) compar ing model-predicted ratios of various species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

3⁄4 Investigate the performance of the model at selected observationa l sites characterized by different chemical regimes that may be encountered either spatially or during different seasons to help identify any in adequacies in the model and to provide a better understanding of conditions under wh ich model inferences may be weak.

3⁄4 Compare hourly 24-hour average, and episode averages of the PM c onstituents 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 hour ly and 24-hour averages by site and subregion to help iden tify any site-specific biases.

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

3⁄4 Evaluate for total sulfur (SO 2 + SO 4 ), nitrate (HNO 3 + NO 3 ) and ammonia (NH 3 +

NH 4 ).

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 c onditions (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 attenti on to the model performance at the Class I areas for SO 4 NO 3 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 ex ist and to and highlight differences between model runs. Experience in ozone/PM modeli ng is the best basis upon which to identify obviously flawed simulation results. Effort s to improve the CMAQ model’s base case performance will be made if needed (i.e., to reduce the discrepancies between model estimatesMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-22 and observations), and consistent with the project resources a nd 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

PM 2.5 ~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 re gional haze modeling is an evolving science, these performance goals are not fully established and certainly not univers ally 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 us eful in three distinct phases or ‘levels’ of an air quality modeling study and all will be used as approp riate in the VISTAS Phase II modeling with CMAQ. These levels are

Level I

. Model algorithm evaluation and configuration testing;

Level II . Model performance testi ng, 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 em issions control scenario analyses.

Many of the Level I sensitivity tests with CMAQ have already been completed in the Phase I configuration and diagnosti c analyses. However, given that open community nature of CMAQ and the frequent science updates to the m odel 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 inte gral part of the Phase II CMAQ model performance evaluation activity. Level II sensitiv ity 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 comp ensatory 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 refine ment and data collection programs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-23 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 es tablish 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 m odeling methods to corroborate the performance findings and control strategy response of the prim ary air quality simulati on model. Mindful of

EPA’s endorsement of corroborat ive modeling methods and the ri gorous use of ‘weight of evidence’ investigations, we recommend that th e 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 exp ected range of model uncertainty and to corroborate the general

effectiveness of the Phase II CMAQ regional haze control stra tegies. 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 back ground 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 e ither the 2013-2017 or 2014- 2018 five year periods. A State is required to set progress goals fo r 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 Re duction 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 visibilit y. The major features of EPA’s recommended visibility proj ections are as follows:

• Monitored data should be used to define current air quality.

• Monitored concentrations of PM 10 are divided into six major components, the first

five of which are assumed to be PM 2.5 and the sixth is PM 2.5-10

. 3⁄4 SO4 (sulfate);
3⁄4 NO3 (particulate nitrate);
3⁄4 OC (organic carbon);
3⁄4 EC (elemental carbon);May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-24 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 predicte d concentrations of each component.

• Component-specific relative reduction fact ors are multiplied by current monitored values to estimate future component concentrations.

• Estimates of future component concentra tions 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 measur ed sulfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].

Associated with each PM species is an ex tinction coefficient that c onverts concentrations (in

 g/m 3 ) to light extinction (in inverse mega meters, Mm -1 ). Sulfate and nitr ate are hygroscopic so relative humidity adjustment factors [f(RH)] are us ed 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 propert ies. EPA has defined site-specific monthly average f(RH) values that will be used in the vi sibility 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.

b Sulfate = 3 x f(RH) x [SO 4 ]

b Nitrate = 3 x f(RH) x [NO 3 ]

b EC = 10 x [EC]

b OM = 4 x [OM]

b Soil = 1 x [Soil]

b CM = 0.6 x [CM]

The total light extinction (b ext ) is assumed to be the sum of the light extinction due to the six PM species listed above plus Ra yleigh (blue sky) background (b Ray ) that is assumed to be 10 Mm -1

.

b ext = b Ray + b Sulfate + b Nitrate + b EC +b OM + b Soil + b CM

The total light extinction (b ext ) in Mm -1 is related to visual range (VR) in km using the following relationship:

VR = 3912 / b ext

The Regional Haze Rule requires that visibility be expressed in terms of deciview (dv) that uses natural logarithms of the extinction as follows:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-25 dv = 10 ln(b ext /10)

Modeling results are used in a relative fashi on 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 sp ecies 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 visibili ty is ranked using IMPROVE reconstructed PM mass (RCM) extinction equation for each year that comprise s 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-yearbaseline period and then calculate the fiveyear average of the 20% best and 20% wors t days. Document which days during the five-year baseline period comprise the 20% be st 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 concentratio n estimates in the current and future-year

simulation across the 20% best and 20% worst observed visibil ity days for each year in the five-year baseline period. Average acro ss the five-years the average PM species

concentrations for the 20% best and 20% wors t 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-yea r 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 concentr ations 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, perfor m 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 th e grid containing the monitor. For the

VISTAS Phase II 36 km modeli ng, 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 center ed on the IMPROVE monitor (EPA, 2001).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Exec_Sum.doc ES-26 VISTAS Phase II Future-Year Modeling

The VISTAS Phase II future-year mode ling will use the 2002 MM5 meteorological conditions. That is, we will assume that the mete orological conditions for the future-year are the same as for 2002. This will allow for the comparison of the changes in visibi lity at Class I areas from the current (2002) to future-year due to ch anges in air emissions. This means that the effects of climate change and climatic va riations 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 us ed in the 2002 Typical Year emissions scenario would be used in the future-year. Future-year Typical Year emissions fo r 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 us ed 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 m odel 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 quali ty. 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). Th ese calculations are repeated for each future-year emissions

scenario.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-1 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 emi ssions and air quality modeling activities to be performed by the contractor team of ENVIRON In ternational Corp, the University of California at Riverside, and Alpine Geophysics, LLC. Deve lopment 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 as sociated with the management of regional haze, visibility and other air quality issues in the S outheastern 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 fi gures, VISTAS is comprised of the states of Alabama, Florida, Georgia, Kent ucky, 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 a nd man-made pollutants, and potential solutions. Stakeholders are encouraged to participate at th e 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. Thro ugh 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 ma ndatory Class I Federal areas which impairment results from manmade air pollu tion” (40 CFR 51.300). States are required to develop State Implementation Plans (SIPs) to attain visibility standards, and Tribes al so may opt to assume responsibility for visibility programs under 40 CF R Part 49 by developing Tribal Implementation Plans (TIPs). The goal of the Regional Haze Ru le (RHR) is to achieve natural visibility conditions at the Federally manda ted Class I areas by 2064. To ac hieve 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 re quires the first visibility SIP/TIP be submitted to EPA by 2007 - 2008 to demonstrate progress towa rd 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 Trib es can start their planning process for submitting their plans to EPA that we re originally due by 2008.

Regional haze is linked to fine particulate (PM 2.5 ) for which EPA has a new standard.

