## Appendix F

CENRAP Technical Support DocumentInternational Corporation Air Sciences

Draft Report

Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans

Prepared for: Central Regional Air Planning Association 10005 S. Pennsylvania, Suite C

Oklahoma City, OK 73159

Prepared by: ENVIRON International Corporation 101 Rowland Way, Suite 220 Novato, CA 94945

and

University of California at Riverside

College of Engineering Center for Environmental Research and Technology Riverside, Ca 92517

September 12, 2007

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700September 2007

i TABLE OF CONTENTS

Page

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

1.1 Background................................................................................................................ 1-1 1.2 CENRAP Organizational St ructure and Work Groups.............................................. 1-3
1.3 Overview of 2002 Annual Emissions a nd Air Quality Modeling Approach............. 1-4 1.3.1 Modeling Protocol ............................................................................................... 1-4 1.3.2 Quality Assurance Project Plan (QAPP).............................................................. 1-5
1.3.3 Model Selection ................................................................................................... 1-6 1.3.3.1 MM5 Meteorological Model Configuration for CENRAP Annual Modeling ................................................................................ 1-6 1.3.3.2 SMOKE Emissions Model Configuration for CENRAP Annual Modeling ................................................................................ 1-7 1.3.3.3 CMAQ Air Quality Model Configuration for CENRAP Annual Modeling ................................................................................ 1-7 1.3.3.4 CAMx Air Quality Model Configuration for CENRAP Annual Modeling ................................................................................ 1-8 1.3.4 Modeling Domains............................................................................................. 1-13 1.3.5 Vertical Structure of Modeling Domain ............................................................ 1-13
1.3.6 2002 Calendar Year Selection ........................................................................... 1-15
1.3.7 Initial Concentrations and Boundary Conditions............................................... 1-16 1.3.8 Emissions Input Preparation .............................................................................. 1-17 1.3.9 Meteorological Input Preparation ...................................................................... 1-17 1.3.10 Photolysis Rates Model Inputs........................................................................... 1-19 1.3.11 Air Quality Input Preparation ............................................................................ 1-20 1.3.12 2002 Base Case Modeling and M odel Performance Evaluation ....................... 1-21 1.3.13 2018 Modeling and Visibility Projections ......................................................... 1-21 1.3.14 Additional Supporting Analysis......................................................................... 1-24 1.4 Organization of the Report....................................................................................... 1-25

2.0 EMISSIONS MODELING.................................................................................................. 2-1

2.1 Emissions Modeling Overview.................................................................................. 2-1 2.1.1 SMOKE Emissions Modeling System Background ............................................ 2-1 2.1.2 SMOKE Scripts ................................................................................................... 2-3 2.1.3 SMOKE Directory Structures.............................................................................. 2-5 2.1.4 SMOKE Configuration ........................................................................................ 2-5 2.1.5 SMOKE Processing Categories ........................................................................... 2-7
2.1.6 2002 and 2018 Data Sources.............................................................................. 2-10 2.1.7 Temporal Allocation .......................................................................................... 2-12 2.1.8 Spatial Allocation............................................................................................... 2-13 2.2 Stationary Point Source Emissions .......................................................................... 2-14
2.2.1 Data Sources ...................................................................................................... 2-15 2.2.2 Emissions Processing......................................................................................... 2-16September 2007

ii 2.2.3 Uncertainties and Recommendations................................................................. 2-17 2.3 Stationary Area Sources........................................................................................... 2-17
2.3.1 Data Sources ...................................................................................................... 2-17 2.3.2 Emissions Processing......................................................................................... 2-20
2.3.3 Uncertainties and Recommendations................................................................. 2-20
2.4 On-Road Mobile Sources......................................................................................... 2-21
2.4.1 Data Sources ...................................................................................................... 2-21 2.4.2 Emissions Processing......................................................................................... 2-22
2.4.3 Uncertainties and Recommendations................................................................. 2-22
2.5 Non-Road Mobile Sources....................................................................................... 2-23
2.5.1 Data Sources ...................................................................................................... 2-23 2.5.2 Emissions Processing......................................................................................... 2-25
2.5.3 Uncertainties and Recommendations................................................................. 2-26 2.6 Biogenic Sources ..................................................................................................... 2-26
2.6.1 Data Sources ...................................................................................................... 2-26 2.6.2 Emissions Processing......................................................................................... 2-26
2.6.3 Uncertainties and Recommendations................................................................. 2-27
2.7 Fire Emissions.......................................................................................................... 2- 27
2.7.1 Data Sources ...................................................................................................... 2-27 2.7.2 Emissions Processing......................................................................................... 2-28
2.7.3 Uncertainties and Recommendations................................................................. 2-28
2.8 Dust Emissions......................................................................................................... 2-2 9
2.8.1 Data Sources ...................................................................................................... 2-29 2.8.2 Emissions Processing......................................................................................... 2-29
2.8.3 Uncertainties and Recommendations................................................................. 2-30
2.9 Ammonia Emissions ................................................................................................ 2-30
2.9.1 Data Sources ...................................................................................................... 2-31 2.9.2 Emissions Processing......................................................................................... 2-31
2.9.3 Uncertainties and Recommendations................................................................. 2-32
2.10 Oil and Gas Emissions............................................................................................. 2-32
2.10.1 Data Sources ...................................................................................................... 2-32 2.10.2 Emissions Processing......................................................................................... 2-32
2.10.3 Uncertainties and Recommendations................................................................. 2-32
2.11 MMS Off-shore Gulf of Mexico Emissions ............................................................ 2-33
2.11.1 Data Sources ...................................................................................................... 2-33
2.11.2 Uncertainties and Recommendations................................................................. 2-34 2.12 Off-shore Shipping Emissions ................................................................................. 2-34
2.12.1 Data Sources ...................................................................................................... 2-34 2.12.2 Emissions Processing......................................................................................... 2-34
2.12.3 Uncertainties and Recommendations................................................................. 2-35
2.13 2018 Growth and Control ........................................................................................ 2-35
2.13.1 Data Sources ...................................................................................................... 2-35 2.13.2 Emissions Processing......................................................................................... 2-36
2.13.3 Uncertainties and Recommendations................................................................. 2-36 2.14 2018 Base G C1 Control Sensitivity........................................................................ 2-37
2.14.1 Data Sources ...................................................................................................... 2-37 2.14.2 Emissions Processing......................................................................................... 2-37
2.14.3 Uncertainties and Recommendations................................................................. 2-37September 2007

iii 2.15 Emissions Summaries .............................................................................................. 2-38

3.0 MODEL PERFORMANCE EVALUATION.................................................................... 3-1

3.1 Evaluation Methodology............................................................................................ 3-1 3.2 Operational Model Evaluation Approach .................................................................. 3-2
3.3 Model Performance Goals and Criteria ..................................................................... 3-2 3.4 Key Measures of Model Performance ....................................................................... 3-2 3.5 Operational Model Performance Evaluation ............................................................. 3-4 3.5.1 Sulfate (SO4) Model Performance....................................................................... 3-4 3.5.2 Nitrate (NO3) Model Performance ...................................................................... 3-4 3.5.3 Organic Matter Carbon (O MC) Model Performance........................................... 3-5 3.5.4 Elemental Carbon (EC) Model Performance....................................................... 3-9
3.5.5 Other PM 2.5 (Soil) Model Performance ............................................................... 3-9 3.5.6 Coarse Mass (CM) Model Performance .............................................................. 3-9 3.6 Diagnostic Model Performance Evaluation............................................................. 3-13 3.6.1 Diagnostic Model Performance in January 2002............................................... 3-13 3.6.2 Diagnostic Model Performance In April............................................................ 3-16 3.6.3 Diagnostic Model Performance In July ............................................................. 3-16 3.6.4 Diagnostic Model Performance In October ....................................................... 3-17
3.7 Performance at CENRAP Class I Areas fo r the Worst and Best 20 Percent Days.. 3-17 3.7.1 Caney Creek (CACR) Arkansas ........................................................................ 3-17 3.7.2 Upper Buffalo (UPBU) Arkansas ...................................................................... 3-19 3.7.3 Breton Island (BRET), Louisiana ...................................................................... 3-20 3.7.4 Boundary Waters (BOWA), Minnesota............................................................. 3-21 3.7.5 Voyageurs (VOYA) Minnesota ......................................................................... 3-22 3.7.6 Hercules Glade (HEGL) Missouri ..................................................................... 3-23 3.7.7 Mingo (MING) Missouri ................................................................................... 3-24 3.7.8 Wichita Mountains (WIMO), Oklahoma........................................................... 3-25 3.7.9 Big Bend (BIBE) Texas..................................................................................... 3-26 3.7.10 Guadalupe Mountains (GUMO) Texas.............................................................. 3-27
3.8 Model Performance Evaluation Conclusions........................................................... 3-28

4.0 VISIBILITY PROJECTIONS............................................................................................ 4-1

4.1 Guidance for Visibility Projections ........................................................................... 4-1 4.2 Calculation of Visibility and 2018 UR P Point from IMPROVE Measurements....... 4-2 4.2.1 Calculation of Visibility fr om IMPROVE PM Measurements............................ 4-3 4.2.1.1 Original and New IMPROVE Equations...................................................... 4-5 4.2.1.1.1 Original IMPROVE Equation.............................................................. 4-5 4.2.1.1.2 New IMPROVE Equation.................................................................... 4-6 4.2.1.1.3 Justification for Using the New IMPROVE Equation......................... 4-7
4.2.2 Calculation of the Baseline Conditions................................................................ 4-8 4.2.3 Data Filling for Sites with Insufficient Valid Data to
Calculate Baseline Conditions ......................................................................... 4-8
4.2.4 Natural Conditions............................................................................................... 4-8
4.2.5 2018 URP Point ................................................................................................... 4-9 4.3 EPA Default Approach to Visibility Projections....................................................... 4-9September 2007

iv 4.3.1 Mapping of Modeling Results to the IMPROVE Measurements ...................... 4-10 4.3.2 Using Modeling Results to Proj ect Changes in Visibility ................................. 4-11 4.4 EPA Default 2018 Visibility at CENRAP and Nearby Class I areas and Compar isons to 2018 URP Goals .......................................... 4-12 4.4.1 Example 2018 Base G Visibility Proj ections for Caney Creek, Arkansas ........ 4-12 4.4.1.1 EPA Default 2018 Visibility Projections.................................................... 4-12 4.4.2 Summary 2018 Visibility Projec tions Across Class I Areas ............................. 4-16 4.5 2018 Visibility Projections for Base G C1 Control Scenario .................................. 4-20

5.0 ADDITIONAL SUPPORTING ANALYSIS ..................................................................... 5-1

5.1 Comparison of CENRAP 2018 Visibility Projections with Other Groups................ 5-1 5.1.1 Comparison of CENRAP , VISTAS, MRPO and WRAP Visibility Projections........................................................................... 5-1 5.2 Extinction and PM Species Spec ific Visibility Projections and Comparisons to 2018 URP Point .................................................................. 5-5 5.2.1 Total Extinction Glidepaths ................................................................................. 5-5 5.2.2 PM Species specific Glidepaths........................................................................... 5-7 5.3 Alternative 2018 Visibility Projection Software...................................................... 5-11 5.4 PM Source Apportionment Modeling...................................................................... 5-12 5.4.1 Definition of CENRAP 2002 and 2018 PM Source Apportionment Modeling 5-12
5.4.2 CENRAP PSAT Visualization Tool .................................................................. 5-14 5.4.3 Source Contributions to Visibility Impairment at Class I Areas ....................... 5-15 5.4.3.1 Caney Creek (CACR) Arkansas ................................................................. 5-16 5.4.3.2 Upper Buffalo (UPBU) Arkansas ............................................................... 5-17 5.4.3.3 Breton Island (BRET) Missouri.................................................................. 5-18 5.4.3.4 Boundary Waters (BOWA) Minnesota....................................................... 5-18 5.4.3.5 Voyageurs (VOYA) Minnesota .................................................................. 5-18 5.4.3.6 Hercules Glade (HEGL) Missouri .............................................................. 5-18 5.4.3.7 Mingo (MING) Missouri ............................................................................ 5-19 5.4.3.8 Wichita Mountains (WIMO) Oklahoma..................................................... 5-19 5.4.3.9 Big Bend (BIBE) Texas.............................................................................. 5-19 5.4.3.10 Guadalupe Mountai ns (GUMO) Texas..................................................... 5-19 5.5 Alternative Visibility Projection Procedures ........................................................... 5-20
5.5.1 Treatment of Coarse Mass and Soil................................................................... 5-20
5.6 Alternative Model .................................................................................................... 5-21
5.7 Effects of International Trans port on 2018 Visibility Projections........................... 5-22
5.7.1 Elimination of International Contribu tions to 2018 Visibi lity Projections........ 5-23 5.7.1.1 EPRI’s Analysis of Effect s of International Contributions......................... 5-24 5.7.1.2 CENRAP Results From Elimination International Transport ..................... 5-27 5.7.2 Glidepaths Based on Controllable Extinction.................................................... 5-29 5.8 Use of Original IMPROVE Equation ...................................................................... 5-32 5.9 Visibility Trends ...................................................................................................... 5- 33

6.0 REFERENCES............................................................................................................... 6 -1September 2007

v APPENDICES

Appendix A: Model Performance Evaluation of the 2002 36 km MM5 Meteorological Model Simulation used in the CENRAP Modeling and Compar ison to VISTAS Final
2002 36 km MM5 and WRAP Interim
2002 36 km MM5 Simulations Appendix B: File Names, Data Source a nd Type and Description of Emissions Used in the 2002 Typical a nd 2018 Base G Emissions Inventories Appendix C: Model Performance Evaluation for the CMAQ 2002 Base F Base Case Simulation in the CENRAP Region Appendix D: 2018 Visibility Projections for CENRAP Class I Areas Using 2002 Typical and 2018 Base Case Base G Emission Scenario CMAQ Results and EPA Default Projection Method and Comparison with 2018 Uniform Ra te of Progress (URP) Glidepaths Appendix E: CAMx PM Source Apportionment Technology (PSAT) Extinction (Mm -1 ) Contributions for the 2002 Worst and Best 20 Percent Days at CENRAP Class I Areas Appendix F: Extinction and PM Species-S pecific 2018 Visibility Projections and Comparisons with 2018 URP Points

TABLES

Table 1-1. Federal Mandated Class I Areas in the CENRAP States..................................... 1-3 Table 1-2. MM5 Meteorological Model Configuration for CENRAP 2002 Annual Modeling (Johnson, 2007)............................................. 1-9
Table 1-3. SMOKE Emissions Model Confi guration for CENRAP Annual Modeling ..... 1-10 Table 1-4. CMAQ Air Quality Model Confi guration for CENRAP Annual Modeling...... 1-11 Table 1-5. CAMx Air Quality Model Confi guration for CENRAP Annual Modeling ...... 1-12 Table 1-6. Ground-level ambient data monitoring networks and
stations available in the CENRAP states for calendar year 2002 used in the model performance evaluation. .............................................. 1-20 Table 2-1. Summary of SMOKE scripts ............................................................................... 2-4 Table 2-2. Summary of SMOKE directories......................................................................... 2-5 Table 2-3. Representative model da ys for 2002 base year simulation.................................. 2-6 Table 2-4. 2002 modeled holidays ........................................................................................ 2-6 Table 2-5. Assignments of months to four seasons for use of seasonal inventory files in SMOKE ................................................................................... 2-7 Table 2-6. Assignments of months to two seasons for use of seasonal inventory files in SMOKE ................................................................................... 2-7 Table 2-7. CENRAP Typ02G emissions categories ............................................................. 2-9 Table 2-8. CENRAP anthropogenic emissions inventory contacts..................................... 2-11 Table 2-9. New Canadian spatial surrogates....................................................................... 2-14 Table 2-10. PM 2.5 speciation profil es for coal-burning sources ......................................... 2-16 Table 2-11. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory ................................................................................... 2-18September 2007

vi Table 2-12. New Temporal Profile Assignmen ts for CENRAP Area Source SCCs ............ 2-19
Table 2-13. Non-road mobile-source inve ntory temporal configuration .............................. 2-24 Table 2-14. Non-road agricultural em issions categories where the MRPO Base K inventory was used instead of the CENRAP inventory in Iowa ........... 2-24 Table 2-15. Non-road oil and gas development equipment categories that Texas provided emissions to be used instead of the CENRAP inventory .. 2-25 Table 2-16. Fugitive and road dust SCCs.............................................................................. 2-29 Table 3-1. Model performance goals and criteria used to assist in interpreting modeling results. .............................................................................. 3-3 Table 4-1. Definition of IMPROVE PM Components from Measured IMPROVE Species.............................................................................. 4-4 Table 4-2. Mapping of CMAQ V4.5 mode led species concentrations to IMPROVE PM components .............................................................................. 4-10
Table 4-3. Observed and Modeled Extinction by Species Averaged Across the Worst 20 Percent Days in 2002 at CACR........................................ 4-14 Table 4-4. 2000-2004 Baseline, 2018 URP Point, and Projected 2018 Visibility and Percent of Meeting the 2018 URP Point for the 2018 Base G and 2018 C1 Control Strategy CMAQ Simulations............... 4-22 Table 5-1. Comparison of CENRAP and EPA MATS 2018 visibility projections at CENRAP and nearby Class I areas ............................................. 5-12

FIGURES

Figure 1-1. Regional Planning Organizations engaged in Regional Haze Modeling............. 1-2 Figure 1-2. National Inter-RPO 36 km modeling domain used
for the CENRAP 2002 annual SM OKE, CMAQ and CAMx modeling............ 1-13 Figure 1-3. MM5 34 vertical layer definitions and scheme for collapsing the 34 layers down to 19 layers for the CENRAP CMAQ and CAMx 2002 annual modeling ....................................... 1-14 Figure 1-4. 2018 visibili ty projections expresse d as a percent of meeting the 2018 URP point for the 2018 BaseG CMAQ base case simulation using the EPA default (EPA, 2007) Regular RRF and alterna tive projections procedures
that set the RRFs for CM=1.0 and CM&SOIL=1.0........................................... 1-23 Figure 2-1. Flow diagram of majo r SMOKE processing steps needed by all source categories........................................................................................ 2-2 Figure 2-2. Flow diagram of SM OKE/BEIS2 processing steps............................................. 2-3 Figure 2-3. Summary of Typ02G and Base18G SO2 emissions by CENRAP state and major s ource sector (tons per year).................................... 2-39 Figure 2-4. Summary of Typ02G and Base18G NOx emissions by CENRAP state and major s ource sector (tons per year).................................... 2-40 Figure 2-5. Summary of Typ02G and Base18G VOC emissions by CENRAP state and major s ource sector (tons per year).................................... 2-40 Figure 2-6. Summary of Typ02G and Base18G PM2.5 emissions by CENRAP state and major s ource sector (tons per year).................................... 2-41 Figure 2-7. Summary of Typ02G and Base18G PM10 emissions by CENRAP state and major s ource sector (tons per year).................................... 2-41September 2007

vii Figure 2-8. Summary of Typ02G and Base18G NH3 emissions by CENRAP state and major s ource sector (tons per year).................................... 2-42 Figure 2-9. Summary of Typ02G and Base18G CO emissions by CENRAP state and major s ource sector (tons per year).................................... 2-42 Figure 3-1. Monthly fractional bias (%) for sulfate (SO4) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation........ 3-6 Figure 3-2. Monthly fractional bias (%) for nitrate (NO3) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation........ 3-7 Figure 3-3. Monthly fractional bi as (%) for organic matter carbon (OMC) across the CENRAP region for the CMAQ 2002
36 km Base F ba se case simulation...................................................................... 3-8 Figure 3-4. Monthly fractional bi as (%) for elemental carbon (EC) across the CENRAP region for the CMAQ 2002 36 km
Base F base case simulation............................................................................... 3-10 Figure 3-5. Monthly fractional bias (%) for other PM 2.5 (Soil) across the CENRAP region for the CMAQ 2002 36 km Base F
base case simulation........................................................................................... 3-11 Figure 3-6. Monthly fractional bias (%) for coarse mass (CM) across the CENRAP region for the CMAQ 2002 36 km Base
F base case simulation. ...................................................................................... 3-12 Figure 3-7. January 2002 performa nce at CENRAP CASTNet sites for SO2 (top left), SO4 (top right), HNO3 (middle left), NO3 (middle right), Total NO3 ( bottom left) and NH4 (bottom right)............. 3-14 Figure 3-8. January 2002 performa nce at CENRAP AQS sites for SO2 (top left), NO2 (top right), O3 (bottom left) and CO (bottom right) ......... 3-15 Figure 3-9. Daily extinction model performance at Caney Creek (CACR), Arkansas for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-18 Figure 3-10. Daily extinction model performance at Upper Buffalo (UPBU), Arkansas for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-19 Figure 3-11. Daily extinction mode l performance at Breton Island (BRET), Louisiana for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-20 Figure 3-12. Daily extinction model performance at Boundary Waters (BOWA), Minnesota for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-21 Figure 3-13. Daily extinction mode l performance at Voyageurs (VOYA), Minnesota for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-22 Figure 3-14. Daily extinction model performance at Hercules Glade (HEGL), Missouri for the wo rst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-23 Figure 3-15. Daily extinction mode l performance at Mingo (MING), Missouri for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-24September 2007

viii Figure 3-16. Daily extinction model pe rformance at Wichita Mountains (WIMO), Oklahoma for the worst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-25 Figure 3-17. Daily extinction model performance at Big Bend (BIBE), Texas for the worst (top) and best (bottom) 20 percent days during 2002........ 3-26 Figure 3-18. Daily extinction model pe rformance at Guadalupe Mountains (GUMO), Texas for the wo rst (top) and best (bottom) 20 percent days during 2002.............................................................................. 3-27 Figure 4-1. Comparisons of observed light extinction with reconstructed light extinction using the ne w (left) and original (right) IMPROVE equations at the Gr eat Smoky Mountains National Park.................. 4-7 Figure 4-2. Linear Glidepath for Caney Creek (CACR), Arkansas that linearly connects the 26.36 dv Baseline Conditions in 2004 with the 11.58 dv Natural Conditions in 2064 resulting in
a 22.91 dv 2018 URP Point.................................................................................. 4-9 Figure 4-3a. 2018 Visibility Projections and 2018 URP Glidepaths in Deciview for Caney Cree k (CACR), Arkansas and Worst 20 Percent (W20%) days Using 2002/2018 Base G CMAQ 36 km Modeling Results. .................................................................. 4-14 Figure 4-3b. 2018 Visibility Project ions and 2018 URP Glidepaths in Deciview for CACR, Arka nsas and Best 20 Percent (B20%) days Using 2002/2018 Base G CMAQ 36 m Modeling Results. ...................... 4-15 Figure 4-3c. Comparison of Observed (left) and 2002 Base G Modeled (right) Daily Extinction for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) days in 2002..................................... 4-15 Figure 4-3d. Differences in M odeled 2002 and 2018 Base G CMAQ Results (2018-2002) Daily Extinction for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) Days in 2002.................................... 4-16 Figure 4-4. 2018 Base G CMAQ Visi bility Projections for CENRAP and Nearby Class I areas Us ing DotPlots that Express 2018 Visibility as a Percenta ge of Meeting the 2018 URP Point On the Deciview Linear Glidepath .................................................................... 4-17 Figure 4-5. Absolute Model Esti mated Changes in Extinction (Mm -1 ) by PM Species for Class I Ar eas in the CENRAP region (top) and Near the CENRAP region (bottom). ........................................................... 4-18 Figure 4-6. Percent Change In Modeled Extinction by PM Species Averaged Across the 20 02 Worst 20 Percent Days for Class I areas in the CENRAP region (top) and Near the CENRAP region (bottom)................................................................... 4-19 Figure 4-7. CMAQ-Estimated Re ductions in Annual Average SO4 (left) and NO3 (right) Fine Particle Concentrations Between
the 2018 Base G Base Case and 201 8 Base G C1 Control Strategy Case......... 4-20 Figure 4-8. 2018 Visibility Projectio ns as a Percentage of Meeting the 2018 URP Point (i.e., DotPlot) for the 2018 Base G
and 2018 Base G C1 Control Strategy Emission Scenarios. ............................. 4-21 Figure 5-1. DotPlot comparing the CENRAP, VISTAS, MRPO and WRAP 2018 visibility projec tions expressed as a percentage of achieving the 2018 URP goal. ......................................................................... 5-3September 2007

ix Figure 5-2. 2002 Base F SO2 emissions (left) as LOG10(tons/year)
and differences in 2018 and 2002 Ba se F SO2 emissions (tons/year)................. 5-3 Figure 5-3. Exampled back traj ectories to Voyageurs National Park for two of the worst 20 percent days from 2002: December 13, 2002 (Julian Day 347) and November 28, 2002 (Julian Day 332) ................ 5-4 Figure 5-4. 2018 Visibility Proj ections and 2018 URP Glidepaths in extinction (Mm -1 ) for Caney Creek (CACR), Arkansas and Worst 20% (W20%) days using 2002/2018 Base G CMAQ 36 km modeling results .............................................................. 5-6 Figure 5-5. CMAQ 2018 Base G visibility projections and \ comparison of ability to achieve the 2018 URP point using the EPA default deciview and a lternative total extinction Glidepaths....... 5-6 Figure 5-6. 2018 Visibility Proj ections and 2018 URP Glidepaths for SO4 (top left), NO3 (top right), EC (middle left), OMC (middle right), Soil (bottom left) and CM (bottom right) in extinction (Mm -1 ) for Caney Creek (CACR), Arkansas and Worst 20 Percent Days using 2002/2018 Base G CMAQ 36 km modeling results .............................................................. 5-9 Figure 5-7. Ability of total and species specific 2018 visibility projections to achieve 2018 URP points............................................................ 5-10 Figure 5-8. 30 source regions used in the CENRAP 2002 and 2018 CAMx PSAT PM source apportionment modeling.................................. 5-14 Figure 5-9. PSAT source category by PM species contributions to the average 2000-200 4 Baseline and 2018 projected extinction (Mm -1 ) for the worst 20 percent visibility days at Caney Creek (CACR), Arkansas ........................................................... 5-16 Figure 5-10. PSAT source region by s ource category contributions to the average 2000-200 4 Baseline and 2018 projected extinction (Mm -1 ) for the worst 20 percent visibility days at Caney Creek (CACR), Arkansas. .................................................................. 5-17 Figure 5-11. PSAT source category by PM species contributions to the average 2000-200 4 Baseline and 2018 projected extinction (Mm -1 ) for the best 20 percent visibility days at Caney Creek (CACR), Arkansas. .................................................................. 5-17 Figure 5-12. Sensitivity of 2018 visibility projections to various methods that assume all CM, all CM and Soil and all CM and part of the Soil is natural ............................................................................. 5-21 Figure 5-13. Comparison of CAMx 2 018 visibility projections with 2018 URP points for CENRAP and nearby Class I areas.................................. 5-22 Figure 5-14. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and CENRAP PSAT (bottom) international
source contributions to ammoni um sulfate at Class I areas............................... 5-25 Figure 5-15. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and CENRAP PSAT (bottom) international
source contributions to ammonium nitrate, organic carbon mass (OCM or OMC) and elemen tal carbon (EC) at Class I areas. ...... 5-26September 2007

x Figure 5-16. Elimination of in ternational sources from 2018 visibility projections and comparison with 2018 URP point at CENRAP Class I areas ......................................................................... 5-29 Figure 5-17. Glidepaths and 2018 visibili ty projections based on visibility due to U.S. anthropogenic emissions at CENRAP Class I areas....................... 5-32 Figure 5-18. Comparison of 2018 Base G visibility projections using the New (NIA) and Old (OIA) IMPROVE algorithms expressed as a percen tage of achieving the 2018 URP point visibility improvements ............................................................................ 5-33 Figure 5-19a. Time series of observed IMPROVE reconstructed light extinction (New IMPR OVE) at Caney Creek (CACR), Arkansas for the average of the Worst 20 Percent days (top), Best 20 Percent days (m iddle) days and all IMPROVE sampling days during the period of record ........................................................ 5-35 Figure 5-19b. Time series of obs erved IMPROVE reconstructed light extinction (New IMPR OVE) at Upper Buffalo (UPBU), Arkansas for the average of the Worst 20 Percent days (top), Best 20 Percent days (m iddle) days and all IMPROVE sampling days during the period of record. ....................................................... 5-36 Figure 5-19c. Time series of obser ved IMPROVE reconstructed light extinction (New IMPROVE) at Breton Island (BRET), Louisiana for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record ................. 5-37 Figure 5-19d. Time series of obs erved IMPROVE reconstructed light extinction (New IM PROVE) at Boundary Waters (BOWA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record ................................ 5-38 Figure 5-19e. Time series of observed IMPROVE reconstructed light extinction (New IM PROVE) at Voyageurs (VOYA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 Percent days (m iddle) days and all IMPROVE sampling days during the period of record. ....................................................... 5-39 Figure 5-19f. Time series of obs erved IMPROVE reconstructed light extinction (New IMPROVE) at Hercules Glade (HEGL), Missouri for the average of the Worst 20 Percent days (top), Best 20 Percen t days (middle) days and all IMPROVE sampling days during the period of record. .................................... 5-40 Figure 5-19g. Time series of obs erved IMPROVE reconstructed light extinction (New IMPROVE) at Mingo (MING), Missouri for the average of the Worst 20 Percent days (top), Best 20 Percent days (m iddle) days and all IMPROVE sampling days during the period of record. ....................................................... 5-41September 2007

xi Figure 5-19h. Time series of obs erved IMPROVE reconstructed light extinction (New IMPR OVE) at Wichita Mountains (WIMO), Oklahoma for the average of the Worst 20 Percent days (top), Best 20 Percen t days (middle) days and all IMPROVE sampling days during the period of record. .................................... 5-42 Figure 5-19i. Time series of observed IMPROVE reconstructed light extinction (New IM PROVE) at Big Bend (BIBE), Texas for the average of the Worst 20 Percent days (top), Best 20 Percent days (m iddle) days and all IMPROVE sampling days during the period of record. ....................................................... 5-43 Figure 5-19j. Time series of observed IMPROVE reconstructed light extinction (New IMPR OVE) at Guadalupe Mountains (GUMO), Texas for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record ..................................... 5-44September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-1 1.0 INTRODUCTION

This Technical Support Document (TSD) desc ribes the Central Regional Air Planning Association (CENRAP) regional em issions and air quality modeling to support the ce ntral states Regional Haze Rule (RHR) State Implementati on Plans (SIPs). Th e CENRAP 2002 annual emissions and air quality modeling was performed by the contractor team of ENVIRON International Corporation (ENVIRON) and the Univ ersity of California at Riverside (UCR).

