west virginia department of environmental protection



# Appendix C.1 VISTAS Modeling Protocol

Promoting a healthy environment.

### [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.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-1 5.0 MODEL INPUT PREPARATION PROCEDURES

In this section we describe the procedures to be used to develop the CMAQ model inputs for the Phase II 2002 annual and potentially additional ~60 days epis odic simulations. We also discuss how the CAMx model inputs will be generate d if the optional task to run an alternative model to corroborate the CMAQ results is selected. The development of the CMAQ meteorological and emissions inputs are discusse d first followed by the science options to be used by CMAQ. The procedures for devel oping the initial and boundary conditions and photolysis rates inputs are then discussed along with the model application procedures.

5.1 Meteorological Inputs to Emi ssions and Air Quality Models

The emissions and air quality models require certain meteorological input data including wind fields, estimates of turbulent eddy disper sion, humidity, temperature, clouds, and actinic flux. Spatially gridded and hourly varying meteorological data are needed to estimate biogenic, mobile source emissions, and plume-rise for large, elevated point sources. Meteorological data are needed to drive chemical transport models for solving atmospheric diffusion and chemistry equations for model species. Because observed da ta are not available fo r the full gridded model domain, numerical meteorol ogical models are used to provide these inputs.

The National Center for Atmospheric Resear ch (NCAR)/Pennsylvania State University (PSU) Fifth-Generation Mesoscale Model (MM5 ) (v3.6) is being used by the VISTAS meteorological modeling contactor, Baron Advan ced Meteorological Systems (BAMS) (Olerud, 2004) to simulate meteorology at a 36-km reso lution for calendar year 2002 over the entire

continental United States and incl uding portions of Canada, Mexic o, and the Atlantic and Pacific Oceans. MM5 is also being applied over the s outheastern U.S. using a 12 km resolution grid. The MM5 is a three-dimensional prognostic mete orological model that is used not only for meteorology studies but also for air quality studies. Some of th e physics used in the simulation include one-way nesting; nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and mixing ratio; explicit treatmen t of moisture; cumulus cloud parameterization; vertical mixing of momentum in the mixed laye r; PBL process paramete rization; atmospheric radiation; sea ice treatment; and snow cover (see Chapter 2 for more details).

After the MM5 simulation is completed, the MM5 output files are transferred to the emissions and air quality modeling team and anal yzed by the Meteorological Gatekeeper. The Meteorological Gatekeeper performs two main role s. To provide an independent evaluation of

the 2002 MM5 simulation that also serves to determine whether the MM5 data have been transferred correctly from the VISTAS meteorol ogical modeling contractor . And to process the 2002 MM5 output using Version 2.2 of the Mode ls-3 CMAQ Meteorological-Chemical Interface Processor (MCIP) to generate meteorological fields that will be used for emissions processing and air quality simulation.

5.1.1 MCIP Reformatting Methodology

The Models-3 Community Multiscale Air Qual ity (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particles) air quality problems. But befo re running the CMAQ Chemical Transport Model (CCTM), the MM5 generated mete orological data must be pr e-processed and converted toMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-2 Models-3 consistent data stru ctures. MCIP version 2.2 will be used to preprocess the MM5 meteorological output. The “pass through” option in MCIP will be used in the VISTAS Phase II modeling. One of MCIP’s functions is to translate meteorological parameters from the output of the Pennsylvania State University/National Ce nter for Atmospheric Research (PSU/NCAR) Mesoscale Modeling System Generation 5 (MM5) to the Models-3 input/output applications program interface (I/O API format) which is required for operation of Models-3 CMAQ processors. Some other necessary parameters not available from the meteorological model are estimated with appropriate diagnostic algorithms in the program. The key functions of MCIP

include:

1. Reading in meteorological model output files 2. Extraction of meteorological data for CTM window domain 3. Interpolation of coarse meteorol ogical model output for finer grid 4. Collapsing of meteorological pr ofile data if coarser vertical resolution data is requested 5. Computation or passing through surface and PBL parameters 6. Diagnosing of cloud parameters 7. Computation of species-speci fic dry deposition velocities 8. Generation of coordinate dependent meteorol ogical data for the generalized coordinate

CCTM simulation 9. Output meteorological data in Models-3 I/O API format

The MCIP processor transforms the data into I/O API format while also calculating several new data fields (e.g. low, middle, and high cloud fr actions) that are not readily available in the raw MM5 output. It also inter polates temperature and wind spee d to observation height (1.5m and 10m, respectively). The MCIP processor cu lls a minimum of six cells about the domain periphery to minimize edge eff ects in the MM5 simulation. MCIP can be used to further reduce the rows or columns in the MM5 data so that the domain definition for the MCIP output files precisely matches the domain used in the air quality modeling. MCIP also allows MM5 layers to be “collapsed” (i.e., some layers can be aggregated). When feasible it is desirable to use the same layer structure in the air qual ity model as in the MM5 to prevent errors associated with aggregating layer data and to maintain consis tency between data produced by the meteorological model and those used by the chem istry-transport model. Howeve r, due to computational costs associated with using large number of vertical layers, ve rtical layer collapsing is typically used to reduce the total number of layers used by the CCTM. In the VISTAS Phase II modeling we will collapse from 34 layers in MM5 output into 19 layers for the CMAQ air quality simulations. The first 8 layers of CMAQ, up to approximately 450 m AGL, will match th e MM5 vertical layer structure exactly. The MM5 layers are then “doubled up” in CMAQ, up to a height of approximately 3,500 m AGL. The region top for CMAQ is the same as used by MM5, 100mb (approximately 15 km AGL). The 36 km analys is domain contains 148 columns, 112 rows, and 19 layers. The 12 km analysis domain covers 168 columns, 177 rows, a nd 19 layers. More details on the CMAQ modeling domain definitions ar e provided in Chapter 4 with the vertical layer structure of MM5 and MCIP/CMAQ shown at Table 4-3.

5.1.2 Products of the Meteorological Input Development Process

The meteorological input development proce ss produces three two-dimensional and four three-dimensional daily meteorological and ge ophysical output data in the Models-3 I/O API format. These CCTM-ready meteorological input f iles are used in both emissions processing andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-3 the CCTM simulations. The met fields are 36 km and 12 km horizontal resolution on a Lambert Conformal Projection (LCP) coordina te system with 19 vertical si gma layers extending from the surface to the 100 mb pressure level. The data f iles include three-dimensional gridded fields of u- and v-wind components, vertical velocity, te mperatures, Jacobian, Jacobian weighted air density, total air density, water vapor, cloud wate r content, rain water content, ice and snow mixing ratio, layer heights, and vertical exchange coefficients. Two-dime nsional gridded fields of latitude and longitude, squared map-scaled fa ctor, surface temperatures and pressures, 1.5 and 10 meter temperature, planetary boundary heights, rainfall, tota l cloud fraction, snow cover, deposition velocities, u* and w*, surface roughness length, as well as dominant land use category are also developed.

Table 5-1 shows the configuration to be used in MCIP version 2.2 for processing the 2002 MM5 output to produce CCTM-ready meteorology input files.

Table 5-1. MCIP V2.2 Configuration used In the VISTAS Phase II Modeling.
Module or option Values or setting Additional Information

PBL value computation option 1 Use PBL value from input meteorology

Radiation fields 1 Use radiation fields from input meteorology

Dry deposition option 2 Use Models-3 (Pleim) dry deposition

routine

Output interval 60 Unit is in minutes

Vertical layer structure 19 layers See Chapter 4

5.2 Development of Emissions Model Inputs and Resultant Inventories

The base year emissions inventory for Phas e 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 March of 2004.

Non-VISTAS state emissions are based on inventories obtained by the Study Team and determined to be representative of the 2002 episod e year. Western State base year point and area source emissions for 2002 were provided by the WR AP RPO. Additionally, an inventory of point source resolved agricultural fire emissi ons were provided by WRAP and utilized in the modeling. The CENRAP RPO provided VISTAS with an inventory of 2002 area source

ammonia emissions which were also used in developing the remaining non-VISTAS emissions.

For the remaining U.S. domain, point s ource projections were based on EPA’s 2001 modeling platform inventories a nd area source, nonroad mobile, a nd fire emissions were based on EPA’s preliminary 2002 NEI. VMT and MOBI LE input files were taken from EPA’s preliminary 2002 FTP site and used in running MOBILE6 nationally.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-4 Mexican and Canadian emissions are be ba sed on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. At this time,

these inventories are the same as in Phase I.

For purposes of air quality model validati on, actual 2002 calendar year emissions for EGU and fire activity will be used, while during strategy and future year emission runs, “typical year” emissions for these categories will be processed.

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 emi ssions 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 modeling, the revised modeling will use actual 2002 calendar year emissions for EGU and fi res, while “typical year” emissions for these categories will be processed during the strategy runs.

These emissions will then be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for ai r quality modeling using Version 2.0 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal, spatial, and speciation profiles and cross-reference data currently provided with the 2.0 release of the model augmented with any r ecommended and approved emission profile data provided by the emissions inventor y contractor or obtained from EPA prior to initial emissions modeling. The processing will be adjusted for e ach run to account for the specific air quality model (AQM) input required by CMAQ.

5.2.1 Emissions Modeling Methodology

Emissions inventory development for photoc hemical modeling must address several source categories including: (a) st ationary point sources, (b) ar ea sources, (c) on-road mobile sources, (d) non-road mobile sources, and (e) biogeni c sources. For this analysis, these estimates must be developed to support the episode that is being modeled (i.e., the historical base year when the episode actually occurred; 2002).

Development of an emissions inventory cust omized for the VISTAS region requires a merging of: (a) the most recent pertinent regional inventory and (b ) available high-resolution, locale-specific emissions estimated by local, state, and regional agencies in the VISTAS region. Local air regulatory and transportation planning agencies are generally the best sources of domain specific activity and cont rol factors to use in developing the base year emissions. Often,

these local emissions data sets come from a vari ety of sources, frequently in different formats.

The study team will acquire emissions es timate data from Emissions Inventory Contractor, in the NIF 3.0 format for purposes of generating the emission inventory base year files necessary for Phase II of the VISTAS contr act. These data will be augmented with highway

mobile source data submitted to the VISTAS em ission inventory contractors from the VISTAS participating States.

Contacts with VISTAS’ emission inventory contractors and the U.S. EPA will be established and formal requests made for inve ntory corrections, updates and ancillary dataMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-5 pertinent to the modeling of emissions in their jurisdictions. Where feasible and consistent with project resources and schedule, these updated data sets will be acquired and will be used to create day-specific modeling inventories specific to the VISTAS domain for the base year episodes to be modeled.

5.2.2 Set-up of SMOKE Over the VISTAS Domain

SMOKE will be configured to generate point, area, nonroad, highway, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs will be maintained in separate source category files in order to allow maximum flexibility in producing alternate strategies. Settings for each of the source categori es are discussed in relevant sections below. With the exception of biogenic and highway mob ile source emissions that are generated using the, respectively, BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions will be processed using the month, day, and hour specif ic temporal profiles of the SMOKE model.

To produce an emissions inventory to support annual modeling, representative time periods will be selected and modeled. Area, nonroad, and point sources will be modeled as a block of Thursday, Friday, Saturday, Sunday, Monda y one per month (total of 60 days modeled). On-Road motor vehicles will be represented by a entire single week for each month. This selection criteria allows for the representation of day-of-the-week vari ability in the on-road motor vehicles, and models a representation of th e meteorological variability in each month. Holidays will be modeled as Sundays. A list of modeled holidays is provided in Table 5-2. The biogenic emissions will be modeled on a day specific basis (365 days).

Table 5-2. SMOKE Modeled Holidays.

Date Julian Day Holiday Description January 1, 2002 2002001 New Year's Day March 29, 2002 2002089 Good Friday May 27, 2002 2002147 Memorial Day July 4, 2002 2002185 July 4th September 2, 2002 2002245 Labor Day November 28, 2002 2002332 Thanksgiving Thurs November 29, 2002 2002333 Thanksgiving Fri December 24, 2002 2002358 Christmas Eve December 25, 2002 2002359 Christmas Day

Population will be used as a gridding defau lt for all source categories when the assigned surrogate would cause SMOKE to drop emissions . This can be a case when the county-level emission inventories are prepared using surroga tes other than those available for modeling

purposes.

The domain for the Phase II episode will be identical to the Phase I domain, which is based on the EPA’s 36-km national CMAQ domain, illu strated in Figure 5-1 below (details on the modeling domains are provided in Chapter 4).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-6 Figure 5-1. EPA 36-km National CMAQ Domain.

The parameters for the SMOKE runs are as follows:

Episodes : 2002 Calendar Base Year. Optional tasks for episodic modeling of up to 60 days that will likely be from 2003.

Future Years : To be determined.

Output Time Zone : Greenwich Mean Time (zone 0)

Projection : Lambert Conformal with Alpha=33, Be ta=45, Gamma=-97, and center at (- 97,40).

Domain :

• 36 Kilometer Grid: Origin at (-2736, - 2088) kilometers with 148 rows by 112 columns and 36-km square grid cells.

• 12 Kilometer Grid: Origin at (108, -1620) kilometers with 168 rows by 177 columns and 12-km square grid cells.

Layer Structure : The CMAQ layer structure will be 19 layers, with specific layer positions defined in the meteorology files (see Chapter 4).

CMAQ Model Species : The CMAQ initial configuration will be for the CB-IV chemical mechanism with PM. The model species in the emission input files will be: CO, NO,May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-7 NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO 3, POA, PSO4, and PMC.

Meteorology Data : Daily (25-hour). SMOKE requires th e following five types of MCIP outputs: (1) Grid cross 2-d, (2) Grid cross 3-d, (3) Met cross 2-d, (4) Met cross 3-d, and (5), Met dot 3-d. These files need to matc h the grid projection and overlap with the emissions modeling region but can be larger in the horizontal directions than the modeling region shown in Figure 1. Therefore, the data files for the 36 Kilometer grid domain will be at least 90 columns by 132 rows

Elevated Sources : All sources will be treated by SMOKE as potentially elevated. No plume-in-grid sources will be modeled. Wildfi re emissions will be handled as point

sources.

Producing 365 day-specific input files for a ll source categories pl aces a burden on available computing facilities, da ta management systems, and w ould adversely affect the Phase II schedule. Selecting representati ve model days for some or all of the source categories reduces the processing and file handling requirements to a more manageable level, and in most cases does not compromise the accuracy of the emissions files.

Other current or recent projects undertak en by EPA, WRAP and LADCo have used a selection approach for all of the source categorie s (except biogenics) that use a representative weekday/Saturday/Sunday either for each month or each season to model all of the emissions files. In an attempt to better represent the level of temporal and spatial de tail available for each source category, we have develope d a more detailed strategy.

Biogenic emissions will be modeled for each episode day, using the daily meteorology. Point sources, including CEM and fire emissions will be modeled for each episode day to take advantage of the available day-specific emissi ons and meteorology. Area sources, including

non-road mobile and dust emissions do not util ize meteorological data, and are temporally allocated by monthly, daily and hour ly profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved by selecti ng representative Thursday, Friday, Saturday, Sunday, and Monday profiles for each month.

Motor vehicle emissions are influenced by me teorological variabilit y, but the processing requirements for daily motor vehicle emissions were determined to be prohibitive under the current schedule. Rather than utilizing averag ed meteorological data or pre-calculated motor vehicle emissions, a single week per month was se lected for modeling. This week was selected from mid-month, to try to best represent the av erage temperature ranges for the month, and also adjusted to exclude holidays that would require atypical processing. The area source modeling

dates were also selected from these ra nges to simplify data handling procedures.

2002 On-Road Mobile Sources Represented by the Following Weeks : January 13-19 February 10-16

March 10-16 April14-20 May 12-18May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-8 June 9-15 July 14-20 August 11-17 September 15-21

October 13-19

November 10-16

December 15-21

5.2.3 Development of Point Source Emissions

Stack parameters are often more important to the reliability of the air quality modeling results than the emissions rates themselves. Stack parameter data are frequently incorrect, especially in some of the current regional modeling inventories and careful QA is required to assure that the point source emi ssions are properly located both hor izontally and vertically on the modeling grid. To screen for si mple, but potentially serious invent ory errors such as these, the study team has modified procedures originally developed by EPA to quality assure, augment, and where necessary, revise, stack parameters to examine the accuracy of the point source emissions, as well as standardize procedures to identify and correct stack data errors. These procedures will be implemented in the NIF to IDA conversion step of the inventory development. Additionally, SMOKE has a number of built-in QA procedures designed to catch missing or out-of-range stack parameters. Thes e procedures will also be invoked in the processing of the point source data.

For the Phase II initial base line modeling, we will be se parating the point source emissions into EGU and non-EGU categories. Th e non-EGU category will not be using any day or hour-specific emissions. All non-EGU point sour ce emissions will be te mporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allocation

factors. These factors will be based on the cros s-reference and profile data supplied with the SMOKE 2.0 version and will be supplemented with relevant data provided to the study team by

VISTAS or its contractors.

For EGU sources with EPA reported CEM data or with hourly emissions provided by stakeholders, actual hourly data will be used. For those s ources where EPA CEM data are utilized, NOx, SO2, and heat input-based hour-sp ecific profiles were developed and applied to NOx, SO2, and all other emissions , respectively. This ensured th at the annual emission values provided by the EI contractor were maintained, but distributed using hourly to annual profiles. For sources providing hour-specifi c data and where they were approved by the State in which they operated, those data were substituted fo r EPA CEM-based emissions and distributions.

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input

data were collected from the 2002 Continuous Em issions Monitoring (CEM ) datasets, and used to develop unit-level temporal distributions. The hour, day of week, and monthly specific temporal profiles will be used in conjunction with th e EI supplied emissions data to calculate hourly EGU emissions by unit.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-9 All point sources will be spatially allocated in the domain based on th e stationary source geographic coordinates. If a point source is missi ng its latitude/longitude coordinates, the source will be placed in the center of its respective county.

5.2.4 Development of Area and Non-Road Source Emissions

All area and non-road source emissions will be temporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allo cation factors. These

factors will be based on the cross-reference and profile data supplied with the SMOKE 2.0 beta version and will be supplemented with relevant data provided to the stu dy team by VISTAS or

its contractors. Area and non-road sources will be spatially allocated in the domain based on SCC-based spatial allocation factor files. If an area or non-road source SCC does not have an existing cross-reference profile assigned to it, the county-level emissions will be allocated by population density in the respective county.

A crustal PM transport factor will be applied to fugitive dust emission sources that have been identified in U.S. EPA modeling to have on ly a portion of its mass transportable from the source of the emission generation. The EPA’s studi es indicate that 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simulation. For th is reason, the VISTAS emissions contractor will apply county-specific fugitive dust emissions trans port factors to these sources to adjust PM emissions accordingly. The fugitive dust emissions adjustment is done by the VISTAS emissions contractor prior to receipt by the emissions and air quality modeling Team. Thus, no additional adjustment will be made in the emissions modeling. (for adjustment factors see: http://www.epa.gov/ttn/chief/emch /invent/statusfugdustemissions_082203.pdf ).

5.2.5 Development of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE will be used to develop the base year on-road mobile source emissions estimates for CO, NOx, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates w ill be combined with gridded, episode-specific temperature data to calculate th e gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link loca tion and subsequently summed to the grid cell level, the off-network em issions estimates are spatially allocated based on a combination of the FHWA version 2.0 high way networks and population. For the VISTAS 36/12 km modeling, no link based data will be us ed. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by th e meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:

• Hourly and daily minimum/maximum temperatures;

• Facility speeds;

• Locale-specific inspection/maintenance (I/M) control programs, if any;

• Adjustments for running losses;

• Splitting of evaporative and exhaust emi ssions into separate source categories;

• VMT, fleet turnover, and changes in fu el composition and Reid vapor pressure (RVP).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-10 The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and maintenance program in effect, type of oxygenated fuel program in effect, alternative vehicle mix pr ofiles, RVP of in-use fuel, operating mode) that

direct the calculation of the MOBILE6 emissions factors.

5.2.6 Development of Biogenic Source Emissions

A revised version of a commonly used biogenic emissions model, the Biogenic Emissions Inventory System (BEIS), has recen tly been developed and tested by EPA over two separate modeling domains/episodes. This vers ion of the model (BEIS-3, v0.9) contains several changes over BEIS-2, including the following:

• Vegetation input data -- are now base d on a 1-km Biogenic Emissions Landuse Database (BELD3) vegetation data base,

• Emission factors – many updates includi ng some recent NARSTO modifications,

• Environmental algorithm -- includes a sunl it/shaded leaf solar radiation model.

A series of sensitivity modeli ng simulations has been complete d and concluded that the more recent BEIS-3 methodology will impact base case model ozone predictions in most parts of the U.S. The preliminary tests have also shown that the newer biogenic emissions do not appear to have a large effect on: 1) the control signal response, 2) relative reduction factors resulting from a projected emissions change, or 3) overall regi onal model performance in the eastern U.S.

For this particular application of BEIS-3, version 0.9 as currently incorporated in the SMOKE processor will be used. This means that: 1) soil NO emissions shall be prepared without the input of specific soil moisture a nd precipitation data and 2) MEOH emissions will not be modeled explicitly. Otherwise, the mo deling should be identical to a BEIS-3 (v1.0) application.

The BELD-3 landuse data on a Lambert confor mal grid at 1-km resolution have already been developed, are available, and will be used to estimate biogenic emi ssions in this study. The BEIS model also requires as input hourly, gri dded temperature and solar radiation data to estimate biogenic emissions, and these data will be derived from the MM5 predictions.