PM 2.5 SIPS are to be submitted 3 years after designation of PM 2.5 nonattainment areas. EPA intends to perform the fina l designations of the PM 2.5 nonattainment areas by December 2004,

which would make the PM 2.5 SIP due by December 2007. As re gional haze is intricately linked

to PM 2.5, the PM 2.5 and regional haze SIPs are in the proc ess of being aligned so they would both be due by December 2007, if the PM 2.5 final designations occur as planned. EPA also intends to designate 8-hour nonattainment areas by April 2004 th at would make 8-hour ozone SIPs also dueMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-2 Figure 1-1. Regional Planning Organizations Engaged in Regional Haze Modeling.

in 2007. States are considering whether to use th e integrated one-atmosphere VISTAS base year modeling for ozone modeling in addition to PM 2.5 and regional haze. Those decisions will be based on schedule constraints, model performa nce, appropriateness of the episodes and feasibility. These regulatory drivers emphasize the importance of completing the VISTAS Phase I modeling to test and evaluate model configur ations in 2003 and starting the annual modeling in 2004. These issues also emphasize the importance in testing the models for ozone as well as

PM 2.5 and visibility.

VISTAS is one of five Regional Planning Orga nizations (RPOs) in th e United States that are implementing the requirements of th e RHR. The purposes of VISTAS are:

> To promote the coordinated efforts of Stat e, Local, and Tribal air agencies to meet federal regional haze and visibility requirements;

> To identify regional or common air management issues;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-3 Figure 1-2. VISTAS Study Region Showing the Class 1 Areas.

> To conduct research and undertake othe r activities to provide the membership with scientific and technological inform ation which may be of value in the development of agency strategies; and

> To develop strategies on regional haze for use by memb er states and tribes, where appropriate, in developing their implementation programs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-4 The Southeastern States Air Resource Mana gers (SESARM) has been designated by the US Environmental Protection Agency as th e 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 de termine the optimal m odeling configuration for use in the subsequent Phase II visibility assessment. Accordin gly, Phase I entailed a comprehensive literature review of recent relevant visibility studies using various photochemical/aerosol modeling platforms in or der to assess and identify appropriate model configurations, data bases, a nd 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 simula tions that have been completed related to regional haze and PM 2.5 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 regiona l 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 eval uating one-atmosphere PM/ozone models (ENVIRON, 2003c);

3⁄4 Develop and implement a plan for tes ting and evaluating alternative science configurations of the recommended Phas e I model(s) and document the results (ENVIRON, 2003d);

3⁄4 Prepare a Task 6 report pr escribing the model set-up, data base development, performance testing, and control strategy eval uation 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) Wo rkgroup; (b) the Data (monitoring) Workgroup; and (c) the Planning Workgroup. Under Phase I, the VISTAS Technical Analysis Workgroup (TAWG) managed the comprehensive model conf iguration 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 fo rth in this protocol.

For the meteorological component of the Phase I modeling, SESARM contracted with Baron Advanced Meteorological Systems (BAM S) to apply the PSU/NCAR Mesoscale ModelMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-5 (MM5) in multiple configurations and to eval uate its performance against surface and aloft meteorological observations. The emissions modeling compone nt 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, te sting, and applying the emissions m odeling 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 exch ange 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/r egional haze simulations plus additional shorter duration episodes. After detailed performance testing, the modeling system will then be exercised with a variety of emi ssions 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 sa me 36/12 km horizontal grid system used in Phase I. A potentially large number of annua l (and episodic) model simulations will be performed; the list below reflects current plans:

3⁄4 2002 Initial Annual Run

. The initial annual model si mulations and performance evaluations using the 2002 inventory for VI STAS 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 configur ation(s) recommended by the Phase I work, to evaluate updates to the model and mode l inputs and to refine model performance.

3⁄4 2002 Revised Annual Run

. A subsequent annual 2002 simu lation using revised 2002 modeling inventory for VISTAS and non-VI STAS 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 modele d 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-s elected future period. Additional future year inventories may also be modeled. The obj ective of these future year model runs is

to establish the modeled air quality basis against which th e 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 theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-6 foregoing simulations and analyses have been completed. A combination of annual and episodic runs is anticipated.

Closely integrated with the annual (and possi bly episodic) meteorological, emissions and air quality modeling will be ongoing project management, technical review, and quality assurance activities performed under the guidan ce of the VISTAS Contra cting Officer and the TAWG. The modeling team members will partic ipate 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 acquisi tion, modeling input developm ent activities, and project management activities, four other Phase II activi ties will be performed, consistent with the Quality Assurance (QA) plan:

Data Gatekeepers

The VISTAS Phase II emissions and air quali ty modeling team are receiving emissions, meteorological and air quality data from other VIST AS contractors or other sources and as a first line of QA, we have defined a Gatekeeper func tion 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 w ho will post data, reports and results to the project website and archiv e all key data generated in the project.

3⁄4 Air Quality Data Gatekeeper . Obtain air quality da ta as appropriate for model input development and model performance evaluation and assure quality of all air quality data obtaine d, consistent with approved QA plan. This gatekeeper will also provide doc umentation 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 m odeling 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 w ith approved QA plan and develop all emissions modeling files to support mode ling runs for 2002. This gatekeeper will also develop the chemical speciation files and temporal and spatial allocation files necessary to convert a nnual 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-7 3⁄4 Data Management Gatekeeper : This gatekeeper will maintain the VISTAS Modeling Website including posting modeli ng 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 electroni c files to VISTAS st ates, 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 proc essing is tedious, time consuming and involves complex manipulation of ma ny 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. Th is 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 Screenin g 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 th e 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 cautio nary or warning messages during the emissions processing. We will redi rect the SMOKE output to log files and review the log files for serious error message s. 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 pr ocessed emissions as da ily totals according to species, source category and county a nd state boundaries. These summaries will then be compared with summary data prepared fo r the pre-processed emissions, e.g., state and county totals for emissions from the augmented emissions data.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-8 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 da ily total emissions spa tial distribution. For a 20 day simulation this produces approximate ly 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simula tions 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 tota l 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 th at display the diurna l 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 inconsiste nt with other days fo r no reason (e.g., not a weekend) and compare ag ainst 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 im mediately identify a problem in a control strategy. For example, if a VISTAS states SO 2 control strategy is being analyzed and there are changes in emissions for other pollutants or for SO 2 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 cont ractor (BAMS) will have primary responsibility in the QA/QC of the MM5 meteorological fields. However, the emissions and air quality modeling team will also pe rform some QA/QC of the meteorol ogical 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 Gate keeper 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;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-9 3⁄4 Evaluate upper-air MM5 meteorological es timates by comparison them to upper-air observations and satellite images; and

3⁄4 Compare the VISTAS 2002 MM5 simulati on 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 a nd output data include the following:

• Verification that correct conf iguration 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 se ts are used when running each model.