1.1 Background

The 1977 Clean Air Act Amendments (CAAA) adde d a new Section 169A for the protection of visibility in Federal Class I areas (specific natio nal parks, wilderness areas and wildlife refuges). Section 169A(a)(1) of the CAAA established the national goal for vi sibility protection: “Congress hereby declares as a national goal 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.” The CAAA require States to submit SIPs containing emission limits, schedules of compliance and to “promulgate regulations to assure reasonable progress toward meeting the natio nal goal” (Section 169A(a)(4)). In response to these mandates EPA promulgated the Regional Haze Rule (RHR) on July 1, 1999 that requires States to “establish goals (expressed in deciviews) that provide for reasonable pr ogress towards achieving natural visibility conditions” at Class I areas. The States’ RHR SIPs are due December 17, 2007 and an important component of the SIP will be the 2018 Reasonable Progress Goals (RPGs) toward achieving natural conditions in 2064. Re gional air quality models are used to project visibility to 2018 to determine the level of visibil ity improvement that is expected to be achieved in 2018. This information, along with other sources, can be used by the states to assist in setting
their 2018 RPGs.

CENRAP is one of five Regiona l Planning Organizations (RPOs) that have responsibility for coordinating development of SIPs and Tribal Impl ementation Plans (TIPs) in selected areas of the U.S. to address the requirements of the RHR. CENRAP is a regional partnership of states, tribes, federal agencies, stakehol ders and citizen groups establishe d to initiate and coordinate
activities associated with the ma nagement of regional haze and other air quality issues within the CENRAP states. The CENRAP region includes states and tribal lands located within the boundaries of Arkansas, Iowa, Ka nsas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma
and Texas.

The CENRAP Emissions and Air Quality Modeling Team is composed of staff from ENVIRON and UCR, with assistance and coordination from the CENRAP states, tribes, federal agencies and stakeholders. The ENVIRON/UCR Team perfor ms the emissions and air quality modeling
simulations for states and tribes within the CENRAP region, providi ng analytical results used in developing implementation plans under the RHR. Figure 1-1 shows the states included in each of the five RPOs in the U.S., including CENRAP. Table 1-1 lists the Cl ass I areas within the
CENRAP states.

CENRAP is performing emissions and air qual ity modeling to project visibility to 2018. The modeling results will be used to determine the level of visibility improvement expected in 2018September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-2 under various emission scenarios. States will us e these results to assist in determining their 2018 RPGs toward achieving natural conditions in 2064.

Figure 1-1. Regional Planning Organizations engaged in Regional Haze Modeling.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-3 Table 1-1. Federal Mandated Class I Areas in the CENRAP States.

Class I Area Acreage Federal Land Manager Public Law
Arkansas Caney Creek Wilderness Area 14,460 USDA-FS 93-622 Upper Buffalo Wilderness Area 12,018 USDA-FS 93-622
Louisiana Breton Wilderness Area 5,000+ USDI-FWS 93-632
Minnesota Boundary Waters Canoe Area Wilderness 810,088 USDA-FS 99-577 Voyageurs National Park 114,964 USDI-NP 99-261
Missouri Hercules-Glade Wilderness Area 12,314 USDA-FS 94-557 Mingo Wilderness Area 8,000 USDI-FWS 95-557
Oklahoma Wichita Mountains Wilderness 8,900 USDI-FWS 91-504
Texas Big Bend National Park 708,118 USDI-NP 74-157 Guadalupe Mountains National Park 76,292 USDI-NP 89-667

1.2 CENRAP Organizational Structure and Work Groups

The governing body of CENRAP is the Policy Oversight Group (POG) that is made up of voting members representing states and tribes with in the CENRAP region and non-voting members representing local agencies, the EPA and other federal agencies. The work of CENRAP is accomplished through five standing workgroups:

• Monitoring;

• Emissions Inventory;

• Modeling;

• Communications; and

• Implementation and Control Strategies.

Participation in workgroups is open to all intere sted parties and the POG may form additional ad hoc workgroups to address specific issues (e.g., a Data Analysis workgroup was formed).

The RHR requires the states, and the tribes that ma y elect to, submit the first SIPs and TIPs that address progress toward natural conditions at fe derally mandated Class I areas by December 17, 2007. 40 CFR 51.308 (Section 308) discusses the following four core requirements to be
included in SIPs/TIPs and Best Available Retrofit Technology (BART) requirements:

1. Reasonable progress goals;
2. Calculations of baseline and natural visibility conditions;
3. A Long-term strategy for regional haze;
4. A Monitoring strategy and other impl ementation plan requirements; and
5. BART requirements for regional haze visibility impairment.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-4 One of CENRAP’s goals is to provide support to states and tribes to meet each of these requirements of the RHR and to develop scientif ically supportable, economical and effective control strategies that the st ates and tribes may adopt to reduce anthropogenic effects on visibility impairment at Class I areas. One comp onent of CENRAP’s suppor t to states and tribes as part of compliance with the RHR is performi ng emissions and air quality modeling. These activities were implemented to:

• obtain a better understanding of the causes of visibility impairment and to identify potential mitigation measures for visib ility impairment at Class I areas;

• to evaluate the effects of alternative control strategi es for improving visibility;
and

• to project future-year air quali ty and visibility conditions.

In October 2004, CENRAP selected the team of ENVIRON and UCR to perform their Emissions and Air Quality Modeling.

The CENRAP Emissions and Air Quality Modeling Team performs regional haze analyses by operating regional scale, three-dimensional air quality models that simulate the emissions, chemical transformations, and tr ansport of gaseous and partic ulate matter (PM) species and consequently the effects on visibili ty in Class I Areas in the central U.S. A key element of this work includes the integration of emissions i nventories and emissions models with regional transport models. The general services provi ded by the CENRAP Emissions and Air Quality Modeling Team include, but are not limited to:

• Emissions processing and modeling;

• Air quality and visibili ty modeling simulations;

• Analysis, display, and reporti ng of modeling results; and

• Storage/quality assurance of the modeling input and output files.

The CENRAP 2002 annual Emissions and Air Qua lity Modeling Team performs work for the CENRAP Modeling Workgroup thr ough direction from the CENRAP Technical Director and
CENRAP Executive Director.

1.3 Overview of 2002 Annual Emissions a nd Air Quality Modeling Approach

The CENRAP 2002 annual emissions and air quality modeling was initiated on October 16, 2004 and involved the preparation of numerous databases, model simulations, presentations and reports. Much of the modeling analyses have been posted to the CENRAP modeling website at: http://pah.cert.ucr.e du/aqm/cenrap/index.shtml . There were numerous ve rsions and iterations of the modeling and interim results. The results presented in this TSD focus on the final modeling results and key findings in their development. The reader is refe rred to the modeling website for interim products.

1.3.1 Modeling Protocol

A Modeling Protocol was prepared at the outset of the study to serve as a road map for performing the CENRAP emissions and air quality modeling and to communicate the modelingSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-5 plans to the CENRAP participants. The M odeling Protocol was prepared following EPA guidance for preparation at the time it was prepared (EPA, 1991; 1999, 2001) and took into account CENRAP’s long-term plan (CENRAP, 2003) and the modeling needs of the RHR SIPs. The first version (Version 1.0) of the Modeli ng Protocol was dated November 19, 2004. Based on comments received from CENRAP, the M odeling Protocol was updated to the current Version 2.0 (Morris et al., 2004a) that was dated December 8, 2004. This Modeling Protocol can be found on the CENRAP modeling Website at:

http://pah.cert.ucr.edu/aqm/cenrap/doc s/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf

1.3.2 Quality Assurance Project Plan (QAPP)

A Quality Assurance Project Plan (QAPP) was prepared for the CENRAP emissions and air quality modeling study that described the qu ality management functions performed by the modeling team. The QAPP was pr epared and was based on the national consensus standards for quality assurance (ANSI/ASQC, 1994), followed EP A’s guidelines for quality assurance project plans for modeling (EPA, 2002) and for QAPPs (EPA, 2001) and took into account the
recommendations from the North American Research Strategy for Tropospheric Ozone (NARSTO) Quality Handbook for modeling pr ojects (NARSTO, 1998). The EPA and NARSTO guidance documents were developed specifically for modeling projects, which have different quality assurance concerns than environmental monitoring data collection projects. The work performed in this project involves modeling at the basi c research level and for regulatory/planning applications. In order to use model outputs for these purposes, it must be
established that each model is scientifically sound, r obust, and defensible. This is accomplished by following a project planning process that incor porates the following elements as described in the EPA modeling guidance document:

• A systematic planning process including id entification of assessments and related performance criteria;

• Peer reviewed theory and equations;

• A carefully designed life-cycle devel opment process that minimizes errors;

• Documentation of any changes from original plans;

• Clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can understand the model output;

• Input data and parameters that are accura te and appropriate for the analysis; and

• Output data that can be used to help inform decision makers.

The CENRAP QAPP can be found at:

http://pah.cert.ucr.edu/aqm/ cenrap/docs/CENRAP_QAPP_Nov_24_2004.pdf ).

A key component of the CENRAP emissions a nd air quality modeling QAPP was the graphical display of model inputs and outpu ts and multiple peer-review of each step of the modeling process. This was accomplished through use of the CENRAP modeling website where modelers posted displays of work products (e.g., emissions plots, model outputs, etc.) for review by the CENRAP modeling team, modeli ng workgroup and others. Th is website can be found at: http://pah.cert.ucr.e du/aqm/cenrap/index.shtml

.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-6 1.3.3 Model Selection

The selection of the meteorolog ical, emissions and air quality models for the CENRAP regional haze modeling was based on a review of previous regional haze modeling studies performed in the CENRAP region (e.g., Pitchford et al., 2004 ; Pun, Chen and Seigneur, 2004; Tonnesen and Morris 2004) as well as elsewhere in the United St ates (e.g., Morris et al, 2004a; Tonnesen et al., 2003; Baker, 2004). The CENRAP emissions and air quality Mode ling Protocol (Morris et al., 2004a) provides details on the justification for model selection and the formulation of the different models. Based on previous work (e.g., CENRAP, WRAP, VISTAS, MRPO, BRAVO and EPA), CENRAP selected the following models for use in mode ling PM and regional haze in
the central states:

3⁄4 MM5: The Pennsylvania State University/Na tional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological M odel (MM5 Version 3.6 MPP) is a nonhydrostatic, prognostic meteorological model routin ely used for urban- and regional-scale photochemical, fine particulate, and regional haze regulatory modeling studies (Anthes and Warner, 1978; Chen and Dudhia, 2001; Stau ffer and Seaman, 1990, 1991; Xiu and Pleim, 2000).

3⁄4 SMOKE : The Sparse Matrix Operator Kernel Em issions (SMOKE) modeling system is an emissions modeling system that generates h ourly gridded speciated emission inputs of mobile, non-road, area, point, fire and bioge nic emission sources for photochemical grid models. (Coats, 1995; Houyoux and Vukovich, 1999) . As with most ‘emissions models’, SMOKE is principally an emission processing system and not a true emissions modeling
system in which emissions estimates are simulated from ‘first principles’. This means that, with the exception of mobile and biogenic sources , its purpose is to provide an efficient tool for converting an existing base emissions inve ntory data into the hour ly, gridded, speciated, and formatted emission files required by an air quality model.

3⁄4 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, PM, visibility and acid deposition at a regional scale for ex tended periods of time (Dennis, et al., 1996; Byun et al., 1998a; Byun and Ch ing, 1999, Pleim et al., 2003).

3⁄4 CAMx: ENVIRON’s Comprehensive Air Qua lity Model with Extensions (CAMx) modeling system is also a state-of-science ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, PM, visibility and acid deposition at a regional scale for extended periods of time. (ENVIRON, 2006).

1.3.3.1 MM5 Meteorological Model Configuration for CENRAP Annual Modeling

Application of the MM5 for the 2002 annual modeling on a 36 km grid for the continental US was performed by the Iowa Department of Natu ral Resources (IDNR; Johnson, 2007). Details of the 2002 36 km MM5 model applica tion and evaluation procedures carried out by IDNR may be found in Johnson, 2007. Application of the MM5 m odel on a 12 km grid covering the Central States for portions of 2002 was performed by EPA Region VII and the Texas Commission on Environmental Quality (TCEQ).September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-7 The MM5 (Version 3.63) configur ation used in the generation of the meteorological modeling datasets consists of the following (see Table 1-2 for more details):

3⁄4 36 km grid with 34 vertical layers; 3⁄4 12 km nested grid for episodic modeling; 3⁄4 For 12 km runs use two way nesting (wit hout feedback) within the 36 km grid; 3⁄4 Initialization and boundary conditions from Eta analysis fields;

o Eta 3D and surface analysis data (ds609.2);

o Not using NCEP global tropospheric SST data (ds083.0) ;

o Observational enhancement (LITTLE_R)

 NCEP ADP surface obs (ds464.0)

 NCEP ADP upper-air obs (ds353.4) 3⁄4 Pleim-Xiu (P-X) land-surface model (LSM); 3⁄4 Pleim-Chang Asymmetric Conv ective Mixing (A CM) PBL model; 3⁄4 Kain-Fritsch 2 cumulus parameterization; 3⁄4 Mixed phase (Reisner 1) cloud microphysics; 3⁄4 Rapid Radiative Transfer Model (RRTM) radiation; 3⁄4 No Shallow Convection (ISHALLO=0); 3⁄4 Standard 3D FDDA analysis nudging outside of PBL; and 3⁄4 Surface nudging of the winds only.

1.3.3.2 SMOKE Emissions Model Configurat ion for CENRAP Annual Modeling

SMOKE supports area, mobile, fire and point so urce emission processing and includes biogenic emissions modeling through a rewrite of the Bi ogenic Emission Inventory System, version 3 (BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis ). SMOKE has been available since 1996, and has been used for emissions pr ocessing in a number of regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigne d and improved with thesupport of the U.S. Environmental Protection Agency (EPA), for use with EPA's Models3/CMAQ ( http://www.epa.gov/asmdnerl/models3 ). The primary purposes of the SMOKE redesign were support of: (a) em issions processing with user-selected chemical mechanisms and (b) emissions processing for reactivity assessments.

As an emissions processing system, SMOKE has far fewer ‘science c onfiguration’ options compared with the MM5 and CMAQ models. Ta ble 1-3 summarizes the version of the SMOKE system that was used and the sources of data that were employed in constructing the required modeling inventories.

1.3.3.3 CMAQ Air Quality Model Configurat ion for CENRAP Annual Modeling

CENRAP used CMAQ Version 4.5 with the “SOA mods enhancement”, described below, and used the model configuration as shown in Tabl e 1-4. The model was se t up and exercised on the same 36 km grid that was used by WRAP and VISTAS, the 36 km RPO national grid. CENRAP performed 12 km CMAQ sensitivity tests and f ound little change in mode l performance with a large penalty in computation time. Conseque ntly, at the February 7, 2006 CENRAP ModelingSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-8 Workgroup Meeting a decision was made to pr oceed with the CENRAP emissions and air quality modeling using just the 36 km nati onal RPO grid (Morris et al., 2006a).

Initial CMAQ 2002 simulations performed by VISTAS found that the model greatly underestimates organic mass carbon (O MC) concentrations, especially in the summer. A review of the CMAQ formulation found that it failed to treat Secondary Or ganic Aerosol (SOA) formation from sesquiterpenes and isoprene and also failed to account for the fact that SOA can become polymerized so that it is no longer volatil e and stays in the particle form. Thus, VISTAS updated the CMAQ SOA module to include thes e missing processes and found much improved OMC model performance (Morris et al., 2006c). CENRAP test ed the CMAQ Version 4.5 with SOAmods enhancement and found it performed much better for OMC than the standard versions of CMAQ Version 4.5. Therefore, CMAQ Versi on 4.5, with the enhanced SOAmods (Morris et al., 2006c), was adopted for the CENRAP modeli ng. CMAQ Version 4.5 is available from the CMAS center ( www.cmascenter.org ).

1.3.3.4 CAMx Air Quality Model Configurat ion for CENRAP Annual Modeling

CAMx Version 4.40 was applied using similar op tions as used by CMAQ. CAMx was used initially in side-by-side comparisons with CM AQ. Comparative model performance results and other factors for CAMx V4 and CMAQ V4.4 with SOAmods were presente d at the February 7, 2006 CENRAP modeling workgroup meetings that found (Morris et al., 2006b):

• No one model was consistently performing better than the other over all species and averaging times.

• Both models performed well for sulfate.

• CMAQ’s winter nitrate over-predicti on tendency not as large as CAMx’s.

• CAMx performed slightly better than CMAQ for elemental carbon (EC).

• CMAQ performed much better than CA Mx for organic mass carbon (OMC).

• Both models over-predicted Soil a nd under-predicted coarse mass (CM).

• CMAQ ran faster than CAMx due to MPI multi-processing capability.

• CAMx required much less disk space than CMAQ.

Based on these factors, CMAQ was selected as the lead air quality model for the CENRAP regional haze modeling with CAMx the secondary corroborative model. However, CAMx also contained a PM Source Apportionm ent Technology (PSAT) capability that was used widely in the CENRAP modeling. Table 1-4 lists the main CAMx configuration used for the CENRAP annual modeling that was selected, in part, to be consistent with the CMAQ model configuration (Table 1-4). One exception to this was that the CAMx PSAT simulations used the Bott
advection solver rather than the PPM advection solver. The PPM advection solver is typically used in the standard CAMx and CMAQ runs. Bo tt, however, is more computationally efficient and the high computational requirements of th e CAMx PSAT runs dictated this choice.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-9 Table 1-2. MM5 Meteorological Model Configuration for CENRAP 2002 Annual Modeling (Johnson, 2007).
Science Options Configuration Details/Comments
Model Code MM5 version 3.63 Grell et al., 1994

Horizontal Grid Mesh 36 km 36 km grid 165 x 129 dot points RPO MM5 Grid

Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coordinate system

Grid Interaction No Feedback IFEED=0 Initialization Eta first guess fields/LittleR Boundary Conditions Eta first guess fields/LittleR Microphysics Reisner I Mixed Ice Look up table

Cumulus Scheme Kain-Fritsch 2 On 36 and 12 km Grids Planetary Boundary Layer ACM PBL

Radiation RRTM Vegetation Data USGS 24 Category Scheme Land Surface Model Pleim-Xiu Land Surface Model (LSM)

Shallow Convection None Sea Surface Temperature Eta Skin Spatially varying Thermal Roughness Garratt

Snow Cover Effects None 4D Data Assimilation Analysis Nudging on 36 and 12 Surface Nudging Wind Field Only Integration Time Step 90 seconds Simulation Periods Annual 2002 for 36 km 12 km episodic only

Platform Linux Cluster Done at IDNR 1

1 Twelve km episodic modeling completed by EPA Region VII and the Texas Commission on Environmental Quality.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-10 Table 1-3. SMOKE Emissions Model Configuration for CENRAP Annual Modeling.
Emissions Component Configuration Details/Comments

Emissions Model SMOKE Version 2.3 Several versions of SMOKE used during course of the study

Horizontal Grid Mesh 36 km 36 km grid 148 x 112 cells RPO National Grid

Area Source Emissions CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states (Pechan, 2005d,e) Other States: '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction (Pechan, 2005c)

On-Road Mobile Sources CENRAP Domain: CENRAP VMT data Updated '02 developed by CENRAP states (Reid et al., 2004a)
Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction (Pechan, 2005c)

Point Sources CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states and stakeholders (Pechan, 2005a,b)
Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction (Pechan, 2005c)

Off-Road Mobile Sources CENRAP Domain: CENRAP State 2002 EI Updated '02 developed by CENRAP states (Pechan, 2005d,e)
Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction (Pechan, 2005c) Biogenic Sources SMOKE BEIS-3 BELD3 vegetative database

Mexican Sources 1999 Emissions for 2002 and 2018 http://www.epa.gov/ttn/chief/net/mexico.html; (ERG, 2006)

Canadian Sources 2000 Emissions for 2002 and 2020 Emissions for 2018 http://www.epa.gov/ttn/chief/net/canada.html

Temporal Adjustments Seasonal, day, hour Based on latest collected information and CEMbased profiles

Chemical Speciation Revised CBM-IV Chemical Speciation Updated January 2004

Gridding Revised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/ Growth and Controls CENRAP developed Pechan (2005a,b)

Quality Assurance QA Tools in SMOKE 2.0 Follow QAPP (Morris and Tonnesen, 2004) and QA refinements (Morris and Tonnesen, 2006) Simulation Periods Annual 2002 for 36 km Episodic periods at 12 kmSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-11 Table 1-4. CMAQ Air Quality Model Configur ation for CENRAP Annual Modeling.
Science Options Configuration Details/Comments

Model Code CMAQ Version 4.5 w/ SOAmods Secondary Organic Aerosol enhancements as described by Morris et al., (2006c)

Horizontal Grid Mesh 36 km annual 36 km covering continental U.S; some episodic 12 km sensitivity runs were also performed 36 km grid 148 x 112 cells RPO National Grid Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5 Grid Interaction One-way nesting Initial Conditions ~15 days full spin -up Separately run 4 quarters of 2002

Boundary Conditions 2002 GEOS-CHEM dayspecific 2002 GEOS-CHEM day specific 3-hour average data

Emissions

Baseline Emissions Processing See SMOKE model configuration MM5 Meteorology input to SMOKE, CMAQ Sub-grid-scale Plumes No Plume-in-Grid (PinG) Chemistry Gas Phase Chemistry CBM-IV Aerosol Chemistry AE3/ISORROPIA

Secondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) w/ SOAmods update Schell et al., (2001); Morris et al., (2006c)

Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processes N2O5 Reaction Probability 0.01 – 0.001

Meteorological Processor MCIP Version 2.3 Includes dry deposition and snow cover updates Horizontal Transport
Numerical Scheme PPM advection solver

Eddy Diffusivity Scheme K-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approach Vertical Transport Eddy Diffusivity Scheme K-theory Diffusivity Lower Limit Kzmin = 0.1 to 1.0 Land use dependent Kzmin

Deposition Scheme M3dry Directly linked to Pleim-Xiu Land Surface Model parameters

Numerics

Gas Phase Chemistry Solver Euler Backward Iterative (EBI) solver

Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 for 36 km Episodic periods at 12 km Integration Time Step Calculated Internally 15 minute coupling time stepSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-12 Table 1-5. CAMx Air Quality Model Configuration for CENRAP Annual Modeling. Science Options Configuration Details

Model Code CAMx Version 4.40 Available at: www.camx.com Horizontal Grid Mesh 36 km annual 36 km covering continental U.S 36 km grid 148 x 112 cells Vertical Grid Mesh 19 Lay ers 17 Layers sync'd w/ MM5 Grid Interaction Two-way nesting Initial Conditions ~15 days full spin -up Separately run 4 quarters of 2002

Boundary Conditions 2002 GEOS-CHEM dayspecific 2002 GEOS-CHEM day specific 3-hour average data

Emissions

Baseline Emissions Processing See SMOKE model configuration MM5 Meteorology input to SMOKE, CAMx Sub-grid-scale Plumes No Plume-in-Grid (PinG) Consistent with CMAQ Chemistry Gas Phase Chemistry CBM-IV with Isoprene updates

Aerosol Chemistry ISORROPIA equilibrium Dynamic and hybrid also available but not used Secondary Organic Aerosols SOAP

Cloud Chemistry RADM-type aqueous chemistry Alternative is CMU multi-section aqueous chemistry N2O5 Reaction Probability None Meteorological Processor MM5CAMx Horizontal Transport

Eddy Diffusivity Scheme K-theory with Kh grid size dependence Vertical Transport Eddy Diffusivity Scheme K-Theory Diffusivity Lower Limit Kzmin = 0.1 to 1.0 Land use dependent Kzmin Planetary Boundary Layer No Patch Deposition Scheme Wesely

Numerics Gas Phase Chemistry Solver CMC Fast Solver

Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme. PSAT w/ Bott scheme.
Simulation Periods Annual 2002 at 36 km Integration Time Step Wind speed dependentSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-13 1.3.4 Modeling Domains

The CENRAP emissions and air quality mode ling was conducted on the 36 km national RPO domain as depicted in Figure 1-2. This domai n consists of a 148 by 112 array of 36 km by 36 km grid cells and covers the continental Unite d States. Sensitivity simulations were also performed for episodes on a 12 km modeling domain covering th e central states, however the results were very similar to the 36 km results so CENRAP elected to proceed with the 2002 annual modeling using the 36 km domain for com putational efficiency (Morris et al., 2006a).

-2736 -2304 -1872 -1440 -1008 -576 -144 288 720 1152 1584 2016 2448 -2088 -1872 -1656 -1440 -1224 -1008

-792 -576 -360 -144

72 288 504 720 936 1152 1368 1584 1800

Figure 1-2. National Inter-RPO 36 km modeling domain used for the CENRAP 2002 annual SMOKE, CMAQ and CAMx modeling.

1.3.5 Vertical Structure of Modeling Domain

The MM5 meteorological model was exercised usi ng 34 vertical layers from the surface to a pressure level of 100 mb (approximately 15 km above ground level). Both the CMAQ and CAMx air quality models can employ layer collap sing in which vertical layers in the MM5 are combined in the air quality model, which improve s computational efficiency. The sensitivity of the CMAQ model estimates to the number of ve rtical layers was evaluated by the Western Regional Air Partnership (WRAP) and Visibility Improvements Stat e and Tribal Association of the Southeast (VISTAS) (Tonnesen et al., 2005; 2006; Morris et al., 2004a). CMAQ model simulations were performed with no layer collapsing (i.e., the sa me 34 layers as used by MM5) and with various levels of layer collapsing. Th ese studies found that using 19 vertical layers upSeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-14 to 100 mb (i.e., same model top as MM5) and ma tching the eight lowest MM5 vertical layers near the surface produced nearly id entical results as with no la yer collapsing. They also found that very aggressive layer collapsing (e.g., 34 to 12 layers) produced results with substantial differences compared to no layer collapsing. Th erefore, based on the WR AP/VISTAS sensitivity analysis, CENRAP adopted the 19 vertical laye r configuration up to th e 100 mb model top. Figure 1-3 displays the definition of the 34 MM5 ve rtical layers and how th ey were collapsed to 19 vertical layers in the air qua lity modeling performed by CENRAP.

Figure 1-3. MM5 34 vertical layer definitions and scheme for collapsing the 34 layers down to 19 layers for the CENRAP CMAQ and CAMx 2002 annual modeling. 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 0September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-15 1.3.6 2002 Calendar Year Selection

The calendar year 2002 was selected for CENRAP regional haze annual modeling as described in the CENRAP Modeling Protocol (Morris et al., 2004a). EPA’s applicable guidance on
PM 2.5 /Regional Haze modeling at that time (EPA, 2001) identified specific goals to consider when selecting modeling periods for use in dem onstrating reasonable progr ess in attaining the regional haze goals. However, since there is much in common with the goals for selecting episodes for annual and episodic PM 2.5 attainment demonstrations as well as regional haze, EPA’s current guidance addresses all three in a common document. (EPA, 2007) At the time of the modeling period selection EPA had also published an updated summary of PM 2.5 and Regional Haze Modeling Guidance (Timin, 2002) that served, in some respects, as an interim placeholder until the final guidance was issued as part of the PM 2.5 /regional haze NAAQS implementation process that was ultimately publ ished in April 2007 (EPA, 2007). The interim EPA modeling guidance for episode selection (E PA, 2001; Timin, 2002) was consistent with the final EPA regional haze m odeling guidance (EPA, 2007).

EPA recommends that the selection of a modeli ng period derive from th ree principal criteria:

3⁄4 A variety of meteorological c onditions should be covered that includes the types of meteorological conditions that produce the worst 20 percent a nd best 20 percent visibility days at Class I areas in the CENRAP States during the 2000-2004 baseline period;

3⁄4 To the extent possible, the modeling data ba se should include days for which enhanced data bases (i.e. beyond routine aerometric a nd emissions monitoring) are available; and

3⁄4 Sufficient days should be available such that relative response factors (RRFs) can be based on several (i.e., > 15) days.

For regional haze modeling, the guidance goes furt her by suggesting that th e preferred approach is to model a full, representative year (EPA, 2001, pg. 188). Moreove r, the required RRF values should be based on model results averaged ov er the 20 percent worst and 20 percent best visibility days determined for each Class I ar ea based on monitoring data from the 2000 – 2004 baseline period. More recent EPA guidance (Timi n, 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 ep isodes including: (a) choose periods which have already been mo deled, (b) choose periods which are drawn from the years upon which the current design values ar e based, (c) include w eekend days among those chosen, and (d) choose modeling periods that meet as many episode selec tion criteria as possible
in the maximum number of nonattainme nt or Class I areas as possible.