5.2.7 Wildfires, Prescribed Burns, Wind Blown Dust and Sea

Salt Source Emissions

Wildfires and Prescribed Burns

Wildfire and prescribed burn emissions will be handled separately from the standard area source input files. The study team expects to receive monthly estimates of fire emissions from the EI contractor, which include burn acreage an d biomass loading information for the VISTAS states. Depending on the completeness and quality of the data received, attempts will be made to calculate spatial and temporal dist ributions of the fire emissions, rather than relying on standard distribution profiles. Also, the study team will a ttempt to calculate vertical distribution of the fire emissions, based on fire size and bi omass involvement. The SMOKE 2.0 can model fire plume rise if provided with the following variables:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-11 PTOP – Top of the fire plume profile (meters above ground level) PBOT – Bottom of the fire plume pr ofile (meters above ground level) Lay1 – The percent of the emissions en trained in the first modeling layer

The WRAP Fire Emissions Joint Forum Em issions Inventory Report (FEJF,2002) has documented an approach to calculating these plum e descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to ca lculate representative hourl y plume profiles. These profiles are then used by SMOKE2.0 to distribute the vertical em issions for the fires. To successfully model fires as elevated point source s, the data provided by the EI contractor will need to include both the day or da ys on which the fire occurs, and a spatial identifier of the fire location. At a minimum, a latitude and longitude of the fire location can be used, while a polygon coverage would be preferable.

In addition, wildfire and prescribed burn data, including emissions estimates and plume rise distributions, will be obtai ned from the WRAP contractor and used to supplement the inventory for the WRAP states.

Windblown Dust

PM 10 and PM 2.5 emissions from wind erosion of natural geogenic sources (SCCs 2730100000 [total] and 2730100001 [dust devils]) will be excluded from the resulting modeling files using a 100 percent reducti on in the control packets.

Sea Salt

CMAQ currently treats sea salt as an inert PM species. That is, the sea salt is not allowed to chemically interact with other species, such as producing particulate sodi um nitrate. There are plans to update CMAQ to have chemically active sea salt, but it is unclear whether such an update will occur during the Phase II modeling. Accordingly, the initial Phase II modeling will be conducted without any sea salt emissions. If CMAQ is updated to treat chemically active Sea Salt, or if CAMx is run using its full-science op tions, then Sea Salt emissions will be generated using appropriate procedures (e.g., as was done in Phase I).

5.2.8 Speciation and Reform atting of Emissions

SMOKE will be run to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM-IV , CB-IV or CB4). Note that VISTAS is evaluating whether the CB-IV or SAPRC99 photochemical mechanisms are more appropriate for the Phase II modeling and may switch to the SAPRC99 mechanism for the revised base case modeling that will be initiated in September 2004. The SMOKE model will also reformat the emissions estimates for use in CMAQ modeling. For each model-ready emissions inventory, SMOKE will produce at a minimum five (5) separa te air quality model-ready files: low-level point source, area source, elevated point source, mobile source, and biogenics. Other source categories, such as EGU and fire emissions may also be handled as separate air quality modelready files.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-12 5.2.9 Development of Modeling Inventories

The emissions inventories modeled for the VISTAS Phase II can be grouped into three distinct types: 1) 2002 annual i nventories, 2) Future Year epis odic inventories, using Phase I definitions, and 3) Alternate Base Year Episodic Inventories. In all cases, the Study Team expects to receive the emissions inventory data for the VISTAS states from the Emissions Inventory contractor, a dd non-VISTAS states and Canadian and Mexican data acquired from alternate EPA and/or RPO sources, and produce the CMAQ ready emissions files.

5.2.9.1 2002 Annual Inventories

The initial 2002 annual base inventory will be produced in two phases, coinciding with the delivery of the MM5 model results for the first 8 months and last 4 months of the 2002 calendar year. The initial inventor y will consist of all of the re quired source categories: area, nonroad, point, motor vehicle and biogenics.

A revised 2002 annual inventory will be modeled late r in the project , using updated emissions as supplied by the EI contractor. This may require re-production of all or some of the source categories. A “typical” inventory will be produced, based on the revised 2002, and replacing the revised actual emissions for EGUs and fires with “typical” EGU and fire emissions, as provided by the EI contractor. The Study Team will also produce up to 3 future year scenario annual runs, based on the 2002 meteorology. The composition of these future year runs is yet to

be determined.

5.2.9.2 Future Year Phase I Episodic Inventories

Utilizing emissions data provided by the EI contractors, the Study Team will model up to 2 alternate future-year scenar ios, using 2 of the Phase I episodes and the accompanying MM5 meteorological data: (1) January 2- January 21, 2002, and (2) July 7 to July 28, 2001. This may require re-modeling of all of the source categories, with th e exception of the biogenics.

5.2.9.3 Alternate Year Episodic Inventories

To capture visibility conditions that are not otherwise represented by 2002 conditions, we may perform episodic air quality modeling for up to 60 additional episode days selected from 2003 or another year. Emissions inventories for these additional days will be prepared by the Study Team and the requisite meteorological mode ling results would be provided prior to any modeling. The additional episode days will be modeled for a single base case inventory.

5.2.10 Products of the Emissions Inventory Development Process

In addition to the CMAQ-ready input files ge nerated for each hour of the days modeled in the Phase II annual run, a number of quality assura nce (QA) files will be prepared and used to check for gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distri bution of the emission provides insight into the quality and accuracy of the emissions inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-13 C

Visualizing the model-ready emissions with the scale of the plots set to a very low value, we can determine whether there are areas omitted from the raw inventory or if emissions sources are erroneously lo cated in water cells.

C

Spot-check the holiday emissions files to c onfirm that they are temporally allocated like Sundays.

C

Producing pie charts emission summaries that highlight the contribution of each emissions source component (e.g. nonroad mobile).

C

Normalizing the emissions by population for each state will illustrate where the inventories may be deficient and provide a reality check of the inventories.

C

Spot check vertical allocati on of point sources using PAVE. We will use state inventory summaries prepared prior to the emissions processing to compare against SMOKE output report totals generated after each major step of the emissions generation

process.

To check the chemical speci ation of the emissions to CB-IV terms and the vertical allocation of the emissions, we will compare re ports generated with SMOKE reports to target these specific areas of the processing. For spec iation, we will compare th e inventory import state totals versus the same state totals with the speciation matrix applied.

For checking the vertical allocation of the emissions, we will creat e reports by source, hour, and layer for randomly selected states in the domain. We will create these reports for a representative weekday in each of the episodes for each of these selected states.

The quantitative QA analyses of ten reveal significant deficienci es in the input data or the model setup. It may become necessary to tailo r these procedures to tr ack down the source of each major problem. As such, we can only outline the basic quantitative QA steps that we will perform in an attempt to reveal the underlying problems with the inve ntories or processing. Following are some of the reports that may be generated to review the processed emissions:

C

State and county totals from i nventory for each source category

C

State and county totals after spatia l allocation for each source category

C

State and county totals by day after tempor al allocation for each source category for representative days

C

State and county totals by model species after chemical specia tion for each source

category

C

State and county model-ready totals (after spatial allocation, te mporal allocation, and chemical speciation) for each source categ ory and for all source categories combined

C

If elevated source selection is chosen by user, the report indicati ng which sources have

been selected as elevated and plume-in-grid will be included.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-14 C

Totals by source category code (SCC) from the inventory for area, mobile, and point

sources

C

Totals by state and SCC from the inventory for area, mobile, and point sources

C

Totals by county and SCC from the invent ory for area, mobile, and point sources

C

Totals by SCC and spatial surrogates code for area and mobile sources

C

Totals by speciation profile code for area, mobile, and point sources

C

Totals by speciation profile code and SCC for area, mobile, and point sources

C

Totals by monthly temporal profile c ode for area, mobile, and point sources

C

Totals by monthly temporal profile code and SCC for area, mobile, and point sources

C

Totals by weekly temporal profile c ode for area, mobile, and point sources

C

Totals by weekly temporal profile code and SCC for area, mobile, and point sources

C

Totals by diurnal temporal profile c ode for area, mobile, and point sources

C

Totals by diurnal temporal profile code and SCC for area, mobile, and point sources

C PAVE plots of gridded inventory pollutants for all pollutant s for area, mobile, and point

sources

5.3 Quality Model Configuration and Input Preparation

5.3.1 CMAQ Science Configuration

This section described the model configurati on and science options to be used in the Phase II modeling effort. The recommendations ar e based on testing and model evaluations of several models or model configurations carried out in the VISTAS Phase I study. Table 5-3 summarizes the proposed configuration for CMAQ. The latest version of CMAQ is currently

Version 4.3 that was released October 2003. Howe ver, EPA is expected to release an updated version of CMAQ in mid 2004, and this version will likely be used in the Phase II modeling. In addition, we will perform simulations for at le ast January and July using the CMAQ 4.3 and the new versions of CMAQ to determine if the CMAQ updates substantially change the model predictions or performance.

In the CMAQ base configuration we will run both the 36 km and 12 km grids using oneway grid nesting where the boundary conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ BCON processor. The base configuration of CMAQ will use 19 vertical layers up to a region top of 100 mb (approximately 15 km AGL).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-15 The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion with a minimum eddy diffusion constant (Kz) of 0.1 m 2 /s. Note that the CMAQ default minimum Kz value is 1.0

m 2 /s. However, based on theoretical considera tions and the Phase I testing and evaluation a 0.1

m 2 /s minimum Kz values was selected for Phase II modeling.

The MCIP2.2 will be used to process the MM5 data using the “pass through” option.

The AERO3/ISORROPIA aerosol chemistry sche me will be used for inorganic aerosol thermodynamics. During the Phase I testing of the CMAQ model it was noted that the

AERO3/ISORROPIA aerosol modules failed to c onserve sulfur and nitrogen mass. A mass conservation patch developed by the Georgia In stitute of Technology was added to the model that renormalized the total sulfur, reactive nitroge n and reduced nitrogen species after the call to

the aerosol modules to conserve sulfur and nitrogen mass in the model. Sensitivity simulations were performed with and wit hout the mass conservation patch. Although the mass conservation patch did not affect model performance, the VISTAS science team believes that mass

conservation is a fundamental char acteristic of the real-world atmosphere so have retained the mass conservation patch for the Phase II mode ling. The SORGAM scheme, which includes a reversible thermal equilibrium, will be used for secondary organic aerosols.

Phase I testing included comparisons of the three gas phase chemistry mechanisms: the Carbon Bond Mechanism version 4 (CB4, Gery et al., 1989), a recent update of CB4 (CB4-2002, Jeffries et al., 2002), and the SAPRC99 (Carter, 1999). Although Phase I testing did not show notably better performance for any one mechan ism, the use of either CB4-2002 or SAPRC99 chemistry in the Phase II modeling is desirable because these mechanisms employ the most

recent available chemical kinetics data and are mo re scientifically defensible than the older CB4 mechanism. The computational cost of the SAPRC 99 chemistry is nearly twice that of the CB4. Since CB4-2002 is only implemented in the slower SMVGEAR chemistry solver, the computational penalty of using CB4-2002 chemistry over using CB4 with the EBI chemistry solver is even greater than that for SAPRC99. Given that the initial te sting of the CMAQ model

in the first half of 2004 will be more of a sh akedown of the modeling system, including the 2002 MM5 data and initial 2002 emission estimates, and the three chemical mechanisms produce similar model performance, then we propose to use the CB4 chemical mechanism for the initial 2002 base case modeling. In the mean time, under Task 7 of the Phase II work effort the sensitivity of the CMAQ model to emission re ductions using the CB4 and SAPRC99 chemistries will be evaluated using the January 2002 and July 2001 Phase I episodes to determine whether the two mechanisms have different responses. Based on this evaluation, and considerations of whether CB4-2002 has been implemented in CMAQ using the faster EBI chemistry solver, a decision will be made on the choice of the ga s-phase photochemical mechanism for the revised 2002 base case modeling to be initiated in September 2002.

Finally, recent environmental chamber expe riments at UCR suggest that both CB4-2002 and SAPRC99 may underestimate th e reactivity of arom atic compounds. UCR and others expect

to test new versions of CB4 and SAPRC chem istry during 2004 independently of the VISTAS work effort. Although updates in aromatic chemis try will primarily affect urban areas, it is desirable to perform Phase II modeling using th e most up-to-date version of the chemistry possible. We will report any pertinent findings or new chemical mechanisms to VISTAS as they

become available.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-16 Table 5-3. Proposed Phase II Model Configuration for the CMAQ. Model Option CMAQ

Model Version Version 4.4 (April 2004)

Horizontal Resolution 36/12 km No. Vertical Layers NZ = 19

Horizontal Advection PPM

Vertical Advection PPM

Horizontal Diffusion Spatially Varying

Vertical Diffusion K V (Eddy Diffusion) MM5 Configuration Pleim-Xiu/ACM MM5 Processing MCIP2.2 Pass Through Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver MEBI/Hertel Secondary Organic Aerosol SORGAM Aqueous-Phase Chemistry RADM Aerosol Chemistry AE3/ISORROPIA Dry Deposition Pleim-Xiu

Plume-in-Grid Off

Initial Concentrations CMAQ Default Boundary Conditions Initial: Seasonal 2001 GEOS-CHEM Revised: 3-Hourly 2002 GEOS-CHEM

Emissions NEI 1999 v2 CMU NH3 Adjustments

5.3.2 Spin-Up Initialization

For the 2002 annual CMAQ modeling, the model will be exercised separately for four quarters. The 2002 MM5 modeling started on D ecember 17, 2001 at 12Z. Thus, allowing for 12 hours of spin up of the MM5 model, CMAQ will be initialized at 00Z on December 18, 2001. This results in a 13 day spin up period for CMAQ and the first quarter run segment of 2002. For the other quarter run segments of 2002, CMAQ w ill be initialized with a 15 day spin up period.

5.3.3 Boundary Conditions

The Phase I modeling included comparisons of several schemes for producing boundary

conditions for the 36-km doma in, including the following:

• Fixed, default values at each edge, with ve rtical variation based on CMAQ default data.

• Clean boundary conditions.

• Seasonal mean output from the GEOS -CHEM model for a 2001 simulation

In addition, for the July 2001 episode we exam ined the following three boundary conditions:

• Fixed CMAQ default values;

• 3-hourly temporally and spatially vary ing BCs based on the 2001 GEOS-CHEM output

• Monthly average GEOS-CHEM output for July 2001May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-17 Sensitivity experiments showed th at model performance in the VI STAS states during conditions of high visibility impairment was relatively in sensitive to variations in the boundary conditions, although differences in concentrations did occur. However, it is likely that boundary conditions will have a larger effect on visibili ty for clean days and for future conditions as visibility begins to improve. Moreover, boundary conditions will have a more significant effect on natural background visibility. In fact the BCs had a la rger effect on the cl eaner January 2002 model estimates than the dirtier July 1999 and 2001 episodes. Therefore, as a long-term effort we recommend that boundary conditions for the 36 km continental US domain be obtained from a global model such as GEOS-CHEM. However, because global model results are not yet available for 2002, in the initial Phase II modeling we will use seasonal average diurnally varying data from a 2001 GEOS-CHEM data to provide boundary conditions for the 36 km

domain. If 2002 GEOS-CHEM results become av ailable, the BCs will be updated with high temporal resolution (e.g., 3-hourly) day-specif ic GEOS-CHEM output. Boundary conditions for the 12 km nest will be obtained from the CMAQ 36-km simulation.

5.3.4 Photolysis Rates

Several chemical reactions in the atmosphere are initiated by th e photodissociation of various trace gases. To accurately represent the complex chemical transformations in the atmosphere, accurate estimates of these photodisso ciation rates must be made. The Models-3 CMAQ system includes the JPROC processor, whic h calculates a table of clear-sky photolysis rates (or J-values) for a specific date. JPROC uses default values for to tal aerosol loading and provides the option to use default column O3 data or to use TOMS data for total column O3. Previously, in the Phase I modeling we used TO MS data, and we will continue to use TOMS data for Phase II. There are a few days in 2002 fo r which TOMS data is not available, and we will used default column O3 data or previous days data for those days.

JPROC produces a "look-up" tabl e provides the photolysis rate s as a function of latitude, altitude, and time (in terms of the number of hours of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six la titudinal bands (10o, 20o, 30o, 40o, 50o, and 60o N), se ven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), and hourly values up to " 8 hours of deviation from local noon. During model calculations, photolysis rates for each model grid cell are estimated by first interpolating the clear-sky photolysis rates from the look-up table using the grid cell latit ude, altitude, and hour angle, followed by applying a cloud correction factor.

The photolysis rates input file must be pr epared as separate look-up tables for each simulation day. The modeling team has already prep ared scripts to automate the production of photolysis rate files for each day of the annual s imulation. Photolysis files are ASCII files, and these will be visually checked for selected days to verify that p hotolysis are within the expected

ranges.

5.4 CAMx Model Inputs

If the optional task to apply the CAMx m odel to corroborative the CMAQ results were selected, then the model inputs would be prepared in a manner consistent with the CMAQ inputs. The same modeling domain and grid structure as used for the CMAQ modeling of 2002 wouldMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-18 be used for CAMx (see Chapter 4). Meteorol ogical inputs would be generated using the MM5CAMx processor using options similar to the MCIP “pass through” option and with a 0.1

m 2 /s minimum Kz. The CAMx initial and boun dary conditions (IC/BC) inputs would be generated from the CMAQ IC/BC inputs using the CMAQ-to-CAMx IC/BC processors. The CAMx emission inputs would be generated us ing the CMAQ-ready emission inputs and the CMAQ-to-CAMx emission processor.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-1 6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assuran ce procedures that will be used in the Phase II modeling. More details are provided in the VISTAS Phase II Quality Assurance Project Plan (QAPP) that is under preparation.

6.1 Quality Assurance Objectives

In December 2002, the USEPA publish exte nsive guidance on developing a Quality Assurance Project Plan (QAPP) for modeling st udies (EPA, 2002). The objective of a QAPP is to ensure that a modeling study is scientifically sound, robust, and defensible. The new EPA guidance suggests that a QAPP should include the following elements :

• a systematic planning process including 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 requir ing the highest level of quality assurance.

The QAPP also provides a valuable resource for project management. It can be used to document data sources and assumptions used in the modeling study, and it can be used to guide project personnel through the data processing and model applica tion process to ensure that

choices are consistent w ith the project objectives.

The modeling team has already developed QA documents and procedures in the Phase I

effort. For the Phase II effort these will be enhan ced and modified to assure that it conforms to the EPA QAPP guidance.

The guidance document also addresses mode l development, coding and selection of models, and model performance requirements. For the Phase II modeling we are using an existing EPA sponsored model. Thus, our QAPP will focus primarily on documenting data sources and QA of data processing performed by the model team. In addition, because no official EPA guidance currently exists for visibility mo del performance, a major objective of our QAPP will be to propose and define model performa nce evaluation procedures. QA objectives for specific aspects of the project are discussed below, and these will be incorporated into a QAPP that conforms to the EPA guida nce document for modeling studies.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-2 6.2 Emissions Model Inputs and Outputs

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical step in performing air quality modeling studies . Because emissions processing is tedious, time consuming and involves complex ma nipulation of many different type s 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.

As part of the VISTAS Phase II QA effort, an “Emissions Gatekeeper” function will be implemented similar to that used effectively in Phase I. The Study Team envisions the role of this Gatekeeper as one to perform quality assurance activ ities on the following emission inventory (EI) data:

(1) EI data obtained from the VISTAS emissions inventory contractors; and (2) The emission inventory to be used for m odeling outside of the St ates in the VISTAS region.

Specifically, the Emissions Gatekeeper will re view the content and format of the provided emission inventories ensuring an appropriate appr aisal of the emissions data and estimates for the VISTAS States. Other tasks will include any additional translation from mass emissions files into the emissions modeling input file struct ure necessary for mode ling. The Study Team will supplement these activities with QA checks on the intermediate and model output files using internal and public domain visua lization and diagnostic packages.

We propose to continue with multistep emi ssions the QA/QC approach applied in the Phase I modeling. This includes the initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA/QC 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 thre e separate groups involved in the QA/QC of the emissions is much more likely to catch any erro rs prior to the air qua lity model simulations.

6.2.1 Emissions Modeling QA/QC

EMS and EPA Input Screening Error Checking Algorithms : Although the SMOKE emissions model will be used for emissions processing, so me of the more advanced EMS input error checking algorithms will be used to screen the data and identify potential emission input errors. Additionally, EPA has issued a revised stack QA and augmentation procedures memorandum that will be used to identify and augment any outlying stacks.

SMOKE error messages : SMOKE provides various cautionary or warning messages during the emissions processing. We will redirect the SMOKE output to log files and review the log files for serious error messages. An archive of the lo g files will be maintained so that the error messages can be reviewed at a later date if necessary.

SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of processed emissions as daily totals according to species, source category and county and state boundaries. These summaries will then be compared withMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-3 summary data prepared for the pre-processe d emissions, e.g., state and county totals for emissions from the augmented emissions data.

6.2.2 QA of the Model-Ready Emissions Impacts

The goal of the post-processed emissions summary QA is to detect possible errors in the final, model-ready binary emissions files by pr eparing summary plots that characterize spatial and temporal patterns in the emi ssions data. This step is designed to catch errors that may be missed in the internal SMOKE QA procedures. We will use a QA/QC post-processing program that read the CMAQ-ready I/O API emissions file formats for each of the major source categories (mobile, area, point, biogenic , fire) and produce the following plots.

Spatial Summary : We will sum the emissions for all layers and for all 24 hours that is used to prepare a PAVE plot showing the daily total emissions spatial distribution. For a 20 day simulation this produces approximately 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simulations these plots will be presented as tons per day. The objective of this step is to identify errors in spatial distribution of emissions.