• Evaluation of CCTM results to verify that m odel 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 cr itical 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, wh ich is discussed above. The major QA issue specifically associated with the air quality mode l simulations is verification that the correct science options were specified in the model itself and that the corre ct input files were used when running the model. For the CMAQ model we em ploy a system of naming conventions using environment variables in the compile and run sc ripts that guarantee th at correct inputs and science options are used. We also employ a redunda nt naming system so that the name of key science options or inputs are included in the na me 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 direct ory 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 gene rate 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 prep ared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions inputs can be discovered by viewing theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-10 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 Visibi lity Impairment in the VISTAS Region

VISTAS is currently developing a comprehensive c onceptual model of visibility impairment in the southeastern US. Brewer, Holman and Ho rnback (2003) have reported a preliminary analysis to characterize the components of PM 2.5 and their contributions to visibility impairment in the VISTAS region. The analyses was ba sed 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 ac res 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 pl ains to the Southern Appalachian Mountains and west ern 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 th e case of Pensacola) for a tota l 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 SEAR CH Sites in VISTAS regionMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-11 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) calcula tes visibility impairment th rough fine mass light extinction that is attributable to light scattering due to air molecules (Rayleigh scattering), scattering by ammonium sulfate, (NH 4 ) 2 SO 4 ; ammonium nitrate, NH 4 NO 3 ; 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 impa irment) 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 va ries from 18 -25 km at the northern IMPROVE

sites and 27-48 km at the coas tal 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% clea rest days (lowest extinction), there is less spatial variation in extinction th an is observed for the 20% haziest days (see Figures 1-4 and 1- 5). Visual range on the 20% cl earest 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

NH 3 NO 3 (NH 4 ) 2 SO 4 RayleighDolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FLShining Rock, NC Figure 3. Light Extinction on 20% Poorest Visibility Days - IMPROVE 1998 - 2001

50 100 150 200 250

0May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-12 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, GAYorkville, GABirmingham, ALCenterville, ALGulfport, MS*Oak Grove, MS*Pensacola, FLOld Fort, FL 50 100 150 200 250 300Extinction (Mm-1) Coarse*

Soil

Organics

EC

NH 4 NO 3 (NH 4 ) 2 SO 4 Rayleigh Figure 4. Light Extinction on 20% Poor est Visibility Days - SEARCH 1999 - 2001

*coarse not measured at 2 MS sitesExtinction (Mm-1) Coarse

Soil

Organics

EC

NH 3 NO 3 (NH 4 ) 2 SO 4 RayleighDolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FLShining Rock, NC Figure 5. Extinction on 20% Best Visi bility Days - IMPROVE 1998 - 2001

0 10 20 30 40 50 60May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-13 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 freque ntly in the winter quarters. Variation in sulfate concentrations is the greate st contributor to seasona l variation in extinction in the VISTAS region.

Ammonium sulfate, (NH 4 ) 2 SO 4 , fine particles are the larg est contributors to light

extinction for both the 20% clea rest and 20% haziest days at all IMPROVE and all SEARCH sites in all years and all quarters. (NH 4 ) 2 SO 4 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 da ys. On the 20% clearest days at the SEARCH sites, organic carbon (OC) ma ss can be greater than (NH 4 ) 2 SO 4 mass.

Ammonium nitrate, NH 4 NO 3 , typically contributes 0-15% of the fine particle mass and fine mass light extinction at the SEARCH and IMPROVE sites. NH 4 NO 3 has the highest contributions at the urban Birmingham and Atlant a SEARCH sites and less contribution at the

more rural SEARCH and IMPROVE sites. Available NH 4 measurements for the SEARCH and

IMPROVE sites indicate that SO 4 is not fully neutralized, partic ularly during the summer quarter (ARS, 2002b). NH 4 will preferentially neutralize SO 4 particles before reacti ng with nitric acid to

form NH 4 NO 3 . Modeling analyses suggest that NH 4 NO 3 formation in the southeastern U.S. is limited by NH 4 availability in the atmosphere (Odman et al., 2002). NH 4 NO 3 volatizes at higher temperatures, and therefore NH 4 NO 3 particle concentrations are higher in the fall and winter months than in the warmer months. On a daily basis, NH 4 NO 3 is most likely to be formed earlyAtlanta, GAYorkville, GABirmingham, ALCenterville, ALGulfport, MS*Oak Grove, MS*Pensacola, FLOld Fort, FLExtinction (Mm-1) Coarse*

Soil

Organics

EC

NH 4 NO 3 (NH 4 ) 2 SO 4 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

0May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-14 in the morning when temperatures are generally c ooler and relative humidity higher than later in the day when conditions are hotter and drier.

Organic carbon, OC, has the second largest co ntribution to fine particle mass and fine mass light extinction for the 20% haziest days at a ll sites (see Figures 1-4 and 1-5). On the 20% clearest days at IMPROVE sites, NH 4 NO 3 has an equal or sometimes larger contribution to fine mass extinction than OC (Figures 1-6 and 1-7). Th e contribution of OC to fine mass extinction is relatively constant across the qua rters of the year. The IMPROVE and SEARCH 24-hr samples do not allow separation of primary OC from s econdary 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 fo r 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 orga nic aerosols (e.g. from biogenic emissions) have small cont ributions to total OC fine mass in the fall and winter months and larger contributions in th e spring and summer months.

Elemental carbon, EC, typically contributes 3- 15% to fine particle mass and fine mass light extinction at the IMPROVE a nd 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 emissi ons 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 c ontributions of different types of combustion sources (e.g. forest fire, prescrib ed forest burning, land clearing, agricultural burning, residential wood combustion, or industria l 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. SEAR CH 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 th e other IMPROVE and SE ARCH sites in the southeastern U.S. Coarse mass generally contri butes 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 m easures 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 na tional ambient air quality standard for PM 2.5 for an annual average (15 μ g/m 3 ) is measured to be exceeded in se veral urban areas in the southeastern states (see Figure 1-8). The annual PM 2.5 standard is not exceeded at any of the IMPROVE sites. No exceedances of the 24-hour standard (65 μ g/m 3 ) have been reported through 2002, except

those related to forest fire events.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-15 Figure 1-8 . Counties in the southeastern U.S. with 1999-2001 annual PM 2.5 Design Values that exceed the fine particulate annual standard.

1.4 Summary of Phase I Findings

The Phase I program entailed sy stematic meteorological, emi ssions, and air quality model testing and evaluation to identify the most reliable , scientifically valid, a nd operational efficient modeling configuration for visibi lity assessment in the Southeastern U.S. Collaboratively, the VISTAS TAWG and modeling contra ctors 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 Phas e 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 Mode l (MM5) in multiple configurations and to evaluate its performance agai nst surface and aloft meteorologi cal observations. Moreover, BAMS scientists participated with the emissions and air quality modeling team in assessing how the air quality modeling system responded to sensit ivity features of the meteorological model. Significant findings of the Phase I MM5 meteorol ogical 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 co mpendium 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 PM 2.5 annual standardMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-16 Following the Phase I MM5 modeling protoc ol (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 evol ution in key state fi elds including mixing heights, temperatures, and winds?