Due to limited available resources CENRAP was re stricted to modeling a single calendar year. The RHR uses the five-year base line of 2000-2004 period as the starting point for projecting future-year visibility. Thus, th e modeling year should be selected from this five-year baseline period. The 2002 calendar year, which lies in the middle of the 2000-2004 Baseline, was selected for the following reasons:

3⁄4 Based on available information, 2002 appears to be a fairly typical year in terms of meteorology for the 5-year Baseline period of 2000-2004;September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-16 3⁄4 2003 and 2004 appeared to be colder and we tter than typical in the eastern US;

3⁄4 The enhanced IMPROVE and IMPROVE Protoc ol and Supersites PM monitoring data were fully operational by 2002. Much less IM PROVE monitoring data was available during 2000-2001, especially in the CENRAP region;

3⁄4 IMPROVE data for 2003 and 2004 were not yet available at the time that the CENRAP modeling was initiated; and

3⁄4 2002 was being used by the other RPOs.

1.3.7 Initial Concentrations and Boundary Conditions

The CMAQ and CAMx models were operated sepa rately for each of four quarters of the 2002 year using a ~15 day spin up peri od (i.e., the models were starte d approximately 15 days before the first day of interest in each quarter in or der to limit the influence of the assumed initial concentrations, e.g., start June 15 fo r quarter 3 whose first day of inte rest is July 1). Sensitivity simulations demonstrated that with ~15 initialization days, the influence of initial concentrations (ICs) was minimal using the 36 km Inter-R PO continental U.S. modeling domain. Consequently, clean ICs were sp ecified in the CMAQ and CAMx modeling using a ~15 day spin up period.

Boundary Conditions (BCs) (i.e., the assumed con centrations along the later edges of the 36 km modeling domain, see Figure 1-2) were base d on a 2002 simulation by the GEOS-CHEM global circulation/chemistry model. GEOS-CHEM is a three-dimensional global chemistry model driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling and Assimilation Office . It is applied by research groups
around the world to a wide range of atmospheric co mposition problems, including future climates and planetary atmospheres using gene ral circulation model me teorology to drive the
model. Central management and support of the model is provided by the Atmospheric Chemistry Modeling Group at Harvard University.

A joint RPO study was performed, coordinate d by VISTAS, in which Harvard University applied the GEOS-CHEM global model for the 2002 calendar ye ar (Jacob, Park and Logan, 2005). The University of Houston (UH) was re tained to process the 2002 GEOS-CHEM output into BCs for the CMAQ model (Byun, 2004). The GEOS-CHEM simulations for the RPOs used GEOS meteorological observations for the y ear 2002. These were obtained from the Global Modeling and Assimilation Office(GMAO) as a 6- hourly archive (3-hour for surface quantities such as mixing depths). The data through August 2002 were from the GEOS-3 assimilation,
with horizontal resolution of 1 o x1 o and 55 vertical layers. The data after August 2002 were from the updated GEOS-4 assimilation, w ith horizontal resolution of 1 o x1.25 o and 48 vertical layers (note 1 o latitude is equal to approximately 110 km ). The GEOS-CHEM out put was processed by mapping the GEOS-CHEM chemical compounds to the species in the CBM-IV chemical mechanism used by CMAQ/CAMx and mapping th e GEOS-CHEM vertical layers to the 19 layer vertical layer structure used by CM AQ/CAMx in the CENRAP modeling (Byun, 2004). The results were day-specific three-hourly BC inputs for the CMAQ model. The CMAQ2CAMx processor was then used to tr ansform the CMAQ day-specific 3-hour ly BCs to the format used by CAMx.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-17 There were several quality assu rance (QA) checks of the BCs generated from the 2002 GEOSCHEM output. The first QA/QC check was a range check to assure reasonable values. The BCs were compared against the GEOS-CHEM outputs to assure the mapping and interpolation was performed correctly. The code used to ma p the GEOS-CHEM output to the CMAQ BC format was obtained from UH, reviewed and the BC ge neration duplicated for several time periods during 2002.

1.3.8 Emissions Input Preparation

The CENRAP SMOKE emissions modeling was based on an updated 2002 emissions data for the U.S. (Pechan, 2005c,e; Reid et al., 2004a,b), 1999 emissions data for Mexico (ERG, 2006),
and 2000 emissions data for Canada. These data we re used to generate a final base 2002 Base G Typical (Typ02G) annual emissions database. Nu merous iterations of the emissions modeling were conducted using interim databases before a rriving at the final Base G emission inventories (e.g., Morris et al., 2005). The 2018 Base G base case emissions (Base18G) for most source categories in the U.S. were based on projections of the 2002 inventory assuming growth and control (Pechan, 2005d). 2018 EGU emissions were based on the run 2.1.9 of the Integrated Planning Model (IPM) updated by the CENRAP stat es. Canadian emissions for the Base18G scenario were based on a 2020 inventory, wh ereas the Mexican 1999 inventory was held
constant for 2018.

The Typ02G and Base18G emission inventories represent significant improvements to the preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the preliminary 2002 modeling served its purpose to de velop the infrastructure for modeling large emissions data sets and producing annual emissions simulations, much of the input data (both as inventories and ancillary data) were placeholders for actual 2002 da ta that were being prepared through calendar year 2005. As these actual 2002 da ta sets became available, they were integrated into the SMOKE modeling and QA syst em that was developed during the preliminary modeling, to produce a high-quality emissions data set for use in the final CMAQ and CAMx modeling. The addition of entirely new invent ory categories, like marine shipping, added complexity to the modeling. By the end of the emissions data collection phase, there were 23 separate emissions processing streams covering a variety of sources categories necessary to general model-ready emission inpu ts for the 2002 calendar year.

Details on the emissions modeling are provided in Chapter 2 with additional information contained in Appendix B.

1.3.9 Meteorological Input Preparation

The 2002 36 km MM5 meteorological modeling was conducted by the Iowa Department of Natural Resources (IDNR) who also performed a preliminary model performance evaluation (Johnson, 2007). CENRAP performed an additi onal MM5 evaluation of the CENRAP 2002 36 km MM5 simulation that included a comparativ e evaluation against the final VISTAS 2002 36
km MM5 and an interim WRAP 2002 36 km simu lation (Kemball-Cook et al., 2004). KemballCook and co-workers (2004) found the following in the compar ative evaluation of the CENRAP,
WRAP and VISTAS 2002 36 km MM5 simulations , (details are provided in Appendix A):September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-18 Surface Meteorological Performa nce within the CENRAP Region

• The three MM5 simulations (CENRAP, VI STAS and WRAP) obtained comparable model performance for winds and humidity that were within model performance
benchmarks.

• The WRAP MM5 simulation obtained better te mperature model performance than the
other two simulations due to the use of surface temperature data assimilation.

o In the final WRAP MM5 simulation the use of surface temperature assimilation was dropped because it introduced instabili ty in the vertical structure of the atmosphere.

• For all three runs, the Northern CENRAP doma in had a cold bias in winter and a warm
bias in summer.

Surface Meteorological Performa nce outside the CENRAP Region

• All three runs had similar surface wind model performance in the western U.S. that was outside the model performance benchmarks

• For temperature, the WRAP MM5 simulation ha d the best performance overall due to the surface temperature data assimilation that was dropped in the final WRAP run.

• The three runs had comparable humidity perf ormance, although WRAP exhibited a larger
wet bias in the summer and the southwestern U.S.

Upper-Air Meteorological Performance

• The VISTAS and CENRAP MM5 simulations were better able to reproduce the deep convective summer boundary layers compar ed to the WRAP MM5 simulations, which exhibited a smoother decrease in temper ature with increase in altitude.

• CENRAP and VISTAS MM5 simulations bett er simulated the surface temperature
inversions than WRAP.

• WRAP was better able to simulate the surface temperature.

• All three models exhibited si milar vertical wind profiles.

Precipitation Performance

• In winter, all three MM5 simulations exhi bited similar, fairly good, performance in reproducing the spatial distribution and ma gnitudes of the monthly average observed precipitation.

• In summer, all runs had a wet bias, particular ly in the desert southwest where the interim WRAP run had the largest wet bias.

In conclusion, the VISTAS simulation appear ed to perform best, the CENRAP MM5 model performance was generally between the VISTAS and WRAP performance, with performance
more similar to VISTAS than WRAP. Alt hough the interim WRAP MM5 simulation performed best for surface temperature due to the surfa ce temperature data assimilation, the surface temperature assimilation degr aded the MM5 upper-air performance including the ability to
assimilate surface inversions and was ultim ately dropped from the final WRAP MM5 simulations (Kemball-Cook et al., 2005).September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-19 The IDNR 12 km 2 MM5 simulations were also evaluated and compared with the performance of the 36 km MM5 simulation (Johnson et al., 2007) . The IDNR 36 km and 12 km MM5 model performance was similar (Johnson, 2007), which supported the findings of the CMAQ and CAMx 36 and 12 km sensitivity simulations that ther e was little benefit of using a 12 km grid for simulating regional haze at rural Class I areas (Morris et al., 2006a). Ho wever, as noted by Tonnesen and co-workers ( 2005; 2006) and EPA modeling guidance (1991; 1999; 2001; 2007) this finding does not necessarily hold for 8-hour ozone and PM 2.5 modeling that is characterized by sharper concentration gradients and frequently occurs in the urban environment as compared to the more rural nature of regional haze.

1.3.10 Photolysis Rates Model Inputs

Several chemical reactions in the atmosphere are initiated by the photodi ssociation of various trace gases. To accurately represent the comple x chemical transformations in the atmosphere, accurate estimates of these phot odissociation rates must be ma de. The Models-3/CMAQ system includes the JPROC processor, which calculates a table of clear-sky photoly sis rates (or J-values) for a specific date. JPROC uses default values fo r total aerosol loading a nd provides the option to
use default ozone column data or to use measur ed total ozone column data. These data come from the Total Ozone Mapping Spectrometer (TOMS) satellite data. TOMS data that is available at 24-hour averages was obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . Day-specific TOMS data was used in the CMAQ radiation mode l (JPROC) to calculate photolysis rates. The TOMS data were missing or erroneous for severa l periods in 2002: Augu st 2-12; June 10; and November 18-19. Thus, the TOMS data for Augu st 1, 2002 was used fo r August 2-7 and TOMS data for August 13 was used for August 8-12. Si milarly, TOMS data for June 9 was used for June 10 and data for August 17 was used for Augus t 18-19. Note that the to tal column of ozone in the atmosphere is dominated by stratospheric ozone which has very little day-to-day variability so the use of TOMS data within a week or two of an actual day introduces minimal uncertainties in the modeling analysis.

JPROC produces a "look-up" table that provide s photolysis rates as a function of latitude, altitude, and time (in terms of the number of hour s of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six latitudinal 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 lati tude, altitude, and hour angle, followed by applying a cloud correction (attenuatio n) factor based on the cloud inputs from
MM5.

The photolysis rates input file was prepared as separate look-up tables for each simulation day. Photolysis files are ASCII files that were visua lly checked for selected days to verify that photolysis are within the expected ranges.

2 The IDNR twelve 12 km annual simulation domain was not sufficient for CENRAP’s needs, thus Bret Anderson with EPA Region 7 in cooperation with Texas complete d an episodic 12km simulation on a larger domain.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-20 The Tropospheric Ultraviolet and Visible (TUV) Radiation Model ( http://cprm.acd.ucar.edu/Models/TUV/ ) is used to generate the phot olysis rates input file for CAMx. TOMS ozone data and land use data were used to develop the CAMx Albedo/Haze/Ozone input file for 2002. As fo r CMAQ, the missing TOMS data period in the fall of 2002 was filled-in using obs erved TOMS data on either side of the missing period using the same procedures as described above for CMAQ. Default land use specific albedo values were used and a constant haze value used, corr esponding to rural conditions over North America.

1.3.11 Air Quality Input Preparation

Air quality data used with the CMAQ and CA Mx modeling systems include: (1) Initial Concentrations (ICs) that are the assumed initia l three-dimensional concentrations throughout the modeling domain.; (2) the Boundary Conditions (BCs ) that are the concentrations assumed along the lateral edges of the RPO national 36 km mode ling domain; and (3) air quality observations that are used in the model performance evalua tion (MPE). The MPE is discussed in Section 3 and Appendix C of this TSD.

As noted in Section 1.3.7, CMAQ default clean In itial Concentrations (IC s) were used along with an approximately 15 day spin up (initializ ation) period to elim inate any significant
influence of the ICs on the modeled concentrations for the days of interest. The same ICs were used with CAMx as well. Both CMAQ and CAMx were run for each quarter of the year. Each quarter’s model run was in itialized 15 days pr ior to the first day of interest (e.g., for quarter 3, Jul-Aug-Sep, the model was initialized on June 15, 2002 with the first mode ling day of interest July 1, 2002). The CMAQ Boundary Conditions (BCs) for the Inter-RPO 36 km continental U.S. grid (Figure 1-2) were based on day-speci fic 3-hour averages from the output of the GEOSCHEM global simulation model of 2002 (J acob, Park and Logan, 2005). The 2002 GEOSCHEM output was mapped to the species and verti cal layer structure of CMAQ and interpolated
to the lateral boundaries of the 36 km grid shown in Figure 1-2 (Byun, 2004).

Table 1-6 summarizes the surf ace air quality mon itoring networks and the number of sites available in the CENRAP region th at were used in the model perf ormance evaluation. Data from these monitoring networks were also used to evaluate the CMAQ and CAMx models outside of the CENRAP region.

Table 1-6 . Ground-level ambient data monitoring networks and stations available in the CENRAP states for calendar year 2002 used in the model performance evaluation.

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 11 CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, 1 Week 3

NADP WSO4, WNO3, WNH4 Weekly 23 EPA-STN Speciated PM 2.5 Varies; Varies 12 AIRS/AQS CO, NO, NO 2 , NO
x , O 3 Hourly; Hourly 25September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-21 1.3.12 2002 Base Case Modeling and Model Performance Evaluation

The CMAQ and CAMx models were evaluated against ambient measurements of PM species, gas-phase species and wet deposition. Table 1-6 summarizes the networks used in the model evaluation, the species measured and the averagi ng times and frequency of the measurements. Numerous iterations of CMAQ and CAMx 2002 base case simu lations and model performance
evaluations were conducted du ring the course of the CENRAP modeling study, most of which have been posted on the CENRAP modeling website ( http://pah.cert.ucr.e du/aqm/cenrap/cmaq.shtml ) and presented in pr evious reports and presentations for CENRAP (e.g., Morris et al., 2005; 2006a,b). Details on the final 2002 Base F 36 km CMAQ base case modeling performance evaluation are provided in Chapter 3 and Appendix C (because of the similarity between 2002 Base F and 2002 Base G and resource
constraints the model evaluation was not re-co nducted for Base G). In general, the model performance of the CMAQ and CAMx models for sulfate (SO4) and elemental carbon (EC) was good. Model performance for nitrate (NO3) was variable, with a summer underestimation and
winter overestimation bias. Performance for or ganic mass carbon (OMC) wa s also variable, with
the inclusion of the SOAmods enhancement in CMAQ Version 4.5 greatly improving the CMAQ summer OMC model performance (Morris et al ., 2006c). Model performance for Soil and coarse mass (CM) was generally poor. Part of the poor performance for Soil and CM is believed
to be due to measurement-model incommensurab ility. The IMPROVE measured values are due, in part, to local fugitive dust sources that are not captured in the model’s emission inputs and the 36 km grid resolution is not conducive to modeling localized events.

1.3.13 2018 Modeling and Visibility Projections

Emissions for the 2018 base case were generated following the procedures discussed in Section 1.3.8 and Chapter 2. 2018 emissions for Electr ical Generating Units (EGUs) were based on simulations of the Integrated Planning Model (IPM) that took into the a ccount the effects of the Clean Air Interstate Rule (CAIR) on emissions from EGUs in CAIR states using an IPM realization of a CAIR cap-and -trade program. Emissions for on-road and non-road mobile sources were based on activity growth and em issions factors from the EPA MOBILE6 and NONROAD models, respectively. Area sources and non-EGU point sources were grown to 2018 levels (Pechan, 2005d). The Canadian ye ar 2000 emissions inventory was replaced by a Canadian 2020 emissions inventory for the 2018 CMAQ/CAMx simulations. The following
sources were assumed to remain constant between the 2002 and 2018 base case simulations:

• Biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model;

• Wind blown dust associated with non-agricu ltural sources (i.e., natural wind blown fugitive dust);

• Off-shore emissions associated with o ff-shore marine and oil and gas production activities;

• Emissions from wildfires;

• Emissions from Mexico; and

• Global transport (i.e., emissions due to BCs from the 2002 GEOS-CHEM global chemistry model.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-22 The results from the 2002 and 2018 CMAQ and CA Mx simulations were used to project 2018 PM levels from which 2018 visibility estima tes were obtained. The 2002 and 2018 modeling
results were used in a relative sense to scale the observed PM concentr ations from the 2000-2004 Baseline and the IMPROVE monitoring network to obtain the 2018 PM projections. The 2018/2002 modeled scaling factors are called Re lative Response Factors (RRFs) and are
constructed as the ratio of modeling results for the 2018 mode l simulation to the 2002 model simulation. Two important regional haze metrics are the average visibility for the worst 20 percent and best 20 percent days from the 2000-2004 five-year Baseline. For the 2018 visibility projections, EPA guidance recomm ends developing Class I area and PM species specific RRFs using the average modeling results for the worst 20 percent days during the 2002 modeling period and the 2002 and 2018 emission scenarios. The results of the CENRAP 2018 visibility projections following EPA guidance procedures (EPA, 2007a) are provided in Chapter 4 and Appendix D. CENRAP has also de veloped alternative procedures fo r visibility projections that are discussed in Chapter 5 and Appendix D. For example, much of the coarse mass (CM) impacts at Class I area IMPROVE monitors is be lieved to be natural a nd primarily from local sources that are subgrid-scale to the modeled 36 km grid so are not represented in the modeling. So, one alternative visibility projection approach is to set the RRF for CM to 1.0. That is, the CM impacts in 2018 are assumed to be the same as in the observed 2000-200 4 Baseline. Similarly, the Soil impacts at IMPROVE monitors are likely mainly due to local dust sources so another alternative approach is to set th e RRFs for both CM and Soil to 1.0.

The 2018 visibility projections for the worst 20 percent days are compared against a 2018 point on the Uniform Rate of Progress (URP) glidepath or the “2018 URP point”. The 2018 URP point is obtained by constructing a linear visib ility glidepath in deciviews from the observed 2000-2004 Baseline (EPA, 2003a) for the worst 20 pe rcent days to the 2064 Natural Conditions (EPA, 2003b; Pitchford, 2006). Wh ere the linear glidep ath crosses the year 2018 is the 2018 URP point. States may use the modeled 2018 visibi lity to help define their 2018 RPG in their RHR SIPs. The 2018 URP point is used as a benchmark to help judge the 2018 modeled visibility projections and the state’s RPG. However, as noted in EPA’s RPG guidance “The glidepath is not a presumptive target, and States may establish a RPG that provides for greater, lesser, or equivalent visibili ty improvement as that descri bed by the glidepath” (EPA, 2007b). Chapter 4 and Appendix D present the 2018 visibil ity projections for the CENRAP Class I areas and their comparisons with the 2018 URP point using EPA default vi sibility projection procedures (EPA, 2007a) and EPA default URP glidepaths (EPA, 2003a,b; 2007b).

Various techniques have been de veloped to display the 2018 visibi lity modeling results including “DotPlots” that display the 2018 vi sibility projections as a per centage of meeting the 2018 point on the URP glidepath. A value of 100% on the DotPlo t indicates that the Cl ass I area is predicted to meet the 2018 point on the URP glidepath. Ov er 100% means the 2018 visibility projection obtains more visibility improve ments (reductions) than required to meet the 2018 point on the URP glidepath (i.e., projected value is below th e glidepath). And less th an 100% indicates that fewer visibility improvements are projected than are needed to meet the 2018 point URP on the glidepath (i.e., above the glidepath). Figure 1- 4 displays a DotPlot that compares the 2018 visibility projections from the CENRAP 2018 Base G CMAQ simulation with the 2018 URP point using the EPA default RRFs and alternativ e RRFs that set the CM and Soil RRFs to unity (i.e., assume CM and Soil are natural so rema in unchanged from the 2000-2004 Baseline). For these results, the 2018 visibility projections at the Hercules Glade (HEGL1) Class I area meets the 2018 point on the URP glidepath (100%), wher eas the 2018 visibility projections at CaneySeptember 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-23 Creek (CACR), Mingo (MING) and Upper Bu ffalo (UPBU) achieve more visibility improvements than needed to meet the 2018 URP point so are below the 2018 URP glidepath. However, the 2018 visibility projections at Br eton Island comes up slightly short (~5%) of meeting the 2018 point on the URP glidepath and Wichita Mountains (WIMO) comes up approximately 40% short of meeting the 2018 poin t on the URP glidepath. Class I areas at the northern (e.g., VOYA, BOWA and ISLE) and southern (e.g., BI BE and GUMO) boundaries of the U.S. also fall short of achieving the 2018 URP point. High contributi ons of international transport and/or natural sources (e.g., wind blown dust) affect th e ability of these Class I areas to be on the URP glidepath. These issues are di scussed in more detail in Chapters 4 and 5.

CMAQ BaseGa Method 1 predictions for CENRAP+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Regular RRF

CM RRF=1

CM&SOIL RRFs=1

CENRAP non-CENRAP Figure 1-4. 2018 visibility projections expressed as a percent of meeting the 2018 URP point for the 2018 BaseG CMAQ base case simulation using the EPA default (EPA, 2007) Regular RRF and alternative projections procedures that set the RRFs for CM=1.0 and CM&SOIL=1.0.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-24 1.3.14 Additional Supporting Analysis

CENRAP performed numerous sup porting analyses of its modeli ng results includ ing analyzing alternative glidepaths and 2018 pr ojection Approaches and performi ng confirmatory analysis of the 2018 visibility projections. Details on the additional suppor ting analysis are contained discussed in Chapter 5, which include:

• The CENRAP 2018 visibility projections we re compared with those generated by VISTAS and MRPO. There was close agr eement between the CENRAP and VISTAS 2018 visibility projections at almost all co mmon Class I areas. With the only exception being Breton Island where the CENRAP’s projections were s lightly more optimistic than VISTAS’. The MRPO 2018 visibility projectio ns were less optimistic than CENRAP’s
at the four Arkansas-Missouri Class I area that may have been due to CENRAP’s BART
emission controls in CENRAP states not included in the 2018 MRPO inventory.

• Extinction based glidepaths were developed and the CENRAP 2018 visibility projections were shown to produce nearly identical estimates of ach ieving the 2018 URP point when using total extinction glidepaths as when the linear deciview glidepaths were used. With the extinction based glidepaths the analys is of 2018 URP could be made on a PM species-by-species basis where it was shown that 2018 extinc tions due to SO4 and, to a lesser extent, NO3 and EC, achie ve the URP, but the other sp ecies do not and in fact extinction due to Soil and CM is projected to get worse.

• 2018 visibility projections were made us ing EPA’s new Modeled Attainment Test Software (MATS) program and the CENRAP Typ02G and Base18G modeling results. The CENRAP 2018 visibility projections exac tly agreed with those generated by MATS with three exceptions: Breton Island, Boundary Waters and Mingo Class I areas, At these three Class I areas MATS did not produce any 2018 visibility projections due to
insufficient data in the raw IMPROVE da tabase to produce a valid observed 2000-2004
Baseline. CENRAP used filled data for these three Class I areas.

• PM Source Apportionment Technology (PSAT) modeling was conducted to estimate the contributions to visibility impairment at Class I areas by source region (e.g., states) and major source category. Source contributions were obtained for a 2002 and 2018 base case and the PSAT modeling results were im plemented in a PSAT Visualization Tool that was provided to CENRAP states and ot hers. Major findings from the PSAT source apportionment modeling include the following:

o Sulfate from elevated point sources was the highest source ca tegory contribution to visibility impairment at CENRAP Cla ss I areas for the worst 20 percent days.

o International transport cont ributed significantly to vi sibility impairment at
CENRAP Class I areas on the southern (BIBE and GUMO) and northern (BOWA and VOYA) borders of the U.S. and to a lesser extent at WIMO as well.

• Alternative visibility project ions were made assuming that coarse mass (CM) alone and
CM and Soil were natural in origin that confirmed the orig inal 2018 visibility projections.

• Visibility projections were made using an alternative model (CAMx) that verified the projections made by CMAQ.

• The effects of International Transport were examined several ways and found that the inability of the 2018 visibility projections to achieve the 2018 URP point at the northern
and southern border Class I areas was due to high contributions due to International Transport.September 2007

F:\CENRAP_Modeling\TSD\draft#3\Chapter_1_Intro3.doc 1-25 • Visibility trends for the worst 20 percent da ys, best 20 percent days and all monitored days were analyzed at CENRAP Class I areas using the period of record IMPROVE
observations. At most Class I areas there was insufficient years of data to produce a discernable trend. In addition, there was significant year-to-year variability in visibility impairment with episodic events (e.g., w ildfires and wind blown dust) confounding the analysis.

1.4 Organization of the Report

Chapter 1 of this TSD presents background, an overview of the approach and summary of the results of the CENRAP meteorological, emi ssions and air quality modeling. Appendix A contains more details on the meteorological model evaluation discussed in Ch apter 1. Details on the emissions modeling are provided in Chapte r 2 and Appendix B. The model performance evaluation is given in Chapter 3 and Appendi x C. The 2018 visibility projections and comparisons with the 2018 URP point are provided in Chapter 4 with mo re details given in Appendix D. Chapter 5 contains additional supporting analysis with details on the PM source apportionment modeling and alternative proj ections provided in Appendices E and F, respectively. Chapter 6 lists the references cited in the report.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-1 2.0 EMISSIONS MODELING

2.1 Emissions Modeling Overview

For the emissions modeling work conducted in support of CENRAP air quality modeling, we used updated 2002 emissions data for the U.S ., 1999 emissions data for Mexico, and 2000 emissions data for Canada to generate a fi nal base 2002 Base G Typical (Typ02G) annual
emissions database. Numerous iterations of the emissions modeling were conducted using interim databases before arriving at the final Base G emission inventories. The 2002 and 2018 emissions inventories and ancillary modeli ng data were provided by CENRAP emissions inventory contractors (Pechan and CEP, 2005c, e; Reid et al., 2004a,b; Coe and Reid, 2003), other Regional Planning Organizations (RPO s) and EPA. Building from the CENRAP preliminary 2002 database (Pechan and CEP. 2005e) and 2018 projections (Pechan, 2005d), we integrated several updates to the inventories and ancillary data to create final emissions input files; the final simulations ar e referred to as 2002 Typical and 2018 Base G, or Typ02G and Base18G. We used the Sparse Matrix Operat or Kernel Emissions (SMOKE) version 2.1 processing system (CEP, 2004) to prepare the inve ntories for input to the air quality modeling systems. The SMOKE simulations documented in this report include emissions generated for annual CMAQ and CAMx simulations at a 36-km model grid resoluti on, and a short-term CMAQ test simulation at a 12-km model grid resolution. We perform ed the modeling and quality assurance (QA) work based on the CENRAP modeling Qu ality Assurance Project Plan (QAPP; Morris and Tonnesen, 2004) and Mode ling Protocol (Morris et al., 2004a).

The Typ02G and Base18G emission inventories represent significant improvements to the preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the preliminary 2002 modeling served its purpose to de velop the infrastructure for modeling large emissions data sets and producing annual emissions simulations, much of the input data (both as inventories and ancillary data) were placeholders for actual 2002 da ta that were being prepared through calendar year 2005. As these actual 2002 da ta sets became available, they were integrated into the SMOKE modeling and QA syst em that was developed during the preliminary modeling, to produce a high-quality emissions data set for use in the final CMAQ and CAMx modeling. The addition of entirely new invent ory categories, like marine shipping, added complexity to the modeling. By the end of the emissions data collection phase, there were 23 separate emissions processing streams covering a variety of sources categories necessary to general model-ready emission inpu ts for the 2002 calendar year.

2.1.1 SMOKE Emissions Modeling System Background

The purpose of SMOKE (or any emissions processor) is to process the raw emissions reported by states and EPA into gridded hou rly speciated emissions require d by the air quality model.
Emission inventories are typically available as an annual total emissions value for each emissions source, or perhaps with an average-day emissions value. The air quality models, however, typically require emissi ons data on an hourly basis, for each model grid cell (and perhaps model layer), and for each model species . Consequently, emissions processing involves (at a minimum) transformation of emission inve ntory data by temporal allocation, chemical speciation, spatial allocation, and pe rhaps layer assignment, to achie ve the input requirements of the air quality model. For the CENRAP modeling effort, all of these steps were needed. InSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-2 addition, CENRAP processing requ ires special MOBILE6 processi ng and growth and control of emissions for the future-year i nventories. Finally, the biogeni c emission processing using BEIS2 includes additional processing steps. SMOKE form ulates emissions modeling in terms of sparse matrix operations. Figure 2-1 shows an exampl e of how the matrix a pproach organizes the emissions processing steps for anthropogenic emi ssions, with the final step that creates the model-ready emissions being the merging of all the different processing streams of emissions into a total emissions input file for the air qua lity model. Figure 2-1 does not include all the potential processing steps, which can be different for each source category in SMOKE, but does include the major processing steps listed in the previous paragraph, except the layer assignment. Specifically, the inventory emissions are arranged as a vector of emissions, with associated
vectors that include characterist ics about the sources such as its state and county or source classification code (SCC). SMOKE also creates matr ices that will apply the gridding, speciation, and temporal factors to the vector of emissions . In many cases, these matrices are independent from one another, and can theref ore be generated in parallel. Th e processing approach ends with the merge step, which combines the inventory em issions vector (now an hourly inventory file) with the control, speciation, and gridding ma trices to create model-ready emissions.

Figure 2-1. Flow diagram of major SMOKE processing steps needed by all source categories.