Vertical Profile : For point sources the emissions total fo r each layer will be summed and plotted

to show the vertical distributi on of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The objective of this st ep is to identify possible errors in vertical

distribution of emissions.

Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared that display the di urnal variation in total hourly emissions. The objective of this step is to identi fy 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 th e daily total emissions across the domain as a function of time. The objective of this step is to identify particular da ys for which emissions appear to be inconsistent with other days for no reason (e.g., not a weekend) and compare against the general trend.

Control Strategy Spatial Displays : Spatial summary plots of the da ily total emissions differences between a control strategy and base case emission s scenarios will be generated. These plots can be used to immediately identify a problem in a control strategy. Fo r example, if a VISTAS states

SO 2 control strategy is being analyzed and there ar e changes in emissions for other pollutants or

for SO 2 outside of the VISTAS states problems in em issions processing can be identified prior to the air quality model simulation.

6.3 Meteorological Model Outputs

As part of the VISTAS Phase II QA effort, a “Meteorological Gatekeeper” function will be implemented similar to that used effectivel y in Phase I (Tesche and McNally, 2002a,b,c). The task of the Gatekeeper is to provide an i ndependent review and qua lity assurance of the meteorological modeling and related data sets developed by the VISTAS meteorological modeling contractor (BAMS) and used subsequently by the emi ssions and air quality modeling teams. This Gatekeeper QA review serves two sp ecific purposes: (a) to en sure that any potentialMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-4 problems with the data sets (should they exist) are identified and corrected in a timely manner, and (b) to provide the study team with information to support ongoing CMAQ model performance testing and sensitivity analyses. In the case of meteorology, the Gatekeeper’s independent QA analysis of the MM5 meteorological data sets serves to provide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model performance and sensitivity analyses.

In addition to having personal responsibility for the quality and chain of custody of the meteorological data sets supplie d by other VISTAS contractors, the Meteorological Gatekeeper will be responsible for ensuring and maintaining th e integrity of the data files uploaded to the project website. This website, hosted by UCR, serves as the repository of data for the ENVIRON/UCR/Alpine m odeling centers and for the VISTAS TAWG participants. In performing the Gatekeeper quality assurance activ ity, one of the first steps is to conduct an independent operational evaluation on the MM5 model results at 36 km and 12 km grid scale.

This evaluation covers surface and aloft wind dir ection, temperature, mixing ratio, precipitation, and planetary boundary layer (PBL) depths on a c ontinental scale (36 km) and subregional scale (12 km) basis. The specific techniques to be used are described in the MM5 model performance protocol prepared for EPA for annual modeling (M cNally and Tesche, 2002). The Gatekeeper will also perform supplemental, ad hoc analys is of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to exercise the Meteorological Chemistry Interface Processor (MCIP) version 2.2 is to read the MM5 outputs from BAMS and produce binary input files for the CMAQ Chemical Transport Model (CCTM) to provide the complete set of parameters necessary in the emissions processing and air quality modeling.

In summary, the quality assurance plan fo r the meteorological data will include the following elements:

3⁄4 Upon receiving the MM5 and MCIP 2.2 output files from BAMS, we will verify the integrity of the file transfer (e.g., no missing and/or corrupted files);

3⁄4 Since the CMAQ modeling domain is a subset of the MM5 domain, we will verify that the modeling domain and vertical layer st ructures in the MCIP files are identical to the CMAQ modeling domain;

3⁄4 We will select several days of the MM5 output and reprocess the MM5 files with MCIP v2.2 using the predetermined MCIP op tions. We will then compare the MCIP files with those provided by BA MS to verify that we obtain identical results from the MCIP processing.

3⁄4 We will create horizontal and vertical plot s of temperature, pressure, precipitation, modeled flow patterns, PBL heights, etc. to assess whether the MC IP output fields are reasonable;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-5 3⁄4 The VISTAS 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the WRAP 2002 MM5 simulation as an independent QA and evaluation of the database.

3⁄4 We will make the plots available on the VISTAS website for viewing and download.

6.4 Air Quality Model Inputs and Outputs

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 m odeling system, where these include the MCIP, JPROC, ICON, BCON 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. The majo r QA issue specifically associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the name of key science options or inputs are included in the name of CMAQ executable program, in the name of the CMAQ output files, and in the name of the directory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files. For example, if a model simulation is performed using the CB4 mech anism, all compile and run scripts contain the variable definition “$MECH = CB4”, and this vari able is hard coded into the script for the executable name, the output file name, and the ou tput directory name. This procedure produces long file/directory names but it eff ectively prevents mistakes or ma kes mistakes readily apparent if they do occur.

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. For example, if we perform simula tion with the SAPRC mechanism, instead of editing the original scripts to specify “$MECH = SAPRC” we will creat e a parallel directory structure with a new set of scripts to perform the SAPRC simulations. This provides a pe rmanent archive of the scripts that were used in performing model simulations . In addition, output from the model simulationMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-6 will be directed to a log file th at provides a record of input file names, warning messages etc that

will be archived.

We will also perform a post-processing QA of the CMAQ output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions inputs can be di scovered 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 quick ly comparing various

model simulations.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-1 7.0 MODEL PERFORMANCE EVALUATION

7.1 OVERVIEW

As noted in Chapter 1, Appendix A presents a summary of the Scope of Work (SOW) for the VISTAS Phase II Emissions and Air Quality Modeli ng activities. This Phase II SOW delineates the tasks, approach and schedule for carrying out the various technical activities described in greater detail in this modeling protocol. The SOW in A ppendix A is broken down into a base effort that focuses on annual modeling of 2002 and several opti onal tasks to be evaluated by VISTAS. The ultimate objective of the Phase II work effort in 2004 is to obtain a properly evaluated operational emissions and air quality modeling system for the 2002 annual calendar year so that control strategy evaluation can begin in 2005. Given the formal cont ractual role that the SOW plays in guiding the work products of the Emissions and Air Quality Modeling Team in Phase II, where ambiguities might arise regarding the Task 4a/4b model eval uation effort, the SOW takes precedence over this Protocol Document. Consistent with the spir it of a modeling protocol for regulatory decisionmaking, this document lays out the ‘roadmap’ for achieving an adequately tested modeling system for regulatory usage. But, obviously, this does not m ean that every analysis identified in this chapter will be carried out or is indeed even possible gi ven the Phase II schedule and resources, the existing aerometric data bases, and present technology c onstraints. The roadmap guides the way to the

desired destination – in this cas e, an evaluated, operational PM/regional haze modeling system – but does not commit the driver to exploring every side street and back country road along the way. Indeed, one expectation of the VISTAS TAWG is a close working relationship with the modeling

team to ensure that the available resources and sc hedule are applied most efficiently in reaching the aforementioned goal.

This chapter describes a range of model testing methodologies potentially available to the Emissions and Air Quality Modeling study team in its efforts to adequately ev aluate the performance of the CMAQ air quality modeling system for th e 2002 annual period. Since one cannot know at this juncture the specific performance problem s that may arise in the initial 2002 CMAQ testing (Task 4a) or the final year 2002 evaluation (Task 4b), we set forth in this chapter a broad range of methods and techniques that may be brought to bear in examini ng CMAQ model performance. We identify the core operational evaluation procedures , recommended in EPA (2001) guidance, that will be performed in Tasks 4a and 4b. We also descri be a broad range of additional performance testing methods that may be worth considering, if necessa ry. Implementation of one or several of these various techniques would fall under Optional Task 14 (Enhanced Model Performance Evaluation). The Optional Task 14 represents a means for ma rshalling additional VISTAS funding, or perhaps supplemental funding from other stakeholder gr oups, for the purpose of enhancing the core operational evaluation presently budgeted for Tasks 4a and 4b. Should portions or all of Optional Task 14 be invoked, we strongly recommend that this be guided by a cooperative effort by the modeling team and the TAWG to ensure that th e highest priority “supplemental evaluations” be selected and properly carried out.

Clearly, not all of the supplemental evaluative techniques identified in this chapter will ultimately be performed. There are three reasons for this:

3⁄4 The Phase II SOW places clear limits on the resources available to perform model evaluation analyses. Accordingly, some ev aluation steps, while desirable, simplyMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-2 may not be possible given current funding levels;

3⁄4 The Phase II SOW places stringent schedul e demands on the model evaluation. A number of the model performance evaluation methods introduced in this chapter (e.g. Weight of Evidence analyses, diagnostic testing with individual measurement networks, PM indicator species and ratios analyses, use of the alternative CAMx model) could very likely require more time to carry out given their quasi-research

nature. Since VISTAS Phase II is not a m odel research and development effort, but rather an operational evaluation of ex isting modeling systems for regulatory decision-making, some interesting, but time consuming analyses simply may not be possible given the present schedule; and

3⁄4 To conform to the EPA PM guidance docum ents requirements for PM model testing, it may not be necessary to conduct many of the diagnostic and Weight of Evidence tests identified in this protocol. Indee d, an adequate evaluation of the Phase II modeling system may be possible through st raightforward application of the core operational performance evaluation pro cedures identified in EPA’s 2001 draft guidance.

At a minimum, the evaluation of the CMAQ modeling system for the Phase II annual 2002 simulation (and for any additional episodes that might be chosen by VISTAS) w ill be consistent with EPA’s draft guidance on PM model testing. This guidance essentially calls for an operational evaluation of the model focusing on a specific set of gas phase and aerosol chemical species and a suite of statistical metrics for quantifying model response over the annual cycle. The emphasis is on assessing: (a) How accurately the model predicts observed concentrations? and, (b) How accurately does the model predict responses of predicted air quality to changes in inputs? States are encouraged to utilize the evaluation procedures set forth in the earlier 1991 guidance document (EPA, 1991) for gas phase species and the newer ( 2001) guidance of PM species. Thus, in carrying out the initial Task 4a operational evaluation and the subsequent Task 4b final evaluation, we will implement the suggested EPA performance testing methodologies for the key gas phase and aerosol species. Since these methods are explicitly presente d in EPA’s guidance docum ent, there is no need to repeat them here.

Subject to the availability of time and Opti onal Task 14 resources, the Phase II evaluation will also attempt to employ other testing methods beyond those in the EPA guidance document. In addition, if the initial Task 4a evaluation evolves smoothly without problems arising in the early performance testing efforts, resources and/or time c onserved in such a situation might then be used to underwrite on or more scientifically rigorous model performance investigations (e.g., those afforded by the rich observational networks such as the continuous speciation SEARCH measurements and the EPA Supersite data). To this end, we discuss in this chapter various supplemental performance testing methods that mi ght be worth pursuing gi ven adequate, additional

resources and time.

We conclude by again emphasizing that most important goal of the Phase II CMAQ evaluation is to determine whether the aggregate m odeling system (model codes plus input data sets and observational data for testing) offers suffi ciently reliable and accurate results that public decision-makers may have reasonable confidence in using the model to help choose betweenMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-3 alternative regional haze reduction scenarios. If the CMAQ model evaluation, as outlined in this chapter, provides sufficient evidence that the modeling system is operating reliably and in conformance with measurements and scientific exp ectations, then specific justifications explaining why the model is acceptable for developing regiona l haze strategies will be offered in the 2002 CMAQ Modeling Summary Report. Conversely, should the evaluation determine that the modeling system suffers from important flaws or errors that undermine its reliability or use, these findings will also be documented, together with recommendations regarding the use of alternate methods, steps to improve the model and/or data base, or other approaches.

7.2 Context for the VISTAS Phase II Model Evaluation

We begin the discussion of the Phase II modeling evaluation met hodology by reviewing how the CMAQ model output is used in regional haze applications to project changes in visibility 1

. When designing a model performance evaluation, it is important to understand how the modeling results will ultimately be used. EPA has published tw o versions of draft guidance for fine particulate and regional haze modeling (EPA, 2000; 2001), utilizi ng a Fine Particulate Guidance Workgroup to provide technical input in the development of both documents 2 . More recently, EPA has provided an informal update on the PM/regional haze m odeling guidance (Timin, 2002) and conducted a PM model evaluation workshop (see, for example, Timin, 2004; Boylan, 2004) shedding additional light on what the final guidance document might contain.

A key concept in EPA’s guidance for addressing regional haze issues is that the modeling

results should be used in a relative sense to scal e or roll back the observed particulate matter (PM) concentrations from which light extinction is estimated. Adopting the recommendations from the Federal Land Managers’ Air Quality Related Values Workgroup (FLAG), estimates of visibility are obtained from observed fine particulate concentr ations using the following IMPROVE reconstructed mass extinction equation (FLAG, 2000):

b ext = 3{f(RH)[(NH 4 ) 2 SO 4 ]} + 3{f(RH)[NH 4 NO 3 ]} + 4{f’(RH)[OC]} + 10[EC] + 1[IP] + 0.6[CM] + b rayleigh

where:

b ext is the estimated extinction coefficient (Mm -1 ); [SO 4 ] is the sulfate concentration assumed to be ammonium sulfate; [NO 3 ] is the particulate nitrate concentration assumed to be ammonium nitrate; [OC] is the organic carbon concentration; [EC] is the elemental carbon concentration;

1 Details on the definitions of model performan ce evaluation statistical measures and graphical displays in three reports to VISTAS: (a) the Ph ase I Task 2 model configuration report (ENVIRON, 2003b), (b) the Task 4a/b model sensitivity a nd CMAQ configuration report (ENVIRON, 2003d), and (c) the Final Phase I Evaluation and Model Sensitivity Analysis report (ENVIRON, 2004). Additional details are also presented in Table 7-4.

2 Members of the VISTAS modeling team participat ed on this work group over the two-year span of

its activities.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-4 [IP] is the inorganic primary fine particulate (< 2.5 : ) concentration excluding primary sulfates and nitrates; [CM] is the coarse particulate (> 2.5 : and < 10 : ) concentration;

b rayleigh is the light-scattering due to Rayleigh scattering (assumed to be 10 Mm -1 ); f(RH) is a relative humidity adjustment factor for the sulfate and nitrates; and f’(RH) is a relative humidity adjustment factor for OC that is assumed to be 1.0.

The relative humidity (RH) values used in th e above equation are monthly- specific and Class I area-specific values based on a long term average (EPA, 2003a).

The regional haze rule expresses reasonable progress in terms of changes in the deciview ( ) dV) from the current to future year conditions. Deciview (dV) is expressed as the natural logarithm of the extinction coefficient (b ext ) to Rayleigh scattering:

Deciview = 10 ln(b ext /10)

) dV = 10 ln[(b ext ) future / (b ext ) base ]

In this framework, changes in visibility in te rms of percentage changes in extinction over a natural “clean” background visibility rather than ch anges in deciview. Changes in deciview or extinction are essentially mathematically iden tical with a 1% change in deciview being approximately equivalent to a 10% change in extinction. Both visibility parameters will be calculated in the Phase II model applications.

The VISTAS Phase II model testing will concen trate on an operational evaluation of those model predictions that are most necessary for estimating visibility (i.e., NH 4, SO 4, NO 3, OC, EC, IP and CM). Where feasible and supported by sufficien t measurement data, we will also evaluate the modeling system for its ability to accurately estimate gas-phase oxidant and precursor/product species since correct, unbiased simulation of gas- phase photochemistry is a necessary element of reliable regional haze predictions. This evalua tion will be carried out across the full VISTAS domain for the entire year and also on subdomai ns and month-by-month basis to help build confidence that the modeling system is operating corr ectly. With this context in mind, we next turn to the philosophy of the model evaluation process.

7.3 Multi-Layered Model Testing Process

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) affirms the recommendations of numerous modeling scientists over the past decade (see, for example, Dennis et al., 1990; Tesche et al., 1990, 1994; Seigneur et al., 1998, 2000; Russell and Dennis, 2000; Ar nold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a comprehensive, multi-layered approach to m odel performance testing should be performed, consisting of the four components: operational, diagnostic, mechanistic (or scientific) and probabilistic. As applied to regional PM/visibility models, this multi-layered framework may be viewed conceptually as follows:

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

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-5 used to characterize visibility (i.e., sulfate, nitrate, ammonium, or ganic carbon, elemental carbon, PM 2.5 , and PM 10 ). This evaluation examines whether the measurements are properly represented by the model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”;

> Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 ) and associated oxidants (e.g., ozone and nitric acid) ; PM size distribution; temporal variation; spatial variation; mass fluxes; and component s of light extinction (i.e., scattering and absorption);

> Mechanistic Evaluation : Tests the ability of the model to predict the response of PM and visibility to changes in variables such as emissions and meteorology; and

> Probabilistic Evaluation: Takes into account the uncertainties associated with the model predictions and observations of PM and visibility.

Within the constraints of the Phase II schedule a nd budget resources the VISTAS Phase II effort will attempt to include elements of each of these co mponents. The operational evaluation will obviously receive the greatest attention since this is the primarily thrust of EPA’s 2001 PM guidance. However, we will consider, where feasible and appropriate, diagnostic and mechanistic tests (e.g., use of probing tools, indicator species and ra tios, aloft model evaluations, urban vs. rural performance analyses), traditional sensitivity simu lations to explore uncertainty, and the use of alternative science platforms (e.g., CAMx). The scope of these additional diagnostic and mechanistic tests will be shaped by the resources available in Optional Task 14 and the timing of when such analyses are commissioned relative to the Phase II schedule and resources.

Before discussing the types of testing pro cedures available for the above evaluation components, we first identify the surface and aloft data sets that are available to support these comparisons.

7.4 Development of Consistent Evaluation Data Sets

7.4.1 Surface Measurements

The ground-level model evaluation database w ill be developed using several routine and research-grade databases. The first is the routine gas-phase concentrati on measurements for ozone, NO, NO 2 and CO archived in EPA’s Aerometric Information Retrieval System (AIRS/AQS)

database. Other sources of information come from the various PM monitoring networks in the U.S., with particular emphasis in the central and easte rn U.S. These include the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends Network (CASTNET), (c) Southeastern Aerosol Re search and Characterization (SEARCH), (d) EPA

PM 2.5 and PM 10 Mass Networks (EPA-FRM), (e) EPA Sp eciation Trends Network (STN); (f) National Acid Deposition Network (NADP) and (g) EPA Supersites (EPA-SPEC) networks. Typically, these networks provide ozone, other gas phase precursors and product species, PM, and visibility measurements. Noteworthy is the continuous speciated PM 2.5 measurements availableMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-6 from the SEARCH network for 2002 modeling period. For 2003 episode modeling, additional continuous PM speciated data will be available from the FOCUS network.

As an example, the IMPROVE network give s daily (24-hour) average mass concentrations every 3 days for SO 4 , NO 3 , organic carbon (OC), elemental carbon (EC), soil (IP), CM, PM 2.5 and

PM 10 . These data are available at approximately 38 sites in the VISTAS 12 km domain. In addition, hourly values of light extinction and deciview are av ailable at several of th ese sites. The SEARCH network provides 24-hour as well as continuous (hourly) speciated measurements of PM 2.5 components and other specifics from 8 stations, depending on the time period (Hansen et al., 2003).

We will use data from these and the other observa tional databases listed in Table 7-1, supplemented with the routine AIRS/AQS data, as appropriate, for CMAQ model performance testing.

Care must be taken in selecting data that are representative of regional concentrations and the prediction of regional haze at the sensitive Class I areas within the VISTAS (see Table 7-3). For example, the criteria and other pollutants in the AIRS/AQS database are t ypically urban-oriented. Thus, would not expect CMAQ predictions for thes e species using a 36/12 km grid mesh to be ideally suited to simulating urban-scale concentra tions. With other data sets (e.g., SEARCH) it may be possible to reproduce the urban-to-rural gradients in PM 2.5 components. While finer grid meshes (e.g., 4 km) might be more appropr iate, high-resolution modeling at 4 km scale on an annual time frame for the VISTAS region is prohibitive given current computer technology and model run times. Accordingly, some selectivity is needed in assembling pertinent measurement sites for comparisons with regional-scale model predictions and the interpretation of the modeled and measured comparisons must be made with care.

Another important consideration is that diffe rent PM monitoring networks may use different measurement approaches that “measure” different amount s of the same species th at are also different from the modeled species. For example, 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. As was done in Phase I, the model will be evaluated separately for each networ k. While Phase II is clearly not aimed at new model development or algorithm refinement, information discerned from model performance across

the various networks should be useful in late r model refinement activities and network design improvements. Finally, the mapping of the modeled species to the monitored data will also have to be performed in a consistent fashion.

The VISTAS Phase I air quality data asse ssment report (ENVIRON, 2003c) provides more details on the ambient monitoring data available for Phase II modeling.

7.4.2 Aloft Measurements

In recent years, the use of instrument aircraft in support of regulatory monitoring and research programs has become much more commonplace. Ind eed, in the upper Midwest, the Lake Michigan Air Directors Consortium (LADCo) has been centr ally involved in aircraft programs to support model development and applications studies for seventeen (17) years, beginning with pioneering flights in 1987. Supplementing the long-term sampling performed by LADCo in the Midwest, there

have been other occasional intensive airborne sampling campaigns throughout the easter n U.S. (e.g., the 1999 SOS field program which provided aloft data for our evaluation of CMAQ for the July ’99 episode), that have produced very useful inform ation for air quality model performance testing.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-7 Fortunately, during CY-2002, there were at least tw o mature airborne field programs underway in the eastern U.S. One was centered over the Midwest, the other on the mid-Atlantic coast. A brief characterization of these potentially valuable CMAQ m odel evaluation data sets is given here. Note that the advanced modeling evaluation using non-r outine data sets like aircraft data, would be covered under Optional Task 14 (see Appendix A).