3⁄4 Are cloud cover and precipitation fiel ds unbiased and reasonably accurate?

3⁄4 Do the fields lead to accep table 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 pe riods 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 iter ppx 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) mi xed phase (Reisner 1) microphysics, (c) Rapid Radiative Transfer Model (RRTM) radiation, (d) snow effect s turned on, and (e) analysis nudging of surface and aloft winds, te mperature, and moisture on the 36 km and 12 km grids. A variety of statistical time series plots, statisti cal tables, and spatial plots were generated to quantify and display the modeling resu lts, both at the ground and aloft.

Key findings distilled from the wo rk of Olerud (2003d,e) included:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-17 o The MM5 (ver 3.6) is most sensitive to the choice of planetary boundary layer (PBL) and soil (LSM) schemes 1 ;

o There was no ‘definite winner’ among th e 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 th e 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 pe rformance 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, base d on the available results.

Further details and interpretati on of the MM5 confi guration analyses ar e described in the

citations mentioned above and are also availa ble 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 activitie s 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 em issions-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 we re 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-18 3⁄4 Although the SMOKE computational me thodology provides quick executions times, the large number and sizes of input , output and intermediate data files require substantial time and disk sp ace 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 em issions processing were significant, notwithstanding the computa tional 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 lo cal Ultra 320 RAID 5 disk array.

Based on the SMOKE processing time requireme nts 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. Thes e extreme file size requirements will significantly affect the amount of time required for emissions genera tion, file handling a nd 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 t ogether with results of pertinent modeling sensitivity experiments is expected to be delivered to VISTAS in early May 2004.

The identification of the Phas e II model configuration consid ered three main, and often competing, issues:

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

(2) Model Performance : Preferable to use model op tions that produce better model performance.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-19 (3) Run Time : Faster run times are preferable.

Key findings from the Phase I CM AQ configuration testing include:

3⁄4 Duration of CMAQ Spin-up Initialization Period: Our experience with the VISTAS Phase I and Western Regiona l 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 th e 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 importa nt as the length of the spin up period is the need to operate the model in a consis tent fashion across scenarios. For the

VISTAS Phase II annual mode ling, the 2002 annual run will be performed in four quarters. The first quarter (January – March, 2002) will be in itialized with ~13 spin up days (starting 00Z on December 18, 2001) with the ot her 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 essentia lly no change in the basic attributes of model performance. The concentration changes were greater for those species that are emitted mainly aloft (e.g., SO 2 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 lin early proportional to the number of vertical laye rs, 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 precipita tion 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 significan tly 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 m 2 /s and 0.1 m 2 /s. ModelMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-20 performance using the two minimum Kz values was mixed, with no one configuration consistently producing supe rior model performance over the other across all species, lo cations and periods. Although EPA recommends using a 1.0

m 2 /s minimum Kz, the consensus of the VISTAS modeling group was that a 0.1

m 2 /s was more technically co rrect because use of a 1 m 2 /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 m 2 /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 la nd-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) ar e believed to overstate ammonia emissions during colder months. EPA has performed inve rse modeling to estimate the level of ammonia emissions needed to match measurements and developed monthly ammonia emission adjustment factors th at 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 performan ce for the January 2002 episode using the 50% ammonia emissions adjustment f actor. For the Phase II modeling, the ammonia emissions inventory has been updated using the CMU ammonia model

and state submissions so additiona l adjustments may not be needed.

3⁄4 Adjustment to NH3 Emissi ons 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 coul d 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 a nd Canadian Emissions: CMAQ runs with and without emissions from Canada and Mexico produ ced nearly identical model performance in the VISTAS Southeast US region. Us e of Canadian and Me xican emissions is more technically justifiabl e, 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 te sts were performed, but they did not substantially improve model performance and were difficu lt to technically justify. Thus, for Phase II the MM5 PBL depth estimates will be used as is.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-21 3⁄4 Use of Global Chemistry Transport Mo del Output for Boundary Conditions: Three boundary condition (BC) sensitivity te sts 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 sp ecific 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 200 4. Thus, in the mean time the initial 2002 CMAQ modeling would use monthly averag e 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 th e 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 usi ng the MM5 with the Pleim-Xiu LSM and ACM PBL (PX_ACM) configuration (no MC IP PBL patch needed). Thus, based on better science and improved model perf ormance, 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 CM AQ. 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 identi cal. For nitrate, CB4-2002 produced degraded model performance for the Ja nuary 2002 episode. Given the muchMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-22 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 us ing the CB4 and SAPRC99 chemistries produced different model perf ormance, but both exhibited similar skill so that one simulation was not clearly performing better than the other across all species, locations and periods. Not te sted, however, was whether one chemical mechanism behaved differently in response to emission reductions over another. SAPRC99 chemistry represents more cu rrent 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 th e 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 performan ce over the standard CMAQ modal AE3 approach and required signifi cantly 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 modeli ng with CAMx an op tional 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 m 2 /s over a 1.0 m 2 /s minimum vertical diffusion coefficient (Kz); and (2) use of the mass conservatio n patch. Regarding the 0.1 m 2 /s minimum Kz, the VISTAS science team believes it is more technically correct and, as described by Morris and co-workers (2004), it produced bette r nighttime ozone performance, whereas PM performance was mixed. The conservation of sulf ur and nitrogen is a fundamental property of the real atmosphere so we believe that ju stifies the use of the mass conservation patch.

The CMAQ run times are highly dependent on th e 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 elapse d 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/dayMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-23 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 ma de in Phase I, the Phase II emissions and air quality modeling protocol is intended to spec ify the procedures for performing the base year modeling and the evaluation of emi ssion control strategies for futu re years. The protocol will prescribe model set up methods, procedures fo r performance evaluation, sensitivity analysis, uncertainty examination, and the va rious statistical metrics and gra phical 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 Assuran ce Plan. The QA plan defined how quality assurance tasks will be performed and sets forth guidelines for accountab ility in correcting erro rs identified through the various review activities. Although comple ted 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 in terface 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 hist orical year base case, typical base year, future year, and stra tegies; (f) procedures for model evaluation, sensit ivity analyses, and uncertainty examination; (g) documentation; (h), data transfer, backup and archiving; (i) design and maintenance of project website, (j) qua lity assurance plans for each element of the Phase II modeling including accountability for co rrecting 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 Qual ity Modeling Study is be ing carried out by ENVIRON, Alpine and UCR un der 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 Coordinato r is the Contracting Officer for the study;

3⁄4 James Boylan of the Georgia Environmen tal Protection Division is the Technical

Contact for the VISTAS Emissi ons and Air Quality Modeling;