Temporal processing includes both seasonal or monthly adjustments and day-of-week adjustments. Emissions are known to be quite different for a ty pical weekday ve rsus a typical Saturday or Sunday. For the day-of-week temporal processing step, emissions may be processed using representative Monday, weekday, Saturday, and Sunday for each month; we refer to this type of processing here as MWSS processing (n ote that because SMOKE operates in Greenwich Mean Time [GMT] then Monday would include some of local time Sunday so needs to be processed separately from the typical weekday). This approach significan tly reduces the number of times the temporal processing step must be run. In the sections below, we have identified the cases in which we have used the MWSS proce ssing approach. Figure 2-2 provides a schematic diagram of SMOKE/BEIS2 processing steps used in this project to generate biogenic emissions rates for Volatile Organic Compounds (VOCs) a nd oxides of nitrogen (NOx). Because biogenic emissions are temperature sensitive, they are ge nerated for each day of 2002 using day-specific meteorological conditions from the MM5 meteorological model.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-3 Figure 2-2. Flow diagram of SMOKE/BEIS2 processing steps.

2.1.2 SMOKE Scripts

The scripts are the interface that emissions mode lers use to run SMOKE and define the set up
and databases used in the emi ssions modeling so are important for anyone wishing to reproduce the CENRAP SMOKE emissions modeling. Many iterations of the CENRAP SMOKE emissions modeling were performed using updated and corrected emissions data and assumptions resulting in the creation of numer ous SMOKE modeling script s during the course of the study. For the CENRAP annual 2002 SM OKE emissions modeling, the default SMOKE script set up, which is based on source categories, was used to configure the scripts. We made
several modifications to the default SMOKE sc ripts to modularize th em, add error checking loops, and break up the report and lo gs directories by source categor y. The result is one script for each major source category being modeled that calls all of the SMOKE programs required for simulating that source category. 16 major sour ce categories were m odeled by SMOKE for CENRAP. An addition seven SMOKE scripts were also run to set up the emissions modeling. Table 2-1 lists all of the SMOKE scripts used for the 2002 base year modeling and the SMOKE programs called by each script. In addition to the source-specific scripts listed in Table 2-1, we also listed the SMOKE utility scripts that actua lly call executables, manage the log files, and manage the configuration of the SMOKE simulations.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-4 Table 2-1. Summary of SMOKE scripts.

Source Category Script Name SMOKE Programs/Functions Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_ar_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport Area fire /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_arf_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport Offshore Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_ofsar_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport
Non-road *

Mobile /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_nr_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport Fugitive dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_fd_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport Road dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_rd_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport
Ammonia * /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_nh3_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport
On-roadMobile (non-VMTbased) /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_mb_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport

On-road non-USMobile (non-VMTbased) /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_nusm_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport

On-road Mobile (VMT-based) /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_mbv_base02f.csh smkinev, mbsetup, grdmat, spcmat, premobl, emisfac, temporal, smkmerge, smkreport WRAP Oil and Gas /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_wog_base02f.csh smkinev, grdmat, spcmat, temporal, smkmerge, smkreport Point /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_pt_base02f.csh smkinev, grdmat, spcmat, laypoint, temporal, smkmerge, smkreport Offshore point /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_ofs_base02f.csh smkinev, grdmat, spcmat, laypoint, temporal, smkmerge, smkreport Canadian Point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_bsf_base02f.csh smkinev, grdmat, spcmat, laypoint, temporal, smkmerge, smkreport All point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_alf_base02f.csh smkinev, grdmat, spcmat, laypoint, temporal, smkmerge, smkreport Biogenec /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smk_bg_base02f.csh Normbies3, tmpbies3, smkmerge

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/make_invdir.csh builds output file names and directories n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/smk_run.csh Calls SMOKE executables for everything but projection, controls, and QA n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/qa_run.csh Calls the SMOKE executables for running QA program & names the input/output directories for reports n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ scripts/run/36km/smoke_calls.csh Calls smk_run.csh, qa_run.csh, configuration and management n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ Assignes/ASSIGNES.cenrap_base02f.cmaq.cb4 p25 Sets up the environment variables for use of SMOKE

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ Assignes/smk_mkdir Creates the input/output
directories n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ Assignes/setmerge_files.scr Sets up the output environment variables for the smkmerge
program

* The nr and nh3 where farther divided to nrm and nry and nh3m and nh3y for the monthly/seas onal and yearly inventoriesSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-5 2.1.3 SMOKE Directory Structures

The SMOKE directories can be divi ded into three broad categories:

1. Program Directories : These directories contain the model source code, assigns files, scripts and executables needed to run SMOKE.

2. Input Directors : These directories contain the raw emissions inventories, the meteorological data and th e ancillary input files.

3. Output Directories : These directories contain all of the output from the model. Also, the output directories contain the MOBILE6 input files.

The directories are desc ribed in the Table 2-2. The final pre-merged emission file names and sources of the data re provided in Appendix B.

Table 2-2. Summary of SMOKE directories.

Category Directory Location Directory Contents /home/aqm2/edss2/ cenrap02f/subsy s/smoke/src SMOKE source code /home/aqm2/edss2/ cenrap02f/subsys/smoke/assigns SMOKE assigns files

/home/aqm2/edss2/ cenrap02f/subsys/sm oke/scripts SMOKE make and run scripts Program

/home/aqm2/edss2/ cenrap02f/subsys/smoke/Linux2_x86pg SMOKE executables

/home/aqm2/edss2/ cenrap02f/data/ met MCIP out metrology files /home/aqm2/edss2/ cenrap02f/data/ge_ dat SMOKE ancillary input files Input

/home/aqm2/edss2/ cenrap02f/data/inventory/cenrap2002 Raw emissions inventory files /home/aqm2/edss2/ cenrap02f/data/run_base02f/static Non-time dependent SMOKE intermediate outputs and MOBILE6 inputs /home/aqm2/edss2/ cenrap02f/ data/run_base02f/scenario Time dependent SMOKE intermediate outputs /home/aqm2/edss2/ cenrap02f/data/run_base02f/outputs Model-ready SMOKE outputs Output

/home/aqm2/edss2/ cenrap02f/dat a/reports SMOKE QA reports

2.1.4 SMOKE Configuration

SMOKE was configured to generate emissions for all months of 2002 on the 36-km unified RPO modeling domain (Figure 1-2). For the anth ropogenic emissions sources that use hourly meteorology and daily or hourly data (i.e., on-road mob ile sources, point sour ces with CEM data, point source fires and biogenic sources) we confi gured SMOKE to represent the daily emissions explicitly. For the non-meteorology dependent emi ssions, we used a representative Saturday, Sunday, Monday, and weekday for each month as surrogate days for the entire month’s emissions (we refer to this as the MWSS processing approach). For these non-meteorology dependent emissions sources we explicitly repres ented the holidays as Sundays. Table 2-3 lists the days that we modeled as representative da ys in the months that we simulated for the 2002 base year modeling. Table 2-4 lists the holid ays in 2002 that were modeled as Sundays.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-6 We used the designations in Table 2-5 to de termine which months fell into each season when temporally allocating the seasonal emissions inve ntories. Some of the inventories for the Electrical Generating Units (EGUs) were r eceived for Winter and Summer. Table 2-6
determines which months fell into each seasonSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-7 Table 2-5. Assignments of months to four seasons for use of seasonal inventory files in SMOKE.

Table 2-6. Assignments of months to two seasons for use of seasonal inventory files in SMOKE.

2.1.5 SMOKE Processing Categories

Emissions inventories are typically divided into area, on-road mobile, non-road mobile, point, and biogenic source categories. These divisions arise from differing methods for preparing the inventories, different ch aracteristics and attributes of the categories, and how the emissions are processed through models. Generally, emissions inventories are divide d into the following source categories, which we refer to la ter as “SMOKE processing categories.”

• Stationary Area Sources : Sources that are treated as being spread over a spatial extent (usually a county or air district) and that are not movable (as compared to non-road mobile and on-road mobile sources). Because it is not possible to collect the emissions at each point of emission, they are estimated ove r larger regions. Exam ples of stationary Month Season January Winter February Winter
March Winter April Winter May Summer June Summer July Summer August Summer September Summer October Winter
November Winter
December WinterSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-8 area sources are residential heat ing and architectural coatings . Numerous sources, such as dry cleaning facilities, may be tr eated either as stationary area sources or as point sources.

• On-Road Mobile Sources : Vehicular sources that travel on roadways. These sources can be computed either as being spread over a sp atial extent or as being assigned to a line location (called a link). Data in on-road invent ories can be either em issions or activity data. Activity data consist of vehicle m iles traveled (VMT) a nd, optionally, vehicle speed. Activity data are used when SMOKE will be computing emission factors via another model, such as MOBILE6 (U.S. EPA, 2005). Examples of on-road mobile sources include light-duty gasoline vehi cles and heavy-duty diesel vehicles.

• Non-Road Mobile Sources : These sources are engines th at do not always travel on roadways. They encompass a wide variet y of source types from lawn and garden equipment to locomotives and airplanes. Em ission estimates for most non-road sources
come from EPA’s NONROAD model (OFFROAD in California). The exceptions are emissions for locomotives, airplanes, pleas ure craft and commercial marine vessels.

• Point Sources : These are sources that are identifie d by point locations, typically because they are regulated and their locations are available in re gulatory reports. In addition, elevated point sources will have their emi ssions allocated vertically through the model layers, as opposed to being emitted into only the first model layer. Point sources are often further subdivided into electric generati ng unit (EGU) sources and non-EGU sources, particularly in criteria inventories in which EGUs are a primary source of NO
x and SO 2

. Examples of non-EGU point sources include chemical manufacturers and furniture
refinishers. Point sources are included in both criteria and toxics inventories.

• Biogenic Land Use Data : Biogenic land use data characteri ze the types of vegetation that exist in either county-total or grid cell values. The biogenic land use data in North America are available using two different se ts of land use categories: the Biogenic Emissions Landcover Database (BELD) version 2 (BELD2), and the BELD version 3 (BELD3) (CEP, 2004b).

In addition to these standard SMOKE processing categories, we have added other categories either to represent specific emissions processes more accurately or to integrate emissions data that are not compatible with SM OKE. Examples of emissions sectors that fall outside of the SMOKE processing categories include emissions generated from process-based models for representing windblown dust a nd agricultural ammonia (NH 3 ) sources. An emissions category with data that are not compatible with SMOKE is one with gridded emissions data sets, such as
commercial marine sources. Another nonstandar d emissions category that we modeled was emissions from fires. All of the emissions categor ies that we used to build CENRAP simulations are described in detail in the following sections. Continuing the enhancement of the emissions s ource categories that we initiated during the preliminary 2002 modeling, we further refined the cat egories from the standard definitions listed above to include more explicit emissions s ectors. The advantage of using more detailed definitions of the source categories is that it leads to more flexibility in designing control strategies, substituting new inventory or profile data into the modeling, managing the input and output data from SMOKE and conducting QA of the SMOKE outputs. The major drawback to defining more emissions source categories is the increased level of complexity and computational requirements (run times and disk space) that results from having a larger number of input data sets. Another motivation behind separating the various emissions categories is related to the size and flexibility of the input data. Some data sets, like the CENRAP on-roadSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-9 mobile inventory, were so large that we had to process them se parately from the rest of the sources in the on-road sector due to computatio nal constraints. We also separated the non-road mobile and ammonia sectors into yearly and mont hly inventories to facilit ate the application of uniform monthly temporal prof iles to the monthly data. Add itional details about how we prepared the emissions inventorie s and ancillary data for modelin g are described in Sections 2.2 through 2.16. Table 2-7 summarizes the entire group of source sect ors that composed simulation Typ02G. Each emissions sector listed in the tabl e represents an explicit SMOKE simulation. As discussed in Section 2.1.2 below, after finishing all of the source-specific simulations, we used SMOKE to combine all of the data into a single file for each day for input to the air quality modeling systems. Each subsection on the emissions sectors describes each s ector in terms of the SMOKE processing category, the year covered by th e inventory, and the source(s) of the data.

Additional details about the inventories are also provided, including any modifications that we made to prepare them for input into SMOKE.

Table 2-7. CENRAP Typ02G emissions categories.

Emissions Sector Abbreviation*
Fires as Point Sources (WRAP, CENRAP, VISTAS) Alf

Area Sources (All domain) ar

CENRAP area fires arf
Area fires, Anthropogenic (All domain, excluding WRAP and CENRAP) arfa

Area fires, Wild (All domain, excluding WRAP) arfw
Biogenic b3
Ontario, Canada, point-source fires bsf
Fugitive dust fd

WRAP on-road mobile mb

CENRAP on-road mobile mbv_CENRAP

Other US on-road mobile mbv
Monthly CENRAP/MRPO anthropogenic NH 3 nh3m
Ammonia from annual inventory (CENRAP) nh3y
WRAP anthropogenic NH 3 nh3
Seasonal/Monthly non-road mobile (WRAP, CENRAP, MW) nrm

Annual non-road mobile nry
On-road Mobile (Non-US) nusm
Offshore shipping (Gulf, Atlantic) ofs
Offshore area (Gulf) ofsar
Stationary point (All domain, including offshore) pt

Road dust rd
Windblown dust (All domain) wb_dust
WRAP oil and gas wog

*These abbreviations are used in the file na ming of the SMOKE output files for each sector.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-10 Emissions models such as SMOKE are com puter programs that convert annual or daily estimates of emissions at the state or county le vel to hourly emissions fluxes on a uniform spatial grid that are formatted for input to an air quality model. For the Typ02G and Base18G emission inventories we prepared em issions for CMAQ version 4.5 us ing SMOKE version 2.1 on the UCR Linux computing cluster. SMOKE integrates annual county-level emissions inventories with source-based temporal, spatial, and chemi cal allocation profiles to create hourly emissions fluxes on a predefined model grid. For elevated sources that requi re allocation of the emissions to the vertical model layers, SMOKE integrates meteorology data to derive dynamic vertical profiles. In addition to its capacity to represen t the standard emissions processing categories, SMOKE is also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3) model for estimating biogenic emissions fluxes (U.S. EPA, 2004) and the MOBILE6 model for estimating on-road mobile emissions fluxes from county-level vehicle activity data (U.S. EPA, 2005a).

SMOKE uses C-Shell scripts as us er interfaces to set configuration options and call executables. SMOKE is designed with flexible QA capabilities to generate standard and custom reports for checking the emissions modeling process. After modeling all of the source categories individually, including those categories generated outside of SMOKE, we used SMOKE to merge all of the categories together to create a single CMAQ input file per simulation day. Also, for use in the CAMx modeling, we converted the CMAQ-rea dy emissions estimates to CAMx-ready files using the CMAQ2CAMx converter. Additional tec hnical details about the version of SMOKE
used for final simulations are available from CE P (2004b). All scripts, data , and executables used to generate the Typ02G and Base18G emissi ons for CMAQ and CAMx are archived on the CENRAP computing cluster.

2.1.6 2002 and 2018 Data Sources

This section describes the procedures that th e CENRAP followed to collect and prepare all emissions data for Typ02G and Base18G simulati ons. We discuss the sources of all inventory and ancillary data used for simu lations. CENRAP worked with em issions inventor y contractors, other RPOs, and EPA to collect all of the data that constitute the simulation. Table 2-8 lists all of the contacts for the various U.S. anthropogenic emission inventories we used. For the CENRAP inventories, this table lists the contacts for the contractors who prepared the inventories; for the
non-CENRAP inventories it lists th e contacts at the RPOs who pr ovided us inventory data. We
obtained the emissions inventories for Canada a nd Mexico from the U.S. EPA Emissions Factors and Inventory Group (EFIG) via the Clearing house for Inventories and Emissions Factors (CHIEF) website ( http://www.epa.gov/ttn/chief/index.html ).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-11 Table 2-8. CENRAP anthropogenic emissions inventory contacts. Source Category Emissions Data Contact
WRAP All Tom Moore, Western Governors' Association Phone: (970) 491-8837 Email: mooret@cira.colostate.edu
CENRAP 2002 Consolidated Inventory Randy Strait, E.H. Pechan & Assoc., Inc. Phone: 919-493-3144 Email: rstrait@pechan.com NH3 Inventory, Prescribed and Agricultural Fires, and On-road mobile
emissions Dana Sullivan, Sonoma Technology, Inc. Phone: 707-665-9900 Email: dana@sonomatech.com Gulf Off-shore platform and support vessel emissions Holly Ensz, Minerals Management Service Phone: (504) 736-2536 Email: holli.ensz@mms.gov
VISTAS All Greg Stella, Alpine Geophysics, LLC, Phone: 828-675-9045 Email: gms@alpinegeophysics.com
MANE-VU All Megan Schuster, MARAMA, Baltimore, MD USA Phone: 410-467-0170 Email: mschuster@marama.org
MRPO All Mark Janssen, LADCO, Des Plaines, IL, USA Phone: 847-296-2181 Email:janssen@ladco.org

As mentioned above, the refinement of these invent ories involved splitting some of the inventory files into more specific source sectors. As th e stationary-area-source emissions sector has traditionally been a catch-all for many types of sources, this is the inventory sector that required the greatest amount of preparation. Upon receiv ing all stationary-area- source inventories we extracted fugitive dust, road dust, anthropogenic NH 3 , and for the non-WRAP U.S. inventories, stage II refueling sources. We retained the dust sources as separate ca tegories that we would further refine with the application of transport factors (see Section 2.8).

We collected the ancillary data used for SMOKE modeling from several sources. SMOKE ancillary modeling data include:

• Temporal and chemical allocation factors by state, county, and source classification code (SCC);

• Spatial surrogates and cross-re ference files for allocating c ounty-level emissions to the model grid;

• Hourly gridded meteorology data;

• Stack defaults for elevated point sources;

• MOBILE6 configuration files;

• A Federal Implementation Standards (FIPS) codes (i.e., country/s tate/county codes) definition file;September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-12 • A Source Category Classification (SCC) codes definition file;

• A pollutant definition file; and

• Biogenic emission factors.

Except for the meteorology data and the MOBILE6 conf iguration files, we us ed default data sets provided by EPA as the basis for all of the ancillary data except for temporal profiles used for Electric Generating Units (EGUs). These profiles were developed based on CEM data from 2000 through 2003 (Pechan and CEP, 2005c). CENRAP provided the mete orology data for the simulations at 36-km and 12-km grid resoluti ons (Johnson, 2007). The inve ntory contractor who prepared the MOBILE6 inventorie s provided the MOBILE6 configurat ion files either directly or via an RPO representative; details about the sources of the MOBILE6 inputs are provided in
Section 2.4. We made minor modifications to the chemical allocation, pollutant definition, and country/state/county codes files for new sources , pollutants, or counties contained in the inventories that we had not prev iously modeled. We made major modifications to the temporal and spatial allocation inpu ts, as described below.

2.1.7 Temporal Allocation

Temporally allocating annual, daily, or hourl y emissions inventorie s in SMOKE involves combining a temporal cross-reference file and a temporal profiles file.

• Temporal cross-reference files associate m onthly, weekly, and diur nal temporal profile codes with specific inventory sources, through a combination of a FIPS (country/state/county) code, an SCC, and sometimes for point sources, facility and unit
identification codes.

• Temporal profiles files contai n coded monthly, weekly, and di urnal profiles in terms of a percentage of emissions allocated to each tem poral unit (e.g., percentage of emissions per month, weekday, or hour).

As a starting point for the temporal allocation da ta for simulations, we used the files generated by emission inventory contractors (Pechan an d CEP, 2005c). Based on guidance from the developers of some of the invent ory files, we enhanced the tem poral profiles and assignments for some source categories (Pechan, 2005b).

We modified the temporal allocation data for th e simulations to improve the representation of temporal emissions patterns for certain source categories. We implemented the adjusted profiles in SMOKE by modifying the tempor al cross-reference file for the applicable FIPS and SCC
combinations.

Updated temporal profiles for EGUs were made available for MRPO in the MRPO Base K inventory. Since the non-road emissions for IA and MN were monthly emissions developed by MRPO, new temporal profiles were created for all the SCCs in these emissions files for these two states only. The monthly prof ile was uniform and the weekly and diurnal profiles were kept the same as were modeled for the rest of the country.

An updated temporal profile, profile 485, based on NOAA 1971-2000 population weighted average heating degree days for home heat ing area source emissions was obtained fromSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-13 VISTAS. This profile provide d state specific updates for hom e heating emissions and was applied to the full inventor y in place of profile 17XX.

Other additions to the Base02G temporal allocation data include d updates that made by other RPOs that are applicable to their inventories. These other updates to the temporal allocation files
included

• VISTAS continuous emissions monitoring (CEM)-specific profiles for EGUs in the VISTAS states;

• VISTAS agricultural burning profiles;

• Wildfire and prescribed fire profiles developed by VISTAS for the entire U.S.;

• MANE-VU on-road mobile profiles;

• WRAP weekly and diurnal road dust profiles;

• WRAP diurnal wildfire, agricultural fire , and prescribed fire profiles; and

• WRAP on-road mobile weekly and diurnal profiles.

Finally, for all of the monthly and seasonal emissions inventorie s, we modified the temporal cross-reference files to apply uniform monthly profiles to th e sources contained in these inventories. The monthly variability is inherent in monthly and seasonal inventories and does not need to be reapplied through the temporal al location process in SMOKE. The inventories to which we applied uniform monthl y temporal profiles included:

• WRAP, CENRAP, and MRPO non-road mobile sources;

• WRAP on-road mobile sources;

• WRAP road dust; and

• CENRAP anthropogenic ammonia.

2.1.8 Spatial Allocation

SMOKE uses spatial surrogates and SCC cross-refe rence files to allocate county-level emissions inventories to model grid cells . Geographic information system (GIS)-calculated fractional land use values define the percentage of a grid cell that is cove red by standard sets of land use categories. For example, spatial surrogates can defi ne a grid cell as bein g 50% urban, 10% forest, and 40% agricultural. In addition to land use categories, spatia l surrogates can also be defined by demographic or industrial units, such as population or commercial area. Si milar to the temporal allocation data, an accompanying spatial cross-reference file associates the spatial surrogates (indexed with a numeric code) to SCCs. Spatial a llocation with surrogates is applicable only to area and mobile sources that are provided on a count y level basis. Point sources are located in the model grid cells by SMOKE ba sed on the latitude-l ongitude coordinates of each source. Biogenic emissions are estimated based on 1-km 2 gridded land use information that is mapped to the model grid using a processing program such as the Multimedia Integrated Modeling System (MIMS) Spatial A llocator (CEP, 2004).

We used various sources of spatial surrogate information for the U.S., Canada, and Mexico
inventories in the simulations. For the U.S. a nd Canadian sources, we used the EPA unifiedSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-14 surrogates available through the EFIG web site (EPA, 2005c). For the 36-km grid, EPA provides these data already formatted for SMOKE on the RP O Unified 36-km domain that we used for the simulations. We modified the spatial surrogate s for Canada on the RPO Unified 36-km domain by adopting several surrogate categ ories that were enhanced by the WRAP. Table 2-9 provides details about the new Canadian spatial surrogate s that were developed by the WRAP and used for CENRAP simulations. For modeling Mexico, we used Shapefiles developed for the Big Bend Regional Aerosol and Visibility Observations Study (BRAVO) mode ling to create surrogates for Mexico on the RPO Unified 36-km domain (EPA, 2005c).

Table 2-9. New Canadian spatial surrogates.

Attribute Base02a Code Shapefile Reference

Land area 950 can_land93_land Natural Resources Canada (1993)
AVHRR land cover data

Water area 951 can_land93_water Natural Resources Canada (1993)
AVHRR land cover data

Forest land area 952 can_land93_forest Natural Resources Canada (1993)
AVHRR land cover data
Agricultural land area 953 can_land93_agri Natural Resources Canada (1993)
AVHRR land cover data

Urban land area 954 can_land93_urban Natural Resources Canada (1993)
AVHRR land cover data

Rural land area 955 can_land93_rural Natural Resources Canada (1993)
AVHRR land cover data
Airports 956 can_airport U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 1:1,000,000 scale data
Ports 957 can_port U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 1:1,000,000 scale data

Roads 958 can_road1m Natural Resources Canada (2001) National Scale Frameworks data

Rail 959 can_rail1m Natural Resources Canada (1999) National Scale Frameworks data

2.2 Stationary Point Source Emissions

Stationary-point-source emissions data for SM OKE consist of (1) Inventory Data Analyzer (IDA)-formatted inventory files; (2 ) ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx; and (3) meteorology data for calcula ting plume rise from the elevat ed point sources. This section describes where CENRAP obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-15 2.2.1 Data Sources

For the stationary-point-source inventories in Typ02G and Base18G, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the BRAVO 1999 Mexican inventory. The BRAVO inventory was updated with entirely new
inventories for the six nort hern states of Mexico for stationary area, as well as stationary point, on-road mobile, and off-road mobile sources. Emi ssions for the southern states of Mexico were
included for the first time in CENRAP simula tions Typ02G and Base18G. These data were provided by ERG, Inc., who completed an up dated 1999 emissions i nventory for northern Mexico (ERG, 2006b) and delivered these data to the WRAP. The CENRAP stationary-point inventory consisted of annual c ounty-level and tribal data provided in A ugust of 2005 (Pechan and CEP, 2005e). The WRAP (ERG, 2006a) and VISTAS Base G (MACTEC, 2006) stationarypoint inventories consisted of an annual data set and monthly CE M data for selected EGUs. The WRAP and VISTAS provided these data direc tly to CENRAP. We downloaded the MANE-VU stationary-point inventories from the MANE -VU web sites. MRPO base K data was downloaded and processed for SMOKE modeli ng by Alpine Geophysics under contract from MARAMA. UCR entered into a nondisclosure ag reement with Environment Canada to obtain version 2 of the 2000 Canadian point-source i nventory. This inventory represented a major improvement over the version of the data that we had used in the preliminary 2002 modeling.

Reductions anticipated from BART controls for electric genera ting units (EGU) in Oklahoma, Arkansas, Kansas, and Nebraska were included in projections of 2018 emissions. These anticipated reductions were based on actual operating conditions and estimated control
efficiencies from utilities.

Newly permitted coal-fired util ities were included in 2018 proj ections. Conservatively, no IPM projected new units were removed from the simulation with the addition of the permitted
facilities.

Due to missing or clearly erroneous stack paramete rs, several facilities in CENRAP states were relegated to default stack prof iles based on SCC in the NEI QA process. Prioritizing for the largest emissions sources, these default parame ters were corrected by CENRAP States and updated files were provided to modeling contra ctors. Final IDA input files Typ02G and Base18G for point sources reflect State corrections.

For coal-fired point and area sources, The EP A Office of Air Quality and Planning Standards (OAQPS) determined that the organic carbon fr action in the speciation profile code "NCOAL" was not representative of most coal combustion occu rring in the U.S. This profile has an organic carbon fraction of 20%, which includes an adjustme nt factor of 1.2 to account for other atoms (like oxygen) attached to the ca rbon. OAQPS has reverted back to the profile code "22001" for coal combustion, which has an organic carbon fract ion of 1.07% (again in cluding the 1.2 factor adjustment). This is the same profile that EPA used for previous ru lemaking efforts including the Heavy Duty Diesel Rule and Non-Road Rule , which were proposed (a nd publicly reviewed) prior to the introduction of the NCOAL profile.

The consensus in OAQPS is that the NCOAL pr ofile has a high organi c carbon percentage
because it is based on measurements of combustion of lignite coal. With the exception of Texas, lignite is not widely used in the U.S.. Thus, OAQPS staff stoppe d relying on this profile as a national default profile. A new coal speciati on profile developed base d on Eastern bituminousSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-16 coal combustion (since much of the coal burned in th e U.S. is of this type) is being developed by EPA's Office of Research and Developm ent but was not completed for this study.

The profile recently developed for MRPO by Carn egie Mellon was provided to CENRAP and is representative of combustion of eastern bituminous coal. This profile is a more appropriate profile for most facilities in the U.S. than the default NCOAL profile.

Additionally, the "22001" profile has been fla gged as problematic because of the apparent inadvertent switching of the organic carbon and elemental carbon fractions, which are 1.07% and 1.83% respectively. The report discovering the disc repancy in the profile did not offer a clear
alternative to correct the problem (MACTEC, 2003).

CENRAP has continued to use th e NCOAL factor for facilities bu rning lignite in North Dakota and Texas. For the remainder of the U.S., the MRPO profile, CMU, was used. The NCOAL factor was modified reducing th e organic carbon by half and assigning the remainder to PM 2.5

. The modification was at the request of Texas a nd was reflective of the original study for the NCOAL factor conducted in Texas (Chow, 2005). Table 2-10 summarizes the PM 2.5 speciation profiles for the NCOAL, 2201 and CMU specia tion profiles for coal burning sources.

Table 2-10. PM 2.5 speciation profiles for coal-burning sources.

Profile POC PEC PNO3 PSO4 PM2.5

NCOAL 0.1000 0.0100 0.0050 0.1600 0.7250
22001 0.0107 0.0183 0.0000 0.1190 0.8520 CMU 0.0263 0.0315 0.0036 0.0447 0.8938

Final simulations used improved temporal alloca tion and speciation inform ation relative to the preliminary 2002 modeling; the rest of the ancill ary data for modeling st ationary point sources stayed the same (Mansell et al., 2005).