During 2002, the Wisconsin Department of Natural Resources (WDNR) and the Midwest RPO (MRPO) (who funded the Jacko aircraft) co llaborated on the support of airborne sampling using two aircraft that, along with ground-ba sed measurements, provided a 3-dimensional representation of air pollution concentrations acr oss the upper Midwest with some flight paths extending south to include the Mammoth Cave, KY and Dolly Sods, WV Class I areas in the VISTAS domain. The goal of the WDNR/MRPO flights was to collect aloft air quality and meteorological data to support model evaluation and da ta analyses. The aircraft flights were aimed at: (1) characterizing high fine particle and ozone episodes, (2) characterizing air quality over the Class I areas in the upper Midwest (Isle Royale National Park and Seney National Wildlife Refuge in northern Michigan) on both clean and hazy da ys, and (3) characterizing urban areas in the

Midwest.

As indicated in Table 7-2, airborne sampli ng was performed over a broad region of the Midwest (including portions of the VISTAS states) from 1 June to 22 November. Lasting 3-5 hours, the WDNR and Jacko aircraft sampled a variety of aerometric parameters (depending upon the flight and aircraft) including wind speed, wind directi on temperature, dew point, relative humidity, pressure, O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg, SO 4 , OC, EC,

PM 2.5 , and light scattering (Neph). Still photographs documenting visibility were also collected. Presently, the full WDNR/MRPO aircraft database , from the first flights in 1987 to the recent sampling in 2003 is being aggregated into a master data base archive.

At the University of Maryland, research ers have been using ground-based monitors, radiosondes, profilers, and instrumented aircra ft to make observations each year since 1992. Parameters measured included meteorology; selected trace gases; fine particulate chemistry, microphysics and optical properties across broad regions of the middle Atlantic coast. During 2002, the University Research Foundation’s Aztec- F aircraft instrument suite included O 3 , NO, CO, SO 2 samplers, as well as a NO 2 closed-path tunable diode laser sy stem, and a differential GPS-based meteorology (T, RH) and horizontal wind ( u and v horizontal components) data system. Aztec-F flights were made from 23 May to 3 October, typically lasting 3 hours.

The volume of aircraft information availabl e for CMAQ performance testing during 2002 is quite significant and, as discussed later, this data set will be used as extensively as possible. Historically, aircraft data sets have been used only sporadically in evaluating model performance aloft. To our knowledge, the VI STA Phase II evaluation under optional Task 14 could represent the most thorough utilization of aircraft data fo r regional model ozone/PM model evaluation yet attempted.

7.5 Model Evaluation Tools

This section introduces the various statistical m easures, graphical tools, and related analytical procedures that have proven useful over the year s in evaluating grid-based chemical transportMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-8 models. Many of the methodologies mentioned belo w were utilized to one degree or another in Phase I. Where appropriate, they will also be us ed in the 2002 annual and ep isodic evaluation of the CMAQ modeling system. However, as with Phase I, while we plan on calculating a rich variety of statistical performance metrics, only a very limited subset of these measures will actually be relied upon to form judgments concerning model acceptability and in the final reporting.

The current methodologies and statistical me trics may be augmented, as necessary and appropriate, by new measures that may become available during the course of the Phase II modeling. Definitions of the statistical measures for gas-phase species such as ozone are provided in the Phase I Task 4 report (ENVIRON, 2003d); measures geared more toward fine particulate aerosol species are identified later in this chapter. In sum, th e Phase II evaluation will employ similar methods and

evaluation tools used in Phase I but the evaluation will remain open to emerge nt methods for testing model performance where necessary and consistent with the project schedule and resources.

7.5.1 Statistical Performance Metrics

EPA’s 2001 PM and regional haze guidance suggests a suite of metrics for use in evaluating model performance. The standard set of statis tical performance measures suggested by EPA for evaluating fine particulate models include: (a) normalized bias; (b) normalized gross (unsigned) error; (c) fractional bias; (d) fractional gross error; and (e) fractional bias in standard deviations.

These measures are subsumed within the list of me trics that were calculated on a routine basis in Phase I using the UCR analysis package. (These are identified in Table 7- 4). The UCR evaluation software will generate these statistical measures for each model simulation performed for each analysis region (see below). In parallel, the AG MAPS software will be used to generate complimentary statistical measures which combine the methods recommended in the EPA ozone guidance (EPA, 1991; 1999) and the newer 2001 PM guidance. From past regional PM model

evaluations we have found the fractional bias and fractional error to be the most useful summary measures and we will focus mainly upon them in Phase II, but not to the exclusion of others that are found to yield discriminating power. For oz one and other gas pha se species (NO, NO 2 , SO 2 ) we will

include use the traditional statistical measures (Tesche et al., 1990; EPA, 1991, 1999) develop with MAPS as an adjunct to the UCR analysis measures.

Typically, the statistical metrics are calcula ted at each monitoring site across the full computational domain for all simulation days. In the Phase II CMAQ evaluation, we will stratify the performance statistics across relevant space and time scales. As part of the Task 4a/b operational evaluation, the gas-phase and aerosol statistical m easures shown in Table 7-4 will be computed for the full 36 km and 12 km domains, as well as fo r the individual RPOs (including VISTAS of course) and on other subdomains as appropriate. Temporally, we will compute the statistical measures for the appropriate averaging times: 1 hr for ozone, and gas-phase precursors such as NO,

NO 2 , CO, SO 2 ; 8-hr for ozone, and 24 hr for sulfate, nitr ate, PM and other aerosol species. These results will then be averaged over annual, m onthly, and seasonal periods for display, further analysis, and reporting. Should it become necessary as part of model performance diagnosis, we will consider aggregating the statistics in othe r ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipitation vs. non-precipitation da ys, (d) month of the year, and (e) the 20% haziest/cleanest days, in order to help elucidate model performance problems. Absent performance difficulties, these supplemental time/space analyses would only be considered if supported under directed Optional Task 14 funding. In subregional performance testing, the focus would likely be onMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-9 the Class I areas and sites where enhanced m onitoring (EPA STN locations, the hourly/daily SEARCH sites) within the VISTAS 12 km domain (Hansen et al., 2003) is available.

As part of the operational evaluation, the metric s defined in Table 7-4 will be calculated for each gas phase species and each fine particulate species in the extinction equation as well as separately for SO 4 , NO 3 and ammonium (NH 4 ) on both the 36 km and 12 km domains. In any diagnostic evaluations that are performed, we will examine the model’s ability to estimate the gaseous species listed above from EPA’s guidan ce (EPA, 2001). However, in reality ambient gaseous species in 2002 are principally available for ozone, NO 2 , SO 2 , and CO. Since most of the gaseous air quality monitors are located in urban settings, these data sets will be of somewhat restricted value in evaluating the 36/12 km regi onal-scale CMAQ predictions. Under Optional Task 14, we would consider the merits of assembling a rural-oriented gaseous species observational model performance evaluation database for use w ith CMAQ. This database could be composited with measurement from rural sites from the AQS, CASTNet, NPS, STN, and SEARCH networks. If commissioned under Optional Task 14 by VISTAS, this rural vs. urban scale operational evaluation would be conducted to augment the more traditional operational evaluation usi ng all valid data sets.

7.5.2 Graphical Representations

The Task 4a/b core operational air quality model evaluation will utilize numerous graphical displays to facilitate quantitative and qualitativ e comparisons between CMAQ predictions and measurements, many of which were used in the Phase I Final Report (ENVIRON, 2004). Together with the statistical metrics listed in Table 7-4, th e graphical procedures are intended to help: (a) identify obviously flawed model simulations, (b ) guide the implementation of Task 4a/4b performance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ simulations. These graphical tools are intended to depict the model’s ability to predict the observed fine particulate and gaseous species concentrations.

The Phase I modeling helped to refine the su ite of graphical tools most effectively in assessing model performance and the differences between the baseline CMAQ runs and sensitivity experiments. The core graphical displays to be considered for use in Phas e II include the following:

> Spatial mean concentration time series plots; > Time series plots at monitoring locations; > Ground-level gas-phase and particulate concentration maps (i.e., tile plots); > Concentration scatterplots stratified by station, by time, and by network; > Bias and error stratified by concentration; > Bias and error stratified by time; > Histogram plots of the statistical metric s, stratified by day, by pollutant, by subregion (e.g., 12 km vs. 36 km, by RPO), and by monitoring network; and > Quantile-Quantile (Q-Q) plots.

These graphical displays will be generated, were a ppropriate for the full annual cycle as well as for monthly and seasonal periods. The displays will be generated with a consistent suite of products including the UCR analysis tools, Alpine MAPS software and ENVIRON evaluation software.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-10May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-11 7.5.3 MAPS/Flying Data Grabber Routines

This section describes the procedures we w ould implement for the aloft gas phase and PM model evaluation with CMAQ using aircraft da ta should Optional Task 14 be commissioned by VISTAS. This aloft performance evaluation would employ aircraft data sets from various sampling programs carried out over the Midwest and eastern U.S. during 2002. Details on how these data are used to evaluate CMAQ performance aloft together with findings from our aloft model evaluations with the 10-21 July 1999 episode are described in the Phase I Final Report (ENVIRON, 2004).

The principal challenge in using the aircraft data for meteorological and photochemical

model evaluations relates to the ‘incommensurab ility’ of Lagrangian aircraft observations with Eulerian (i.e., fixed location) volume-averaged model estimates (see, for example, Hanna, 1994). Aircraft data are essentially continuous, high fr equency Lagrangian samples having response times

on the order of 30 seconds or less. In contra st, CMAQ model estimates represent hourly-averaged values. Thus, the aircraft data must be averaged in some manner to yield quantities that are at least qualitatively comparable to the air quality fields estimated by CMAQ in the grid volume(s) through which the aircraft passes. The objective is to de velop hourly-average time series of measurements and model estimates that are as nearly comparable as possible.

The procedures proposed for processing the al oft meteorological and air quality observations and CMAQ model predictions have been described in several science reports prepared in connection with the Lake Michigan Ozone Study (Tesche and McNally, 1993a-d, 2001) and more recently the Houston-Galveston 1-hr ozone SIP modeling that utilized the TexAQS 2000 data base (Tesche and Jeffries, 2002). These methods, formalized within AG’s Flying Data Grabber (FDG ) model, were employed in Phase I for the 13-21 July 1999 episode. The methods used are substantial extensions of the techniques pioneered a decade ago by Sche re and Wayland (1989) for the Regional Oxidant Model (ROM2.0) evaluation agains t the NEROS database and by Barchet and Dennis (1990) for the RADM/ADOM evaluation (Dennis et al., 1990).

For a typical aircraft (or helicopter flight), the Flying Data Grabber first identifies the specific time interval during which the aircraft was located in a given CMAQ model grid cell along the flight path. The observations are then integr ated to produce mean, standard deviation, bias and

error estimates for the variable measured within each grid cell of the flight path. This averaging process produces an observed, averaged time series for the above statistical quantities along the flight path. Note, that these time intervals are characteristically much smaller than the one-hour model averaging time. Flight path statistics, together with the mean modeled and observed horizontal winds along the flight paths are also produced by the FDG. The maximum and minimum values during each time segment within a grid cell are also recorded.

The FDG methodology further assumes that the air quality model estimates vary approximately linearly during each hour. It is then straightforward to construct an estimated time series of the modeled values that corresponds to the above-described observed time series derived from the aircraft data. The measurements and model estimates, now on roughly comparable time and space scales, are subsequently processed with the MAPS statistical/graphical software tools described in the Phase I Task 4a report (ENVIRON, 2003d). The statistics of principal interest are

the mean values of the observed and modeled concen trations together with estimates of bias andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-12 imprecision (i.e., gross error). A va riety of graphical representations are also produced to facilitate evaluation an intercomparison.

7.5.4 Use of Multiple Evaluation Packages

In Phase I model evaluation (ENVIRON, 2004) relied principally on the UCR evaluation package that was originally developed for WRAP and then enhanced by VISTAS. This package produce scatter plots by site, day or all sites and days , time series plots and statistical measures. In Phase I modeling an error was introduced in the exte nsion of the package to treat 12 km grids that caused some delays as we attempte d to resolve the 36 km and 12 km performance issues. In Phase II we will attempt to prevent such a reoccurrence by using additional software evaluation packages specifically, the Alpine MAPS package will be used to not only corroborate the UCR results but also provide some additional model evaluation measures and displays (e.g. spatial average time series plots). In addition; ENVIRON’s evaluation software that focuses on the eval uation of the model at Class I areas for the 20% best and 20% worst days will be employed to identify how the model is predicting PM species on these key days and iden tify any days from the 20% worst and 20% best days 3 where model performance is so suspect that use of the modeling results on these days to project visibility improvements may not be desirable.

7.5.5 Probing Tools and Allied Methods

The CMAQ model evaluation in Tasks 4a and 4b will employ routine operational evaluation methods and standard statistical metrics (Table 7- 4) and graphical displays to support the assessment

of whether the model is shown to perform with sufficient accuracy and reliably for its intended purpose. Ideally, this operational evaluation will confirm that the modeling system is performing consistent with its scientific formulation, technical implementation, and at a level that is at least as

reliable as other current state-of-science methods . Should unforeseen model performance problems arise in the initial or refined year 2002 model simu lations, it may be necessary to draw into the evaluation supplemental diagnostic tools to aid in model testing. These diagnostic techniques are loosely referred to as “probing tools”. The actual n eed for their use, if any, can only be determined once the initial 2002 CMAQ operational evaluation is completed. Should such diagnostic methods actually be needed, their usage would be funde d by Optional Task 14 and/or Optional Task 15 (Contingency). The scope of these additional analys es into the Task 4a/b evaluation process will be closely coordinated with the VISTAS TAWG, with due consideration being given to the Phase II schedule and resources. Below, we identify the type s of probing tools that could be brought to bear under Optional Tasks 14 and/or 15 should their use become necessary at some later time.

Current ‘One-Atmosphere” models such as CMAQ and CAMx have been outfitted with a number of “probing tools” that have proven to be very useful in testing and improving model performance and in evaluating emissions control strategies. Among the probing tools available in one or both models are: (a) ozone source appor tionment technology (OSAT) algorithms, (b) process analysis (PA), and (c) the direct decoupled met hod (DDM) for sensitivity analysis. In addition, the

3 Mention here and elsewhere in this protocol of evaluation efforts for the 20% best and worst days is in addition to the planned evaluation of all days in the annual cycle. While EPA visibility guidance addresses the 20% days, and our evaluation is tailored to follow the guidance, the overall CMAQ evaluation will address all days in the year.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-13 project team is implementing PM Sour apporti onment techniques in CMAQ and CAMx. VISTAS may choose to evaluate these tools as part of the Phase II modeling exercise. It is currently unclear what funding will be available under Optional Tasks 14 and/or 15 for these options.

Source Apportionment Technology: CAMx contains a suite of “source attribution”

methods. One such method is Ozone Source Apportionment Technology (OSAT). OSAT tracks ozone formation based on how groups of ozone precursors contributed to ozone formation. Thus, OSAT decides whether ozone formation is NOx or VOC limited in each grid cell at each time step,

and bases ozone attributions on the relative amounts of the limiting precursor from different sources that are present in that grid cell at that time step. These incremental ozone attributions are integrated throughout the model run. The method is generally applicable and has been widely used to aid model diagnosis in the performance testing phase and to guide control strategy formulations as well.

A new PM Source Apportionment Technology (PSAT) is being implemented in CAMx funded by the MRPO that is in its final stages of tes ting. A Tagged Species S ource Apportionment (TSSA) approach has also been implemented in CM AQ and tested for nitrate (Tonneson, 2004, personal communication).

Decoupled Direct Method (DDM): Various forms of the Decoupled Direct Method (DDM) have been installed in CMAQ and CAMx, based on the original work of Dunker and co-workers (Dunker, 1981; 1984; Dunker et al., 2002) and research ers at Georgia Institute of Technology (GIT). In general, the DDM method: (a) calculates first order sensitivities dC/dP where C is a concentration output and P an input parameter 4 , (b) promotes accuracy by using consistent numerical methods and the same time steps for concentrati ons and sensitivities, (c) optimizes the code for efficiency, but not at expense of accuracy, and (d) calculates sensitivities with respect to parameters representing pollutant sources – emissions, BCs and ICs. Finally, the DDM provides a flexible and powerful user interface for defining various sensitivities including:

> Emissions resolved by geographic area. > Emissions resolved by source category. > BCs optionally resolved by boundary edge (N, S, E, W, Top). > All sensitivities available relative to sources of individual species (NO, PAR, etc.) or species group (VOC, NOx or ALL). > Simultaneously calculate sensitivities to many initial condition, boundary condition and emissions parameters.

In recent comparisons between CAMx DDM sensitivities and brute-force sensitivities (calculated from +/- 20% perturbations) Dunker et al., (2002a,b) reported that sensitivities of ozone with respect to area source NOx and VOC emissions were calculated and results indicated that the agreement between DDM and brute force sensitiv ities is excellent. DDM implementation into CMAQ is reported by Kumar (2003).

Process Analysis (PA): Photochemical air quality model simulations are usually evaluated primarily in terms of their ability to simulate observed O 3 data. There is an increasing awareness that

4 Recent research by Prof. Russell and coworkers at GIT has led to the extension of the CMAQ DDM method to include second order sensitivity coefficients (see, Hakami et al., 2003).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-14 chemical mechanisms, and air quality models must also be evaluated in terms of their ability to simulate the fundamental chemical processes that control O 3 formation and the sensitivity of O 3 to emissions reductions (Arnold et al., 1998). Proce ss analysis is a method for explaining model simulations by adding algorithms to the AQM to store the integrated rates of species changes due to

individual chemical reactions and other sink a nd source processes (Jeffries and Tonnesen, 1994; Tonnesen, 1995). By integrating these rates over time and outputting them at hourly intervals, process analysis provides diagnostic outputs that can be used to explain a m odel simulation in terms of the budgets of free radicals, production and loss of odd oxygen and O 3 , and conversion of NOx to inert forms, as well as the effects of transpor t and other sink and source terms. Of particular importance to the VISTAS modeling, process analysis can also improve model diagnosis and performance evaluation efforts by identifying proce sses that are ‘out of balance’ (Tesche and Jeffries, 2002), by identifying situations for whic h the model formulation and/or implementation should not be expected to apply and by suggesting how ambient data can be used to evaluate model accuracy for key terms in the chemical processing of VOC and NOx (e.g., Imre et al., 1998).

Process Analysis (PA) is implemented in both CMAQ and CAMx and each model supports three complementary aspects of the method: (a) th e integrated process rate (IPR), (b) integrated reaction rate (IRR) and (c) chemical process analysis (CPA). Several versions of process analysis (PA) have been implemented in air quality m odels (AQMs) including both trajectory models (Tonnesen, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and De nnis, 2000; Arnold et al., 1998; and Wang, 1997).

The fundamental approach in all versions of PA is similar: The AQM is modified to calculate the integral over time of the individua l sink and source processes and each chemical reaction. These integrated sink/source process rate s (IPR) and integrated reaction rates (IRR) can then be stored to a file and analyzed using a pos t-processor, or some processing can be performed internally in the model and a more limited set of process diagnostic information is output directly by the AQM. Chemical process analysis (CPA) is an improvement on the IRR method whereby some of the processing of IRR information is intern alized within the AQM to output chemically meaningful parameters directly (e.g., budget terms for O 3 , NOx and odd oxygen).

Process analysis measures for aerosol chemis try have not been analyzed as much as for ozone chemistry. Although the ozone chemistry proce ss analysis is directly related to secondary sulfate and nitrate formation, there is additional process analysis information available in the aerosol modules that are not extracted in either CMAQ or CAMx. In particular, information on sulfate formation and oxidants from the aqueous-phase m odule and on the sulfate/nitrate equilibrium from the aerosol thermodynamics module would be a usef ul addition to the current process analysis

output.

Because application of all three of thes e probing tools--source apportionment, DDM, and Process Analysis—are computational intensive and require a fair amount of analysis time to reap the benefits of using the methods, they do not lend themselves directly to annual simulations. However, each method has potential for use in addressing key episodic periods or geographical locations in the VISTAS domain where performance in the 2002 simulation may present a problem or where particular attention needs to be focused on emissions controls (a specific Class 1 area for example). In such focused applications, one or more of these probing tools may indeed serve a purpose and will be considered where appropriate.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-15May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-16 7.6 Phase II Model Evaluation Procedures

EPA guidance (EPA, 2001, pg. 227) suggests that the performance evaluation focus on two

aspects:

> How well is the model able to replicate observed concentrations of components of

PM 2.5 , and total observed mass of PM 2.5 ? and

> How accurately does the model characterize the sensitivity of ch anges in component concentrations to changes in emissions?

Recognizing that the former is much easier to accomp lish than the latter, EPA goes on to declare that testing of a model’s reliability in estimating the actual effects of emissions changes is the more important. Over the past 20 years, a substantia l body of information and analytical techniques has been developed to address the first aspect. Unfortunately, even today there are little rigorous methods available for quantifying the accuracy and precision of a model’s predictions of ozone, PM or visibility changes as the result of emissions cha nges. In this section we explain how the VISTAS Phase II testing will address the first aspect of the performance evaluation, i.e., how does the model compare against observed data. In section 7.9 we consider the second performance consideration.

7.6.1 Assessment of Ground-Level Gas-Phase and Aerosol Species

Given that visibility is expressed in terms of extinction built off of individual components of fine particulate matter, the model should be evaluate d separately for each of the key fine particulate matter components that make up the extinction coeffi cient. Current EPA guidance suggests that the

model should also be evaluated for ammonium as well as several key gas-phase species that are important for fine particulate modeling. For particulate species this includes SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , elemental carbon (EC), organic carbon (OC), IP, mass of individual constituents of IP, and coarse matter (CM). The gaseous species include ozone (O 3 ), HNO 3 , NO 2 , PAN, NH 3 , NOy, SO 2 , CO, and H 2 O 2

.