3⁄4 Michael Abraczinskas of the North Ca rolina Department of Environment and

Natural Resources is the Technical Cont act for the VISTAS MM5 Meteorological Modeling;

3⁄4 Laura Boothe of the North Carolina De partment of Environmental and Natural

Resources is the Technical contact fo r the VISTAS Emissions Inventory;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-24 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 Proj ect 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 Wilkin son of Alpine are leading the emissions modeling efforts;

3⁄4 Dennis McNally of Alpine, Chris Emer y 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 mainta in regular contact with the VISTAS Contracting Office, Technical Contacts, ke y members of the TAWG and other VISTAS Contractors through regular conference calls and em ails. 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 fr om 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 re solved 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 qua lity modeling system for the 2002 and 2018 annual calendar years so that contro l 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 Phas e II. Chapter 2 also identifies the Phase I recommendations concerning the optimal configurat ions and science options to be used in theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-25 SMOKE emissions and CMAQ air quality models. Details of the modeling domain specification for annual modeling and the data se ts to be used in developing model inputs and in support of the model performance evaluation and strategy applica tion are presented in Chapter 4. Subsequent, in Chapter 5, the procedures for preparing emissi ons 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 activ ities, respectively. In Chapter 9, we outline the procedures to be used in utilizing the a nnual (and potentially episodic) air quality modeling results to develop future year visibility estimat es 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 sche dule (Chapter 12) are di scussed in turn. The VISTAS Phase II SOW is provided in Appendix A. The core procedures to be used in conducting the operational and scie ntific 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. 200 3) 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.comMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 1.doc 1-26 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.eduMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-1 2.0 MODEL SELECTION

This chapter introduces the regional meteorol ogical, emissions and air quality models to be used in the annual PM/regional haze modeli ng for VISTAS Phase II. The specific science configurations for each modeling system are iden tified 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 fi rst 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 emer gent analysis procedures throughout the entire activity. For example a major upda te in the CMAQ code is expect ed to be rel eased around June 2004 that would require a rerunning of the initial CMAQ 2002 base case and reanalysis of the model performance evaluation. Notable limitati ons of the models rele vant to their intended purpose in VISTAS are identifie d. 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 sel ected the following models for use in modeling particulate matter (PM) and regi onal haze in the Sout heast under Phase II:

3⁄4 MM5: The Pennsylvania State University/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorologica l 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 ge nerates hourly gridded speciated emission inputs of mobile, nonroad, area, point, fi re 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’ photochemi cal grid model capable of addressing ozone, particulate matter (PM), visibility a nd acid deposition at regional scale for periods up to one year.

In addition, alternative models may also be applied as part of Phase II modeli ng to corroborate the CMAQ analyses such as:

3⁄4 CAMx: ENVIRON’s Comprehensive Air Qua lity Model with Extensions (CAMx) modeling system is also a state-of-sci ence ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, part iculate 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 optiona l task (see Appendix A).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-2 3⁄4 CMAQ – AIM or CMAQ-MADRID: These two models are variations of CMAQ

that utilize a sectional treatment of PM si ze 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 applicati on and evaluation procedures bei ng 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 deve loped in the 1970s at PSU and first documentedby Anthes and Warner (1978), the MM5 modeling system maintains its status as a state-of-thescience model through enhancements provide d 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 project s 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 succe ssfully applied in c ontinental 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, pub lic availability, and es tablished performance record compared with other a pplications-oriented prognostic mode ls, 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, prognos tic model that has been used widely in regional air quality model applications (S eaman, 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) a nd 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 stor ms, subtropical easterly jets, mesoscale convective complexes, desert mixed la yers, 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-dimens ional wind components (u, v, and w), temperature (T), water vapor mixing ratio (q

v ), and the perturbation pressure (p'). Use of a constant reference-state pressure increases the accuracy of the calculati ons 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 differ ent domains of arbitrary horizontal and verticalMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-3 resolution. The interfaces of the nested grids can be either on e-way or two-way interactive. The model is also capable of using a hydrostatic option, if desired, for co arse-grid applications.

MM5 uses a terrain-following non-dimensiona lized pressure, or "sigma", vertical coordinate similar to that used in many operatio nal and research models. In the non-hydrostaticMM5 (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 inte rpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer (PBL) para meterizations 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 (T g ), based on the insolation, atmos pheric path length, water vapor, cloud cover and longwave radiation. The su rface physical properties of albedo, roughness length, moisture availability, emissivity and therma l 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 pr ognostic equation for the second-or der turbulent ki netic 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-h our 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 evaluati ons of the MM5 modeling system in regulatory air quality application studies ha ve been widely reported in th e 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 a pplication history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scal e 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 an d Sims, 2003), MM5 was selected as the meteorological modeling system for the VISTAS study.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-4 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 Emi ssions (SMOKE) Emissions Processing System Prototype was originally de veloped 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 s ources, its purpose is to provide an efficient, modern tool for convertin g emissions inventory data into the formatted emission files required by an ai r quality simulation model. For mobile sources, SMOKE actually simulates emissions rates based on input mob ile-source activity data , emission factors and outputs from transportation travel-demand models.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing (HPC) as a pplied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach util ized 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 st eps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allo cation have been separated into independent operations wherever possible. The results from thes e steps are merged together at a final stage of processing.

SMOKE supports area, mobile, fire and poi nt 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/chie f/software.html#pcbeis ). SMOKEMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-5 has been available since 1996, and it has been us ed for emissions proce ssing 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. Environm ental 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 ma jor features that make it an attractive component of the VISTAS modeling system (Seppa nen, 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 parti tion pollutants to model species, as long as the appropriate input data are supplied, (b) integrati on 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) c ontrol 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 wh ere possible to be used for multiple source categories, (e) integration with Models-3 file form ats and settings, (f) impr oved data file formats, (g) support of various air qua lity 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 f actors, (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 Environmenta l 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 th e summer of 2003, and SMOKE ver2.0 was released on 30 Sept ’03. Barring unanticipated difficulties with th is release, the Phase II modeling will employ the SMOKE Ver 2.0. The SMOKE executables, script s 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

.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-6 2.3.2 SMOKE Configuration for Phase II Modeling

As an emissions processing system, SMOKE has far fewer ‘scien ce configuration’ options compared with the MM5 and CMAQ mode ls. For a thorough ch aracterization of the

methods that will be used to exercise th e 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 versi on of the SMOKE system to be used and the sources of data to be employed in cons tructing the required m odeling inventories.

2.4 CMAQ Modeling System

2.4.1 CMAQ Overview

For more than a decade, EPA has been de veloping 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 with in a common platform (Dennis, et al., 1996; Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 2003). The or iginal justificati on 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 Qu ality model (CMAQ) in mid1999 as the initial operating scie nce model under the Models-3 framework (Byun et al., 1998b).