2.2.2 Emissions Processing

For Typ02G and Base18G simulations we configur ed SMOKE to process the annual inventories for the U.S., Canada, and Mexico and process ho urly CEM data for the VISTAS. We configured SMOKE to allocate these emissions up to m odel layer 15 (approximately 2,500 m AGL), which roughly corresponds to the maximum planetary bounda ry layer (PBL) heights across the entire domain throughout the year. As coar se particulate matter (PMC) is not an inventory pollutant but is required by the air quality models as input species, we used SMOKE to calculate PMC during the processing as (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMALIZE set to “No,”
we treated the annual inventorie s based on the assumption that th ey represent average-day data based on a seven-day week, rather than average weekday data. We also assumed that all of the volatile organic compound (VOC) emissions in th e inventories are react ive organic gas (ROG),
and thus used SMOKE to convert the VOC to total organic gas (TOG) before converting the emissions into CB-IV speciation for the air qualit y models. To capture the differences in diurnal patterns that are contained in the CEM tempor al profiles for VISTAS and CENRAP states (Base02F), we configured SMOKE to generate daily temporal matrices, as opposed to using a Monday-weekday-Saturday-Sunday (MWSS) temporal allocation approach.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-17 To QA the stationary-point emissions, we us ed the procedures in the CENRAP emissions modeling QA protocol (Morris and Tonnesen, 2004) and a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE for all simulations. These QA graphics are available on the web site at: http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml

2.2.3 Uncertainties and Recommendations

There were issues with the stationary-point emi ssions that we left unres olved at the completion of the Typ02G and Base18G emissions modeling eith er because we did not feel they would have a major impact on the modeling results in CENRAP states or because we di d not have alternative approaches and they represented the best avai lable information. Cana dian emissions for 2000 were found to have a significan t number of missing stack para meters. These stacks when modeled with default parameters frequently resu lted in lower plume heights. Stack parameters
for 2000 were corrected based on cross referencin g sources with the 2005 Canadian inventory for the largest emitting points. Stack parameters for many of the sources with lower emissions remain incorrect, but are assumed to have a less significant impact on CENRAP Class I areas. The 2020 projected emissions for Ca nada were obtained as air quality mode l-ready files from EPA. EPA has not confirmed that missing stack parameters were corrected for the projected inventory. It is assumed that they were not corr ected and default parameters were used instead. Given confidentiality issues that surround Cana dian inventories, EPA processed emissions represent the best available data.

2.3 Stationary Area Sources

Stationary-area-source emissions data for SMOKE consist of IDA-formatted inventory files and ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This sec tion describes where we obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected.

2.3.1 Data Sources

For the stationary area source inventories in the Typ02G and Base18G simulations, we used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the upda ted Mexican inventory, http://www.epa.gov/ttn/chief/net/mexico.html

. The BRAVO inventory was updated with entirely ne w inventories for the si x northern states of Mexico for stationary area, as well as stationary point, on-road mobile, and off-road mobile
sources. Emissions for the southern states of Mexico were included for the first time in CENRAP simulations Typ02G and Base18G. The CENRAP stationary-area inventory consisted of annual county-level and tribal data provide d by in August of 2005 (Pechan and CEP, 2005e). The WRAP (ERG, 2006a) and VISTAS Base G (M ACTEC, 2006) stationa ry-area inventories
consisted of an annual data set. We downloaded the MANE-VU stationary-area inventories from
the MANE-VU web sites. MRPO base K da ta was downloaded and processed for SMOKE modeling by Alpine Geophysics under contract from MARAMA.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-18 To prepare the stationary-area inventories for modeling, we made several modifications to the files by removing selected sources either to model them as separate source categories or to omit them from simulations completely. Using guidance provided by EPA (EPA, 2004b), we extracted fugitive and road dust sources from all stationary-area inventories for adjustment by transport factors and modeling as separate source categories (see Section 2.8). We also extracted and discarded the stage II refueling sources (Table 2-11) from the U.S. i nventories; we modeled these sources with MOBILE6 as part of the on -road mobile-source emissions. We left the stage II refueling emissions in the WRAP stationary -area inventory because the on-road mobile inventory that we received for this re gion did not contain these emissions.

Table 2-11. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory. SCC Description 2501060100 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Total 2501060101 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Displacement Loss/Uncontrolled 2501060102 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Disp lacement Loss/Controlled 2501060103 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service Stations Stage 2: Spillage 2501070100 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service Stations Stage 2: Total 2501070101 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service Stations Stage 2: Displacement Loss/Uncontrolled 2501070102 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service Stations Stage 2: Disp lacement Loss/Controlled 2501070103 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service Stations Stage 2: Spillage

Other steps that we took to prepar e the stationary-area inventories included confirming that there is no overlap between the anthropogenic NH 3 inventory (Section 2.9) and stationary area sources, and moving area-source fires in each regiona l inventory to separate files. In addition to these inventory modifications we made a few cha nges to the ancillary data files for simulation Typ02G, as described next.

Simulation Typ02G used improved temporal and sp atial allocation inform ation relative to the preliminary 2002 modeling; the rest of the ancill ary data for modeling st ationary area sources stayed the same as in the preliminary 2002 modeling (Manse ll et al., 2005) . We adopted enhanced spatial allocation data with additional area-based surr ogates for Canada (Table 2-9), and added surrogates for a missing county in Co lorado (Broomfield) fr om WRAP modeling and QA work. The WRAP had noticed when looking at the Canadian data for the preliminary 2002 modeling that forest fire emissions from the Ca nadian area-source invent ory, which are relatively large sources of CO, NO
x , and PM 2.5 , were being allocated to a su rrogate for logging activities. They found similar discrepancies for other area and non-road SCCs in Canada. To improve the representation of the Canadian emissions, we adopted several land-area-based surrogates developed by the WRAP, such as forested land area, urban land area, and rural land area, and made the accompanying additions to the spatial cross-reference file to associate inventory SCCs with these surrogates. We also added spatial surrogates for Broomfield County, CO; this county was included in the inventory but was not incl uded in the base EPA su rrogates (this county was recently created from portions of other counties).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-19 Improvements to the temporal allocation data for simulation Typ02G included the addition of several FIPS-specific profiles provided by VI STAS and CENRAP contractors (Pechan 2005b). These temporal profiles listed in Table 2- 12 targeted mainly fire and agricultural NH 3 sources, such as open burning and livestock operations, respectively.

Table 2-12. New Temporal Profile Assignments for CENRAP Area Source SCCs.

SCC Description Month Week Diurnal Recommend ation Based

on Profile Data for SCC Description of Similar SCC used to Recommend Profiles

2310001000 Industrial Processes; Oil and Gas Production: SIC 13;All Processes : On-shore; Total: All
Processes 262 7 26 2310000000 Industrial Processes;Oil and Gas Production: SIC 13;All Processes;Total: All Processes 2310002000 Industrial Processes;Oil and Gas Production: SIC 13;All Processes : Off-shore;Total: All
Processes 262 7 26 2310000000 Industrial Processes;Oil and Gas Production: SIC 13;All Processes;Total: All Processes 2461870999 SolventUtilization;Miscellaneous Nonindustrial: Commercial;PesticideApplication: NonAgricultural;Not Elsewhere Classified 258 7 26

2461800000 Solvent Utilization;Miscellaneous Non-industrial: Commercial;Pesticide Application: All Processes;Total: All Solvent Types
2805009200 Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry production - broilers;Manure handling and storage 1500 7 26 2805009300 Miscellaneous Area Sources;Agriculture Production - Livestock;Poultry production - broilers;Land application of manure
2805021100 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - scrape dairy;Confinement 1500 7 26 2805021300 Miscellaneous Area Sources;Agriculture
Production - Livestock;Dairy cattle - scrape dairy;Land application of manure
2805021200 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - scrape dairy;Manure handling and storage 1500 7 26 2805021300 Miscellaneous Area Sources;Agriculture
Production - Livestock;Dairy cattle - scrape dairy;Land application of manure
2805023100 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - drylot/pasture dairy;Confinement 1500 7 26 2805023300 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - drylot/pasture dairy;Land application of manure
2805023200 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - drylot/pasture dairy;Manure handling and storage 1500 7 26 2805023300 Miscellaneous Area Sources;Agriculture Production - Livestock;Dairy cattle - drylot/pasture dairy;Land application of manure
2810020000 Miscellaneous Area Sources;Other Combustion;Prescribed Burning of Rangeland;Total 3 11 13 2810015000 Miscellaneous Area Sources;Other Combustion;Prescribed Burning for Forest Management;TotalSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-20 2.3.2 Emissions Processing

For simulations Typ02G and Base18G we configur ed SMOKE to process the annual stationaryarea-source inventories fo r the U.S., Canada, and Mexico. As PMC is not an inventory pollutant but is required by the air quality models as input species, we used SMOKE to calculate PMC during the processing as (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMALIZE set to “Yes,” we treated the annua l stationary-area inventories based on the assumption that they represent average weekday data, causing SMOKE to renormalize the data to a seven-day estimate before applying any temporal adjustme nts. We also assumed that all of the VOC
emissions in the inventories are ROG and thus used SMOKE to convert the VOC to TOG before converting the emissions into CB-IV speciation for the air quality m odels. We configured SMOKE to use a MWSS temporal allocation approac h, as opposed to a daily temporal approach.

To QA the stationary-area emissions, we used the procedures in the CENRAP modeling QAPP and Modeling Protocol (Morris and Tonnesen, 2004; Morris et al., 2004a) and a suite of graphical summaries. We used tabulated summarie s of the input data and SMOKE script settings to document the data and configuration of SMOKE for all simulations. The graphical QA summaries include, for all emissions output specie s, daily spatial plots summed across all model layers, daily time-series plots, and annual time-se ries plots. These QA gra phics are available on
the UCR/CENRAP web site at http://pah.cert.ucr.edu/aq m/cenrap/emissions.shtml

.

2.3.3 Uncertainties and Recommendations

Most of the issues that we encountered with the stationary area sources related to the removal of
certain SCCs from the base inventories for in clusion as other source categories or complete omission from simulations. We spent considerable effort on ensuring that we did not have overlap between the area inventor y and the other sectors that explicitly represent sources traditionally contained in the area inventory, such as NH 3 and dust.

Both the Canadian and Mexican inventories pres ented minor problems that we resolved for simulation Typ02G but that can be addressed more thoroughly in future simulations. The Canadian inventory we us ed contained data only at the province level, essentially equivalent to a statewide rather than county-level inventory. A higher resolution inventory would have allowed us to use higher-resolution and more accurate spa tial allocation data. Future modeling that uses Canadian data should move to the newly releas ed municipality-level year 2000 inventories for
Canada.

There was a discrepancy between the state and county coding in the Mexican inventory and the SMOKE file that defines acceptable FIPS codes. Di fferences in the ordering of the Mexican state
names between these two data sets led to so me of the Mexican in ventory sources being mislabeled in the SMOKE QA reports. The state codes in the inventory and spatial surrogate
files for two Mexican states were chan ged to be consistent with the SMOKE country/state/count y codes file.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-21 2.4 On-Road Mobile Sources

On-road mobile-source emissions data for SMOKE consist of IDA-formatted emissions and vehicle activity inventor y files, and ancillary data for allo cating the inventories in space, time, and to the Carbon Bond-IV chemistry mechanis m used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected.

2.4.1 Data Sources

The SMOKE processing for CENRAP included tw o approaches for processing on-road mobile sources depending on the source of the data provided. The first approach was to compute mobile emissions values prior to providing them to SM OKE; we call this the pre-computed emissions approach. The second approach was to provide SMOKE with VMT data, meteorology data, and MOBILE6 inputs, and let the SMOKE/MOBILE6 module compute the mobile emissions based on these data; we call this the VMT approach. These approaches are not mutually exclusive for a single SMOKE run; therefore, we performed sing le SMOKE runs in which both approaches were
used as follows:

• Annual VMT for computing CO, NO x , VOC, SO2, NH3 and PM using MOBILE6 for all
CENRAP States.

• Pre-computed, seasonal MOBILE6-based emi ssions of all pollutants for the 13 WRAP states that included pre-speciated PM 2.5 data.

• Annual VMT for computing CO, NO x , VOC, SO2, NH3 and PM using MOBILE6 for the rest of the United States (V ISTAS, MRPO and MANE-VU).

• Pre-computed, annual 1999 emissions of all pollutants for Mexico.

• Pre-computed, annual 2000 emissions of all pollutants for Canada.

For the CENRAP states, STI provided VMT data and MOBILE6 input files for all counties in the CENRAP region (Reid et al., 2004a). MOBI LE6 input files were provided only for the months of January and July for 2002. MOBILE6 input files for the rema ining months of 2002 had to be generated. These da ta were then processed with in SMOKE. Using one set of MOBILE6 input files for each county in the CENRAP states resulted in compute memory requirements that were to large to process all CENRAP states together. Therefore the on-road mobile processing for the CENRAP states was split into two groups for SMOKE processing. The resulting gridded emissions data files were then merged together to obta in an on-road mobile source emissions file for the entire CENRAP region.

For the WRAP states we used actual 2002 data split into California a nd non-California seasonal inventories that were provided by the WRAP (Pollack et al., 2006) . In addition to the standard criteria pollutants, these f iles contained pre-speciated PM 2.5 emissions. For the rest of the U.S. we used annual county-level activity and speed inventories with monthly, county-level MOBILE6 inputs, and hourly meteorology to estimate the hourly emissions with the
SMOKE/MOBILE6 module. For the non-U.S. inventories, we used version 2 of the year 2000 Canadian inventory and the updated 1999 Mexican inventory pr e-computed mobile source
emissions.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-22 2.4.2 Emissions Processing

For the Typ02G emissions modeling we confi gured SMOKE to process the annual on-road mobile emissions inventory data for the WR AP, Canada, and Mexico as pre-computed
inventories. For the non-WRAP states, we used the SMOKE/MOBILE6 integration to process the annual activity inventories and monthly, county-based roadway information. The WRAP inventories contained pre-comput ed speciated PM emissions (Pol lack et al, 2006) so the SMOKE PM speciation module was not use d. The WRAP on-road mobile i nventories were developed to represent seven-day (weekly) average emissions (a s compared to the area source inventory, which represented average weekday emissions). As actual weekly average emissions, we configured SMOKE to process the WRAP on -road mobile source emissions by setting WKDAY_NORMALIZE to “No” in which case the em issions are adjusted to represent weekday and Saturday and Sunday emissions (as in contrast to the area sources where the emissions are just adjusted for Saturday and Sunday). We also assumed that all of th e VOC emissions in the
inventories are ROG and used SMOKE to conve rt the VOC to TOG before converting the emissions into CB-IV speciation for the air qual ity models. We configured SMOKE to create day-of-week specific rather than MWSS, temporal profiles because the WRAP on-road mobile temporal profiles contain weekly prof iles that vary across the weekdays.

As noted previously, the large number of count y roadway inputs for MOBILE6 processed for the non-WRAP portion of the U.S. require d us to split the st ates mobile-source processing into three subsets because of computer memory limitations. Separate MOBILE6 input files were used for each separate county for CENRAP states, where as one MOBILE6 input file was used for several
counties outside of the CENRAP region. The three subsets consisted of two sets of SMOKE/MOBILE6 simulations for the CENRAP and a simulation that computed on-road mobile emissions for the MRPO, VISTAS, and MANE-VU states. We c onfigured MOBILE6 to use weekly temperature averaging for co mputing these emissions within SMOKE.

To QA the on-road mobile emissions, we used the CENRAP emissions modeling QA protocol (Morris and Tonnesen, 2004; Morris et al., 2004a) a nd a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE for simulations Typ0 2G and Base18G. The gr aphical QA summaries include, for all emissions output species, daily sp atial plots, daily time-series plots, and annual time-series plots. These graphics are available at http://pah.cert.ucr.edu/aq m/cenrap/qa_base02b36.shtml#mb

2.4.3 Uncertainties and Recommendations

We approached the on-road mobile emissions preparation for simulation Typ02G from three different directions, which were based on the form of the input inventories and ancillary
emissions data for different re gions of the modeling domain:

• The WRAP region used emissions estimates pre-computed with EMFAC for California
and MOBILE6 for the rest of WRAP states and processed li ke area sources with SMOKE adjusted from weekly to day-of-week emissions.

• The CENRAP, VISTAS, MRPO, and MANE-VU stat es used county-leve l activity data to compute emissions with the SMOKE/MOBILE6 module.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-23 • The non-U.S. parts of the domain also had pre-computer on-road mobile source emissions so used an area-source ap proach for processing with SMOKE.

Different approaches for modeling a single emi ssions sector adds complexity and additional
sources of error and inconsistenc ies to the modeling because of the different assumptions that went into the preparation of the input data. Fo r example, refueling emissions from the on-road mobile sector are represented in the WRAP area- source sector but are computed with MOBILE6 for the rest of the U.S. Not using MOBILE6-b ased emissions for the non-U.S. portion of the domain neglects the effects of the actual 2002 meteorology on thes e emissions. Applying MOBILE6 outside of the U.S. is currently not possible because MOBILE6 is instrumented only for calculating emissions for the U.S. automotive fleet. The result of using MOBILE6 to calculate U.S. emissions and not using it to cal culate the non-U.S. on-road mobile emissions
estimates is that the non-U.S. emissions are not specific to this modeling year and the 2002 meteorological conditions, whereas the U.S. emissions are 2002-specific.

While we used the best available information to compute the on-road mobile emissions for the various portions of the modeling domain, inc onsistent approaches for representing these emissions may lead to unnatural emissions gr adients along political boundaries. We recommend for future work a unified approach for at leas t the U.S. inventories, where either we use MOBILE6 in SMOKE for the entire domain (o r alternative emissions model such as CONCEPT), or we calculate the emissions with MOBILE6 outside of SMOKE and then use the resulting county-based em issions inventories.

2.5 Non-Road Mobile Sources

Non-road mobile source emissions data for SM OKE consist of annual, seasonal, and monthly IDA-formatted emission inventory f iles and ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry mech anism used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the types of QA that we performed to ensure that SMOKE processed the data as expected.

2.5.1 Data Sources

The non-road mobile-source inventories in the Typ02G and Base18G emissions modeling used actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory and the improved 1999 Me xican inventory. The U.S. inve ntories consisted of annual, seasonal, and monthly inventories; the non-U.S. inventories were annual data. Pechan provided
the CENRAP inventories divided between annual data for aircraft, locomotive, and commercial
marine and annual files for all other non-road sources (Pechan and CEP, 2005e). Minnesota substituted the monthly MRPO Base K non-road inventory for the CENRAP inventory in their state. Iowa substituted the monthly estimates for non-road agricultural sources from the MRPO base K inventory for the CENRAP inventory. Texas provided estimates for 2002 non-road emissions in lieu of the CENRAP prepared in ventory. WRAP provided non-road inventories
divided between California and no n-California seasonal inventorie s, further subdivided into aircraft, locomotives, shipping, and all other non-road mobile sources (Poll ack et al., 2006). Note
that the California Air Resources Board us es their own OFFROAD model for California non-September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-24 road emissions, whereas the EPA NONROAD model is used for the rest of the states (with the exception of locomotives, aircraft and shipping). With these data WRAP also provided temporal adjustments to apply to the inventories to sp lit them between weekday and weekend emissions. We used these weekday/weekend splits to derive new weekly temporal profiles for the WRAP sources. The MRPO base K monthly non-road i nventories were obtained from MRPO in NIF
format and were converted to SMOKE format by Wendy Vit of the Missouri DNR. The VISTAS
Base G and MANE-VU non-road mobile inventor ies consisted of annual county-level data (Pechan and CEP, 2005c). We received these inventories directly from the respective RPO inventory representatives. We received the Ca nadian 2000 inventory version 2 from the U.S. EPA EFIG (EPA, 2005d). For Mexico we used the improved 1999 inventory available at http://www.epa.gov/ttn/chief/net/mexico.html

.

Along with adding the WRAP weekday/weekend em issions splits to the temporal allocation files, we also created temporal input files th at apply a flat, uniform monthly profile to the monthly and seasonal non-road inventories. With the monthly and seasonal variability inherent in these inventories, we avoided applying redund ant monthly profiles by splitting the inventories into seasonal/monthly and annual data. We applied the uniform monthly tem poral profiles to the seasonal/monthly inventories and non-uniform monthly temporal profiles to the annual
inventories. How the non-road emissions i nventory data were split into those with monthly/seasonal emission and those with a nnual emissions is pr ovided in Table 2-13.

Table 2-13. Non-road mobile-source inventory temporal configuration. Region Source Temporal Coverage WRAP (non-CA) Non-road mobile Seasonal WRAP (CA) Non-road mobile Seasonal WRAP Aircraft Seasonal
WRAP Locomotive Annual WRAP In-port and near-shore shipping Annual CENRAP All non-road Annual CENRAP, IA Non road Ag. Monthly VISTAS All non-road Annual MRPO and MN All non-road Monthly
MANE-VU All non-road Annual Canada All non-road Annual
Mexico All non-road Annual

Iowa elected to use the CENRAP-sponsored invent ory for all of the non-road categories except for the agricultural equipment categories provi ded in Table 2-14. For these agricultural equipment categories, Iowa elected to use th e Midwest RPO Base K inventory because this inventory provided improvements to the temporal allocation of emissions for the agricultural sector. The Base K inventory in cludes monthly emissions. The monthly emissions are used in the SMOKE IDA files for modeling.

Table 2-14. Non-road agricultural emissions categories where the MRPO Base K inventory was used instead of the CENRAP inventory in Iowa.

SCC SCC Description 22600050xx Off-highway Vehicle Gasoline, 2-Stro ke: Agricultural Equipment (2 SCCs); 22650050xx Off-highway Vehicle Gasoline, 4-Stroke: Agricultural Equipment (11 SCCs); 22670050xx LPG : Agricultural Equipment (3 SCCs); 22680050xx CNG : Agricultural Equipment (3 SCCs); and 22700050xx Off-highway Vehicle Diesel : Agricultural Equipment (11 SCCs).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-25 Texas provided annual and daily emissions for CO, CO 2 , NO
x , VOC, SO 2 , PM10-FIL, and PM25-FIL for several oil and gas field equipmen t non-road categories (Table 2-15). Texas provided authorization to change the pollutant codes fr om PM10-FIL to PM10-PRI and PM25-
FIL to PM25-PRI.

Table 2-15. Non-road oil and gas development equipment categories that Texas provided emissions to be used instead of the CENRAP inventory. SCC SCC Description 2265010010 Off-highway Vehicle Gasoline, 4-Stroke : Industrial Equipment: Other Oil Field Equipment; 2268010010 CNG : Industrial Equipment : Other Oil Field Equipment; and 2270010010 Off-highway Vehicle Diesel : Industrial Equipment : Other Oil Field Equipment

Lancaster County Nebraska provided its own non-road inventory for SCC 2260000000 (Offhighway Vehicle Gasoline, 2-Stroke : 2-Stroke Gasoline except Rail and Marine: All). The CENRAP-sponsored inventories for SCCs starti ng with 226 in Lancaster County were removed
to correct double-countin g of emissions. This adjustment was made by Pechan for Base02b modeling.

2.5.2 Emissions Processing

We configured SMOKE to process all of the non -road mobile emissions inventory data as arealike inventories using spatial surrogates to gr id the county-level emissions. As the WRAP inventories contained pre-comput ed PM emissions, we did not have to use SMOKE to compute coarse mass PM (PMC). The WRAP non-road mob ile inventories represented seven-day average emissions (different from the area inventory, whic h represented weekday average emissions). As actual weekly average emissions, we confi gured SMOKE to process them by setting WKDAY_NORMALIZE to “No.” For the rest of th e non-road mobile inventories we processed the data as weekday average data by se tting WKDAY_NORMALIZE to “Yes.” We also
assumed that all of the VOC emissions in the inventories are ROG and used SMOKE to convert the VOC to TOG before converting the emissions into CB-IV speciati on for the air quality models. We configured SMOKE to create MWSS temporal intermediates rather than daily temporal files because the non-road mobile sources do not use weekly temporal profiles that vary across the weekdays, but do have very different emissions on weekdays versus weekend days.

We divided the non-road mobile emissions mode ling based on whether the data were annual or seasonal/monthly inventories. This split facilitate d the application of uniform monthly temporal profiles to the seasonal/monthly inventories. Af ter processing the non-road emissions as two separate categories, non-road year ly and non-road monthly, we combined them with the rest of
the emissions sectors to create mode l-ready emissions for CMAQ and CAMx. To QA the non-road mobile emissions we used the procedures in the CENRAP emissions modeling QAPP (Morris and Tonnesen, 2004) and Mode ling Protocol (Morris et al., 2004a) and a suite of graphical summaries. We used tabul ated summaries of the input data and SMOKE script settings to document the data and configuration of SMOKE for simulations. The graphical QA summaries include, for all emissions output spec ies, daily spatial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.edu/aq m/cenrap/qa_base02f36.shtml#nrSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-26 2.5.3 Uncertainties and Recommendations

We prepared non-road mobile emissions using a combination of inventories having different temporal resolutions and various forms of anci llary data. These different combinations of information may lead to inconsistencies in how these emissions are represented across the modeling domain. In addition, the Canadian inventories contai n only province-level information and thus have low-resolution sp atial and temporal profiles app lied to them. The Mexican nonroad emissions are deficient in the number of di fferent SCCs contained in the inventory and the availability of spatial surrogates that are applic able to non-road mobile sources. Improvements to the temporal profiles and spatial surrogates c ould provide a more consistent approach to representing the non-road emissions across the entire modeling domain.

2.6 Biogenic Sources

Biogenic emissions data for SMOKE consist of input files to the BEIS3 model (EPA, 2004a). BEIS3 is a system integrated into SMOKE fo r deriving emissions estimates of biogenic gasphase pollutants from land use information, emissi ons factors for different plant species, and hourly, gridded meteorology data. The results of BEIS3 modeling are hour ly, gridded emissions fluxes formatted for input to CMAQ or CAMx. Th is section describes the sources of the BEIS3 input data that we used for the Typ02G and Ba se18G emissions, how we modeled these data and the types of QA that were performed to ensure that SMOKE processed the data as expected.

2.6.1 Data Sources

The BELD3 land use data and biogenic emissions factors that were deve loped during the WRAP preliminary 2002 modeling were used for the CE NRAP biogenic emissions modeling (Tonnesen et al., 2005). These data included BELD3 1-km re solution land use estimates and version 0.98 of
the BELD emissions factors. Since the WRAP and CENRAP use the same 36 km Inter-RPO continental U.S. modeling domai n, CENRAP was able to leverage of the WRAP work performed previously.

2.6.2 Emissions Processing

We used BEIS3.12 integrated in SMOKE to prepar e emissions for the simulations. Most of the preparation for the biogenic emissions processi ng was completed during the preliminary 2002 modeling (Morris et al., 2005). As the modeling do mains did not change from the preliminary 2002 to the final modeling, we re-used the gridded land use data and vegetation emissions factors that we prepared for the preliminary simulations.

To QA the biogenic emissions, we used the CENRAP emissions modeling QAPP (Morris and Tonnesen, 2004) and Modeling Prot ocol (Morris et al., 2004a) and a suite of graphical
summaries. We used tabulated summaries of th e input data and SMOKE script settings to document the data and configuration of SM OKE for simulation Base02b. The graphical QA summaries include, for all emissions output species, daily spatial plots, daily time-series plots, and annual time-series plots. Thes e QA graphics are available at http://pah.cert.ucr.edu/aq m/cenrap/qa_base02b36.shtml#b3September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-27 2.6.3 Uncertainties and Recommendations

The use of newer versions of BEIS (BEIS3.13) and the new MEGAN biogenic emissions models should be considered in future modeling.

2.7 Fire Emissions

Fire emissions data for SMOKE have traditionally been represented as county-level area-source inventories that were placed in only the first vertical model layer. We advanced the representation of fire emissions for air quality modeling by prepar ing portions of the inventory data as point sources with specific latitude-longi tude coordinates for each fire centroid and precomputed plume rise parameters that were deri ved from individual fire characteristics. These
new inventories were based on the fire data products prep ared by a CENRAP emission contractor (Reid et al., 2004b) and modified by the project team to be properly modeled as point sources. These data consist of annual, daily, a nd hourly IDA-formatted em issions inventory files and ancillary data for allocating the inventor ies in space, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section describes where we obtained these data, how we modeled them, and the type s of QA performed to ensure that SMOKE processed the fire emissi ons data as expected.

2.7.1 Data Sources

The fire inventories in the Typ02G emissions in ventory were held cons tant through Base18G. We used actual 2002 fire data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory fire data, a nd actual 2002 fire data for Ontari o, Canada. The inventories used consisted of both area and poi nt source data for the U.S ., Canada, and Mexico. Sonoma Technology, Inc. provided the fire emissions fo r the CENRAP states (Reid et al., 2004b). Air Sciences provided us with the WRAP inventorie s divided among six differe nt fire categories: wildfires, agricultural fires, wildland fire use, natural prescribed, anthropogenic prescribed, and non-Federal rangeland fires (Air Sc iences, 2007a). These inventorie s consisted of annual, daily, and hourly IDA-formatted files with informati on on daily emissions totals and hourly plume
characteristics for each fire. We received simila r fire emission inventories for the other RPOS (Air Sciences, 2007b). We modeled these sources w ith the rest of the st ationary-area-source

sector.

CENRAP received data for 54 fires that occurred in Ontario during the year 2002. Information on the data code abbreviations, da ta definitions, and data units us ed in the raw data files was obtained from Mr. Rob Luik (Dat a Management Specialist) at th e Ontario Minist ry of Natural
Resources ( Rob.Luik@MNR. gov.on.ca ). Emissions for each fire were estimated using the Emission Production Model (EPM)/CONSUME w ithin the BlueSky framework. A fire
identification code is needed to track individual fire s throughout the proce ssing. The unique fire
identification code was created for each fi re by concatenating the FIRE_NUMBER and CUR_DIST fields of the original data. The fire identification c ode also contains the FIPS code of the fire; this information is not used by BlueSky but is needed by BlueSky2Inv, the utility program that converts the BlueSky output to th e SMOKE inventory format. The FIPS code 135000 was used for all fires with longitudes east of –90°, and FIPS code 135059 was used forSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-28 fires west of –90°. These FIPS codes were used to ensure that the fires would be assigned the
correct time zones in later SMOKE processing. Some of the dates provided in the original data included hourly information. In al l cases, the hourly information wa s not used leaving all data at a daily resolution.