As indicated above, for some of the VISTAS subregions there are very few measurements, if any, for most of the gaseous species. But ozone measurements are available and should be integrated into the model performance evaluation. Given the importance of the photochemistry and the radical cycle in forming secondary fine par ticulate matter, the ozone evaluation will provide some insight into the model’s ability in this area. However, given the coarse grid resolution to be used (36 km and 12 km) perhaps greater weight s hould be given to the model evaluation results at the more rural ozone monitors (e.g., CASTNet) comp ared to urban ozone monitors. In addition to the list of PM and gaseous species from EPA’ s guidance document listed above, the Task 4a/4b evaluation will also examine model’s ability to estimate PM 2.5 mass, PM 10 mass, extinction (b ext ) and deciview (dV) using the equation above to obtain ex tinction from fine particulate concentrations.

At some of the IMPROVE sites there are also direct measurements of hourly extinction using transmissometer or nephelometer instruments. 5 Thus, it would be scientifically interesting to

5 The Phase I modeling did not utilize the hourly extinction data in the operational evaluation of the CMAQ and CAMx models due to schedule and resource constraints and because the modelsMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-17 evaluate the model estimated extinction with the hourly measured values at these sites. If such a comparison were to be made, day-specific hourly a nd site-specific RH and f( RH) values could be used to convert model estimated fine particulate matter to extinction rather than the monthly f(RH) EPA recommends for projecting visibility improveme nts (EPA, 2003a). Furthermore, there have been some discrepancies reported in the past between the extinction calculated using the reconstructed mass data and the hourly measurements . Thus, it would be interesting to calculate day-specific extinction using day-specific f(RH) values and the reconstructed mass data for comparison with the direct measurements and the m odel estimates. Whether and to what extend this more detailed investigation into light extincti on is pursued would depend upon the availability of Optional Task 14 authorization.

As part of the CMAQ operational evaluation, model outputs will be compared statistically and graphically to observational data obtaine d from the IMPROVE, SEARCH, CASTNet, EPAFRM, EPA-STN, and other monitoring networks. These monitoring data will be obtained from AIRS, VIEWS, and other appropriate organizations. These comparisons will likely include:

> Daily monthly, seasonal and annual averages for SO 2 , SO 4 , NO 3 , EC, OC, PM 2.5 , and

PM 10 , taking care to exclude periods of sampli ng interference in the observational data. We will look for systematic biases between the model results and IMPROVE observations, and if biases are found, identif y possible sources of error in the model inputs.

> Hourly, high resolution PM species and gase ous species concentrations at sites where available (e.g., SEARCH, AIRS and EPA-Supersites).

> At sites with contrasting aerosol mass loadi ngs, analysis of the temporal behavior of the major scattering and absorbing aerosol constituents along with the visibility trends, to

establish correlations.

The optional Phase II CMAQ diagnostic model evaluations may entail several components, many of which can be identified presently. Of course, the actual diagnostic analyses to be performed and the scope of such analyses can onl y be determined once the initial Task 4a model testing is underway. These diagnostics analyses – to be funded through Optional Task 14 (Enhanced Model Performance Evaluation)-- will need to be carefully defined and rank-ordered in terms of their priority to ensure that they can be accommodated within available resources and schedule. Among the diagnostic model evaluation analyses that could be considered are:

> Evaluate seasonal trends in observations of organic and inorganic aerosol precursors and their effects on PM composition and visibility, and evaluate the ability of the model to capture these seasonal trends.

> Evaluate how well the model simulates various physicochemical processes by: (a) examining observed and modeled correlati ons between various species pairs, and

performance for the major PM components was deemed more important in terms of evaluating alternative model science configurations.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-18 (b) comparing model-predicted ratios of vari ous species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

> Investigate the performance of the model at selected observational sites characterized by different chemical regimes that may be encountered either spatially or during different seasons to help identify any inadequacies in the model and to provide a better understanding of conditions under which model inferences may be weak.

> Compare hourly 24-hour average, and episode averages of the PM constituents for the 2002 Phase I VISTAS annual modeling period as well as the 2003 episodes across all

sites.

> Create scatter plots of modeled vs. obser ved data and hourly and 24-hour averages by site and subregion to help identify any site-specific biases.

> Create time series plots of predicted and observed concentrations as appropriate.

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

> Compare observed versus modeled mass fractions of PM constituents at various sites that are characterized by their proximity or re moteness relative to sources, or by specific meteorological conditions (e.g., frontal passa ge, stagnation, precipitation); these will

enable identification of trends in the mode l of over- or under-prediction of specific PM

constituents under these conditions.

> Calculate the measured and predicted relative abundance of key PM components and compare with EPA guideline recommendations and emergent alternative science recommendations (e.g., removing the soil co mponent from the calculations, use of alternative relative importance equations [i.e., Boylan, 2004]).

> Pay particular close atte ntion to the model performance at the Class I areas for SO 4 , NO 3 , EC, OC, IP 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.

The suite of statistical metrics and graphical t ools identified in the previous section for the core operational evaluation efforts under Task 4a/ 4b would likely also be used to diagnose performance problems with the CMAQ simulations s hould they exist and to highlight differences between model runs. Experience in ozone/PM m odeling is the best basis upon which to identify obviously flawed simulation results. Efforts to improve the CMAQ model’s base case performance will be made, where necessary, warra nted (i.e., to reduce the discrepancies between model estimates and observations), and consistent with the proj ect resources and schedule; however, these model performance improvements efforts must be based on sound scientific principles. “Curve-fitting”

exercises will be avoided.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-19 7.6.2 Assessment of Aloft Gas-Phase and Aerosol Species

A substantial number of aircraft flights were conducted during 2002 over the Midwest and Eastern U.S. Should VISTAS elect to fund the op tional aloft model performance evaluation, we will

endeavor to obtain this information and use it in a scientific performance evaluations of aloft gasphase and aerosol species (see section 7.3.2).

7.7 Performance Goals and Benchmarks

Establishment of performance goals and benc hmarks for regulatory m odeling is a necessary but difficult activity. Here, performance goals re fer to targets that we believe a good performing model should achieve, where as performance benchm arks are based on historical model performance measures for the best performing simulations. Perf ormance goals are necessary in order to provide consistency in model applications and expectations across the country and to provide standardization in how much weight may be accorded modeling study re sults in the decision-making process. It is a problematic activity, though, because many areas present unique challenges (e.g., Houston, San Joaquin Valley, Los Angeles) and no one set of perfor mance goals is likely to fit all needs. Equally concerning is the very real danger that modeling studies will be truncated when the ‘statistics look right’ before full assessment of the model’s reliability is made. This has the potential from breeding built-in compensating errors (Reynolds et al., 1996) as modelers strive to get good statistics as opposed to searching for the explanations for poor performance and then rectifying them. A

NARSTO review of more than two-dozen urban-scal e ozone SIP applications found this tendency to be all too prevalent in the regulatory modeling of the 1990s. (Roth et al, 1997).

Nearly 15 years ago, research sponsored by the California Air Resources Board (Tesche et al., 1990) led to the agency’s adop tion of three performance goals for 1-hour ozone modeling in the

state:

> Unpaired (in time and space) peak prediction accuracy ( ≤ ± 20%); > Mean normalized bias in hourly averaged concentrations ( ≤ ± 15%); and > Mean normalized gross error in hourly concentrations ( ≤ 35%).

These performance goals for 1-hr ozone concentra tions were adapted from previous surveys of several dozen urban-scale photochemical grid modeling studies (principally in California) focusing on ozone episodes of 1 to at most 3 days in dura tion. A surprising number of these studies did not include biogenic VOC emissions in the inventory under the then prevailing belief that biogenics were a negligibly small source category compared to automobile emissions. Most of the studies (Tesche, 1985, 1988; Tesche et al., 1985; 1990) compri sing the data base from which the California ozone performance goals were derived entailed hourly ozone concentrations well above background levels (~40-50 ppb). As a result, it was common practice to use a “cutoff values” ranging between 40 ppb to 60ppb to eliminate prediction-observations pairs that would cause these bias and error residual statistics to become extraordinarily larg e when measured concentrations were low.) Accordingly, normalized statistics such as bias and error proved to be suitable in most applications since the observed concentrations were genera lly high. These three California ozone model performance goals were adopted by EPA (1991) as part of the nationwide photochemical modeling guidelines and have been heavily used since.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-20 However, when these evaluation metrics and goals were later adapted to PM and PM species, difficulties arose because performance statistics that divide by low concentration observations become much less useful. Indeed, some PM species may approach zero (e.g., NO 3 ). In time, this

has led to the introduction of the fractional and norma lized mean bias and error metrics in addition to the mean normalized metrics and related perfor mance expectations based on these alternative

measures.

While the 1-hr metrics and goals still have va lue in interpreting ozone and some gas-phase species performance, it has been necessary to de velop new performance metrics and goals for fine particulates. EPA’s PM guidance document (EPA , 2001) guidance document identifies particulate matter components of interest to include: SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , EC, OC, IP, and mass of individual constituents of inorganic primary particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO 3 , NO 2 , PAN, NH 3 , NO y , SO 2 , CO, and H 2 O 2 . In addition, EPA guidance identifies several potentially useful statistical measures including: (a) accuracy of spatially averag ed concentrations near a monitor, (b) fractional bias in means and standard deviations of pred ictions and observations, (c) normalized bias, (d) normalized gross error, (e) unpaired comparisons between predicted and observed peak concentrations. (Interested readers are referred to the EPA guidance document on the details of these metrics including mathematical formulae and implementation methods.)

As with ozone in the 1980s, actual experience with PM models has led to the development of the current performance expectations for these models. For example, PM 10 SIP model performance goals for mean normalized gross error of < 30% for southern California (SCAQMD, 1997; 2003)

and < 50% for Phoenix (ENVIRON, 1998) have been us ed. As correctly poi nted out by Seigneur and co-workers (2003), the current ability of regi onal PM models to predicting regional PM and visibility is an area of research with improve ments needed for characterizing meteorology and emissions as well as PM models themselves. To this list we would add the need for improvements in model evaluation methodologies as well.

When EPA’s draft guidance was developed nearly f our (4) years ago, an interim set of fine particulate modeling performance goals were suggested 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 regi onal haze modeling is an evolving science, and considerable practical application and performance testing has transpired in the intervening years since these goals were postulated, we consider th em general guidelines. As part of the Phase II model evaluation effort, we will compare the EPA goals in the 2001 guidance document with the results of the CMAQ/CAMx Phase I modeling, toge ther with other recent PM modeling studies (e.g., WRAP, MRPO) to advance a proposed set of pe rformance goals most re levant to the VISTASMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-21 study. Results of the Phase I model evaluation together with recommendations from science workshops (e.g., EPA’s PM Model Performance Evaluation Workshop in February 2004) and recently published scientific studies (e.g., Boylan, 2004) will be used to provide support to these

recommendations.

We regard the above goals as simply general interim guidance. Cert ainly, more information on likely performance expectations will be ava ilable once the Phase I CMAQ/CAMx ozone and PM performance evaluation and sensitivity results ar e fully analyzed. In the VISTAS Phase II modeling, we will generate the model performance statistics listed in Table 7-4 and make comparisons with EPA’s interim goals as one means for:

> Establishing a benchmark on the annual 2002 VISTAS modeling episode, and

> Guiding the interpretation of the CMAQ modeling results.

Equally as useful, we will endeavour to compare th e results of the Phase II multi-species evaluations of CMAQ with the Phase I results for the Jan ’02, Jul ’99 and Jul ’01 episodes as well as modeling results from other studies (e.g., BRAVO, EPA, CRC, etc.), other RPOs, and the modeling research community in general. This will allow judgmen ts about the adequacy of the Phase II modeling results to be placed in the most current perspective possible.

7-8. Diagnostic and Sensitivity Testing

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance e xpectations. Indeed, our experience has been that initial simulations that ‘look very good’, usually do so as the result of comp ensating errors. The norm is to engage in a logical, documented process of model performan ce improvement wherein a variety of diagnostic probing tools and sensitivity testing methods are us ed to identify, analyze, and then attempt to remove the causes of inadequate model performan ce. This is invariably the most technically challenging and time consuming phase of a modeling study. We anticipate that the annual CMAQ

model base case simulations will present some performance challenges that may necessitate focused diagnostic and sensitivity testing in order for them to be resolved. Hopefully, these diagnostic and/or sensitivity tests can be adequately carried out with in the resources and schedule of Tasks 4a/4b. If not, then it may be necessary to draw upon the Optional Task 14 (Enhanced Model Performance Evaluation) and/or Optional Task 15 (Contingenc y) resources to conduct the necessary work. Where practical, diagnostic or sensitivity analyses , if needed, could be performed on selected episodes within the annual cycle, thereby avoi ding the time-consuming task of running CMAQ for the fully 2002 period. Below we identify the types of diagnostic and sensitivity testing methods that might be employed in diagnosing inadequate mode l performance and devisi ng appropriate methods for improving the model response.

7.8.1 Traditional Sensitivity Testing

Model sensitivity experiments are useful in three distinct phases or ‘levels’ of an air quality modeling study and all will be used as appropriate in the VISTAS Phase II modeling with CMAQ.

These levels are:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-22 > Level I

. Model algorithm evaluation and configuration testing;

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

> Level III. Investigation of model output respons e (e.g., ozone, aerosol, deposition) to changes in precursors as part of emissions control scenario analyses.

Most of the Level I sensitivity tests with CMAQ have already been completed in the Phase I configuration and diagnostic analyses. However, given that open community nature of CMAQ and the frequent science updates to the model and s upporting data abases, it is possible that some additional configuration sensitivity testing will be necessary in the early months of Phase II Potential Level I sensitivity runs would be carried out at one or more of the Team’s three modeling

centers.

Potential Level II sensitivity analyses might be helpful in accomplishing the following tasks:

> To reveal internal inconsistencies in the model; > To provide a basis for compensatory error analysis; > To reveal the parameters (or inputs) th at dominate (or do not dominate) the model’s operation; > To reveal propagation of errors through the model; and > To provide guidance for model refinement and data collection programs.

At this time, it is not possible to identify one or more Level II sensitivity runs that might be needed to establish a reliable annual 2002 CMAQ base case. The merits of performing Level II sensitivity testing will depend upon whether performance problem s are encountered in Tasks 4a/4b. Also, the number of tests possible, should performance di fficulties arise, will be limited by the available schedule and Phase II resources under Optional Task 14 (Extended Model Performance Evaluation). Thus, at this juncture, one cannot be overly pr escriptive on the number and emphasis of sensitivity runs that may ultimately be desirable in Phase II. However, from past experience with CMAQ and other models, experience it is possible to identify examples of sensitivity runs could be useful in model performance improvement exercises with the annual 2002 CMAQ simu lation. These include:

> Modified biogenic emissions estimates; > Modified on-road motor vehicle emissions; > Modified air quality model vertical grid structure; > Modified boundary conditions; > Modified fire emissions; > Modified EGU emissions;

> Modified ammonia emission estimates.

> Modified aerosol/N 2 O 5 /HNO 3 chemistry; and

> Modified NH 3 and HNO 3 deposition velocities.

Note that in a few cases (e.g., vertical grid structure, NH 4 emissions estimates), some sensitivity experimentation has already been carried out in Phase I with the Jan ’02, Jul ’01 and Jul ’99 episodes. To the extent that this Phase I in formation can help guide the Phase II diagnostics analyses, we will capitalize on this earlier work.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-23 If necessary, Process Analysis extraction output s can be included in these Level II diagnostic sensitivity simulations in order to provide insight into why the model responds in a particular way to each input modification. Again, the number, co mplexity, and importance of these types of traditional sensitivity simulations can only be determined once the initial CMAQ annual 2002 simulation(s) are executed.

Level III sensitivity analyses have two main pur poses. First, they facilitate the emissions control scenario identification and evaluation pr ocesses. Today, four complimentary sensitivity “tools” can be used in regional photochemical models depending upon the platform being used. These methods include: (a) traditional or ‘brute force’ testing, (b) the direct decoupled method (DDM), (c) Ozone Source Apportionment Technology (OSAT) and PM Source Apportionment Technology (PSAT), and (d) Process Analysis (PA). Each method has its strong points and they will be employed in Phase II where needed. The sec ond purpose of Level III sensitivity analyses is to help quantify the estimated reliability of the ai r quality model in simulating the atmosphere’s response to significant emissions changes. This important model evaluation need is addressed in

further detail in section 7.9 below.

Based on experience in other regional studies , examples of Level III monthly or annual sensitivity runs for Phase II might include:

> Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO 2 emissions; > Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point

source NO

x emissions; > Ozone, sulfate, nitrate, ammonium a nd other aerosol sensitivities to ground level

NO

x emissions; and > Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia.

Of course, traditional ‘brute force’ sensitivity experiments is just one way of quantifying

these or other Level III sensitivities. Other met hods that can be applies include DDM, OSAT, or

PSAT simulations.

The need to perform sensitivity experiment ation (Levels I, II, or III) will depend on the outcome of the Task 4a/4b operational performance evaluations. If such a need arises, the ability to actually carry out selected sensitivity and/or dia gnostic experiments will hinge on the availability of Optional Task 14 and/or 15 resour ces and sufficient time to carry out the analyses. Clearly, selection of the specific analysis method will de pend upon the nature of the technical question(s) being addressed at the time. Note that as part of VISTAS Phase II modeling, Georgia Institute of Technology will be performing emissions sensitivities with CMAQ using the Phase I databases.

7.8.2 Diagnostic Tests

A rich variety of diagnostic probing tools are available for investigating model performance issues and devising appropriate means for improving the model and/or its inputs. Previously, in section 7.4.4 we introduced the suite of ‘probing t ools’ available for use in the CMAQ and CAMx modeling system for use in Phase II. Where the need exists (i.e., if performance problems are encountered) and assuming VISTAS elects to fund th e use of the probing tool applications (under Optional Task 14 or elsewhere), these techniques coul d be employed as appropriate to assist in theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-24 model performance improvement efforts a ssociated with the annual 2002 CMAQ basecase development. Here we describe an additional diagnostic method – indicator species and species ratios -- that is potentially useful not only in m odel performance improvement activities but also in judging the models reliability in estimating the imp acts on air quality from future emissions. This method involves the use of so-called ‘indicator species’ and species ratios. If, during the conduct of Phase II, we determine that application of indicat or species and species ra tio techniques would be beneficial to the study (and if existing project res ources allow), we will di scuss with the TAWG the merits of including this additional probing tool as part of Optional Task 14.

Beginning in the mid 1990s, considerable interest arose in the calculation of indicator species and species ratios as a means of diagnosing phot ochemical model performance and in assessing model credibility in estimating the effects of em issions changes. Major contributions to the development and refinement of this general dia gnostic method over the past decade have been made many scientists including Milford et al., ( 1994), Sillman (1995, 1999), Sillman et al., (1997), Blanchard (2000), Blanchard and Fairley (2001), and Arnold et al., (2003). Indeed, a recent evaluation of CMAQ using indicator species ratios such as O 3 /NO

x , NO

z /NO y (a measure of chemical aging), and O 3 /NO

z (a measure of the ozone production efficiency per NO

x converted), showed not only good agreement with measurements (Arnold et al., 2003) but also convincingly demonstrated the utility of the method for diagnosing model performance in a variety of ways.

Traditionally, indicator species analyses have focused on ozone and its precursor and product species. However the method is e qually applicable to PM species and species ratios given sufficient measurement data for comparisons. With some of the high-resolutions monitoring data available from the SEARCH program and the EPA Supersites, it is indeed feasible to compute relevant indicator species and ratios for PM and its com ponent species. For example, Ansara and Pandis (1998) demonstrated how indicator species ratios c ould be applied to show how the modeled mass of PM might respond to sulfate, nitrate and ammonia emissions-related reductions.

7.9 Corroborative and Weight of Evidence Modeling Analyses

This section identifies additional modeling an alyses that might be worth pursuing under Optional Task 14 (Extended Model Performance Evaluation) to add strength to the core model evaluation efforts already planned as part of Tasks 4a and 4b.

7.9.1 Corroborative Models

Noteworthy in EPA’s new ozone, PM, and regional haze guidance documents is the encouragement of the use of alternative modeli ng methods to corroborate the performance findings and control strategy response of the primary air qua lity simulation model. This endorsement of the use of corroborative methodologies, stems fr om the common understanding that no single photochemical modeling system can be expected to provide exact predictions of the observed ozone and PM species concentrations in a region the si ze of VISTAS, especially over time scales spanning 1-hr to 1 year. Although the photochemical/PM models identified in EPA’s PM/regional haze guidance document possess many up-to-date science and computational features, there still can be important differences in modeled gas-phase and aerosol predictions when alternative models are exercised with identical inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-25 As we discovered in the Phase I CMAQ/CAMx inter-comparisons, the general levels of

difference revealed between the two model’s ozone and PM predictions is typical of what one encounters when inter-comparing alternative state- of-science regional models. These differences provide some insight into the current limits of predictability and reproducibility of today’s best photochemical/PM models. In light of these unders tandable differences in modeling results between state-of-science “One Atmosphere” models, not onl y is the issue of model selection for VISTAS

Phase II critical 6, but the procedures for the selected model (CMAQ in this instance) and interpreting its output are important as well. Thus, recognizi ng the uncertainty that attends even the most sophisticated models, the EPA’s draft PM/regiona l haze modeling guidance explicitly addresses the issue of modeling uncertainty by recommending that alternative models (photochemical and observation-based) be considered in the attainment demonstration ‘weight of evidence’ analyses. Indeed, the Relative Reduction Factor (RRF) appro ach in the formal attainment demonstration process is designed to address the fact that no mode l is fully capable of giving precise predictions. In fact the RRF approach was developed in respons e to the findings of many modelers (as we have also demonstrated in Phase I) that alternative, comparable models give somewhat divergent ozone and secondary aerosol predictions.