The most recent rendition is CM AQ 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 Transpor t Model (CTM) and several pre-processors including the Meteorological-Chemistry Inte rface Processor (MCIP), initial and boundary conditions processors (ICON and BCON) and a phot olysis 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 EP A has funded the Community Modeling and Analysis Systems (CMAS) cente r to support the coordination, upda te and distribution of the Models-3 system.

A number of features in CMAQ’s theoretica l formulation and technical implementation

make the model well-suited for annual PM modeli ng. In CMAQ, the modal approach has been adapted to dynamically represent the PM size di stribution 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, nitrat e, SOA) is assumed to be in the fine modes. The thermodynamics of inorganic aerosol co mposition 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 SO 2 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 isMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-7 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 wher eby VOCs can be converte d to condensable gases that can then form SOA and then evaporat e back into condensable gases depending on atmospheric conditions.

Pleim et al., (2003) describe the newest features impleme nted in CMAQ ver 4.3 (released August 2003). Many of these fe atures are mentioned above; othe rs pertain to details in the model’s chemistry, transport, computer impl ementation, and model operation. Table 2-3 highlights the major options in CMAQ (ver 4.3) fo r different processes and compares them with the recently released CAMx (ver 4.3 ) model in Table 2-4, which is disc ussed 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 brie fly in the introduction. In this section we identify the main science options we recommend for annual PM mode ling with CMAQ. In particular, we propose to run CMAQ (ver 4.3) with the base configurati on as shown in Table 2-3. The model would be set up and exercised on the same nested 36/12 km grid domain us ed in Phase I, employing oneway grid nesting. That is, boundary conditions fo r the 12 km grid simula tion 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 al ong 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 pr ocess 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 m odeling. 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 al l 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 ba se case simulation in September

2004.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-8 2.5 Alternative Models

EPA’s guidance on model selection for PM2.5 SIPs and Regional Haze “reasonable progress demonstrations” do not identify a pref erred photochemical grid modeling system, recognizing that at present there is “no singl e model which has been extensively tested and shown to be clearly superior or easier to us e than several alternat ives” (EPA, 2001, pg. 169.) The agency recommends that models used for PM2.5 SIPS or RH reasonable progress requirements should meet the requirements fo r alternative models. The CMAQ, CMAQ-AIM and CAMx modeling systems all meet these requirements.

We believe that there is potentially signifi cant 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 re liability as CMAQ, given similar effort in preparing model inputs, diagnosing and im proving 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 diffe rences with CMAQ could not be explained

based on their differences in formulation with CMAQ-AIM pe rforming poorer than CMAQ. Accordingly, at this time we cannot reco mmend 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 diagnosti c tool addressing model performance issues that may arise in the establis hment of the CMAQ annual 2002 and episodic base cases. CAMx’s suite of diagnosti c probing tools plus it’s flexi-nesting algorithms make it an attractive tool fo r assisting in the diagnosis of CMAQ performance should unexpected situation arise;

3⁄4 Model Evaluation Corroboration: To provide corroboratio n of the base case model performance evaluation exercises to be pe rformed 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 ge neric and specific future year emissions changes on modeled gas-phase and particulate aerosol co ncentrations 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 performan ce in the annual and episodic base case simulations, and in the estimate of PM2.5 and visibility reductions

associated with future emi ssions 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-9 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, ha ving 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 m odel make it necessary to switch to an alternative ‘One-Atmosphere’ model at some point during Phase II.

The benefits of employing a pair of complimentar y state-of-science air qua lity 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 m odeling 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 phot ochemical/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 modul ar 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 multiscal e ozone issues from the urban- to regionalscale, CAMx has been widely used in recent year s 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-leve ls 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 Implic it 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;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-10 3⁄4 Ability to interface with a va riety of meteorological models including the MM5 and RAMS prognostic hydrostatic meteorologi cal models and the CALMET diagnostic meteorological model (others also compatible);

3⁄4 The Ozone Source Apportionment Tec hnology (OSAT) ozone apportionment technique that identifies the ozone contribution due to ge ographic 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) usi ng an empirical aerosol thermodynamics algorithm.

Culminating extensive model development effort s at ENVIRON and other participating groups, the CAMx (ver 4.3+) code was released in th e 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 ex tension 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 particulat e matter (PM), Mechanism 4 (M4) “oneatmosphere” treatment uses two size secti ons 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 th e 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 with in 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 mode led either using the RADM module (like CMAQ) or the Variable Size-Resolution M odel (VRSM) of Fahey and Pandis (2001), which automatically determine whether wate r droplets can be repr esented by a single ‘bulk’ droplet-size mode or whether it is n ecessary to use fine and coarse droplet-size

modes to account for the different pH effects on sulfate formation.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-11 CAMx (ver 4.3+) provides two ke y 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 a nd coarse) and the efficient RADM bulk aqueous-phase module (as used in CMAQ). Alternatively, more rigorous aer osol simulations (perhaps for shorter episode) may be addressed using the vers ion that treats N-size sections (N is typically 10) and the rigorous, but computationally-ext ensive 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 implementi ng the governing equations on modern parallel computers. In these cases, we will utilize th e 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 opti on 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 verti cal layers that exactly match those used by CMAQ. The PPM advection solver would be used along with the spatially varying (Smagorinsky) horizontal diffusion approach. Vert ical 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 configurat ion 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 scheme s. The SOAP secondary organic aerosol scheme would be used for the base configuration in CAMx.

Note that it may be desirable to exercise CA Mx using its “full-science” configuration for selected periods to investigative sc ientific 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 particulat e and visibility at key coastal sites in the VISTAS domain, especially when looking at cl ean days or natural background; and

3⁄4 The full sectional aqueous-phase chemistr y module may be important for sulfate

formation.May 2004

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 fo rmulating 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 t op performing configuration in the VISTAS region

across variables and locations and was never th e 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 pr edicts very low PBL heights that can appear as “holes” in the spatial distri bution 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, ther e 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 tr ies 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 featur es 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 re lease, 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, th ere is the potential for errors and/or ambiguities

to affect the emissions modeli ng schedule. Should problems ar ise or issues be encountered which would require additional SMOKE runs or potential SMOKE modifications or alternate modeling methods, we will immediately noti fy 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 SM OKE 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. WeMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-13 will keep abreast of SMOKE develo pment 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 CM AQ nitrate performance suspect with winter

overestimations and summer underestimations. Ot her science limitations in the current version of CMAQ include inadequate trea tment of sea salt and the assumption that all secondary PM is in the fine mode. Lack of any two-way grid ne sting limits the ability of the model to properly resolve point source plumes or urban photochemist ry 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 th at needs to be updated.

2.7 Model Input Requirements

Each of the VISTAS modeling system co mponents have signif icant data base requirements. These data needs fall into two categories: those requi red for model setup and operation, and those required for model evaluation testing. Below, we identify the main input data base requirements for the meteorologi cal, 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 fi xed and variable inputs.