2.7.2 Emissions Processing

SMOKE is instrumented to distribute point-source- formatted fire inventories to the vertical model layers either by using a pr e-computed plume rise approach or by computing the plume rise dynamically using actual 2002 meteorology. We app lied both approaches for modeling pointsource fire emissions in simulation Typ02G. For the pre-computed plume rise approach, SMOKE reads an annual inventory fi le with information on fire lo cations, a daily inventory file with daily emission totals for each fire, and an hourly inventory file with hourly plume bottom, plume top, and layer 1 fractions for each fire. SMOKE uses this information to locate the fires on the horizontal model grid and to distribute the plum e of each fire vertically to the model layers. Because some of these fires have plumes th at reach the model top, we set the number of emissions layers for processing these inventorie s to the full 19 layers of the meteorology. We applied this approach to the point-source fi res for the WRAP, CENRAP and VISTAS regions. The alternative plume rise approach uses inform ation on fuel loading and the heat flux of the fires to distribute the fires vertically to the mode l layers. The data are provided to SMOKE in the form of an annual inventory with information on fire locations a nd a daily inventory with daily emission totals for each fire, daily heat flux, and da ily fuel loading. We applied this approach to the point-source fires for Ontario, Canada.

All of the point-source fires used diurnal temporal profiles a nd speciation profiles for VOC and
PM 2.5 developed by Air Sciences (2 007a) during the preliminary 2002 modeling (Morris et al., 2005).

We modeled the area-source fires for U.S. and Cana da as standard stationary area sources. We applied monthly temporal profil es provided by RPOs, flat week ly temporal pr ofiles, and the diurnal profiles developed by Air Sciences for WRAP fires (Air Sciences, 2007a), and for the rest of the RPOs we used diurnal profiles that were provided by them (Air Sciences, 2007b). We used the forestland area surrogate to distribute these emissions fr om the county or province level in the inventories to the model grid cells.

To QA the fire emissions, we used the proc edure in the CENRAP emissions modeling QA protocol (Environ, 2004) and a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE script settings to doc ument the data and configuration of SMOKE for simulation Typ02G. The graphical QA summaries include, for all emissions output species, daily spatial plots, daily time-series plots, annual time- series plots, and verti cal profiles. These QA graphics are available at: http://pah.cert.ucr.e du/aqm/cenrap/qa_typ02g36.shtml

.

2.7.3 Uncertainties and Recommendations

We used forestland spatial surrogates to distribute these county level (province level for Canada) data to the model grid. Using spatial surrogates to locate fires is a crude approach that results in the artificial smearing of the emissions over too large an area. This issue can be remedied bySeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-29 moving to a point-source approach for representing these fires, similar to the approach used by Air Sciences for preparing the WRAP fire inventories.

2.8 Dust Emissions

Dust emissions data for SMOKE have traditiona lly taken the form of county-level stationaryarea-source inventories. As th ese emissions are correlated to meteorology, land use, and vegetative cover, we made several changes to how dust emissions are simulated by SMOKE to take these parameters into consideration. This section describes where we obtained data for windblown, fugitive, and road dust sources, how we modeled them, and the types of QA performed to ensure that SMOKE processed the data as expected.

2.8.1 Data Sources

For the fugitive dust and road dust inventories in the Typ02G em ission scenario, we used actual 2002 data developed by the RPOs for the U.S., ve rsion 2 of the year 20 00 Canadian inventory, and the BRAVO 1999 Mexican inventory. We extract ed the fugitive dust inventories from the stationary-area inventories for each of the RP Os, Mexico, and Canada. Before modeling these
data we further divided them into construc tion/mining sources and ag ricultural sources. We defined the fugitive dust sources in the Base 02f modeling based on guidance provided by EPA (2004b). WRAP provide road dust emission inventorie s (Pollack et al., 2006). For the rest of the RPOs and Canada, we extracted the road dust SCCs from the stationary-are a-source inventories. The BRAVO 1999 Mexico inventory did not contai n any road dust SCCs. Table 2-16 lists the SCCs for the various fugitive and road dust sour ces that we modeled in the Base02f and Typ02G inventories. We applied near-s ource capture transport factors th at are based on county-level vegetative cover to the fugitive and road dust inve ntories to prepare them for input to the air quality models.

For windblown dust, we used gridded emissions prepared outside of SMOKE using a land use and meteorology-based model developed under funding from the WRAP by ENVIRON and UCRiverside (Mansell, 2005; Mansell et al., 2005).

Table 2-16. Fugitive and road dust SCCs. Dust Category SCCs Fugitive dust (construction and mining) 2275085000, 2311000000, 2311010000, 2311010070, 2311020000, 2311030000, 2325000000, 2305070000, 2530000020, 2530000100, 2530000120 Fugitive dust (agricultural) 2801000003, 2801000005, 2801000008, 2805001000 Road dust 2294000000, 2296000000

2.8.2 Emissions Processing

We modeled the fugitive and road dust inve ntories through SMOKE using an area-source approach. We modeled these data on the assumpti on that they represented weekday, rather than seven-day week, emissions and thus used the SMOKE setting WKDAY_NORMALIZE to convert the data to a seven-day average. We configured SMOKE to compute PMC during theSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-30 processing as (PM 10 - PM 2.5 ). Usually the records with dust do not include any other pollutants such as VOC, and NOx. For the few records that did include pollutants ot her than the PM we

split the records where the PMs processed with dust and the non PMs processed with the area. We configured SMOKE to create MWSS temporal in termediates rather than daily temporal files because the dust sources do not use weekly tempor al profiles that vary across the weekdays. As noted above, we used SMOKE to apply near-s ource transport factors to the raw fugitive and road dust inventories to prepare th em for input to the air quality models. We used U.S. transport factors from work done by Pace (2005) and a 2001 land use/land cover database to develop a SMOKE input file of county and SCC-based trans port factors for the U.S., Canada, and Mexico. We applied these factors to create a new set of i nventories adjusted for these transport factors for all regions except VISTAS; the VISTAS dust sources that we received already had the transport factors applied to them.

We calculated the windblown dust emissions outside of SMOKE us ing an internally developed, process-based model. By “process-based” we re fer to an emissions model that integrates information about the processes that lead to th e emissions of interest, in this case windblown dust. The process-based windblown dust model de veloped by the WRAP considers wind speeds, precipitation history, and soil types to derive gridded dust fluxes re sulting from wind disturbances for the modeling domain. More information on this model, its modes of operation, and the configuration used for simulation Base 02a are available in Mansell et al. (2005). To QA the fire emissions, we used the pro cedures in the CENRAP emissions modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of graphical summaries. We used tabulated summarie s of the input data and SMOKE script settings to document the data and configuration of SM OKE for Base02f emissions. The graphical QA summaries include, for all emissions output species, daily spatial plots, daily time-series plots, and annual time-series plots. Thes e QA graphics are available at http://pah.cert.ucr.edu/aq m/cenrap/qa_base02f36.shtml#fd for fugitive dust, http://pah.cert.ucr.edu/aq m/cenrap/qa_base02f36.shtml#rd for road dust, and http://pah.cert.ucr.edu/aq m/cenrap/qa_base02b36.shtml#wbd for windblown dust.

2.8.3 Uncertainties and Recommendations

There are several improvements that should be made to the dust emissions modeling in future simulations. We will expand the list of fugitive dus t SCCs that we extract from the stationaryarea-source inventories for applic ation of transport factors. This expanded list is based on recent work by EPA (2004b). We will also explore improve ments to the assumptions that we used for generating emissions with the WRAP windblown dust model. Areas of improvement in the
windblown dust model include refinements to th e land use data and soil characteristics, additional information about agricultural activi ties in the WRAP and CENRAP regions, detailed model evaluation on targeted windblown dust case studies, and the app lication of snow-cover and vegetative transport factors to th ese emissions (Mansell et al., 2005).

2.9 Ammonia Emissions

Ammonia (NH 3 ) emissions from agricultural activities are a major source of ammonia and are dependent on many different environmental para meters, such as meteorology, crop and soilSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-31 types, and land use. CENRAP developed NH 3 emissions for the CENRAP states (Pechan and CEP, 2005e). Ammonia emissions were estimate d for 13 source categories using the Carnegie Mellon University (CMU) model and supplemental technical work; 80% of technical work was dedicated to improving emissions estimates for two source categories—li vestock production and fertilizer use. For these two categories, as well as biogenic sources, improvements were made to the activity data and/or emission factors used by the CMU model. For four other source categories (industrial point sources, landfills, ammonia refrigeration, and non-road mobile sources), emissions estimates were prepared independently of the CMU model, and for the remaining six source categories ( publicly owned treatment works, wildfires, domestic animals, wild animals, human respiration, and on-road mob ile sources), emissions estimates were derived by running the CMU model with no alterations.

CENRAP NH 3 model emissions estimates were combined with data provided by the other RPOs to represent agricultural NH 3 emissions in simulations Typ02G and Base18G.

2.9.1 Data Sources

The WRAP provided NH 3 emissions using the WRAP NH 3 model (Mansell et al, 2005) that generated emissions for the followi ng sectors: domestic sources, wild animals, fertilizers, soils, and livestock. MWRPO provided monthly IDA-form atted inventories reflective of base K to CENRAP that they produced from process-base d models of their own, along with temporal profiles and spatial cross-reference information for these sources. Iowa elected to use the
MWRPO estimates of NH 3 emissions for fertilizer applic ation, livestock, and wastewater treatment or SCC 28017XXXXX, 28050XXXXX, and 2630020000 respectively. Minnesota reviewed the MWRPO inventory an d chose to move forward with the CENRAP developed data set. The rest of the U.S., Canada, and Mexico had agricultural NH 3 emissions contained within their annual stationary-ar ea-source inventories.

2.9.2 Emissions Processing

The WRAP NH 3 emissions were processed outsi de of SMOKE using the WRAP NH 3 model and provided to CENRAP as gridded, hourly emissi ons in network common data form (NetCDF) files. CENRAP and MWRPO provided monthly IDA-formatted, county-level NH 3 inventories that were developed separately with process- based models. We modeled these emissions like area sources with SMOKE, applying the temporal profiles and the spatial cross-referencing developed for CENRAP that we received from the MWRPO. The agricultural NH 3 emissions for the rest of the RPOs, Canada, and Mexico are contained within their stationary-area inventories. We applied the SMOKE default tem poral profiles and spatia l surrogates to all nonprocess-based NH 3 emissions.

To QA the NH 3 emissions, we used the procedures in the CENRAP modeling QAPP (Morris and Tonnesen, 2004) and Modeling Prot ocol (Morris et al., 2004a) and a suite of graphical
summaries. We used tabulated summaries of th e input data and SMOKE script settings to document the data and configuration of SMOK E for simulations Typ02G and Base18G. The graphical QA summaries include, for all emissions output species, daily spat ial plots, daily timeseries plots, and annual time-series plot s. These QA graphics are available at http://pah.cert.ucr.e du/aqm/cenrap/index.shtmlSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-32 2.9.3 Uncertainties and Recommendations

Like the other emissions categories that have traditionally been represented as stationary area sources, the agricultural NH 3 emissions sector is affected by interregional inconsistencies in the way these emissions are represented.

During the QA of the Base02a emissions, the WR AP discovered a problem with their soil NH 3 estimates. The emission factor for soil NH 3 that were used in devel oping these data produced too high an emission estimate from this sector. For simulatio ns Base02B through Typ02G, we
therefore removed the soil NH 3 sector completely from the WRAP domain. In future simulations
we will include these emissions with a revised emission factor for NH 3 emissions from soils.

2.10 Oil and Gas Emissions

Emissions from oil and gas develo pment activities have been poorly characterized in the past. Simulations These emissions have been sporadically reported by some states in their stationaryarea-source inventories, but fo r the most part were missing fr om our preliminary modeling. In the Typ02G and Base18G simulations, significant effort was made to better represent oil and gas production emissions explicitly as bo th area and point sources.

2.10.1 Data Sources

Emissions from oil and gas production activities fo r the CENRAP states were included with the
other CENRAP state emission s ource categories (Pechan and CEP, 2005e). We received oil and gas production emissions inventories for the WRAP states and for tribal lands in the WRAP region as stationary-area-source and stationary-point-source I DA-formatted inventories. ERG, Inc. provided the point-source inventories with the rest of the stati onary-point data (ERG, 2006a). ENVIRON provided the area-source oil a nd gas inventories for non-CA WRAP states
and for tribal lands in the WRAP region, along with spat ial surrogates for allo cating these data to the model grid (Russell and Pollack. 2005). Oil a nd gas production emissions data for outside of the WRAP region are contained in th e stationary-area inventories.

2.10.2 Emissions Processing

We modeled the WRAP point-source oil and gas pr oduction emissions in combination with the rest of the stationary-point-s ource emissions. We modeled th e WRAP area-source oil and gas production emissions explicitly as a separate category that include d WRAP and tribal inventories. These data represent weekly average emissions and did not require any renormalization within SMOKE. We used spatial surrogates generated by ENVIRON to allocate these annual county-level emissions to the model grid. For all oil and gas emissions, we applied flat temporal profiles to create hourly inputs to CMAQ and CAMx.

2.10.3 Uncertainties and Recommendations

In future 2002 modeling California oil and ga s production emissions s hould be replaced with revised data provided by the California Air Res ources Board (CARB). In addition, WRAP hasSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-33 updated their oil and gas production inventory for the base and future years in a Phase II work effort that substantially improved the emissions inventory estimates (Bar-Ilan et al., 2007).

2.11 MMS Off-shore Gulf of Mexico Emissions

Offshore area point source emissions include emissi ons in the Gulf of Mexico and off the coast
of California that are associated wi th oil and gas drilling platforms.

2.11.1 Data Sources

We obtained year 2000 IDA-formatted point-source inventories for oil and gas platforms in the
Gulf of Mexico from the Minerals Management Service (MMS) web site: http://www.gomr.mms.gov/homepg/regulate/envi ron/airquality/gulfwid e_emission_inventory/20 00GulfwideEmissionInventory.html

We combined these with point-source data for coastal California provid ed to us by CARB during the preliminary 2002 modeling. We also obtained gr idded area source emissions for platforms in
the Gulf of Mexico from the MMS that we converted to the CENRAP 36-km model grid.

The 2000 MMS Gulf wide Emission Inventory wa s updated as of June 2006 to account for a change in vessel emissions in the non-point sour ce (non-platform) database file. The point source (platform) emission inventory database file has not changed from the original version.
Area source emissions from offshore activities in the Gulf of Mexico were developed from the latest estimates provided by the Minerals Ma nagement Service (MMS). The MMS inventory includes both platform and non -platform sources. The non-plat form area source emissions estimates are spatially allocate d to lease blocks and protraction units throughout the Gulf of Mexico. Temporal and spatial allocation cross- reference data were de veloped from the MMS inventory data and formatted for input to the SMOKE emissions model by Carolina Environmental Programs. These data were provi ded to the CENRAP emissions modeling team for implementation within SMOKE. The spatial allocation surrogates were provided for 4-km grid cells. The UCR team used these surrogates and developed su rrogates for 36-km grid cells.
Because these data are references to lease bloc ks/protraction units, rather than counties, this source category was processed separately form a ll other emissions using a customized reference data and SMOKE run scripts.

We modeled the offshore point and area sources as separate categories in the simulations. We
used SMOKE to locate the offshor e point sources on the model grid and to vertically allocate them into 15 model layers.

To QA the offshore platform emissions, we us ed the procedures in the CENRAP modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 20042) and a suite of graphical summaries. We used tabulated summarie s of the input data and SMOKE script settings to document the data and configuration of SMOKE for simulation Base02a. The graphical QA summaries include, for all emissions output species, daily spatial plots, daily time-series plots, and annual time-series plots. Thes e QA graphics are available at http://pah.cert.ucr.e du/aqm/cenrap/index.shtml for the point and area sources.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-34 2.11.2 Uncertainties and Recommendations

While the MMS data that we used were an improvement over previously modeled Gulf of Mexico platform inventories, the data were de veloped for a different m odeling application that covered only the extreme northwestern portion of th e Gulf, so they are missing large areas of the region of the Gulf that contain drilling platforms. The California offshore inventory represents an initial attempt at compiling an emission inventory for this area and contains very few sources. Future simulations will focus on improving these emissions by expanding the coverage of the offshore platform inventories for both the Gulf of Mexico and the Pacific Coast.

2.12 Off-shore Shipping Emissions

Emission inventory development for regional- and continental- scale air quality modeling has historically neglected offshore emissions s ources beyond 25 miles offshore. Concern over the environmental effects of commercial shipping emissi ons in the Pacific on the coastal states in the WRAP region led to the development of a commerci al marine shipping inve ntory for the Pacific. This inventory of off-shore marine vessels emi ssions made a substantial difference in some of the coastal western PM estimates (e.g., SO4). VI STAS developed an off-shore marine vessels inventory for the entire modeling domain that in cluded the Pacific and Atlantic Oceans and the Gulf Of Mexico. For Typ02G and Base18G emi ssion inventories CENRAP adopted the offshore shipping inventories developed by VISTAS.

2.12.1 Data Sources

Initially we obtained gridded a nnual commercial marine shipping emissions for the Pacific on the 36-km model grid from WRAP for inclusion in CENRAP simu lations in the Base F modeling (Pollack et al., 2006). The commercial marine i nventory contains all of the criteria pollutants
contained in the non-road mob ile-source inventory: CO, NO
x , VOC, NH 3 , SO 2 , PM 10 , and PM 2.5

. This inventory was subsequently updated in the Typ02G and Base18G modeling with the
VISTAS off-shore commercial marine emissions i nventory that covered the Gulf of Mexico and
the Atlantic and Pacific Oceans and was base d on the EPA/ARB SOx Emissions Control Area (SECA) program. Dr. James Corbett (University of Delaware) analyzed off-shore marine vessel
data and worked with ENVIRON/ICF to convert to gridded emissions for the SECA grid. ENVIRON then provided SO 2 , NO X , PM and VOC emissions for the RPO 36-km grid.

2.12.2 Emissions Processing

The commercial marine shipping inventory was not processed through SMOKE. VISTAS provided the data to the as gridded text f iles on the 36-km model grid. These data were reformatted to the NetCDF CMAQ input format with a utility developed by UCR. The VOC inventory was converted to CB-IV speciation and the NO
x and PM 2.5 inventory pollutants to CMAQ input species with SMOKE chemical profil es for commercial shipping sources. No temporal adjustments were applied to these emissions; they use uniform monthly, daily, and diurnal profiles. An SCC for commercial mari ne vessels within the MMS inventory (SCC CM80002200) was accounted for in the commercial marine inventory developed for VISTAS. The duplicate emissions were removed from the MMS inventory prior to processing emissionsSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-35 for Base G simulations. The duplicated emissions amounted to 19,000 TPY of NO X and 3,184
TPY of SO 2 . For simulation Typ02G and Base18G we received binary netCDF file from ENVIRON for one day and that day was used for every day of the year. To QA the commercial marine shipping emissions , we used the procedures in the CENRAP modeling QAPP (Morris and Tonnesen, 2004) and Mode ling Protocol (Morris et al., 2004a) and a suite of graphical summaries. The graphica l QA summaries include, fo r all emissions output species, daily spatial plots, daily time-series plots, and annual time-series plots. These QA graphics are available at http://pah.cert.ucr.e du/aqm/cenrap/index.shtml

.

2.12.3 Uncertainties and Recommendations

As a first attempt at representing shipping emissi ons in the Pacific in international waters, the WRAP and VISTAS 2002 commercial shipping i nventory is a breakthrough in a historically neglected emissions category. As the RPOs evalua te the effects of these emissions on the air quality modeling, we anticipate that there will be refinements to the temporal profiles and to the
vertical allocation of the emissi ons. Many of the stacks of large commercial ships contained in this inventory extend vertically above the first model layer. Futu re versions of this inventory should use higher-resolution temporal adjustme nts and should allocate the emissions to the appropriate model layers. Off-s hore marine shipping activ ity is projected to increase. However, there are also the potential for emission controls on this source category (e.g., SECA program). Given these two off setting activities, the 20 02 off-shore marine shipping emissions were assumed to be unchanged going from 2002 to 2018. Better estimates of 2018 marine emissions are being developed that should be cons idered in future modeling activities.

2.13 2018 Growth and Control

Base18G was based on grown inventories assumi ng on-the-books control strategies. CENRAP
contracted with Pechan to de liver growth and cont rol data for CENRAP and to consolidate growth and control information for other RPOs where available (Pechan, 2005d). The data are applicable to all source categories and pollu tants included in the CENRAP 2002 emission inventory. This includes the following pollutants: sulfur oxides (SO x ), oxides of nitrogen (NO x ), volatile organic compounds (VOC), carbon monoxide (CO), ammonia (NH 3 ), and primary PM 10 and PM 2.5 . Some source categories were held constant between 2002 and 2018 because either stagnant growth was deemed appropriate or insufficient data was available to adequately project future growth or controls. These source categories include the following:

• Wind Blown Dust from non-agri cultural land use categories.

• Emissions from wildfires.

• Emissions from Mexico.

• Global transport sources (i.e., the 2002 GEOS-CHEM boundary conditions).

2.13.1 Data Sources

CENRAP contracted with Pechan to provide gr owth and control factors to be applied with SMOKE for the CENRAP region (Pechan, 2005d). Th ese growth and control parameters were based on growth estimates derived from E GAS 5.0 and control es timates assumed forSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-36 implementation of federal regulations and on- the-books state and local control programs. Emissions projections for electr ic generating units were developed for the RPOs with the Integrated Planning Model (IPM). The RPO 2.1.9 IPM results were subsequently modified by VISTAS, MRPO and CENRAP to reflect planne d new construction and controls. The WRAP provided 2018 EGU estimates developed in coordi nation with State and I ndustry stakeholders. VISTAS, MWRPO and the WRAP provided emi ssions for 2018, having applied growth and control factors outside of SMOKE processing. EPA provided SMOKE processed emissions, applying both growth and cont rols, for Canada for the year 2020. These emissions were provided on the RPO 36-km grid. However, em issions were inexplicably processed for an
alternative vertical st ructure. Alpine Geophysics, under c ontract to VISTAS reallocated the emissions through the vertical layers to more accu rately reflect the vertical structure applied uniformly by the RPOs. The modified data wa s obtained directly from Alpine Geophysics.
Emissions from Mexico were held constant between the inventory ye ar 1999 and modeled 2002 and 2018. Improvements to the Mexican invent ory have been continuously made between generation of the original BRAVO inventory and the presen t improved 1999 inventory. However, given the continued uncertainties in th e improved inventory, no fu ture year projections where attempted by CENRAP.

2.13.2 Emissions Processing

Growth and control factors developed by Pechan (2005d) for Arkansas did not match the final delivered inventory for Arkansas. Arkansas underwent major revisi ons to point and facility IDs in mid-2005. These updates were not available by the delivery date of the growth and control parameters. In coordination with Arkansas, a cr oss-walk was developed to correct the point and facility IDs.

The assumptions that went into the development of controls for engines covered under the RICE
MACT were not consistent with the final rule. Rule penetration values for CENRAP states were adjusted to more accurately reflect the impact of the final rule.

The impact of the refinery global settlements wa s not incorporated into CENRAP modeling until the base G simulations. Control assumptions provided by EPA and referenced in EPA CAIR modeling were applied to the 2018 inventor y. These reductions primarily impacted SO 2 emissions; however, NO X reductions were applied in Oklahoma, Louisiana, and Minnesota.

2.13.3 Uncertainties and Recommendations

The impact of control programs is an area of uncer tainty that will need continued review as the programs are implemented. Development of growth and control assumptions for Mexico will be necessary for continued refinement of the impact of international transp ort. CENRAP obtained estimates of increased prescribed burn activity for the Forest Service after processing of the base G simulations was underway. These estimates of increased activity s hould be reviewed for
inclusion in future simulations. EPA devel oped 2020 estimates of Canadian emissions are assumed to include erroneous stack parameters previously addressed in the 2000 emissions processing. Further review of th is data set is recommended.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-37 2.14 2018 Base G C1 Control Sensitivity

CENRAP conducted a control sensitivity evaluating the impact of point source reductions given a maximum dollar per ton control level. The intent of the control sensitivity was to generate information on the impact of possible control stra tegies in support of the consultation process. The strategies were grouped together under a common set of criteria and not specifically identified by the states. The results of the modeli ng were not intended to be prescriptive; instead, they were intended to be a starting point for cont rol discussions that would require much greater
refinement.

2.14.1 Data Sources

CENRAP contracted with Alpi ne Geophysics to provide an ev aluation of possible additionalcontrols for the 2018 CENRAP point source inventory. These controls were in addition to onthe-books and BART controls a ssumed in the development of Base18F and Base18G emission
scenarios. Base18F IDA files we re enhanced with additional info rmation on base level controls.
The enhanced dataset was then linked with the control data co ntained in the 2006 release of EPA’s AirControlNet software. Alpi ne developed cost curves for NO X and SO 2 in 2005 dollars for the Base18F CENRAP point source inventor y. Staff from Iowa DNR and Kansas DHE worked in conjunction to add area of influenc e data (Alpine Geophysic s, 2006) and distance
calculations to each Class I area in CENRAP. A variety of dollar per ton control levels were
evaluated. CENRAP elected to base the sensitivity on a ma ximum control cost of $5,000 per
ton. This selection was made with the understandi ng that the cost data under-represented the true
cost of retrofit controls and di d not take in to consideration more recent market fluctuations impacting costs of controls and construction. CENRAP refined the se lection by applying controls to only those sources that met the criteria that the ratio of thei r emissions in tons per year to their distance to any Class I area in kilo meters be less than 5. This distance weighting criteria allowed the sensitivity to focus on those sources with the greatest impact. Additional
controls for other RPOs were not considered in this evaluation.

2.14.2 Emissions Processing

Sources considered for control were removed from the IDA files. Growth and control assumptions were applied outside of SMOKE an d delivered to UCR as 2018 emissions. Stack parameter changes as a result of additional controls were not considered in this analysis.

2.14.3 Uncertainties and Recommendations

Given uncertainties in control costs more refined analyses s hould include an evaluation of
retrofit control costs und er present values.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-38 2.15 Emissions Summaries

Appendix B provides details on the source of th e emission files used in the CENRAP Typ02G and Base18G modeling. Also in Appendix B ar e sample emission summary plots, additional plots are available on the CENREAP modeling website: http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml

.

CENRAP has contracted with E.H. Pechan and A ssociates to provide em issions summaries used in the final Typ02G and Base18G modeling in Excel spreadsheets a nd in an Access database that are available on the CENRAP website ( http://www.cenrap.org/projects.asp# ). Figures 2-3 through 2-9 display the, respectively, SO2, NOx, VOC, PM2.5, PM10, NH3 and CO anthropogenic emissions for the CENRAP stat es and the Typ02G and Base18G emission scenarios. Emissions are broken down by major source sector. For the state of Texas the emissions are broken by three groups, northeast Texas, southeast Texas and remainder of Texas (west Texas).

For most states, EGUs are the largest contributor to SO2 emissions (Figure 2-3). As EGU SO2 emissions are generally projected to be reduced in the future, most states show a reduction in total SO2 emissions from 2002 to 2018. One excep tion to this is Louisiana for which non-EGU point source SO2 emissions are greater than for EGU and are projected to increase from 2002 to
2018. The reasons for these increases are uncl ear, but the growth factors for non-EGU points should be examined more carefully.

NOx emissions are fairly evenly distributed across non-EGU point, EGU point, non-road mobile, on-road mobile and area sources for the 2002 Typ02G emissions scenario (Figure 2-4). In 2018,
the contributions of on-road mobile source NOx emissions is reduced dramatically, with some states also showing reductions in EGU NOx emissi ons as well, resulting in all states exhibiting
lower NOx emissions in 2018 than 2002.

VOC emissions are dominated by area, non-road mobile, on-road mobile and non-EGU point sources in both 2002 and 2018 (Figure 2-5). VOC emissions from on-road and non-road mobile source are projected to go down in the future, whereas VOC emissions from non-EGU point and, especially, area sources are projected to increase. Thus, whether a stat e’s total VOC emissions increase or decrease depends on th e relative contributions of mobile versus area sources and the
level of increase in area source VOC emissions. No te that the VOC emissions listed in Figure 2-5 do not include biogenic VOC emissions that would be greater than the anthropogenic VOC emissions shown in Figure 2-5. Note that because biogenic VOC emissions are processed using the SMOKE/BEIS module on the 36 km grid, state- wide biogenic VOC emissions summaries are not readily available.

Primary PM 2.5 emissions are primarily from road dust and fugitive dust, and for some states fires (Figure 2-6). Kansas, Oklahoma, Louisiana and Te xas all have large cont ributions from fires not seen in the other states. Road dust and fugitiv e dust are the most dominate source categories for coarse particulate as well (Figure 2-7).

CENRAP developed a separate ammonia emis sions for 13 categories using the CMU model including livestock and fertilizer that domina tes the ammonia emissions across the CENRAPSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-39 states (Figure 2-8). Several states also have significant ammonia contributions from non-EGU point sources, whereas others do not.

CO emissions are dominated by the on-road and non-road mobile source sectors (Figure 2-9). However, states with fires also see large CO c ontributions from them as well. On-road mobile source CO emissions are projected to go down substantially from 20 02 to 2018, whereas the other source categories are flat.