Mindful of EPA’s endorsement of corroborativ e modeling methods and the rigorous use of ‘weight of evidence’ investigations, we recommend that the most recent version of CAMx may be used in supportive, corroborative ro le to CMAQ in Phase II . Among other things, this will permit us to more explicitly identify the expected range of model uncertainty and to corroborate the general effectiveness of the Phase II CMAQ regional haze c ontrol strategies. Other corroborative modeling methods such as the CMAQ-AIM and CMAQ-MADRID should also be considered. However, as these models are derivatives of CMAQ they woul d not provide as robust independent corroboration

as CAMx.

Thus, we recommend CMAQ as the primary photoc hemical model for use in VISTAS Phase II and CAMx as a supportive model to be used fo r corroboration and are in the identification of compensation errors in the MM5/SMOKE/CMAQ m odeling system. The use of CAMx is under Optional Task 11 in the Phase II SOW that is cu rrently not funded (see Appendix A). Additional

corroborative models should also be considered.

7.9.2 Weight of Evidence Analyses

EPA’s guidance recommends three general types of ‘weight of evidence’ analyses in support of the attainment demonstration: (a) use of ai r quality model output, (b) examination of air quality and emissions trends, and (c) the use of corrobora tive modeling such as obse rvation-based (OBM) or observation-driven (OBD) models. We will consider the use of one or more methods in conducting the Phase II CMAQ modeling because it could significantly strengthen the credibility and reliability of the modeling available to the states for their subs equent use. The exact de tails of the ‘weight of evidence’ analyses must wait until Phase II evolves fu rther. It is premature to prescribe which, if any of the WOE analyses would be performed sin ce the model’s level of performance with the 2002 episode is obviously not known at this time and th e time and remaining project resources available to support WOE analyses is unknown as well. Nonetheless, we outline below our thoughts

6 Model selection is a key issue that all RPOs have addr essed and some such as WRAP and VISTAS have chosen CMAQ. Others, such as the MRPO have adopted CAMx inst ead. Still other RPO’s have yet to decide between these two or another model. Clearly, there does not appear to be any one ‘right’ selection at the present time.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-26 regarding what would likely be considered shoul d the operational CMAQ mode l evaluation need to be bolstered with WOE analyses.

Use of Air Quality Models

. As just discussed, we recommend augmenting the CMAQ annual 2002 and episodic simulations with the use of CAMx to provide additional information on model uncertainty and sensitivity. More specifica lly, we propose to exercise CAMx on a somewhat limited basis to corroborate the key model perfor mance evaluation results and emissions control findings of the primary model, CMAQ. Sec ond, we propose to apply the DDM and OSAT/PSAT methods to develop corroborative informati on on source-receptor relationships and model sensitivities. These supplemental calculations would be performed with one or both models (CMAQ does not contain OSAT/PSAT presently) for one or more key episodes within the annual 2002 cycle. The results of this additional modeli ng would be used directly in the ‘weight of evidence’ analyses to quantify the degree of modeling uncertainty and to corroborate appropriateness of the subsequent PM emissions reductions scenarios.

Use of Emissions and Air Quality Trends. A limited scope emissions and trend analysis could be employed in Phase II to support the ‘wei ght of evidence’ determinations. However, traditionally, these types of analyses are performe d by the lead agency’s own staff. With this expectation, we would coordinate our efforts with the VISTAS TAWG to develop a trends analysis supporting the future year applications of CMAQ.

Use of Corroborative Observational Modeling

. While regulatory modeling studies for ozone attainment demonstrations have traditionally relied upon photochemical models to evaluate ozone control strategies, there has recently been growing emphasis on the use of data-driven models to corroborate the findings of air quality models. As noted, EPA’s guidance now encourages the use

of such observation-based or observation-driven m odels (OBMs/ODMs). As part of Phase II, we will consider the merits of using these techni ques as supportive weight of evidence. While the OBD/OBM models cannot predict future year air qua lity levels, they do provi de useful corroborative information on the extent to which specific s ubregions may be VOC-limited or NOx-limited, for example, or where controls on ammonia or SO2 em issions might be most influential in reducing

PM 2.5 . Information of this type, together with results of DDM and traditional ‘brute-force’ sensitivity simulations, can be extremely helpful in postulating emissions control scenarios since it helps focus on which pollutant(s) to control.

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes

EPA identifies three methods (EPA, 2001, pg. 228) potentially useful in quantifying a model’s reliability in predicting air quality respons e to changes in model inputs, e.g., emissions.

These include:

> Examination of conditions for which subs tantial changes in (accurately estimated) emissions occur;

> Retrospective modeling, that is, modeling before and after historical significant changes in emissions to assess whether the observed air pollution changes are adequately simulated; andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-27 3⁄4 Use of predicted and observed ratios of ‘chemical indicator species’.

We note that in some urban-scale analyses, th e use of weekday/weekend information has been helpful in assessing the model’s response to emissi ons changes. However, we suspect that this approach would not prove feasible with an a nnual episode over geographic domain as large as

VISTAS.

The first two methods have actually been cons idered for over 15 years and were the subject of intensive investigations in the early 1990s in Southern California in studies sponsored by the South Coast Air Quality Management District (Tes che, 1991) and the American Petroleum Institute (Reynolds et al., 1996). To date, neither method has proven useful largely because of the great difficulty in developing historical emissions inve ntories of sufficient quality to make such an analysis credible and the difficulties in removi ng the influences of different meteorological conditions such that the modeling signal reflects only the model’s response to emissions changes. It is difficult enough to construct re liable emissions inventories using today’s modeling technology let alone construct retrospective inventories 5-10 year s ago prior to the implementation of significant emissions control programs or major land use changes. The use of indicator species, however, offers some promise.

However, recent analytical and numerical modeli ng studies have demonstrated how the use of ambient data and indicator species ratios can be us ed to corroborate the future year control strategy estimates of Eulerian air quality models. Blanch ard et al., (1999), for example used data from environmental (i.e., smog) chambers and photochemi cal models to devise a method for evaluating the 1-hr ozone predictions of models due to changes in precursor NOx and VOC emissions. Reynolds et al., (2003) followed up this analysis, augmented with process analysis, to assess the reliability of SAQM photochemical model estimate of 8-hr ozone to precursor emissions cutbacks. With respect to secondary aerosol PM, the recen t CMAQ evaluation by Arnold et al. (2003) clearly demonstrated how the use of indicator species anal ysis could be use to develop insight into the expected reliability and adequacy of a photoche mical/PM model for simulating the effects of emissions control scenarios. These researchers used three indicator ratios (or diagnostic ‘probes’) to quantify the model’s response to input changes:

> The ozone response surface probe [O 3 /NO

x ]; > The chemical aging probe [NO

z /NO y ]; and > The ozone production efficiency probe [O 3 /NO

z ].

By closely examining CMAQ’s response to key input changes, properly focused in time and spatial location, Arnold et al., (2003) were able to conclude that the photochemical processing in CMAQ was substantially similar to that in the atmosphere

Thus, the extension of these techniques to address CMAQ predictions for secondary aerosols will doubtless be quite challenging, but the use of indicator species (e.g., ammonia or HNO 3 limitation for nitrate particle formation) and species ra tios appears to offer, at this time, the only real opportunity to quantify the expected reliability of the air quality model to correctly simulate the effects of emissions changes. In the Phase II CMAQ model evaluation, we will remain alert to opportunities to extend the indicator species ratio an alyses to the problem of fine particulate and regional haze. This is one area where technical collaboration between the Em issions and Air QualityMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-28 Modeling team and the VISTAS TAWG can be especially fruitful in terms of identifying and testing emergent methods for challenging the model’s ability to correctly simulate the effects of future year emissions changes. Finally, we note that this is truly a current research area and as such falls outside the scope of the Phase II modeling effort. However, given its importance, we will remain alert to opportunities to utilize newly available methods s hould this prove feasible within the Phase II

resources and schedule.

Table 7-1 . Ground-Level Ambient Data Monitoring Networks and Stations Available in VISTAS 12
Km Domain for CY-2002. Monitoring

Network Chemical Species Measured Sampling Frequency;

Duration Approximate

Number of

Monitors

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr 38

CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week 74

SEARCH 24.hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); and Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 8

NADP WSO4, WNO3, WNH4 Weekly 100

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr 313 (?)

EPA-SPEC Speciated PM 2.5 Varies; Varies 43 (?)

AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly 6,407

Table 7-2 . Aircraft Sampling Programs Performed in the VISTAS 12 Km Domain for CY-2002.

Aircraft Program Meteorological Parameters & Chemical Species Measured Sampling Program & Flight Duration Approximate

Number of Flights; Days;

Aircraft University of Maryland (UMD);

Univ. Research Foundation (URF) Meteorology: WS, WD, Temp, RH, Air Quality : O 3 , NO, NO 2 , CO,
SO 2, aerosol absorption, aerosol scattering. 23 May to 3 Oct; Typically 3 hrs 54 flights, 54 days,
1 aircraft

Midwest RPO &

Wisconsin DNR Meteorology : WS, WD, Temp, RH, dew point, pressure Air Quality : O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg,
SO 4 , OC, EC, PM 2.5 , light scattering (Neph), visibility pictures. 1 June to 22 Nov; Typically 3-5 hrs 133 flights; 29 days;
2 aircraft (WDNR and Jacko Aircraft)May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-30 Table 7-3. Federal Mandated Class I Areas in the VISTAS States.

State Acreage Federal Land Manager Public Law

Alabama

Sipsey Wilderness Area 12,646 USDA-FS 93-622

Arkansas

Caney Creek Wilderness Area 7,344 USDA-FS 93-622

Upper Buffalo Wilderness Area 9,912 USDA-FS 93-622

Florida

Chassahowitzka Wilderness Area 23,360 USDI-FWS 94-557

Everglades NP 1,397,429 USDI-NPS 73-267

St. Marks Wilderness Area 17,745 USDI-FWS 93-632

Georgia

Cohotta Wilderness Area 33,776 USDA-FS 93-622

Okefenokee Wilderness Ar ea 343,850 USDI-FWS 93-429

Wolf Island Wilderness Area 5,126 USDI-FWS 93-632

Kentucky

Mammoth Cave NP 51,303 USDI-NPS 69-283

Louisiana

Breton Wilderness Area 5,000+ USDI-FWS 93-632

North Carolina

Great Smoky Mountains NP {1} 273,551 USDI-NPS 69-268

Joyce Kilmer-Slickrock Wilderness Area{2} 10,201 USDA-FS 93-622

Linville Gorge Wilderness Area 7,575 USDA-FS 88-577

Shining Rock Wilderness Area 13,350 USDA-FS 88-577

Swanquarter Wilderness Area 9,000 USDI-FWS 94-557

{1} Great Smoky Mountains National Park, 514,758 acres overall, of which 273,551 acres are in North Carolina, and 241,207

acres are in Tennessee. {2} Joyce Kilmer-Slickrock Wilderness, 14,033 acres overall, of which 10,201 acres ar e in North Carolina, and 3,832 acres are

in Tennessee.

South Carolina

Cape Romain Wilderness 28,000 USDI-FWS 93-632

Tennessee

Great Smoky Mountains NP {1} 241,207 USDI-NPS 69-268

Joyce Kilmer-Slickrock Wilderness{2} 3,832 USDA-FS 93-622

{1} Great Smoky Mountains National Park, 514,758 acres overa ll, of which 273,551 acres are in North Carolina, and 241,207

acres are in Tennessee. {2} Joyce Kilmer Slickrock Wilderness, 14, 033 acres overall, of which 10,201 acres ar e in North Carolina, and 3,832 acres are

in Tennessee.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-31 Virginia

James River Face Wilderness 8,703 USDA-FS 93-622

Shenandoah NP 190,535 USDI-NPS 69-268

West Virginia

Dolly Sods Wilderness 10,215 USDA-FS 93-622

Otter Creek Wilderness 20,000 USDA-FS 93-622

Table 7-4. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIEON, 2003b,d for details).

Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Accuracy of paired peak ( A p ) Paired_Peak

peak peak


## O OP

− P peak = paired (in

both time

and space) peak prediction

Coefficient of determination ( r 2 ) Coef_Determ



# ∑ ∑ ∑

= = =



## − −      

−−

N

i N

i i i N

i i i



## OOPP OOPP

1 1 2 2 2

1



## )()( ))(( P i = prediction at

time and

location i ; O i = observation

at time

and location

i ;

P = arithmetic average of P i , i=1,2,..., N;

O = arithmetic average of O i , i=1,2,...,N

Normalized Mean Error ( NME ) Norm_Mean_Err



# ∑ ∑

=

= 

## −

N

i i N

i ii



## O OP

1 1 Reported as %

Root Mean Square Error ( RMSE ) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

## 1      

− 

# ∑

= N

i ii 

## OP N Reported as %

Fractional Gross Error ( F E ) Frac_Gross_Err 

# ∑

= 

## + − N

i ii ii 

## OP OP

N 1 

## 2 Reported as %May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-32 Mean Absolute
Gross Error ( MAGE ) Mean_Abs_G_Err 

# ∑

= 

## − N

i ii 

## OP N 1 

## 1

Mean Normalized
Gross Error ( MNGE ) Mean_Norm_G_Err 

# ∑

= 

# − N

i i ii



# O OP

N 1



# 1 Reported as %

Mean Bias ( MB ) Mean_Bias 

# ( ) 

# ∑

= 

## − N

i ii 

## OP N 1 

## 1 Reported as

concentration (e.g., μg/m 3 )

Mean Normalized Bias ( MNB ) Mean_Norm_Bias 

# ( ) 

# ∑

= 

## − N

i i ii



## O OP

N 1 

## 1 Reported as %

Mean

Fractionalized Bias (Fractional Bias, MFB ) Mean_Fract_Bias 

# ∑

= 

##        

+ − N

i ii ii 

## OP OP

N 1 

## 2 Reported as %

Normalized Mean

Bias ( NMB ) Norm_Mean_Bias



# ∑ ∑

=

= 

## −

N

i i N

i ii



## O OP

1 1 

## )( Reported as %

Bias Factor ( BF ) Bias Factor

1 

## 1 N

i

i i 

## P

N O

= 

##       

# ∑ Reported as

BF:1 or 1: BF or
in fractional notation (BF/1 or 1/BF).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-1 8.0 VISIBILITY ASSESSMENT AND COMPARISON

This chapter provides a summary of how the modeling results will be used to satisfy the requirements of the Regional Haze Rule (RHR) as part of the Section 308 visibility State Implementation Plans (SIPs) and Tribal Implemen tation Plans (TIPs) due in 2007. Note that the VISTAS Phase II modeling results will also likel y be used in some capacity as the regional component in some State’s fine particulate (PM 2.5 ) and 8-hour ozone SIPs that are also due in 2007. However, how the modeling resu lts will be used to address PM 2.5 and 8-hour ozone issues is not discussed in this chapter and will be addre ssed by the pertinent States at a later time. The Phase II modeling results generated in 2004 will lik ely not be the final basis for the Section 308

SIPs as the results will be updated and refined. However, it is important to start planning how the modeling results will be used to project visibility changes, optimize the presentation of results and identify any potential pitfalls early on.

8.1 Regional Haze Rule Requirements

The Regional Haze Rule (RHR) regulations were published by EPA in 1999 (Federal Register 35769, July 1, 1999) and are designed to address the requirements of Section 169A and 169B of the Clean Air Act (CAA). The CAA and RH R call for the protection of visibility at the 156 “mandatory Federal Class I areas.” 1 Section 169A of the Clean Air Act Amendments (CAAA) established a national visib ility goal to remedy existing impa irment due to air pollution at the Class I areas. This is accomplished by defining a visibility goal of “nat ural conditions” to be

achieved at each Class I ar ea by 2064. The RHR requires States with Class I areas to develop SIPs that include reasonable progress goals for improvi ng visibility in each Class I area and emission reduction measures to meet those goals. For the initial control strategy SIPs due in 2007, States are required to adopt progress goals for improvi ng visibility from baseline conditions. The 2000- 2004 five-year period is used to de fine baseline conditions and the first future progress period is 2018, that has been interpreted as the 2013-2017 or 2014-2018 five year periods. A State is required to set progress goals for each Class I ar ea in the State for tw o visibility metrics:

• Provide for an improvement in visibility for the most impaired (i.e., 20% worst) visibility days; and

• Ensure no degradation in visibi lity for the least impaired (i.e., 20% best) visibility days.

The reasonable progress goals must provide for a rate of improvement sufficient to attain “natural conditions” by 2064, or justify any altern ative rate. States are to defi ne controls to meet progress goals every 10 years, starting in 2018, that defines progress periods ending in 2018, 2028, 2038, 2048, 2058 and finally 2064. States will determine whether they are meeting their goals by comparing visibility conditions from one five -year period to another (e.g., 2000-2004 to 2013- 2017). As stated in 40 CFR 51.308 (d) (1), baseli ne visibility conditi ons, progress goals, and

1 Areas designated as mandatory Class I areas are t hose National Parks exceeding 6, 000 acres, wilderness areas and national memorial parks exceeding 5,0 00 acres, and all international parks th at were in existence on August 7, 1977. Since then some new Class I areas have been defined and some existing Class I areas have been expanded. However, these areas are not included as part of the 156 “Federal mandated Class I areas” for which the RHR applies. In this document the term “Class I area” re fers to the 156 “Federal mandated” Class I Areas.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-2 changes in visibility must be expressed in term s of deciview (dv) units. The deciview unit of visibility impairment is de rived from light extinction (b ext ) as follows:

dv = 10 ln (b ext /10)

Where light extinction (b ext ) is expressed in terms of inverse megameters (Mm -1 = 10 -6 m -1 ).

Section 8.2.1.1 below discusses how b ext is calculated using the observed fine particulate

concentrations from the IMPR OVE monitors and S ection 8.2.1.2 below defines how the modeling results will be mapped to the IMPROVE measurements for the pu rpose of projecting visibility improvements.

8.2 EPA Guidance for Complying with Regional Haze Rule

EPA has published three guidance documents that relate to how modeling results should be used to demonstrate compliance with the RHR:

“Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze, Draft 2.1 (EPA, 2001).

“Guidance for Tracking Progress Under the Regional Haze Rule” (EPA, 2003a).

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” (EPA, 2003b).

The first EPA modeling guidance document lis ted above (EPA, 2001) discusses the use of modeling results to demonstrate prog ress toward the RHR goals. This is a draft document that will be updated during 2004. When the draft modeling guidance is updated that may necessitate an update of this Modeling Protocol to keep it cons istent with current EPA guidance. The second document (EPA, 2003a) focuses on monitored visibili ty and how to define the visibility baseline and how to track visibility goals. The third EPA guidance document discusses procedures for defining “natural conditions” for a Class I area that is the visibility goal in 2064. In the discussion below we are assuming that other VISTAS particip ants would define the “natural conditions” for each VISTAS Class I area. In this Chapter we a ddress the use of the mode led and monitored fine particulate concentrations at the Class I areas for projecting visibility cha nges from the current to future years.

VISTAS is currently evaluating EPA’s assumptions for projecting visibility improvements in their guidance documents (EPA, 2001; 2003a,b). If technically justif ied, VISTAS may make modifications to EPA’s procedures based on thei r analysis. For example, recent measurements suggest that, at least for the VIST AS region, sulfate is not always fully neutralized by ammonium

and that ammonium bisulfate and su lfuric acid can exist, especially in the summer. Thus, the EPA approach assuming that sulfate is always comple tely neutralized by amm onium may have to be updated. The current EPA approach also does not account for sea salt that can be a contributor to visibility impairment, especially at coastal sites. VISTAS is also evaluatin g EPA’s default “natural conditions” and its appropriateness for the VISTAS region. Belo w we describe EPA’s approach for projecting visibility improvements, but be aware they may be modified by VISTAS at a later

date.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-3 8.2.1 Procedures for Projecting Visibility Changes

EPA guidance recommends using the model in a relative sense to project future-year visibility conditions (EPA, 2001; 2003a). This is done through the use of Rela tive Reduction Factors (RRFs) that are defined as the ratio of th e future-year to the current-year modeling results. The RRFs are applied to the baseline visibility c onditions to project future-year visibility. The major features of EPA’s recommended visibil ity projections are as follows (EPA, 2003a,b):

• 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);
3⁄4 OF (other fine particulate); and
3⁄4 CM (coarse matter).

• Models are used in a relative sense to develop relative reduc tion 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 wi th 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 ].

In order to facilitate tracking visibility progress, three important vi sibility parameters are required

for each Class I area:

Baseline Conditions : Baseline Conditions represent visi bility for the 20% best and 20% worst days for the initial five-year baseline period of the regional haze program. Baseline

Conditions are calculated us ing monitor data collected during the 2000-2004 five-year period.

Current Conditions : Current Conditions for the best and worst visibility days are calculated from the latest five-year averag e based on the latest five-years of monitored data (which is currently 1998-2002, but expected to be 1999-2003 by the time the initial future-year Phase II visibility projections will be made in late 2004)..May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-4 Natural Conditions : Estimates of natural visibility conditions for the 20% best and 20% worst days at a Class I area that is the goal of the RHR is 2064 that has been defined as visibility conditions that would be expe rienced in the absence of human-caused impairment” (EPA, 2003b).