3⁄4 Topography: High resolution (e.g., 30 sec to 5 mi n) topographic information derived from the Geophysical Data Center global da ta sets from the National Center for Atmospheric Research (NCAR) terrain databa ses 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 us e 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 horizontalMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-14 wind components (u and v), temperature (T), and relative humidity (R H) at the standard pressure levels, plus sea-level pres sure (SLP) and ground temperature (T g ). Here, T g represents surface temperature over land a nd sea-surface temperature over water.

> Water Temperature: Water temperatures required on bot h 36 km and 12 km grids can be derived from the ETA skin temperature vari able. 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 re solved-scale and paramete rized sub-grid scale processes, the model does not require prec ipitation or cloud input. The potential for precipitation and cloud formation enters th rough 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-di mensional fields produced every 3-hr from the NWS ra winsonde wind, temperature, a nd mixing ratio data, and similar analyses generated every three hour s 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 da ta input requirements and data sources in detail.

2.7.3 CMAQ

As described in more detail in Chapter 5, th e CMAQ Chemical Transport Model (CTM) requires the following inputs:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 2.doc 2-15 3⁄4 Three-dimensional hourly meteorological fi elds that will be generated by the CMAQ MCIP2.2 processing of the BAMS MM5 output;
3⁄4 Three-dimensional hourly emi ssions 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 CM AQ-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 topographyMay 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 2.doc2-16Table 2-1. MM5 Meteorological Model Configuration for VISTAS Phase IIScience OptionsConfigurationDetails/CommentsModel CodeMM5 version 3.6 (MPP)Grell et al., 1994Horizontal Grid Mesh36/12 km36 km grid164 x 128 cells12 km grid180 x 189 cellsVertical Grid Mesh34 layersVertically varying; sigma pressure coord.Grid InteractionNo FeedbackIFEED=0InitializationEta first guess fields/LittleRBoundary ConditionsEta first guess fields/LittleRMicrophysicsReisner I Mixed IceLook up tableCumulus SchemeKain-Fritsch 2On 36/12 GridsPlanetary Boundary LayerACM PBLRadiationRRTMVegetation DataUSGS 24CategorySchemeLand Surface ModelP-X Land Surface Model (LSM)Shallow ConvectionNoneSea Surface TemperatureEta SkinSpatially varyingThermal RoughnessGarrattSnow Cover EffectsNone4D Data AssimilationAnalysis Nudging on 36/12Integration Time Step90 secondsSimulation PeriodsAnnual 2002Other episodic periods to be definedPlatformLinux ClusterDone at BAMSRun-Time (expected)10% Real TimePlatform DependentMay 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 2.doc2-17Table 2-2. SMOKE Emissions Model Configuration for VISTAS Phase IIEmissions ComponentConfigurationDetails/CommentsEmissions ModelSMOKE ver 2.0Oct '03 release w/ latest updatesHorizontal Grid Mesh36/12 km36 km grid148 x 112 cellsRPO Unified Grid12 km grid168 x 177 cellsArea Source EmissionsVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesOn-Road Mobile SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesPoint SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS states and stakeholdersOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesOff-Road Mobile SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesBiogenic SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesTemporal AdjustmentsSeasonal, day, hourBased on latest collected information and CEM-based profilesChemical SpeciationRevised CB4 Chemical SpeciationExpected EPA release in Jan '04GriddingRevised EPA Spatial Surrogates UsedGridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/Growth and ControlsVISTAS Special Interest Team Generated DataBase Cases defined by VISTAS Planning WorkgroupQuality AssuranceQA Tools in SMOKE 2.0Independent QA with AG's Established ProtocolSimulation PeriodsAnnual 2002Other episodic periods to be definedPlatformDual Athelon 2600 \+Local 1.8 terabyte Ultra 320 RAID 5 system processing and storageMay 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 2.doc2-18Table 2-3. CMAQ AirQuality Model Configuration for VISTAS Phase IIScience OptionsConfigurationDetails/CommentsModel CodeCMAQ (ver 4.3)Pleim et al., (2003)Horizontal Grid Mesh36/12 km36 km covering cont. U.S; 12 km covering allVISTAS states36 km grid148 x 112 cellsRPO National Grid12 km grid168 x 177 cellsVertical Grid Mesh19 LayersFirst 17 layers sync'd w/ MM5Grid InteractionOne-way nestingInitial Conditions15 days full spin-upSeparately run 4 quarters of 2002Boundary ConditionsGEOS-CHEM annual runInitially use 2001 GEOS-CHEM seasonalvalues; Will be replaced with 2002 GEOSCHEM run.EmissionsBaseline Emissions ProcessingSee SMOKE (Ver 2.0) model configurationMM5 Meteorology input to SMOKE, CMAQDust Transport FractionApplied in emissions before SMOKE Tom Pace updatesNH3 Inventory AdjustmentApplied in emissions before SMOKESub-grid-scale PlumesNo Plume-in-Grid (PinG)PinG for PM not available yetChemistryGas Phase ChemistryCBM-IVSAPRC99 may be considered in Fall 2004Aerosol ChemistryAE3/ISORROPIASecondary Organic AerosolsSecondary Organic Aerosol Model(SORGAM) Schellet al., (2001)Aerosol Mass Conservation PatchYesCloud ChemistryRADM-type aqueous chemistryIncludes subgrid cloud processesN2O5 Reaction Probability0.01 – 0.001Meteorological ProcessorMCIP ver 2.2Includes dry deposition and snow coverupdatesHorizontal TransportEddy Diffusivity SchemeK-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approachVertical TransportEddy Diffusivity SchemeK-theoryDiffusivity Lower LimitKzmin = 1.0Planetary Boundary LayerNo PatchDeposition SchemeM3dry Directlylinked to Pleim-Xiu Land Surface ModelMay 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 2.doc2-19Table 2-3. CMAQ AirQuality Model Configuration for VISTAS Phase IIScience OptionsConfigurationDetails/CommentsparametersNumericsGas Phase Chemistry SolverEuler Backward Iterative (EBI) solverHertel et al (1993) EBI solver ~ 2x faster thanMEBIHorizontal Advection SchemePiecewise Parabolic Method (PPM) schemeSimulation PeriodsAnnual 2002Other episodic periods to be definedIntegration Time StepTBD15 minute coupling time stepPlatformAthlon MP 2600+Run-Time (expected)30-60 daysPlatform DependentTable 2-4 CAMx Air Quality ModelConfiguration for VISTAS Phase IIScience OptionsConfigurationDetailsModel CodeCAMx (ver 4.3+)ENVIRON (2003)Horizontal Grid Mesh36/12 km36 km covering cont. U.S; 12 km covering allVISTAS states36 km grid148 x 112 cells12 km grid168 x 177 cellsVertical Grid Mesh19 Layers17 Layers sync'd w/ MM5Grid InteractionTwo-way nestingInitial Conditions15 days full spin-upSeparately run 4 quarters of 2002Boundary ConditionsGEOS-CHEM annual runInitially use 2001 GEOS-CHEM seasonalvalues; revised run will use 2002 GEOS-CHEMrun.EmissionsBaseline Emissions ProcessingSee SMOKE (Ver 2.0) model configuraton MM5Meteorology input to SMOKE, CAMxDust Transport FractionApplied in emissions before SMOKENH3 Inventory AdjustmentApplied in emissions before SMOKESub-grid-scale PlumesNo Plume-in-Grid (PinG)PinG for PM not available yetChemistryGas Phase ChemistryCBM-IVwith Isoprene updatesAerosol ChemistryISORROPIA equilbriumDynamic and hybrid also availableMay 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 2.doc2-20Table 2-4 CAMx Air Quality ModelConfiguration for VISTAS Phase IIScience OptionsConfigurationDetailsSecondary Organic AerosolsSOAPAerosol Mass Conservation PatchNot identified as neededCloud ChemistryRADM-type aqueous chemistryAlternative is CMU multi-section aqueouschemistryN2O5 Reaction ProbabilityNoneMeteorological ProcessorMM5CAMxHorizontal TransportEddy Diffusivity SchemeK-theory with Kh grid size dependenceVertical TransportEddy Diffusivity SchemeK-TheoryDiffusivity Lower LimitKzmin = 1.0Planetary Boundary LayerNo PatchDeposition SchemeWeselyNumericsGas Phase Chemistry SolverCMC Fast SolverHorizontal Advection SchemePiecewise Parabolic Method (PPM) schemeSimulation PeriodsAnnual 2002Other episodic periods to be definedIntegration Time StepWind speed dependentPlatformAthlon MP 2600+ processorsRun-Time (expected)30-60 daysDual ProcessorMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 3.doc 3-1 3.0 EPISODE SELECTION