### Annual SO2 Emissions by Source Sector

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000

Ar

k a ns

as-02

Ark

a nsa

s -18 Iowa-02

I owa-18

Kansas -02

K ansas-18

Loui s i an

a-

02 Louisiana- 1 8

M

i nnesot a-02

Mi

nnesot a-18 Missouri-02

M

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Ne

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ask

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T

exas - 02

- NE Texas- 18

- NE Texas- 02

- S

E

T

exas - 18

- SE

T

e x as

-02-RE S T

Te

x as-18-REST

Tons per year roaddust point_fire point_negu point_egu

onroad

offroad fug_dust
area_fire

area
ammonia

Figure 2-3. Summary of Typ02G and Base18G SO2 emissions by CENRAP state and major source sector (tons per year).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-40 

### Annual NOX Emissions by Source Sector (tons)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

Arkansas-02

A

r k

a

ns

as

- 1 8 Iowa-02 Iowa-18

Kansas-02

K

ans as

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0 2 Minnes o ta-

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Mi

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i

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O

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Tex a s-02-NE

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Tex a s-02-SE

T

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E

T

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- 0 2-

RE

ST Texas-1 8 -REST

Tons per year roaddust point_fire point_negu point_egu

onroad

offroad fug_dust
area_fire

area
ammonia

Figure 2-4. Summary of Typ02G and Base18G NOx emissions by CENRAP state and major source sector (tons per year).



### Annual VOC Emissions by Source Sector (tons)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000

A

r ka

n sas-02

A

r k an

s as

- 18

I o wa-02 Iow

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na-1 8

Mi

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N ebraska-02 Nebraska-18

Ok

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T

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Te

x as

- 02-

R E S T Texas-18-REST

Tons per year roaddust point_fire point_negu point_egu

onroad

offroad fug_dust
area_fire

area

Figure 2-5. Summary of Typ02G and Base18G VOC emissions by CENRAP state and major source sector (tons per year).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-41 Annual PM25 Emissions by Source Sector (tons)

0 50,000 100,000 150,000 200,000 250,000

A rkan s as-

0 2

A

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s as

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Iowa - 02

Io

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8

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M

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N ebras k a

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Tex

a s - 0 2-

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T

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- SE
Tex

a s - 18

- S E Texas-02-REST

T

e x as-

18

- RE

S T

Tons per year roaddust point_fire point_negu point_egu

onroad

offroad fug_dust
area_fire

area
ammonia

Figure 2-6. Summary of Typ02G and Base18G PM2. 5 emissions by CENRAP state and major source sector (tons per year).

Annual PM10 Emissions by Source Sector (tons)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

A

rka nsas -0

2 Arkansas-18

I owa -02 Iowa-18

Kan sa

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K

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Tex as-

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T

exas-18-NE

Te

xa

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E Texa s-

18-S E Texas-02-REST

Te

xas

-18

-RE ST

Tons per year roaddust point_fire point_negu point_egu

onroad

offroad fug_dust
area_fire

area
ammonia

Figure 2-7. Summary of Typ02G and Base18G PM10 emissions by CENRAP state and major source sector (tons per year).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_2_Emissions1.doc 2-42 Annual NH3 Emissions by Source Sector (tons)

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

Ark

a nsas-02

Ar

kansas- 1 8 Iowa- 0 2

Io

w a-18

Ka

nsas- 02 Kansas-1 8 Louisiana - 02

L ou

i si

a na-18 Minn eso

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Figure 2-8. Summary of Typ02G and Base18G NH3 emissions by CENRAP state and major source sector (tons per year).

Annual CO Emissions by Source Sector (tons)

0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000

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Figure 2-9. Summary of Typ02G and Base18G CO emissions by CENRAP state and major source sector (tons per year).September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-1 3.0 MODEL PERFORMANCE EVALUATION

In this Chapter we summarize the CMAQ mode l performance for the final 2002 36 km Base F
base case simulation. Because the 2002 Base F CMAQ simulation produced nearly identical
results in the U.S. as the final 2002 Base G simulation and limited resource availability,
CENRAP elected not to redo the model evalua tion for the 2002 Base G case. This model performance focuses on the abil ity of the model to predict PM species within the CENRAP region. Details on the model performance are pr ovided in Appendix C. Previously we have documented model performance of interim versions of model base case simulations in reports (Morris et al., 2005) and presentations to the CENRAP Work Groups and POG (e.g., Morris et al., 2006a,b).

3.1 Evaluation Methodology

EPA’s integrated ozone, PM 2.5 and regional haze modeling guida nce calls for a comprehensive, multi-layered approach to model performance tes ting, consisting of the four major components: operational, diagnostic, mechanis tic (or scientific) and probabilistic (EPA, 2007). The CMAQ model performance evaluation effort focuse d on the first two components, namely:

• 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 quantities used to characterize visibility (i.e., sulfat e, nitrate, ammonium, organic carbon, elemental carbon, other PM 2.5 , and coarse matter (PM 2.5-10 ). This evaluation examines whether the measurements are properly represented by th e model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”; and

• 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 components of light extinction (i .e., scattering and absorption).

In this final model performance evaluation for the 2002 Typical Base F CMAQ simulation, the operational evaluation has been give n the greatest attention since this is the primary thrust of EPA’s modeling guidance. However, we have also examined certain diagnostic features dealing with the model’s ability to simulate sub-regi onal, monthly, diurnal, gas phase and aerosol
concentration distributions. In the course of the CENRAP air quality modeling and other modeling processes, numerous diagnostic sensitiv ity tests were performed to investigate and improve model performance. Key diagnostic te sts that were performed and the results are discussed on the CENRAP modeling website: http://pah.cert.ucr.edu/aqm/cenrap/index.shtml

.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-2 3.2 Ambient Air Quality Data used in the Evaluation

The ground-level model evaluation database fo r 2002 was compiled by the modeling team using several routine and research-grade databases. The first is the routine gas-phase concentration measurements for ozone, SO 2 , NO 2 and CO archived in EPA’s Aerometric Information Retrieval System (AIRS) Air Quality System (AQS) data base. Other sources of observed information come from the various PM monitoring networks in the U.S. These include the Interagency Monitoring of Protected Visu al Environments (IMPROVE); Clean Air Status and Trends Network (CASTNET); EPA Speciati on Trends Network (STN) of PM 2.5 species; and National Acid Deposition Program (NADP). During the c ourse of the CENRAP modeling, the numerous
base case simulations were eval uated across the continental U.S. (e.g., Morris et al., 2005). In this section and in Appendix C we focus our evaluation on model performance within the CENRAP region.

3.2 Operational Model Evaluation Approach

The CENRAP modeling databases will be used to develop the visibility State Implementation Plan (SIP) as required by the Regional Haze Rule (R HR). Accordingly, the primary focus of the operational evaluation in this report is on th e six components of fine particulate (PM 2.5 ) and coarse mass (PM 2.5-10 ) within the CENRAP region that are used to characterize visibility at Class
I areas:

• Sulfate (SO4);

• Particulate Nitrate (NO3);

• Elemental Carbon (EC);

• Organic Mass Carbon (OMC);

• Other inorganic fine part iculate (IP or Soil); and

• Coarse Mass (CM).

The model performance for ozone, precu rsors, and product species (e.g., SO 4 , NO 3 , NH 4 and
HNO 3 ) is also evaluated to build confidence that the modeling system is sufficiently reliable to project future-year visibility.

3.3 Model Performance Goals and Criteria

The issue of model performance goals for PM speci es is an area of ongoing research and debate. For ozone modeling, EPA has established perfor mance goals for 1-hour ozone: normalized mean bias and gross error of #± 15% and # 35%, respectively (EPA, 1 991). EPA’s draft fine particulate modeling guidance notes that performance goals for ozone should be viewed as upper bounds of model performance that PM models may not be able to always achieve and that we should demand better model performance for PM co mponents that make up a larger fraction of
the PM mass than those that are minor c ontributors (EPA, 2001). EPA’s final modeling guidance does not list any specifi c model performance goals for PM and visibility modeling and instead provides a summary of PM model performa nce across several historic al applications that can be used for comparisons, if desired. Measuring PM species is not as precise as ozone monitoring. In fact, the uncertain ty in measurement techniques fo r some PM species is likely toSeptember 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-3 exceed the more stringent model performance goa ls, such as those for ozone. For example, recent comparisons of the PM species measurements using the IMPROVE and STN measurement technologies found un certainties of approximately ∀ 20% (SO4) to ∀ 50% (EC) (Solomon et al., 2004).

For the CENRAP modeling we have adopted th ree levels of model performance goals and criteria for bias and gross error as listed in Table 3-1. Note that we are not suggesting that these performance goals be adopted as guidance. Rather, we are just using them to frame and put the PM model performance into context and to facili tate model performance intercomparison across episodes, species, models and sensitivity tests.

Table 3-1. Model performance goals and criteria used to assist in interpreting modeling results

.

Fractional Bias Fractional Gross Error Comment

#∀ 15% # 35% Ozone model performance goal for which PM model performance would be considered “good” – note that for many PM species measurement uncertainties may exceed this goal.

#∀ 30% # 50% Proposed PM model performanc e goal that we would hope each PM species could meet

#∀ 60% # 75% Proposed PM criteria above which indicates potential fundamental problems with the modeling system.

As noted in EPA’s PM modeling guidance, less abundant PM species should have less stringent performance goals (EPA, 2001; 2007). Accordingly, we are also using performance goals that
are a continuous function of aver age concentrations, as proposed by Dr. James Boylan at the Georgia Department of Natural Resources (GA DNR), that have the following features (Boylan, 2004):

• Asymptotically approaching proposed perf ormance goals or criteria (i.e., the ∀ 30%/50%
and ∀ 60%/75% bias/error levels listed in Table 3-1) when the mean of the observed concentrations are greater than 2.5 ug/m 3

.

• Approaching 200% error and ∀ 200% bias when the mean of the observed concentrations are extremely small.

Bias and error are plotted as a function of aver age concentrations. As the mean concentration approaches zero, the bias performan ce goal and criteria flare out to ∀ 200% creating a horn shape, hence the name “Bugle Plots”. Dr. Boylan has defined th ree Zones of model performance: Zone 1 meets the ∀ 30%/50% bias/error performa nce goal and is considered “good” model performance; Zone 2 lies between the ∀ 30%/50% performance goal and

∀ 60%/75% performance criteria and is an area where concern for model performance is raised;
and Zone 3 lies above the ∀ 60%/75% performance criteria and is an area of questionable model performance.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-4 3.4 Key Measures of Model Performance

Although we have generated numerous statistical performance measures (see Table C-2 in Appendix C) that are available on the CE NRAP modeling website, when comparing model performance across months, subdomains, networks, grid resolution, models, studies, etc. it is useful to have a few key measurement statistics to be used to facilitate the comparisons. It is
also useful to have a subset of months within the 2002 year that can repres ent the entire year so
that a more focused evaluation can be conducted. We have found that th e Mean Fractional Bias and Mean Fractional Gross Error appear to be th e most consistent descriptive measure of model performance (Morris et al., 2004b; 2005). The Fractional Bias an d Error are normalized by the average of the observed and predicted value (s ee Table C-2) because it provides descriptive power across different magnitudes of the model and observed concentrations and is bounded by
-200% to +200%. This is in c ontrast to the normalized bias and error (as recommended for ozone performance goals, EPA, 1991) that is norm alized by just the observed value so can “blow up” to infinity as the observed value approach es zero. In Appendix C we perform a focused evaluation of model performance for PM and gaseous species and four months of the 2002 year that are used to represent the s easonal variation in performance:

• January

• April

• July

• October

Scatter plots of model predictions and observations for each PM species are presented for each of the four months along with perfor mance statistics and predicted and observed time series plots at each CENRAP Class I area. Summary plots of monthly fractional bias and error are also presented.

3.5 Operational Model Performance Evaluation

A summary of the operational eval uation is presented below. Just the monthly fractional bias performance metrics for each PM species using bar charts and Bugle Plots are presented in this section. The reader is referred to Appendix C for the complete model performance evaluation.

3.5.1 Sulfate (SO4) Model Performance

Figure 3-1 compares the mont hly SO4 fractional bias across the CENRAP region for the IMPROVE, STN and CASTNet monitoring networks. An underprediction bias is clearly evident the first 8-10 months of the year. This unde restimation bias is greatest across the CASTNet network which persists throughout the year. The SO4 underprediction is not as severe for the STN network and it is minimal by August becoming a slight overprediction in September. For the IMPROVE network, the SO4 fractional bias is < ±20% for the first 2 and last 3 months of the year and ranges from -30% to -50% for the late Spring and Summer months.

Figure 3-1 also includes a Bugle Pl ot of monthly SO4 frac tional bias statistics (for Bugle Plot of fractional gross error see Appe ndix C) and compares them ag ainst the proposed PM modelSeptember 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-5 performance goal and criteria (see Table 3-1). For the STN network, SO4 model performance meets the proposed performance goal for all months. For the IMPROVE network, approximately half of the months achieve the pr oposed PM performance goa l with the other half outside of the goal, but within the performance criteria. Ac ross the CASTNet network, most months are outside of the proposed goal but are within the criteria. The CA STNet fractional bias for some months is right at the performance criteria ( ≤ ±60%). With the exception of two IMPROVE months, the monthly SO4 fractional bias performance statistics achieve the proposed PM model performance goal.

3.5.2 Nitrate (NO3) Model Performance

Monthly NO3 model performance across the CE NRAP region is characterized by a summer underestimation and winter overestimation bias (F igure 3-2). The summer underestimation bias is more severe, exceeding -100%. Whereas, the winter overestimation bias is approximately 50%. So based on statistics alone, it appears the summer underestimation bias is a bigger
concern than the winter overestim ation bias. However, the Bugle Plots in the bottom part of Figure 3-2 show that the summer underestimation bi as occurs when NO3 is very low and is not an important component of PM and visibility im pairment. These summer values occur in the flared horn part of the Bugle Pl ot and the summer NO3 performance, in most cases, achieves the model performance goal and always achieves the performance criteria. Whereas, the winter overstated NO3 performance for the most part do esn’t meet the performance goal and there are
some months/networks that also don ’t meet the performance criteria.

3.5.3 Organic Matter Carbon (OMC) Model Performance

The OMC monthly fractional bias across IMPROVE and STN sites in the CENRAP region are shown in Figure 3-3. The fractional bias fo r OMC at the IMPROVE sites is quite good throughout the year with values ge nerally within ±20%, albeit with a slight winter overestimation
and summer underestimation bias. At the urban STN sites, the model exhibits an underestimation bias throughout the year that ranges from -20% to -50%. The urban underestimation of OMC is a fairly common occurrence and suggests there may be missing sources of organic aerosol emissi ons in the modeling inventory.

The good performance of the model for OMC at th e IMPROVE sites is also reflected in the Bugle Plot (Figure 3-3, bottom) with the bias achieving the proposed PM model performance goal for all months of the year. At the STN sites, however, the OMC bias falls between the proposed PM model performance goal and criteria, with error right at the goal for most months.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-6 Figure 3-1. Monthly fractional bias (%) for sulfate (SO4) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-7 Figure 3-2. Monthly fractional bias (%) for nitrate (NO3) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-8 Figure 3-3. Monthly fractional bias (%) for organic matter carbon (OMC) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-9 3.5.4 Elemental Carbon (EC) Model Performance

The monthly average bias for EC across the IMPROVE and STN monito rs in the CENRAP region are shown in Figure 3-4. The STN networ k exhibits small fracti onal bias year round, whereas the IMPROVE monitoring network exhibits a large underprediction bias in the summer months (-40% to -70%) and much smaller bias in the winter. The B ugle Plot puts the EC performance in context. The low EC concentrati ons at the IMPROVE sites results in bias values in the horn of the Bugle Plot. Thus, EC bias achieves the propos ed PM performance goal for all months of the year.

3.5.5 Other PM 2.5 (Soil) Model Performance

Figure 3-5 displays the monthly variation in the Soil fractional bias using IMPROVE measurements in the CENRAP region. During th e winter months, the model exhibits a very large (> 100%) overestimation bias. With the ex ception of July, the summer monthly bias is toward a slight overprediction but generally less than 20%. The July underestimation bias appears to be driven by impacts of high Soil va lues from wind blown dust events (e.g., see July 2002 discussion in Appendix C). The Bugle Plot indicates that the summer Soil performance achieves the PM performance goal, a few months in the Spring/Fall period fall between the performance goal and criteria and the winter Soil performance exceeds the model performance criteria. Thus, the Soil perf ormance is a cause for concern.

3.5.6 Coarse Mass (CM) Model Performance

The monthly average fractional bias values for CM are shown in Figure 3-6. In the winter the underprediction bias is typically in the -60% to -80% range. In the late Spring and Summer the underprediction bias ranges from -120% to -160%. As this unde rprediction bias is nearly systematic (i.e., an underprediction almost always occurs), then the fractio nal errors are the same magnitude as the bias.

The Bugle Plots clearly show that the CM model performance is a problem. The monthly bias exceeds both the performance goal and criteria for almost every month of the year.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-10 Figure 3-4. Monthly fractional bias (%) for elemental carbon (EC) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-11 Figure 3-5. Monthly fractional bias (%) for other PM 2.5 (Soil) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-12 Figure 3-6. Monthly fractional bias (%) for coarse mass (CM) across the CENRAP region for the CMAQ 2002 36 km Base F base case simulation.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-13 3.6 Diagnostic Model Performance Evaluation

The CASTNet and AQS networks also measure ga s-phase species that are PM precursor or related species. The diagnostic evaluati on of the 2002 36 km Base F CMAQ base case simulation for these compounds and the four seasona l months are presented in Appendix C. The displays for January are provided below as an ex ample; the reader is referred to Appendix C for the rest of the monthly displays.

The CASTNet network measures weekly aver age samples of SO2, SO4, NO2, HNO3, NO3 and NH4. The AQS network collects hourly m easurements of SO2, NO2, O3 and CO. A comparison of the SO2 and SO4 performance provi des insight into whether the SO4 formation rate may be too slow or fast. For example, if SO4 is underestimated and SO2 is overestimated that may indicate chemical conversion rates that are too slow. Analyzing the performance for SO4, HNO3, NO3, Total NO3 and NH4 provides insight into the equilibrium of these species. For example, if Total NO3 performs well but HNO3 and NO3 do not, then there may be issues associated with the partitioning be tween the gaseous and particulate phases of nitrate. Causes for incorrect HNO3/NO3 partitioning could include inadequate ammonia emissions and/or poorly characterized meteorological conditions (e.g., temperature).

3.6.1 Diagnostic Model Performance in January 2002

In January, SO2 is overstated across both the CAST Net and AQS sites with fractional bias values of 38% (Figure 3-7) and 31% (F igure 3-8), respectively. SO4 is understated by -34% across the CASTNet monitors (Figure 3-7) and -12% an d -13% for the IMPROVE and STN networks (Figure C-4a). Wet SO4 deposition is also overs tated in January (+40%, Figure C-4a). Given that SO2 emissions are well characterized, th ese results suggest that the January SO4 underestimation may be partly due to understated transformati on rates of SO2 to SO4 and overstated wet SO4 deposition.

Total NO3 is overestimated by 35% on averag e across the CASTNet sites in the CENRAP region in January (Figure 3-7). HNO3 is underestimated (-34%) and particle NO3 is overestimated (+61%) suggesting ther e are gas/particle equilibrium issues. An analysis of the
time series of the four CASTNet stations reveals that NO3, HNO3 and NH4 performance is actually very reasonable at the west Texa s site and the HNO3 underestimation and NO3 overestimation bias is coming from the east Ka nsas, central Arkansas and northern Minnesota CASTNet sites (see Figure C-3 for site locati ons). One potential contributor for this performance problem could be overstated NH3 emissions. However, the Total NO3 overestimation bias suggests that the model es timated NOx oxidation rate may be too high in January.

The SO2, NO2, O3 and CO performan ce across the AQS sites in Janua ry is shown in Figure 3-8. The AQS monitoring network is primarily an urba n-oriented network. S o, it is not surprising that the model is underestimating concentrations of primary emissions when a 36 km grid is used. NO2 is underestimated by approximately 5% , and CO by approximately 67%. Ozone is also underestimated on average, especially the maximum values above 60 ppb.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-14 Figure 3-7. January 2002 performance at CENRAP CASTNet sites for SO2 (top left), SO4 (top right), HNO3 (middle left), NO3 (middle right), Total NO3 (bottom left) and NH4 (bottom right).September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-15 Figure 3-8. January 2002 performance at CENRAP AQS sites for SO2 (top left), NO2 (top right), O3 (bottom left) and CO (bottom right).September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-16 3.6.2 Diagnostic Model Performance In April

In April there is an average SO2 overes timation bias across the CASTNet (+15%) and underestimation bias across the AQS (-10%) networks (Figures C-42 and C-43). SO4 is underestimated across all networks by -30% to -58% (Figure C-5a). The wet SO4 deposition
bias is near zero. Both SO2 a nd SO4 are underestimated at the west Texas CASTNet monitor in April suggesting SO2 emissions in Mexico are likely understated.

The HNO3 performance in April is interesting with almost perfect agreement except for 5 modeled-observed comparisons that drives the average underprediction bias of -29% (Figure C42). On Julian Day 102 there is high HNO3 at the MN, KS and OK CASTNet sites that is not captured by the model. Given that HNO3, NO3 and Total NO3 are all underestimated by about the same amount (-30%), then part of the underes timation bias is likely due to too slow oxidation
of NOx.

There is a lot of scatter in the NO2 and O3 perf ormance that is more or less centered on the 1:1 line of perfect agreement with bi as values of -8% and -21%, respectively (Figure C-43). CO is underestimated by -72% with the model unable to predict CO concentrati ons above 1 ppm due to the use of the coarse 36 km grid spacing. Mob ile sources produce a vast majority of the CO emissions. So, AQS monitors for CO complian ce are located near roadways, which are not simulated well using a 36 km grid.

3.6.3 Diagnostic Model Performance In July

In July SO2 is slightly underestimated acro ss the CASTNet (-5%) and AQS (-12%) networks (Figures C-44 and C-45). SO4 is more signi ficantly underestimated across all networks (-22% to -53%, as shown in Figure C-6a). Sinc e wet deposition SO4 is al so underestimated, it is unclear why all sulfur sp ecies are underestimated.

The nitrate species are also all underestimated with the Total NO3 bias (-56%) being between the HNO3 bias (-35%) and NO3 bias (-115%). The mode led NO3 values are all near zero with little
correlation with the ob servations, whereas the observed HNO3 and Total NO3 is tracked well
with correlation coefficients of 0.74 and 0.76. These results s uggest that the July NO3 model performance problem is partly due to insuffici ent formation of Total NO3, but mainly due to incorrect partitioning of the Total NO3.

Again, there is abundant scatter in the AQS NO2 s catter plot for July (Figure C-45) resulting in a low bias (0%) but high error (65%). Ozone pe rformance also exhibits a low bias (-15%) and error (20%), but the model is incapable of simulating ozone a bove 100 ppb. Although CO performance in July is better than the previous months, it still has a large underestimation bias of
82%.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-17 3.6.4 Diagnostic Model Performance In October

SO2 is overstated in October across the CASTNe t (+28%) and AQS (+33%) sites (Figures C-46 and C-47). Although SO4 is understated across th e CASTNet sites (-24%), the bias across the IMPROVE (-6%) and STN (0%) sites are near zero (Figure C-7a).

Performance for HNO3 is fairly good with a lo w bias (+12%) and error (30%). But NO3 is overstated ( +34%) leading to an overstatement of Total NO3 (+ 37%). The overstatement of NO3 leads to an overstatement of NH4 as well (Figure C-46)

As seen in the other months, NO2 exhibits a lot of scatter resu lting in a low correlation (0.22) and high error (61%) but low bias (12%). Th e model tends to underpredict the high and overpredict the low O3 observations resulting in a -29% bias and lo w correlation coefficient. CO is also underpredicted (-76%) for the reasons discussed previously.

3.7 Performance at CENRAP Class I Areas for the Worst and Best 20 Percent Days

In this section, and in section C.5 of Appe ndix C, we present the results of the model performance evaluation at each of the CENRAP Cl ass I areas for the worst and best 20 percent days. Performance on these days is critical sinc e they are the days used in the 2018 visibility projections discussed in Chapter 4. For each Class I area we compared the predicted and observed extinction of the worst and best 20 percen t days below. In Appendix C the PM speciesspecific extinction is also compared for the worst 20 percent days.

3.7.1 Caney Creek (CACR) Arkansas

The ability of the CMAQ model to estimate visibility extinction at the CACR Class I area on the 2002 worst and best 20 percent days is provide in Figures 3-9 a nd C-48. On most of the worst 20 percent days at CACR total extinction is dominated by SO4 extinction with some extinction due to OMC. On four of the worst 20 perc ent days extinction is dominated by NO3. The average extinction across the worst 20 percent da ys is underestimated by -33% (Figure 3-9), which is primarily due to a -51% underestima tion of SO4 extinction combined with a 6% overestimation of NO3 extinction (Figure C-48) . Performance for OMC extinction at CACR on the worst 20 percent days is pretty good with a -20% bias and 36% error. EC extinction is systematically underestimated. Soil extinction has lo w bias (-19%) buts lots of scatter and high error (74%), while CM extinction is gr eatly underestimated (bias of -153%).

On the best 20 percent days at CACR the observed extinction ranges from 20 to 40 Mm -1

. Whereas, the modeled extinction has a much larger range from 15 to 120 Mm -1 . Much of the
modeled overestimation of total extinction on th e best 20% days (+44% bias) is due to NO3 overestimation (+94% bias).September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-18 Worst 20% Obs (left) vs Typ02g (right) at CACR1

0 50 100 150 200 250 300 350 400

80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 _ _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at CACR1

0 20 40 60 80 100 120 140

11 14 26 89 98 116 197 266 278 284 287 293 299 302 311 323 329 335 338 353 356 359 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-9. Daily extinction model performance at Caney Creek (CACR), Arkansas for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-19 3.7.2 Upper Buffalo (UPBU) Arkansas

Model performance at the UPBU Class I area for th e worst and best 20 percent days is shown in Figures 3-10 and C-49. On most of the worst 20 percent days at UPBU, visibility impairment is dominated by SO4, although there are also two high NO3 days. The model underestimates the average of the total extinction on the worst 20 percent days at UPBU by -40% (Figure 3-10),
which is due to an underestimation of extincti on due to SO4, OMC and CM by -46%, -33% and - 179%, respectively.

On the best 20 percent days at UPBU, the model performs reasonably well with a low bias (2%) and error (42%). But again, the model has a mu ch wider range in extinction values across the best 20 percent days (15 to 120 Mm -1 ) than observed (20 to 45 Mm -1 ). There are five days in which the modeled NO3 overprediction is quite severe and when those days are removed the range in the modeled and observed extinction on the best 20 percent days is quite similar to the observed, although the model gets much cleaner on the very cleanest modeled days.

Worst 20% Obs (left) vs Typ02g (right) at UPBU1

0 50 100 150 200 250 300 350

128 143 158 170 173 179 191 197 203 206 215 218 221 224 239 242 248 251 254 257 269 296 320 332 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at UPBU1

0 20 40 60 80 100 120

14 26 47 68 89 98 116 119 164 167 176 227 266 278 281 287 290 302 311 323 335 353 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-10. Daily extinction model performance at Upper Buffalo (UPBU), Arkansas for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-20 3.7.3 Breton Island (BRET), Louisiana

The observed total extinc tion on the worst 20 percent days at Breton Island is underestimated by -71% (Figure 3-11), which is due to an underes timation of each component of extinction (Figure C-50) by from -50% to -70% (SO4, OMC and Soil) to over -100% (EC and CM). The observed extinction on the worst 20 percent days ranges from 90 to 170 Mm -1 , whereas the modeled values drop down to as low as approximately 15 Mm -1 . On the best 20 percent days the range
of the observed and modeled extinc tion is similar (roughly 10 to 50 Mm -1 ) that results in a reasonably low bias (-22%), but there is little agreement on which days are higher or lower resulting in a lot of scat ter and high error (54%).

Worst 20% Obs (left) vs Typ02g (right) at BRET1

0 20 40 60 80 100 120 140 160 180

44 47 74 125 143 179 194 197 200 206 215 239 248 251 254 257 260 263 272 281 284 287 305 311 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at BRET1

0 5 10 15 20 25 30 35 40 45 50

8 14 32 38 68 101 119 122 158 170 176 185 188 191 209 212 233 236 245 299 302 335 356 _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-11. Daily extinction model performance at Breton Island (BRET), Louisiana for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-21 3.7.4 Boundary Waters (BOWA), Minnesota

There are three types of days during the worst 20 percent days at BOWA: SO4 days, OMC days and NO3 days (Figure 3-12). Th e two high OMC days are likely fire impact events that the model captures to some extent on one day and not on the other. On the five high (> 20 Mm -1 ) NO3 extinction days the model predicts the observed extinction well on three days and overestimates by a factor of 3-4 on the other two high NO3 days. SO4 is underestimated by - 43% on average across the wors t 20 percent days at BOWA.

With the exception of two days, the model reprodu ces the total extinction for the best 20 percent days at BOWA quite well with a bias and error value of +14% and 22% (Figure 3-12). Without these two days, the modeled and observed extinction both range between 15 and 25 Mm -1

.

Worst 20% Obs (left) vs Typ02g (right) at BOWA1

0 20 40 60 80 100 120 140 160

11 26 146 149 161 170 176 179 188 197 200 203 242 245 251 275 299 314 332 344 347 _ _ _ _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at BOWA1

0 5 10 15 20 25 30 35 40

29 53 62 83 110 116 122 125 134 137 140 185 218 230 239 266 329 350 356 359 _ _ _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-12. Daily extinction model performance at Boundary Waters (BOWA), Minnesota for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-22 3.7.5 Voyageurs (VOYA) Minnesota

VOYA is also characterized by SO4, NO3 and OMC days (Figur e 3-13). Julian Days 179 and 200 are high OMC days that were also high OMC days at BOWA again indicating impacts from fires in the area that is not fully captured by the model. SO4 and NO3 performance is fairly good and, without the fire days, OMC performance l ooks good as well (Figure C-52). On the best 20 percent days there is one day the modeled exti nction is much higher than observed and a few others that are somewhat higher, but for most of the best 20 percent days the modeled extinction is comparable to the observed values.