8.2.1.1 Calculation of Baseline Conditions

Baseline and Current Conditions for Class I ar eas are calculated using fine and coarse particulate concentration measured at Interagenc y Monitoring of Protected Visual Environments (IMPROVE) monitors. Currently, each Class I area in the VISTAS domain has an associated

IMPROVE PM monitor. The IMPROVE monitors do not directly measure visibility, but instead measure speciated fine particulate (PM 2.5 ) and total PM 2.5 and PM 10 mass concentrations from which visibility is obtained through the IMPROV E reconstructed mass extinction equation.

Visibility conditions are estimated starting with the IMPROVE reconstructed 24-hour average particulate matter (PM) mass measurements for six PM species:

• Sulfate [SO 4 ];

• Particulate Nitrate [NO 3 ];

• Organic Matter [OM];

• Elemental Carbon [EC];

• Other Fine Particulate [Soil]; and

• Coarse Matter [CM].

The IMPROVE monitors do not directly monitor some of these species so assumptions are

made as to how the IMPROVE measurements can be adjusted and combined to obtain these six components of light extinction. For example, sulf ate and particulate nitr ate are assumed to be completely neutralized by ammonium and are assume to occur solely in the fine particulate mode (that is any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM IMPROVE measurement). Concentrations for the above six components of light extinction in the IMPROVE reconstructed mass ( RCM) extinction equation are obtained from the IMPROVE measured species as follows:

Table 8-1. Definition of IMPROVE Reconstructed Mass (RCM) Species from Measured IMPROVE Species.

IMPROVE RCM IMPROVE Measured Species

SO 4 1.375 x ( 3 x S)

NO 3 1.29 x NO3_

OM 1.4*OC1 + 1.4*OC2 + 1.4*OC3 + 1.4*OC4 + 1.4*OP

EC EC1 + EC2 + EC3 – OP

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI

CM MT – MF

Where:

• S is elemental sulfur as determined fr om proton induced x-ray emissions (PIXE) analysis of the IMPROVE Module A that is multiplied by 3 to account the presence of oxygen, if S is missing then the sulfate (SO4) measured by ion chromatography analysis of the Module B is us ed to replace (3 x S). It is assumed to be completely neutralized by ammonium (1.375 x SO4).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-5 • NO3_ is the particulate nitrat e measured by ion chromatogr aphy analysis of the Module B. It is assumed to be complete ly neutralized by ammonium (1.29 x).

• The IMPROVE Organic Carbon (OC) measur ements are multiplied by 1.4 to obtain Organic Matter (OM) to adjust the OC mass for other elements assumed to be

associated with OC.

• Elemental Carbon (EC), which is also referre d to as Light Absorbing Carbon (LAC) is determined by TOR analysis and is the su m of EC fractions minus the pyrolized

fraction.

• Soil is determined as a sum of the ma ss of those elements (measured by PIXE) predominately associated with soil, whos e mass is adjusted for oxygen in the common compounds.

• MT and MF are total PM 10 and PM 2.5 mass, respectively.

Associated with each PM species is an extincti on coefficient that converts concentrations (in

 g/m 3 ) to light extinction (in inverse mega meters, Mm -1 ). Sulfate and nitrate 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 increa sing RH to account for the particles taking on water and having higher light scattering properties. Note that some Organic Matter (OM) compounds may also have hygroscopic properti es, 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]

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). These values have been recently updated (SAIC, 2003) and are available at: ftp://ftp.saic.com/raleigh/Regiona lHaze_2002FRHcurve/fRH_analysis/

.

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 Rayleigh (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:

dv = 10 ln(b ext /10)May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-6 The equations above using data from the associated IMPROVE mo nitor are used to estimate the daily average visibility at each Class I area. For each year from the 2000-2004 baseline these daily average visi bility values, in terms of decivi ew (dv), are then ranked from highest to lowest. The 20% worst days visibility for each given year is given as the average visibility across the 20% highest visibility days in deciview. Similarly, the 20% best days are given as the average visibility across the 20% lo west visibility days in terms of deciview. The Baseline Conditions is the average of the 20% be st and 20% worst days across the five-years of

2000-2004. The Current Conditions is the average of the 20% best and 20% worst days based on the latest five-years of available data, which would be 1999-2003 for the VISTAS Phase II analysis being conducted in late 2004.

8.2.1.2 Mapping of Modeling Results to the IMPROVE Measurements

As noted above, to project future-year visibil ity at Class I areas the modeling results are

used in a relative sense to scale current obser ved visibility for the 20% best and 20% worst visibility days using RRFs that ar e the ratio of modeling results for the future-year to current-year. This scaling is done separately for each of the six components of light extinction in the IMPROVE reconstructed mass extinction equa tions. The modeled species do not necessarily exactly match up with the IMPROVE reconstructed mass species, thus assumptions must be made to map the modeled species to the IMPROVE reconstructe d mass species for the purpose of projecting visibility improvements. For example, the models explicitly simulates ammonium and sulfate may or may not be fully neutralized in the model, whereas the IMPROVE rec onstructed mass equations assume sulfate is fully neutralized by ammonium. For the CMAQ Version 4.3 model that was used in VISTAS Phase I modeling, the mapping of modeled species to IMPROVE reconstructed mass (RCM) species listed in Table 8-2 is propose d to be used in the VISTAS Phase II modeling.

Table 8-2. Proposed Mapping of CMAQ V4.3 Modeled Species Concentrations to IMPROVE Reconstructed Mass (RCM) Species.

IMPROVE RCM CMAQ V4.3 Species

SO 4 1.375 x (ASO4J + ASO4I)

NO 3 1.29 x (ANO3J + ANO3I)

EC AECJ + AECI

OM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI

Soil A25J + A25I

CM ACORS + ASEAS + ASOIL

If a different model is used (e.g., CAMx or CMAQ-AIM) or updates to CMAQ change its species definitions, then the speci es mappings would have to be defined specific for that model. Note that in the above species mapping it is assu med that all of the CMAQ estimated sulfate and particulate nitrate are in the fi ne mode. If a fully sectional model was used (e.g., CMAQ-AIM or CAMx), then a decision would have to be made whether to map the coarse mode sulfate and

nitrate to the IMPROVE fine SO 4 and NO 3 species (as implicitly assumed for CMAQ) using mappings in Table 8-2) or to include it in the IMPROVE CM species. In fact this is also an issue for the CMAQ V4.3 modal approach in which th e lognormal size distribution for sulfate and nitrate in the accumulation mode (i.e., ASO4J and ANO3J) may have some mass greater than 2.5

: m, however the current usual convention for CMAQ is to assume all the sulfate and nitrate are in

the fine mode.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-7 8.2.1.3 Using Modeling Results to Project Changes in Visibility

Modeling results are used in a relative fashion to project future-year visibility using relative reduction factors (RRFs). RRFs are expressed of the ratio of the modeling results for the futureyear to the results of the base year and are Class I area and PM species specific. RRFs are applied to the Baseline Conditions observed PM species to project future-year PM levels from which visibility can be assessed usi ng the IMPROVE RCM extinction equations listed above. EPA has identified the following six steps to project futu re-year visibility for the 20% best and 20% worst days (EPA, 2001;2003a):

1. For each Class I area visibility is ranked using IMPROVE reconstructed PM mass (RCM) extinction equation for each year that compri ses the five-year Baseline Conditions (i.e., 2000-2004).

2. Calculate the arithmetic average of the 20% be st and 20% worst visibility days, in deciview (dv), for each year of the five-year baseline period and then calculate the five-year average of the 20% best and 20% worst days. Document which days during th e five-year baseline period comprise the 20% best and 20% worst observed visibility days.

3. Use an air quality model to simulate th e 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 obs erved visibility days for each year in the five-year baseline period. Average across the five-years the average PM species

concentrations for the 20% best and 20% wors t days and for each year in the five-year baseline period. Calculate Cla ss I area and best/worst 20% da ys RRFs for each of the six PM components as the ratio of the five-year average estimated PM species concentration in the future-year to current year.

4. Multiply the 20% best/worst days and PM species dependent RRFs by the observed 24- hour PM species concentrations for each day from the 20% best/wor st 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, perf orm 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 c oncentrations “near” the monitor, EPA (2001) recommends taking a spatial average of PM concen trations across a grid cell resolution dependent NX by NY array of cells centered on the grid contai ning the monitor. For the VISTAS Phase II 36 km modeling, just the model estimates for the grid cell containing the monitor will be used (i.e., NX=NY=1). For the VISTAS Ph ase II 12 km modeling, EPA recommends that NX=NY=3, that is the model estimated PM species concentrations are averaged across 9 grid cells centered on the IMPROVE monitor (EPA, 2001).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-8 8.2.1.4. Exclusion of Days from 20% Best/Worst Conditions

As discussed in Chapter 7, the model perfor mance evaluation will pay particular close attention to the model’s PM performance at ea ch Class I area for the 20% Best and 20% Worst days. If the model performance is extremely poor fo r one or more of these days at a Class I area, it may be desirable to exclude that day when calcu lating the RRFs for visibility projections. For example, if a day is one of th e Worst 20% days at a Class I area and the model estimates clean background concentrations then it would be inappropriate to use the modeling results, even in a relative fashion through the RRFs, to project future-year visibi lity. The modeling Team would identify such suspect days and work with the VI STAS TAWG on whether theyshould be excluded from the visibility projecti ons on a case-by-case basis.

8.3 VISTAS Phase II Future-Year Modeling

The VISTAS Phase II future-year modeling will use the 2002 MM5 meteorological conditions. That is, we will assume that the me teorological conditions for the future-year are the same as for 2002. This will allow for the comparison of the changes in visi bility at Class I areas from the current (2002) to future-y ear due to changes in emissions. This means that the effects of climate change and climatic variations will not be accounted for. Several other decisions concerning the future-year to be modeled, model(s) to be used and modifications to the model inputs to reflect future years do need to be made such as:

8.3.1 Future-Year to be Modeled

Visibility projects are needed for the 20% be st and 20% worst days from the five-year baseline period of 2000-2004 to 2018, which has been interpreted as the five-year future-year periods of 2013-2017 or 2014-2018. The EPA guidance documents are inconsistent on what future-year should be used for the assessment. The 2001 modeling guidance says that the mid-year from the 2013-2017 five-year proj ection period should be used, wh ich would be 2015 (EPA, 2001, pg. 221). The 2003 guidance for tracking progress doesn ’t discuss the future-year that should be modeled, but does mention that go als should be compared across 5- year planning periods “(e.g., 2000-2004 to 2013-2017) and progress should be measur ed as improvement from 2004 to “2018” (EPA, 2003, pg. 1-6). VISTAS and other RPOs will be looking to EPA for providing more definitive guidance as to which future-year should be used for modeling visibility progress and this Modeling Protocol will be updated when such inform ation is available. Thus, in the discussion below we just refer to the “future-year” with th e actual year to be determined during 2004. There may also be some benefits to projecting visibili ty improvements for an interim future-year between 2002 and 2018 to help track progress to the 2018 visibility targets.

8.3.2 Future-Year Emissions

The future-year emissions will be provided to the emissions and air quality modeling team (ENVIRON/Alpine/UCR) by the VIST AS emissions projection contr actor (MacTec). They would be processed into the gridded speciated h ourly three-dimensional emissions inputs for photochemical grid modeling using the SMOKE emi ssions model and the proc edures discussed in Section 5.2. The same biogenic emissions as used in the 2002 Base Case modeling will be used for the future-year modeling. This assumes that the same land use and biomass distribution as

used in the 2002 Base Case emissions would exis t in the future-year emission scenarios. TheMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-9 effects of urban sprawl, increased agricultural, defo restation, locations of new roads, etc. between the current (2002) and future-year would not be accounted for. The same Typical Year fire emissions that were used in the 2002 Typical Year emissions scenario would be used in the futureyear. Future-year Typical Year emissions for Electrical Generating Units (EGUs) would also be used for the future-year scenarios, however for EGUs these would not be identical to the 2002 Typical Year EGU emissions.

8.3.3 Future-Year Initial and Boundary Conditions

The same Initial Conditions (ICs) as used in the 2002 Base Case would be used in the future-year modeling. Because a ~15 day spin up period is being used, ICs will have minimal if any influence on the model estimated concentrations.

As discussed in Section 5.3, the source of the boundary conditions (BCs) to be used along the lateral edges of the continental US nationa l RPO 36 km modeling domain for current (2002) and future-year modeling is being studied by VI STAS. The VISTAS Phase I modeling analyzed several different types of boundary conditions, including the CMAQ default values and seasonal, monthly and 3-hour average values estimate d by the GEOS-CHEM globa l model for a 2001

annual simulation.

The effects of using time varying (3- hourly) BCs produced by the 2001 GEOS-CHEM global model simulation versus the seasonal GE O-CHEM BCs were analyzed using the CMAQ model and the VISTAS Phase I July 2001 episode. These results support the notion that a 2002

GEOS-CHEM simulation would be beneficial to support regional air quality modeling of the 2002 calendar year. Accordingly, VISTAS is pur suing performing a 2002 GEOS-CHEM simulation,

whose results would be used to define BCs for the CMAQ revised basecase simulation in the fall

of 2004.

Initially, BCs based on seasonal average re sults from the GEOS-CHEM 2001 simulation will be used for the VISTAS Phase II CMAQ modeli ng of the initial 2002 base case. If a 2002 GEOS-CHEM simulation is ready in time, then the Phase II modeling 2002 BCs would be updated

with that data. If a 2002 GEOS-CHEM simulati on is performed, there is the possibility that GEOS-CHEM would also be run for the future-year emissions scenario in which case the futureyear CMAQ BCs would be based on the future-year GEOS-CHEM simulation. In the absence of a future-year emissions scenario GEOS-CHEM si mulation, we recommend that the same CMAQ BCs be used in the future-year as used in the 20 02 base year as any adjust ments to them would be arbitrary and speculative.

8.3.4 Other Future-Year Modeling Inputs

All other future-year CMAQ m odeling inputs would be identic al to the 2002 Base Case simulation including meteorology, photolysis rates, geophysical, and other inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-10 8.4 Presentation of Results

There are various ways that the future-year m odeling results can be presented to display the visibility projections and convey the key fi ndings. Although the proj ection of visibility improvements at the Class I areas is a key elemen t of the Regional Haze Rule (RHR), analyzing the results solely at the Class I areas is a very narrow view of them, thus we intend to present the future-year modeling results in other forms to convey a more complete picture.

8.4.1 Projection of Visibility Improvements at Class I Areas

The results of the visibility projections at each Class I area in and around the VISTAS domain would be displayed graphically as well as in tabular summaries. Spread sheets of the results would be generated ordered by state and pr ovided to VISTAS. The vi sibility projections at each Class I area would be compared with the 2 018 visibility progress goal calculated following EPA’s “Guidance for Tracking Progress Unde r the Regional Haze Rule (EPA, 2003a).

Figure 8-1 displays a sample plot of a visi bility progress goal prepared for the Grand Canyon National Park as part of the WRAP Section 309 SIP work effort. The modeled projected visibility could be plot ted on the same figure as a graphical way of presenting the results. Shown as a straight line near the botto m of Figure 8-1 is the EPA defaul t “natural conditions” for the 20% worst visibility days as the green line with green triangles that represents the visibility goal at Grand Canyon of 6.97 dv in 2064. The black diamonds on the left side of the plot are the “current

conditions” that are based on IMPROVE observations for the 20% worst days for five-year periods ending in 1993 (i.e., 1989-1993) to 2001 (i.e., 1997-2001). The “curr ent conditions” for the latest five years of data, which is 1997-2001 in this gr aph (the Section 309 SIP analysis was performed

in 2003 before the 2002 IMPROVE data were availa ble), is assumed as the “baseline conditions” (i.e., 2000-2004) that is the star ting point in 2004 (12.00 dv) for th e glide path or linear uniform rate of progress to natural cond itions in 2064 (purple line with s quares). In this example, the visibility progress goal in 2018 would be 10.83 dv so that a 1.17 dv reduction in visibility is needed by 2018 to meet the visibility progress target in 2018 at the Grand Canyon National Park (GMNP). Note that we would exp ect higher observed visibility le vels for the 20% worst days at Class I areas in VISTAS due to higher density population and higher sulfate concentrations in the eastern US, but Figure 8-1 illustrates the types of gra phs that can be utilized to display visibility progress goals combined with vi sibility progress projections.

Tabular summaries could list for each Class I area, in terms of deciview, the observed Baseline Conditions, the 2018 Progress Goal, the difference (i.e., visi bility progress target) and then the modeled difference for each future-year emissions scenario. Such a table would clearly state which future-year emission scenarios met the RHR 2018 Progress Go als at which Class I

areas.

Another useful display of the projected visibility improvement s are stacked bar charts of extinction showing the contributio ns of each of the six major PM components to light extinction for the IMPROVE measured current period to th e future-year period and the 20% best and 20% worst days. These side–by-side current to futu re-year extinction compar isons can graphically display which extinction components are getting better and which are getting worse. They can also be used to identify why the emission cont rols may not have had the desired effect. ForMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-11 example, an SO 2 emissions control strategy may have had the desired effect in reducing sulfate concentrations but the visibility estimates re main relatively unchanged as the lower sulfate concentrations frees up ammonia that then forms pa rticulate nitrate that compensates for the lower sulfate in the visibility equations. Such a chem ical shift in the extinction budget would be readily apparent in the stacked bar charts. The most useful form s to convey the modeling results will evolve over the project.

Figure 8-1. Example Calculation Of Visibility Pr ogress Goals Using IMPROVE Data Collected At The Grand Canyon National Park For The 20% Worst Days S howing EPA Default “Natural Conditions” (Green Triangles) For The 20% Worst Days That Is The 2064 Goal (6.97 dv),

“Current Conditions” Observed Vi sibility (dv) As Black Triangl es Whose Latest Value (12.00 dv) Is Assumed As The Starting Point For The 2000- 2004 Baseline (First Purple Square), And The

Glide Path From 2004 Baseline To “Natural Cond itions” In 2064 Assuming Linear Uniform Rate Of Progress. 

### Uniform Rate of Reasona ble Progress Glide Path Grand Canyon NP - 20% Worst Days

12.00 11.66 10.83 9.99 9.15 8.31 7.47 6.97

0 5 10 15 20 25 30

1993 1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063

YearHaziness Index Unit:Deciviews

Glide Path Natural Visibility Conditions ObservationMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-12 8.4.2 Spatial Maps of Results

Spatial maps of changes in individual species, total PM mass, extinction and deciview from the 2002 Typical Year to future-year simulations w ould be used to provide a big picture view of the changes in regional air quality and visibility from 2002 to th e future–year. The display of hourly and 24-hour animations as well as longer-term averages (e.g., monthly, seasonal and annual) may provide more insight into the modeling results.

8.4.3 Time Series Plots

Time series plots of concentrations extinction and deciview at key receptor locations, such as Class I areas, may also provide additional insight into the modeling results. Hourly time series for selected periods and locations may provide insi ght into short-term changes, whereas changes in 24-hour concentrations for the year provides inform ation for all days, not just the ~40 days that constitute the 20% best/worst days of the year.

8.4.4 Box Plots and Q-Q Plots

Box and whisker plots of the projected extinctio n and deciview at Class I areas as well as absolute modeled extinction and deciview at all sites can be used to see changes in visibility across all concentrations, not just the 20% best/worst da ys. Quantile-Quantile plots of the changes in frequency distribution projected using RRFs and absolute results from the model would also provide an indication of the change s in visibility from the curren t to future-year across all days.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-1 9.0 DATA MANAGEMENT

Data management and data security procedur es are critical components of the VISTAS regional fine particulate and haze modeling. Very la rge data files are used in each component of the modeling process, including processing of the meteorology data, emissions processing, and visibility modeling with CMAQ. An annual simu lation on the VISTAS 36-km domain requires approximately 2 Terabytes (Tb) of disk storag e while an annual simulation on the VISTAS 12- km domain requires over 3 Tb of disk storag e. This chapter describes data management practices.

For all critical files we w ill maintain backup copies eith er on tapes or redundant disk systems. In addition, because model simulations will be performed separately by ENVIRON, UCR and AG, each institution will maintain its own copy and backup of critical input and output files. Because there are differences in system configurations at each of the 3 modeling centers (ENVIRON, UCR, and Alpine Geophysics) the data backup and archiving are discussed separately for each center, below.

CMAQ generates large output files of which most information is rarely used (for example, model output for layers other than the surface layer). Under Phase I, we extracted layer 1 data for analysis and then deleted the 3- dimensional CMAQ instantaneous concentration CONC output files. However, we permanently sa ved the script, executables and input data so that 3-D files can be regenerated if neede d. During Phase I a ne w version of CMAQ was released that outputs hourly average layer 1 c oncentrations for selected species (ACONC) in

addition to the 3-D instantaneous concentration CONC files. For Phase II we propose to output and archive all species on the layer 1 ACONC file and extract and archive 3-D CONC files just for the last hour of each month so that monthly se nsitivity tests could be performed. In the event the Optional Task 14 is funded to evaluate CMAQ estimates alof t, we will save the 3-D CONC files for periods with aircraft flights. We do not pl an redundant archiving for model output files (including output from MCIP and SMOKE) because these files can be regenerated by repeating model simulations, and this is approach more ef ficient and more cost effective than redundant archiving.

To promote efficient, reliable communicat ion among project participants, the modeling

team has created 4 different listservs to aid in dissemination of information and as a primary means for distributing emissions and air quality modeling information to the VISTAS TAWG.