This chapter provides a brief overview of th e 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 wr iting. However, much of the technical work underpinning the VISTAS episode selection proce ss has been published and peer-reviewed over the later stages of Phase I and it is this body of inform ation that we have distille d in preparing the brief episode selection summary that follows.

3.1 Overview of EPA Guidance

EPA’s current draft guidance on PM2.5/Regi onal 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. Howe ver, since there is much in common with the goals for selecting episodes for annual and episodi c 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 da ta 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 mon itoring 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 epis odes 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 da ys 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 areMay 2004

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 Anal ysis 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 PM 2.5 concentrations. Using the standard Cl assification and Regression Tree (CART) analysis software, these researchers characterized each of the days within these two three-month periods relative to haze and PM 2.5 observations as well as other relevant meteorological factors. This work was based on previous episode charact erization 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 characteri zation analyses, collaborative investigations by VISTAS TAWG participants (e.g., NCDAQ, Geor gia DNR, FL DEP) intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative Baseline modeling periods from a regulatory standpoi nt (Boylan et al., Brew er 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 VI STAS 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 m onitoring 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 recor d. Daily, monthly, and a nnual trends of PM 2.5 concentrations across the three year period were subsequently analyzed (Boylan et al., 2003). Equally as important, the methodologies and decisi ons 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, th e episode selection process culminated in the selection of calendar year (CY) 2002 (1 Janua ry 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 ne w emissions or aerometric data deliveries by EPAMay 2004

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 ba se 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 we re 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 a ttractive candidate episodes to include with the annual 2002 modeling is ongoing and w ill 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 fr om 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-1 4.0 MODELING DOMAINS A ND DATA AVAILABILITY

This chapter summarizes the model domain definitions including the model domain, resolution, map projections and nesting sc hemes 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. Th e 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 hi gh 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 Pl anning Organization (RPO) unified grid

definition for the 36 km continental domain. Th e RPO unified grid consists of a LambertConformal map projection using the map proj ections 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 dimens ion and by 128 cells in the north-south dimension. The CMAQ 36 km grid incl ude 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 n ear 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 do main. 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 suggestio n 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. Finall y, we note that the grid definition for the CMAQMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-2 Meteorology Chemistry Interface Processor (M CIP) 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 vert ical grid used in the MM5 modeling. The MM5 model employed a terrain follo wing coordinate system defined by pressure, using 34 layers that extend from the surface to th e 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 simu lations. 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 an d a 19 layer CMAQ models were compared to ambient monitoring data.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-3 Figure 4-1 . Nesting of 36-km CMAQ Grid in the MM5 36-km Grid.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-4 Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid

.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-5 Figure 4-3. Domain Definition for High Resolution 12-km Grid.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-6 Table 4-3. Vertical Layer Definition For MM5 Si mulations (Left Most Columns), And Approach For Reducing CMAQ Layers By Collaps ing 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 0May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-7 4.3 Data Availability

The CMAQ modeling system requires emissi ons, meteorological, initial and boundary condition (IC/BC) and ozone column data for de fining the inputs to operate the CMAQ Chemical Transport Model and air quality data with whic h 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 m odeling will be founded on revised 2002 emissions developed by VISTAS em ission inventory cont ractors in NEI Input Format (NIF) 3.0. These revised emissions we re reviewed by VISTAS stakeholders and considered complete in January of 2004. Non- VISTAS state emissions will be based on inventories obtained by th e 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 va lidation, 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 categor ies 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 performan ce demonstration and configuration expected to begin in October 2004. Non-VISTAS state em issions 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 m odeling, actual 2002 calendar year emissions will be modeled for EGU and fires for base case model performance evaluation, while “typical year” emissions for these cate gories 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 fo r 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-refere nce data provided with the version 2.0 release of the model augmented with any recommended and appr oved emission profile data provided by the emissions inventory contractor, obtained from EPA, or prepar ed 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 Av ailable 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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-8 If optional modeling of episode periods from 2003 is performed under VISTAS Phase II,

then data from the new FOCUS sites will be av ailable. 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 disc ussed 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).May 2004G:\VISTAS Modeling\Task6 ModelingProtocol\Draft#3\Chapter 4.doc4-9Table 4-4.Overview of Ambient Data Monitoring Networks.Monitoring NetworkChemical Species MeasuredSampling PeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24hr averagehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (seespecies mappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10,PbTypically hourlyaveragehttp://www.epa.gov/air/data/Speciation Trends Network(STN)Speciated PM24-hour average http://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (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 onparameter.Electric Power Research Institute (EPRI),Southern Company, and other companies.http://www.atmospheric-research.comEPA Particulate Matter Supersites Speciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htmMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 4.doc 4-10 4.3.3 Ozone Column Data

Additional data used in the air qualit y modeling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data is availabl e 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 us ing the MM5 prognostic meteorological model by Baron Advanced Meteorological Systems (BAM S). 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 Phas e 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 (IC s) will be used along with a ~15 day spin up period to eliminate any signi ficant influence of the ICs.

The CMAQ Boundary Conditions (BCs) for th e 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 simulati on that would be used to define days specific high time resolved (e.g., 3-hourly) CMAQ BCs.