The listservs are:

• vistas-all@cert.ucr.edu: ge neral project information fo r all interested persons.

• vistas-modeling@cert.ucr.edu: private list for the VISTAS project management team and modeling contractors.

• vistas-emissions@cert.ucr.edu: list for sharing information on emissions processing and QA.

• vistas-met@cert.ucr.edu: list for shari ng information on meteorology modeling and processing of MM5 data using MCIP.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-2 These separate listservs are aimed at provi ding better organization of communications and allowing for detailed discussions of specific topics such as emissions QA.

9.1 Project Website

For the VISTAS Phase I modeling effort, a pr oject webpage at UCR has been used to publish and disseminate all project results. The webpage address is: www.cert.ucr.edu/vistas and it includes sections on model input data, emissions QA, visibility modeling results, and project reports and presentations. Because the Phase I we bpage already contains results for a very large number of model simulations, we propose to establish a new, parallel webpage for Phase II using the address www.cert.ucr.edu/aqm/vistas2 that will be identical in design to the Phase I webpage. The advantages of establishi ng a new webpage include the following:

• Separating Phase I and Phase II results will avoid confusion in presenting or interpreting.

Results.

• The Phase II results will be simpler and more accessible.

• All Phase I results will continue to be ava ilable at the previously published web page

addresses.

As in Phase I, the project webpage will be th e primary mechanism for communicating results of model simulations and analysis to funding agencies and other interested parties.

9.2 Data Transfer

Data transfer among the 3 modeling centers an d between other VISTAS participants or contractors will be accomplished using a combinati on of email, ftp downloads and portable disk drives depending on the size of the data transfer. For data files smaller than a few MB email typically works well and is most efficient. For da ta files of less than ab out 500 MB file transfer protocol (ftp) is typically th e fastest and most efficient method. ENVIRON, UCR and AG each maintain webpages and ftp pages that can be us ed for exchanging data . In addition, each modeling center has severa l portable disk drives with both USB2 and firewire interfaces that can be FedEx among project participants to exchange large data sets. Portable disk drives range in size from 80 to 300 GB and are adequate for al l large files data transfers. The approach

described here has been used throughout the VISTAS Phase I project and has proven to be

economical and efficient.

9.3 Data Backup and Archiving

Data backup and archiving will be performed at each of the modeling centers. Copies of critical project data will be maintained at each modeling center to provide redundant backup of key project data. In addition, ea ch modeling center will perform backups of key project data to tape or redundant disk storage systems. Data storage and back up resources at each modeling

center are described next.

ENVIRON

Over 10,000 Gigabytes (>10 Terabytes, Tb) of disk storage are available to the UNIX/Linux workstations. All of the workstations are networked togeth er and are accessibleMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-3 from each employee’s desktop PC. All workstat ions have CD-ROM drives and can access DLT, 4mm DAT and 8mm Exabyte tape drives for data backup and data transfer . ENVIRON can also create CDs (CD-R and CD-RW) and DVDs (DVD+ a nd DVD-) for data bac kup and distribution. For Phase II modeling, most CMAQ simulations would be performed on a new 9 node Beowulf

Linux Cluster that included one ma ster node and 8 processing nodes. Each node consists of two AMD Athlon 2600+ processors. The master node ha s 2 Gb of memory and is connected to a 2.8 Tb RAID disk system. Each secondary pr ocessing node includes 1 Gb of memory. The ENVIRON Novato computing ce nter also includes approxima tely 10 dual processor Linux workstations with processing sp eeds of 1700+ to 3000+. Three olde r Unix workstations are also available, SUN, DEC and SGI. The Linux computer systems are located in their own room with

their own dedicated air conditioni ng (AC) system. The room in cludes a temperature sensitive power shut off device that will shut off the power to all comput ers in case the AC breaks down so that catastrophic failure due to too high temp eratures does not occur. Backups are made on

IDE disk drives that are removed from the com puter and stored on a sh elf to protect against power surges destroying the backup data.

UC Riverside

Data storage systems at UCR include more than 22 TB of disk space configured as RAID5 disk systems. All computers and disk systems are connected using high speed Gigabit

Ethernet for efficient simulati on and analysis of large datase ts. To provide maximum data security the systems are located behind the UCR firewall and an additional firewall internally within the laboratory. A separate system is used for the project websites and ftp site to allow project data to be accessed thought the UCR T1 internet connection. The data backup/archiving system include 8mm tape drives and DLT and S uper DLT auto loading car tridge system capable of performing unattended archive/ backups of over 1 Tb (uncompressed). Key disk systems have hot-swappable hard drives with stand-by spare drives and redundant power supplies. Copies of critical project data are backed up to tape a nd to a redundant RAID5 disk system. The compute clusters and disk systems are located in a locke d, secure room with a dedicated climate control system and with backup air c onditioning. The laboratory has a full time systems administrator to perform system backups, maintenance and upda tes and Dr. Tonnesen’s group includes a second full time systems administrator who performs we ekly backups of critical data to tape.

Alpine Geophysics

Alpine Geophysics' computing facilities c onsist of SUN Micros ystems SPARCstation computers and a very powerful array of 19 mu ltiprocessor Linux-based workstations. Disk storage systems include 12 Tb of aggregate disk space and over 8 Tb of SCSI and IDE Raid-5 protected space. All client data is stored on at least one RAID-5 protected di sk array. To further protect client data, two of our main servers b ackup their disk drives on a weekly basis to a second server that is physically disconnected from the power supply when not doing an active transfer. This way a catastr ophic power failure will not comp romise the ability for Alpine Geophysics to deliver.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 10.doc 10-1 10.0 DOCUMENTATION

This section describes the documentation that be provided during VISTAS Phase II and the potential for modifications to this Modeling Protoc ol and QA Plan that might become necessary as this phase of the study unfolds.

10.1 Planned Documentation

Documentation associated with the emissi ons and air quality modeling performed during Phase II will include all relevant input data base s and scripts associated with the pre- and postprocessing associated with model input developm ent, model application, sensitivity and diagnostic analyses, and performance evaluations. Interim re ports describing the methodologies and results of the model performance evaluation, model interc omparison, and visibility assessment will be provided. As shown in Appendix A, the interim reports for many of the subtasks will consist of PowerPoint presentations and/or results posted to the project website. For the major tasks, however, we do intend to document the work activities in more formal reports. Such planned reports for the

base effort include:

• Phase II Modeling Protocol (this document)

• Phase II Quality Assurance Project Plan (QAPP)

• 2002 emissions modeling draft and final reports

• Initial 2002 CMAQ modeling summary report

• Revised 2002 CMAQ modeling summary report

• Future-year visibility projections modeling report

• Draft and final Executive Summary report

• Optional Journal Article(s)

In addition, copies of all scri pts, reports and documentation provided to the VISTAS TAWG will be maintained on the project website. A final report summarizing all aspects of the project will also be provided. The list of deliverables for this project is described in a summary of the Phase II scope of work provided in Appendix A.

Reporting on each task in Phase II will consist of documentation of the

 data sources,

 methods,

 results, and findings. Individual task deliverab les shall reflect any changes and revisions

that occur over duration of Phase II. At the completion of Phase II, a draft final Executive Summary report will be prepared that details , documents and summarizes the results. This

documentation will conform to the recommendations set forth in EPA’s "Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze (Jan ‘01)” and any subsequent versions. The final Execu tive Summary report will contain: (a)

 an executive summary abstract that provides a brief overview and summa ry of the modeling effort, emissions and air quality models used, model configuration, model performance evaluation overview and results, and rationale for the selected configuration, (b) technical detail covering all relevant aspects of the Phase II emissions and PM grid modeling, and (c) a di scussion on data accessibility and availability for review by the public. The report will be provi ded in electronic form, (e.g., Word 2000 and pdf formats) and shall be submitted to the VISTAS Pr oject Officer and also posted on the project web

site.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 10.doc 10-2 10.2 Procedures for Updating Modeling Protocol and QA Plan

One of the underlying realizations stemming fr om the Phase I activities was the awareness that the science of ‘One-Atmosphe re’ PM/regional haze modeling is advancing very rapidly. Part of this stems from the parallel activities being carried out by the RPOs; some if it is due to other ongoing 8-hr ozone and PM modeling studies being performed by various states. In addition, EPA is in the process of revising its PM 2.5 and regional haze guidance documents. Collectively, it is quite likely that there will be new opportunities to strengthen the modeling algorithms, input data sets, and evaluation procedures throughout the duration of Phase II. Moreover, the Phase II emissions and air quality modeling activities involve the collaboration with the TAWG as well as other VISTAS contractors who will provide the required emissi ons and meteorological inputs to the SMOKE and CMAQ models. Given the ongoing model refinement activities and the need for strong coordination with other VISTAS contractors, it may be necessa ry to modify certain aspects of this modeling protocol. In this event, modification will be ma de in consultation with the VISTAS TAWG and the revised protocol will be submitted to the workgroup for approval.May 2003

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\References.doc R-1 REFERENCES

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### Appendix A

Summary of VISTAS Phase II Emissions and Air Quality Modeling Scope of Work (SOW) (Updated May 6, 2004)

### [This page intentionally left blank.]VISTAS Phase II 2004 Atmospheric Modeling Scope of Work ENVIRON/Alpine/UCR Draft Version#5: May 6, 2004

INTRODUCTION

ENVIRON International Corporation, Alpine Geophy sics (Alpine) and the University of California at Riverside (UCR) propose to perform the VISTAS Phase II emissions and air quality modeling as described in the Scope of Work (SOW) given below. As was seen in the VISTAS Phase I modeling, the SOW will likely be refined and modified during the course of the project and the ENVIRON/Alpine/UCR team is flexible in achieving the ultimate objectives and requirements needed by VISTAS. In putting together the VISTAS Phase II SOW, the ENVIRON/Alpine/UCR team is dependent on receiving data and information from other participants. The following are our assumed dates for deliverables that we expect to receive key data sources in developing
our SOW.

• January 31, 2004 – Initial 2002 baseline emissions for VISTAS states delivered by VISTAS contractors. (Achieved February 9, 2004.)

• January 31, 2004 – First 8 months of 2002 MM5 modeling results delivered by VISTAS contractors. (Achieved February 20, 2004)

• March 30, 2004 – Last 4 months of 2002 MM5 modeling results.

• April 15, 2004 -- Initial 2018 Future-Year VISTASA States emissions for one scenario and 2 of the Phase I episodes (Jan ’02 and Jul ’01) from VISTAS contractor. Second 2018 scenario available April 30, 2004.

• September 15, 2004 – Revised 2002 baseline emissions for VISTAS states.

• September 15, 2004 – Revised 2002 base year using typical year EGU and fire inventory for VISTAS states.

• September 15, 2004 – 2002 base year inventory and typical year EGU and fire inventory for non-VISTAS states, Mexico, Canada as available from non-VISTAS sources

• October 15, 2004 – Revised Future-Year emissions for VISTAS states, three future year scenarios and the 2002 annual period.

• October 31, 2004 – MM5 modeling results for ~60 episode days from 2003 (or other years as appropriate) for optional episode modeling task.

Key deliverables for the modeling team are as follows:

• Draft modeling protocol by March 5, 2004, final protocol 2 weeks after VISTAS review

comments received.

• Analysis of GEOS-CHEM 2001 hourly and monthly outputs for CMAQ initial and boundary conditions (IC/BC) and recommendation whether to use 2002 GEOS-CHEMfor IC/BC in CMAQ revised 2002 modeling in fall 2004 – due March 2004.

• 2002 initial inventory for Non-VISTAS States, Mexico, Canada by March 15, 2004.

• 2018 initial emissions estimates for Non-VISTAS States, Canada, Mexico and 2 Phase I episodes (Jan 2002 and Jul 2001) by April 15, 2004.

• 2018 CMAQ-ready emissions from SMOKE fo r 2 Phase I episodes (July 2001 and Jan 2002) for one scenario by May 15, 2004 and the other scenario by June 15, 2004.

• 2002 CMAQ-ready emissions from SMOKE for 2002 annual period by May 31, 2004

• Quality Assurance Project Plan (QAPP) for Phase II modeling (June 2004)

• CMAQ model performance evaluation for annual 2002 runs using initial 2002 inventory by August 30, 2004

• Collection of available 2002 revised inventory and typical year EGU and fire for nonVISTAS states, Canada, Mexico by September 15, 2004

• Future year revised inventory for non-VISTAS states, Canada, Mexico by October 15,
2004 for 3 scenarios

• 2002 revised CMAQ-ready SMOKE outputs usi ng revised 2002 inventories by October 15, 2004

• 2002 revised plus typical EGU and fire CM AQ-ready SMOKE outputs by October 31,

2004

• 2002 revised CMAQ annual run and model per formance evaluation by December 1,

2004

• 2002 revised plus typical EGU and fire CMAQ annual run and model performance evaluation by December 31, 2004

• Future year CMAQ-ready SMOKE outputs using revised inventories for first scenario by November 15, 2004, for second and third by December 15, 2004

• Future year revised emissions CMAQ annual modeling runs for 3 scenarios due by January 31, 2005.

• Optional task CMAQ-ready SMOKE outputs for 2003 episodes after January 1, 2005

Table 1 summarizes the task structure, task leader, schedule and deliverables for the VISTAS Phase II proposed SOW. Figure 1 displays the current proposed schedule for the VISTAS Phase II modeling based on the above assumptions on data deliverables to the Modeling Team. This schedule will likely be revised and updated as more refined delivery dates are developed.Figure 1. Current proposed schedule for the VISTAS Phase II modeling (May 6, 2004).Table 1.Summary of proposed VISTAS Phase II Tasks, Task Managers, Deliverables and Costs (shaded tasks are currently not funded, May6,2004).Work ElementManagerDeliverables (Date)Task 1: Management Ralph Morris•Monthly Conference Calls (2004)•3 Meetings with 2 People in Southeast US (2004)•First Draft Modeling Protocol (03/05/04)•Second Draft Modeling Protocol (03/30/04)•Updated Modeling Protocol (as needed)•QAPP (June 2004)•Draft Final Executive Summary Report (01/15/05)•Final Executive Summary Report (01/31/05)Task 2: IC/BCs Analysis Gail Tonnesen•Initial analysis of the need for 3-hr vs. seasonal BCs for CMAQmodeling (02/12/04).•2002 IC/BC recommendations PowerPoint presentation, whichincludes monthly average vs. 3-hr BC CMAQ comparisons forJuly 2001 episode (March 2004)Subtask3a: Air QualityGatekeeperGail Tonnesen•2002 Air Quality MPE database, QA/QC and PowerPointpresentation (04/30/04)•2002 initial CMAQ IC/BC inputs (04/30/04)•Review of 2002 special study (e.g., aircraft data) dataavailability that would be used in Optional Task 14 Enhancedmodel evaluation (05/30/04)Subtask 3b:MeteorologicalGatekeeperDennis McNally•First eight months 2002 MM5 QA/QC Gatekeeper PowerPointpresentation (03/26/04)•CMAQ-ready meteorological inputs for first six months of 2002(03/31/04)•Last four months 2002 MM5 QA/QC Gatekeeper PowerPointpresentation (04/15/04)•CMAQ-ready meteorological inputs for first six months of 2002(04/15/04)Subtask 3c: EmissionsGatekeeperGregory Stella•Recommendation for Mexico and Canadian Emissions(02/29/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointpresentation on initial 2002 emissions (03/31/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointWork ElementManagerDeliverables (Date)presentation on initial future year emissions (5/15/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointpresentation on revised 2002 and future year emissions(10/15/04)•Initial 2002 Base Case emissions inventory for non-VISTASstates (03/15/04)•Revised 2002 emissions inventory for non-VISTAS States(09/15/04)•Initial future-year inventory for non-VISTAS states (05/15/04)•Three future-year emission inventories for non-VISTAS states(09/15/04)Subtask 3d: EmissionsModelingCyndi Loomis•2002 CMAQ-ready emission input using initial emissions for allof 2002 (05/30/04)•Initial 2002 emissions PowerPoint presentation (05/30/04)•2002 CMAQ-ready emission inputs using revised and typicalemissions (10/15/04)•Revised and typical 2002 emissions modeling PowerPointpresentation (10/31/04)•Future-year CMAQ-ready emissions for two scenarios usingrevised emissions (11/15/04 and 12/15/04)•Draft emissions summary report (11/30/04)•Final Phase II emissions modeling report (12/31/04)Subtask 3e: Future-YearEmissions Modeling for 2VISTAS Phase I EpisodesCyndi Loomis•CMAQ-ready initial future-year emission inputs for two VISTASPhase I episodes (05/15/04)•CMAQ-ready future-year emission inputs for second scenarioand two VISTAS Phase I episodes (06/15/04)•Summary PowerPoint presentation on VISTAS Phase I episodefuture-year emissions (06/15/04)Subtask 3f: InterimFuture-Year Modeling for2002Cyndi Loomis•CMAQ-ready future-year emissions for interim future-year and2002 meteorology (12/15/04)•Interim future-year emissions summary PowerPoint presentation(12/30/04)Subtask 4a: Initial 2002CMAQ Modeling and MPET.W. Tesche•PowerPoint presentation of the initial CMAQ model performancefor 2002 annual period (06/30/04)Work ElementManagerDeliverables (Date)•Summary report of CMAQ initial 2002 model performanceevaluation and sensitivity analysis including recommendationsfor “best” configuration (08/30/04)Subtask 4b: Revised 2002CMAQ Modeling and MPERalph Morris•Power Point presentations on the revised CMAQ base casesimulation and sensitivity analysis (11/30/04)•Draft Report of revised CMAQ 2002 Base Case simulation andMPE (12/15/04)•Report on model sensitivity tests (01/15/05)Subtask 4c: Typical year2002 CMAQ ModelingRalph Morris•PowerPoint presentation on 2002 typical year CMAQ modeling(12/31/04)Subtask 4d: Future-YearCMAQ Modeling for TwoScenariosGail Tonnesen•Draft Report on future-year base case modeling for twoscenarios using 2002 and episodic databases (01/31/05)Subtask 4e. InterimFuture-Year CMAQModelingGail Tonnesen•PowerPoint presentation of Interim Future-Year modelingresults (01/31/05)Optional Task 5: 2003Episodes•Starts after completion of future-year emissions modeling (starts~01/01/05)Subtask 5a: EpisodicEmissions Gatekeeperand ModelingCyndi Loomis•Emissions Gatekeeper summary PowerPoint presentation forrevised episodic modeling emissions (2005)•Revised Episodic CMAQ-ready revised 2003 Base Case andTypical Year emission inputs and summary (2005)•Future-year CMAQ-ready episodic inputs for three scenariosand summary report on the episodic emissions modeling (2005)Subtask 5b: EpisodicMeteorologicalGatekeeperDennis McNally•CMAQ MCIP meteorological modeling inputs (2005)•MM5 Gatekeeper evaluation and QA/QC PowerPointpresentation (2005)Subtask 5c: Episodic AQData GatekeeperGail Tonnesen•MPE air quality data database for episodic modeling periods(2005)•AQ data summary presentation for episode periods (2005)•CMAQ IC/BC inputs for “2003” episode periods (2005)Subtask 5d:Episodic Emissions fornon-VISTAS statesGreg Stella•Revised “2003” Base Case, Typical Year and Future-Yearemissions for episodic modeling (2005)Work ElementManagerDeliverables (Date)Subtask 5e. EpisodicBase Case and TypicalYear CMAQ ModelingGail Tonnesen•CMAQ Base Case and MPE and Typical Year modeling reportfor episode periods (2005)Subtask 5f: EpisodicFuture-Year Modeling forThree scenariosRalph Morris•Summary Report on future-year base case modeling for threescenarios using the episodic databases (2005)Subtask 6a: VISTASModeling WebsiteGail Tonnesen•VISTAS modeling website will be maintained and updatedcontinuously during 2004.Subtask 6b: DataTransfer, Storage andRetrievalGail Tonnesen•Performed continuously during 2004oArchive (2004)oData transfer drive (12/31/04)Optional Task 7Evaluate RelativeReduction Responseusing CB-IV vs. SAPRCfor 2 Phase I episodesGail Tonnesen•PowerPoint presentation documenting the emissions reductionsensitivity tests for the January 2002 and July 2001 episodesusing CB4 and SAPRC99 chemistry (06/30/04)Optional Task 8:Emissions SensitivityModeling using Phase IepisodesRalph Morris•PowerPoint presentation and model output files for the emissionreduction sensitivity simulations using winter and summerPhase I episodes (TBD, ~09/30/04)Optional Task 9:Additional Sensitivityusing VISTAS Phase IEpisodesT.W. Tesche•Summary report of additional Phase I sensitivity tests (TBD,09/30/04???)Optional Task 10: JournalPaper on VISTAS Phase IModelingGail Tonnesen•Draft Journal paper (05/31/04)•Draft final Journal paper submitted to Journal; (06/30/04)•Final Manuscript (TBD, 10/31/04???)Optional Task 11:Corroborate Analysisusing Alternative ModelsRalph Morris•Summary presentation of corroborative modeling analysis usingalternative models (01/31/05)Optional Task 12: NaturalConditions ModelingGail Tonnesen•Natural conditions summary presentation (TBD???)Optional Task 13: 2002GEOS-CHEM BCsGail Tonnesen•2002 CMAQ BCs based on 2002 GEOS-CHEM simulation.Work ElementManagerDeliverables (Date)Optional Task 14:Enhanced ModelPerformance EvaluationT.W. Tesche•Summary presentation of the enhanced model performanceevaluation using initial database (08/31/04)•Summary presentation of the enhanced model performanceevaluation using the revised base case results (11/30/04)Optional Task 14:ContingencyRalph Morris•To be determined.