Worst 20% Obs (left) vs Typ02g (right) at VOYA2

0 50 100 150 200 250

5 11 71 86 104 131 146 152 170 179 188 197 200 221 242 245 254 260 272 284 299 332 344 347 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at VOYA2

0 5 10 15 20 25 30 35 40 45

29 47 50 56 116 122 125 128 134 137 140 185 203 218 239 266 275 290 296 329 350 356 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-13. Daily extinction model performance at Voyageurs (VOYA), Minnesota for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-23 3.7.6 Hercules Glade (HEGL) Missouri

On most of the worst 20 perc ent days at HEGL the observed extinction ranges from 120 to 220
Mm -1 whereas model extinction ranges from 50 to 170 Mm -1 (Figure 3-14). However, there is one extreme day with extin ction approaching 400 Mm -1 that the model does a very good job in replicating. Over all the days there is a modest underestimati on bias in SO4 (-39%) and OMC (-39%) extinction, larger underestimation bias in EC (-62%) and CM (-118%) extinction and overestimation bias in Soil (+ 30%) extinction (Figure C-53).

On the best 20 percent days th ere is one day where the model overstates the observed extinction by approximately a factor of four and a handfu l of other days that the model overstates the extinction by a factor of 2 or so, but most of the days both the model and observed extinction
sites are around 40 Mm -1 ±10 Mm -1 . On the best 20 percent days , when the observed extinction is overstated, it is due to overstatement of the NO3.

Worst 20% Obs (left) vs Typ02g (right) at HEGL1

0 50 100 150 200 250 300 350 400

5 149 152 170 173 179 188 191 206 215 218 221 224 239 242 245 248 251 257 260 269 296 320 341 347 Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at HEGL1

0 20 40 60 80 100 120 140 160

14 26 44 68 71 89 113 116 119 134 137 227 266 278 281 287 290 302 308 311 317 335 353 _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-14. Daily extinction model performance at He rcules Glade (HEGL), Missouri for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-24 3.7.7 Mingo (MING) Missouri

The worst 20 percent days at MING are mainly high SO4 days with a few high NO3 days that the model reproduces reasonably well resulting in low bias (+10%) and error (38%) for total extinction (Figure 3-15). The PM species specific performance is fairly good with low bias for SO4 (+4%), good agreement with NO3 on high NO3 days except for one day, low OMC (+23%) and EC (+3%) bias and larger bias in EC (+37%) and CM (-105%) extinction (Figure C-54).

For the best 20 percent days, there is one day the model is way too high due to overstated NO3 extinction and a few other days the model overstate s the observed extinction that is usually due to overpredicted NO3, but on most of the best 20 percent days the modeled extinction is comparable to the observed values. This results in low bias (+12%) and error (36%) for total extinction at MING for the best 20 percent days.

Worst 20% Obs (left) vs Typ02g (right) at MING1

0 50 100 150 200 250 300 350 400

20 77 95 158 173 185 188 191 194 197 206 215 218 221 239 242 245 251 272 296 332 341 _ _ _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at MING1

0 20 40 60 80 100 120 140 160 180 200

14 32 44 62 116 134 137 140 164 167 176 200 227 263 266 278 281 287 290 308 353 356 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-15. Daily extinction model performance at Mingo (MING), Missouri for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-25 3.7.8 Wichita Mountains (WIMO), Oklahoma

With the exception of an overprediction on day 344 due to NO3, observed total extinction on the worst 20 percent days at WIMO is understated with a bias of -42% (Figure 3-16) that is primarily
due to an underestimation of extinction due to SO4 (-48%) and OMC (-69%) (Figure C-55).

CMAQ total extinction performance for the averag e of the best 20 percen t days at WIMO is characterized by an overestimation bias (+21%) on most days that is primarily due to NO3 overprediction on several days. Again the mode led range of extinction on the best 20 percent days (12-60 Mm -1 ) is much greater than observed (20-35 Mm -1 ).

Worst 20% Obs (left) vs Typ02g (right) at WIMO1

0 50 100 150 200 250

29 83 95 101 110 113 125 128 131 143 170 173 176 179 218 221 224 242 245 254 260 269 272 344 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at WIMO1

0 10 20 30 40 50 60

8 11 14 26 32 41 44 47 53 98 116 263 278 281 287 302 311 314 317 323 353 356 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-16. Daily extinction model performance at Wi chita Mountains (WIMO), Oklahoma for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-26 3.7.9 Big Bend (BIBE) Texas

The observed extinction on the worst 20 percent days at BIBE is underpredicted on almost every day resulting in a fracti onal bias value of -72% (Figure 3-17). Every co mponent of extinction is underestimated on average for th e worst 20 percent days (Figur e C-56) with th e underestimation bias ranging from -24% (OMC) to -162% (CM). SO4 extinction, that typically represents the largest component of the total ex tinction is understated by -94%.

The model does a better job in predicting the total extinction at BIBE for the best 20 percent days with average fractional bias and error values of +13% and 19% (Figur e 3-17). With the exception of one day that the obse rved extinction is overestimated by approximately a factor of 2, the modeled and observed extinction on the best 20 percent days at BIBE are both within 12 to
25 Mm -1 . However, there are some mismatches w ith the components of extinction with the model estimating much lower contributions due to Soil and CM.

Worst 20% Obs (left) vs Typ02g (right) at BIBE1

0 20 40 60 80 100 120 140

29 71 89 92 95 101 110 113 116 119 125 128 131 143 167 191 218 221 230 242 245 254 275 329 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at BIBE1

0 5 10 15 20 25

11 14 38 56 77 194 212 263 281 287 299 302 308 320 326 338 344 347 350 353 359 362 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-17. Daily extinction model performance at Big Bend (BIBE), Texas for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-27 3.7.10 Guadalupe Mountains (GUMO) Texas

Most of the worst 20 percent days at GUMO are hi gh dust days with high Soil and CM that is not captured by the model (Figure 3-18). Extinc tion due to Soil and CM on the worst 20 percent days is underestimated by -105% and -191%, respect ively (Figure C-57). Better performance is seen on the best 20 percent days with bias and error for total extinction of 8% and 21%, but the
model still understates Soil and CM.

Worst 20% Obs (left) vs Typ02g (right) at GUMO1

0 10 20 30 40 50 60 70 80 90 100

74 83 92 95 122 131 137 146 161 164 167 170 173 176 191 218 221 245 251 254 260 266 272 329 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs Typ02g (right) at GUMO1

0 5 10 15 20 25

8 14 29 35 38 77 194 200 284 287 299 302 305 311 317 323 326 338 347 353 356 362 _ _ Av
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 3-18. Daily extinction model performance at Guadalupe Mountains (GUMO), Texas for the worst (top) and best (bottom) 20 percent days during 2002.September 2007

F:\CENRAP_Modeling\TSD\dr aft#3\Chapter_3_MPE3.doc 3-28 3.8 Model Performance Evaluation Conclusions

The model performance evaluation reveals that the model is performing best for SO4, OMC and EC. Soil performance is mixed with a winter overestimation bias with lower bias and higher error in the summer. CM performance is poor year round. The operational evaluation reveals that SO4 performance usually achieves the PM model performance goal and always achieves the model performance criteria, although it does have an underestimation bias that is greatest in the summer. NO3 performance is characterized by a wi nter overestimation bias with an even greater summer underestimation bias. However, the summe r underestimation bias occurs when NO3 is very low and when it is not an important co mponent of the observed or predicted PM mass concentrations or component of visibility impairment. Perf ormance for OMC meets the model performance goal year round at the IMPROVE site s, but is characterize d by an underestimation
bias at the more urban STN sites. EC exhibits very low bias at the STN sites and a summer underestimation bias at the IMPROVE sites, but meets the model performance goal throughout the year. Soil has a winter overe stimation bias that is outside of the model performance goal and criteria raising questions whether the model s hould be used for this species. Finally, CM performance is extremely poor with an underpredict ion bias that is outside of the performance goal and criteria. We suspect that much of th e CM concentrations measured at the IMPROVE sites is due to highly localized emissions from fugitive dust sources that are not included in the emissions inventory and would be difficult to simulate using 36 km regional modeling.

Performance for the worst 20 percent days at the CENRAP Class I areas is generally characterized by an underestimation bias. Perf ormance at the BRET, BIBE and GUMO Class I areas for the worst 20 percent days is particul arly suspect and care should be taken in the interpretation of the visibility projections at these three Class I areas.

The CMAQ 2002 36 km model appears to be work ing well enough to reliably make future-year projections for changes in SO4, NO3, EC and OMC at the rural Class I areas. Performance for Soil and especially CM is suspect enough that care should be taken in interpreting these modeling results. The model evaluation focu sed on the model’s ability to predict the components of light extinction mainly at the Class I areas. Additional anal ysis would have to be
undertaken to examine the model’s ability to simulate ozone and fine particulate to address 8-
hour ozone and PM 2.5 attainment issues.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-1 4.0 VISIBILITY PROJECTIONS

This section presents the future-year visibility projections for Class I areas within and near the CENRAP states and their comparison with the 2018 Uniform Rate of Progress (URP) point. As noted in Chapter 1, the Regional Haze Rule (RHR) requires states with Class I areas to develop State Implementation Plans (SIPs) that include reasonable progress goals (RPGs) for improving visibility in each Class I area and emission reduction measures to meet those goals. For the initial SIPs due in December 2007, states are re quired to adopt RPGs for improving visibility from Baseline Conditions. The 2000-2004 five-year pe riod is used to define Baseline Conditions and the first future progress period is 2018. A state is required to set RPGs for each Class I area in the state for two visibility metrics:

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

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

The goal of the RPGs is to provide for a rate of improvement sufficient to be on a course to attain “Natural Conditions” by 2064. Stat es are to define controls to meet RPGs every 10 years, starting in 2018, which defines progre ss 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 a nother (e.g., 2000-2004 to 2013-2017). As stated in 40 CFR 51.308 (d) (1), baseline visibility conditions, reasonable progress goals, and changes in visibility must be expressed in terms of deci view (dv) units. The haze index (HI) metric of visibility impairment, in deciviews, is derived from light extinction (b ext ) as follows:

HI = 10 ln (b ext /10),

Where light extinction (b ext ) is expressed in terms of inverse megameters (Mm -1 = 10 -6 m -1 ). Light extinction (b ext ) is calculated using the ob served fine particulate concentrations from the IMPROVE monitors using either the original or the new IMPROVE aerosol extinction equation. Both equations are discussed below.

4.1 Guidance for Visibility Projections

EPA has published several guidance documents th at relate to how mode ling results should be used to project future-year visibility and how states should define RPGs:

“Guidance on the Use of Models and Other An alyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 and Regional Haze” (EPA, 2007a).

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

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” (EPA, 2003b).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-2 “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” (EPA, 2007b).

The first EPA modeling guidance document list ed above (EPA, 2007) discusses the use of modeling results to project futu re-year visibility. The sec ond EPA guidance document (EPA, 2003a) focuses on monitored visibility, how to defi ne the visibility Baseline Conditions and how to track visibility goals. The third EPA guida nce document discusses procedures for defining
Natural Conditions for a Class I area. Natural Conditions are the visibi lity goal for 2064. Although states may propose altern ative approaches for defining Natural Conditions, in this
section we use the default Natural Conditions at Class I areas (EPA, 2003b; Pitchford, 2006). The final EPA guidance document discusses how states should define their RPGs and their relationship to the 2018 URP point.

The EPA documents discussed above are followed for the visibility projections presented in this section with one notable exception. Some of the EPA documents are based on the original IMPROVE equation (e.g., EPA, 2003a, b). The CENR AP visibility projecti ons are based on the new IMPROVE equation, although projections ba sed on the original IMPROVE equation are also presented as an alternative approach in Ch apter 5. EPA guidance allows for using either the original or the new IMPROVE equation (EPA , 2007a; Timin, 2007). CENRAP, along with the other RPOs, have elected to use the new IMPR OVE equation for their visibility projections.

4.2 Calculation of Visibility and 2018 URP Point from IMPROVE Measurements

EPA guidance recommends using the model in a re lative sense to project future-year visibility conditions (EPA, 2007a). This projection is made using Relative Response Factors (RRFs) that
are defined as the ratio of the future-year modeling results to th e base-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 projection approach are as follows (EPA, 2003a,b; 2007a):

• Monitored data are used to define curr ent visibility Baseline Conditions using IMPROVE monitoring data from th e 2000-2004 five-year base period.

• 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 coarse mass (CM or PM 2.5-10 ). 3⁄4 SO4 (sulfate) that is assumed to be ammonium sulfate [(NH 4 ) 2 SO 4 ]; 3⁄4 NO3 (particulate nitrate) that is a ssumed to be ammonium nitrate [NH 4 NO 3 ]; 3⁄4 OC (organic carbon) that is assumed to be total organic mass carbon (OMC) 3⁄4 EC (elemental carbon); 3⁄4 IP (other fine inorganic particulate or Soil); and 3⁄4 CM (coarse mass).

• Models are used in a relative sense to develop RRFs between baseline and future predicted concentrations of each component.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-3 • PM component-specific RRFs are multiplied by observed Baseline monitored values to estimate future-year PM component concentrations.

• Estimates of future-year component concentr ations are consolidated to provide an estimate of future-year air quality and visi bility using either the original or new IMPROVE equation.

• Future-year model projected visibility is compared with the 2018 point on the URP glidepath to assist in evaluati ng the visibility improvements.

• It is assumed that all measured sulfat e 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 tr acking visibility progress, three important visibility concepts are required
for each Class I area:

Baseline Conditions : Baseline Conditions represent visibili ty for the 20 percent best (B20%) and 20 percent worst (W20%) visibility days for the initial five-year baseline period of the regional haze program. Baseline Conditions are calculated using IMPROVE monitor data collected during the 2000-2004 fi ve-year period and are the st arting point in 2004 for the URP glidepath and 2018 visibility projections.

Natural Conditions : Estimates of natural visibility conditions for the best 20 percent and worst 20 percent days at a Class I area (i.e., visi bility conditions that would be experienced in the absence of human-caused impairment). EPA has defined a set of default Natural Conditions for the original IMPROVE equatio n (EPA, 2003b) that has been updated to the new IMPROVE equation by the Natural Haze Le vels II Committee (Pitchford, 2006) that we have used in this Chapter.

2018 URP Point : The 2018 Uniform Rate of Progress (URP) point is defined by defining a linear glidepath in deciviews starting w ith the 2000-2004 Baseline Conditions in 2004 and ending at Natural Conditions in 2064. Where the linear glidep ath passes through 2018 is the 2018 URP point in deciviews.

4.2.1 Calculation of Visibility fr om IMPROVE PM Measurements

Baseline Conditions for Class I areas are calcul ated using the procedures in EPA’s guidance document (EPA, 2003a) and fine and coarse part iculate matter concentr ations measured at IMPROVE monitors (Malm et al, 2000; Debell et al., 2006). Currently, each Class I area in the
CENRAP domain has an associated IMPROVE 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 IMPROVE equation.

Visibility conditions are estimated star ting with the IMPROVE 24-hour average mass measurements for six PM species:September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-4 • Sulfate [(NH 4 ) 2 SO 4 ];

• Particulate Nitrate [(NH 4 NO 3 ];

• Organic Matter Carbon or Or ganic Mass by Carbon [OMC];

• Elemental Carbon [EC] or Li ght Absorbing Carbon [LAC];

• Other fine particulate [Soil]; and

• Coarse Matter or Coarse Mass [CM].

The IMPROVE monitors do not directly measure so me 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, in the IMPROVE e quation sulfate and particulate nitrate are assumed to be completely neutralize d by ammonium. In addition, only the fine mode (PM 2.5 ) of PM is speciated by the IMPROVE monitor to obtain sulfate and nitrate measurements (that is, any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM IMPROVE measurement). Concentrations for th e above six components of light extinction in the IMPROVE equation are obtained from the IM PROVE measured species using the mappings
shown in Table 4-1:

Table 4-1. Definition of IMPROVE PM Component s from Measured IMPROVE Species. IMPROVE Component IMPROVE Measured Species Sulfate 1.375 x (3 x S)
Nitrate 1.29 x NO 3 - OMC 1.4*OC (original IMPROVE) and 1.8*OC (new IMPROVE)
LAC EC
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 from pr oton induced x-ray emissi ons (PIXE) analysis
of the IMPROVE Module A 1 . To estimate the mass of the sulfate ion (SO 4 = ), S is multiplied by 3 to account the presence of oxygen. If S is missing then the sulfate (SO 4 ) measured by ion chromatography analysis of th e Module B is used to replace (3 x S). For the IMPROVE aerosol extinction calculation, Sulfate is assumed to be completely neutralized by ammonium (1.375 x SO 4 ).

• NO 3 - is the particulate nitrate measured by ion chromatography analysis of the Module B.
For the IMPROVE aerosol extinction calculat ion, it is assumed to be completely neutralized by ammonium (1.29 x NO 3 - ).

• The IMPROVE Organic Carbon (OC) measurements are multiplied by 1.4 to obtain Organic Mass Carbon (OMC) using the original IMPROVE equation and multiplied by 1.8 for the new IMPROVE equation. This adjustment of the measured OC accounts for
mass due to other elements in the OMC besides Carbon.

• Elemental Carbon (EC) is also referred to as Light Absorbing Carbon (LAC).

1 The IMPROVE sampler consists of four independent mo dules (A, B, C and D). Each module incorporates a separate inlet, filter pack and pump assembly and are controlled by a common timing mechanism. Module A measures fine PM mass and elements. Module B measures su lfate and nitrate ions. Module C measures EC and OC. Module D measures PM 10 mass. (see http://vista.cira.colostat e.edu/improve/ fo r more details).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-5 • Soil is determined as a sum of the masse s of those elements (measured by PIXE) predominantly associated with soil (Al, Si, Ca, Fe, K and Ti), adjusted to account for oxygen associated with the common oxide fo rms. Since K and FE are products of the combustion of vegetation, they are both represented in the formula by 0.6 x Fe and K is not shown explicitly.

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

4.2.1.1 Original and New IMPROVE Equations

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

μ g/m 3 ) to light extinction (in inverse megameters, Mm -1 ). Sulfate and nitrate are hygroscopic which means that they can absorb water from the atmosphere which changes their extinction efficiency. This is accounted for through rela tive humidity adjustment factors [f(RH)] that increase the particle’s extinction efficiency with increasing RH to account for the particles taking on water Note that some OMC may also ha ve hygroscopic properties, but the IMPROVE equations assume OMC is non-hygroscopic.

There are currently two IMPROVE equations that are used to convert the measured PM concentrations to light extinc tion, the original (or old) and the new IMPROVE equations.

4.2.1.1.1 Original IMPROVE Equation

The original IMPROVE equation th at converts PM species concentr ations to light extinction is given as follows:

b Sulfate = 3 x f(RH) x [Sulfate]
b Nitrate = 3 x f(RH) x [Nitrate]
b EC = 10 x [EC]
b OMC = 4 x [OMC]
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 are available in the final EPA guidance document (EPA, 2003a) and 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 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 OMC + 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 ,September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-6 for b ext in Mm -1

.

The Regional Haze Rule requires that visibility be expressed in terms of a haze index (HI) in units of deciviews (dv), whic h is calculated as follows:

HI = 10 ln(b ext /10)

4.2.1.1.2 New IMPROVE Equation

The new IMPROVE equation is nonlinear in SO 4, NO3 and OMC concentrations accounting for the different light scattering efficiency characteri stics as a function of concentrations for these three species. It is expressed as follows:

b Sulfate = 2.2 x f S (RH) x [Small Sulfate] + 4.8 f S (RH) x [Large Sulfate]
b Nitrate = 2.4 x f S (RH) x [Small Nitrate] + 5.1 f S (RH) x [Large Nitrate]
b EC = 10 x [Elemental Carbon]
b OMC = 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass]
b Soil = 1 x [Fine Soil]
b CM = 0.6 x [Coarse Mass]
b NaCl = 1.7 x f SS (RH) x [Sea Salt]
b NO2 = 0.33 x [NO 2 (ppb)]

The total Sulfate, Nitrate and OMC are each split into two fractions, representing small and large
size distributions of those co mponents. As noted in Table 4-1, the OMC is 1.8 times the
IMPROVE OC measurement in the new IMPROVE algorithm, compared to 1.4 times the IMPROVE OC measurement in the original IM PROVE equation. New te rms have been added for Sea Salt (important for coastal areas and pos sibly other areas)and fo r light absorption by NO2 (only used where NO2 observati ons are available). As none of the CENRAP Class I area IMPROVE sites measure NO2 concentrations, then this component of the new IMPROVE equations was not used. Site-specific Rayleigh scattering for each IMPROVE monitoring site is used in the new IMPROVE equation, as compared to a constant 10 Mm -1 value assumed in the original IMPROVE equation.

The apportionment of the Small an d Large components of Sulfate, Nitrate and Organic Mass is
done as follows:

[Large Sulfate] = [Total Sulfate] / 20 x [Total Sulfate], for [Total Sulfate] < 20 μ g/m 3

[Large Sulfate ] = [Total Sulf ate], for [Total Sulfate] > 20 μ g/m 3

[Small Sulfate] = [Total Su lfate] – [Large Sulfate]

The same equations are used to apportion To tal Nitrate and Total OMC among their Large and Small components.

The total extinction (b ext ) in the new IMPROVE equations is the sum of all the extinction components associated with each PM species. The new IMPROVE equation adds Sea Salt andSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-7 NO 2 as noted above. In addition, site-specific Rayleigh bac kground is used with the new IMPROVE equation:

b ext = b Ray + b Sulfate + b Nitrate + b EC +b OMC + b Soil + b CM + b NaCl + b NO2

The Haze Index (HI) and Visual Range (VR) are calculated from the tota l extinction from the new IMPROVE equation using the same formulas as given above for the original IMPROVE equation.

4.2.1.1.3 Justification for Using the New IMPROVE Equation

The new IMPROVE equation was developed using the latest scientific information on PM species extinction properties combined with f itting reconstructed ligh t extinction based on
IMPROVE measured PM and NO2 concentrations with actual co-located measured light extinction (e.g., nephelometer measur ements). Figure 4-1 displays example comparisons of 24- hour light extinction using the original and ne w IMPROVE equations compared against 24-hour nephelometer measurements of light extinction at the Great Smoky Mountains Class I area IMPROVE monitor. The origin al IMPROVE equation has a bi as toward understating light extinction at the high end and overstating it at the low end, whereas th e new IMPROVE equation does a better job in estimating light extinction from measured PM at all extinction levels. Because the new IMPROVE equation is based on mo re recent science and fits the observed light extinction values better, the CENRAP states have elected to perform th eir primary visibility projections using the new IMPROVE equation. Results using the original IMPROVE equation are presented in Section 5 as an alternative approach.

Figure 4-1. Comparisons of observed light extinction wi th reconstructed light extinction using the new (left) and original (right) IMPROVE equations at the Great Smoky Mountains National Park.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-8 4.2.2 Calculation of the Baseline Conditions

The visibility Baseline Conditions for the worst 20 percent and best 20 percent days is calculated
from the IMPROVE observations from the 20 00-2004 period for each Class I area following EPA’s guidance (EPA, 2003a). The basic procedur es for calculating the Baseline Conditions are
as follows:

1. Determine whether the observed IMPROVE data for each site and year satisfies EPA’s minimal data capture criteria (E PA, 2003a). If there are less than three years with valid data capture for the 2000-2004 Baseline then th e Baseline Conditions can not be calculated and data filling is needed.
2. For each year in the 2000-2004 period with suff icient valid data, rank the visibility in
terms of extinction or decivi ew using either the original or new IMPROVE equation and monthly average f(RH) factors (EPA, 2003a).
3. For the worst 20 percent days, ex tract the 20% most impaired vi sibility days for each year (similarly for best 20 percent days extract 20% cleanest days). With a complete yearly data capture of IMPROVE 1:3 day sampling fr equency this would re sult in 24 worst 20 percent and 24 best 20 percent days in a year.
4. For each worst 20 percent (or best 20 percent) day in each year, calculate 24-hour average visibility extinction using the IMPROVE meas urements and either the original and new IMPROVE equation, convert the daily extinc tion to daily deciview and then average across each year to get yearly average deciview extinction for the worst 20 percent (or best 20 percent) days for each valid year from the 2000-2004 period.
5. Average the annual average deciview worst 20 pe rcent (or best 20 perc ent) days deciview across each valid year in the 2000-2004 period (minimum of 3 valid years required) to get the worst 20 percent (or best 20 percent) Baseline Conditions.

4.2.3 Data Filling for Sites with Insufficient Va lid Data to Calculate Baseline Conditions

Three CENRAP Class I areas did not contain su fficient IMPROVE observa tions during the fiveyear 2000-2004 Baseline to have three valid years of data from which Baseline Conditions could
be constructed: Breton Island (BRET), Louisiana; Boundary Wa ters (BOWA), Minnesota and Mingo (MING), Missouri. For these three Class I ar eas, data filling was used to obtain sufficient data so that at least three-years of valid data were available from which Baseline Conditions
could be calculated. These data filled IMPROVE databases were prepared and made available
on the VIEWS website. More information on the data filling procedures can be found at the VIEWS website: ( http://vista.cira.colostate.edu/views/ ).

4.2.4 Natural Conditions

EPA has published default Natural Conditions fo r Annual Average and the worst 20 percent and best 20 percent days based on the original IMPROVE equation (EPA, 2003b). These default Natural Conditions have been updated to th e new IMPROVE equation by the Natural Haze Levels II Committee (Pitchford, 200 6). These default Natural Conditions are used as the anchor point for the glidepaths in 2064 and are provided in Appendix D for the CENRAP Class I areas.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-9 4.2.5 2018 URP Point

The 2018 point on the Uniform Rate of Progress (U RP) glidepath is constr ucted by generating a linear glidepath in deciviews from the Baselin e Conditions in 2004 to Natural Conditions in 2064. Where the linear glidepat h crosses 2018 is the 2018 point on the URP glidepath or the 2018 URP point. Figure 4-2 displays an exampl e linear glidepath for the Caney Creek Class I
area in Arkansas. There are th ree years of sufficient valid IMPROVE data during the 2000-2004 Baseline (2002, 2003 and 2004) with values of 27.21, 26.52 and 25.34 dv result ing in worst 20 percent Baseline Conditions of 26. 36 dv that is placed as the st arting point in 2004 for the glidepath. The ending point for the glidepath is 11.58 dv which is the default Natural Conditions for the worst 20 percent days (EPA, 2003b; Pitc hford, 2006). The linear glidepath crosses 2018 at 22.91 dv which becomes the 2018 URP point.



### Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

26.36 25.38

22.91

20.45

17.99

15.52

13.06
11.58

0 5 10 15 20 25 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation

Figure 4-2. Linear Glidepath for Caney Creek (CACR) , Arkansas that linearly connects the 26.36 dv Baseline Conditions in 2004 with the 11. 58 dv Natural Conditions in 2064 resulting in a 22.91 dv 2018 URP Point.

4.3 EPA Default Approach to Visibility Projections

For CENRAP’s model application for a single year (2002), EPA’s regional haze modeling guidance recommends developing Cl ass I area-specific and PM sp ecies-specific RRFs based on the average concentrations for the worst 20 pe rcent days from 2002 (EPA, 2007). Thus, this isSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-10 the methodology used to project 2018 visibility es timates in this section. For example, if SO4(2002) i and SO4(2018) i are the model estimated sulfate c oncentrations for the 2002 worst 20 percent days (i=1...N) at a give n Class I area for th e 2002 and 2018 emission scenarios then the
RRF for sulfate and this Class I area is given by:

RRF(SO4) i = ∑ SO4(2018) i / ∑ SO4(2002) i

4.3.1 Mapping of Modeling Results to the IMPROVE Measurements

As noted above, to project future-year visibility at Class I areas the modeling results are used in a
relative sense to scale current observed visibil ity for the worst 20 percent and best 20 percent visibility days using RRFs that are the ratio ofmodeling results for the future-year to currentyear. This scaling is done separately for each of the six components of light extinction in the IMPROVE equations. The CMAQ modeled species do not necessarily exactly match up with the IMPROVE PM species, thus assumptions must be made to map the modeled species to the IMPROVE PM species for the purpose of projecti ng visibility improvements. For example, CMAQ explicitly simulates ammonium and sulfate may or may not be fully neutralized in the model by ammonium, whereas the IMPROVE equations assume sulfate is fully neutralized by ammonium. For the CMAQ Version 4.5 (September 15, 2005 release) model, the mapping of modeled species to IMPROVE equation PM species is listed in Table 4-2.

Table 4-2. Mapping of CMAQ V4.5 modeled species concentrations to IMPROVE PM components.
IMPROVE Component CMAQ V4.3 Species

Sulfate 1.375 x (ASO4J + ASO4I) Nitrate 1.29 x (ANO3J + ANO3I)

LAC AECJ + AECI OMC AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI Soil A25J + A25I CM ACORS + ASEAS + ASOIL

For the CENRAP visibility projections usi ng the 2002 Typical and 2018 base case Base G emission scenarios, the secondary organic ae rosol (SOA) module in CMAQ V4.5 was modified (SOAmods) to include additional processes rela ted to the generation of SOA from biogenic emissions. In particular, three new species have been added that represent SOA products from biogenic emission compounds that is not include d in the standard version of CMAQ V4.5 (Morris et al., 2006c):

• ASOC1 – SOA from biogenic so urces (e.g., terpenes and isoprene) that has become polymerized so is no longer volatile.

• ASOC2 – SOA from biogenic sesquiterpen e and higher reactivit y and higher yield monoterpene emissions.

• ASOC3 – SOA from bioge nic isoprene emissions.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-11 Thus, the species mapping for Organic Mass Carbon (OMC) and the CMAQ V4.5 SOAmods
version of the model used in CENRAP 2018 visibil ity projections is as given in Table 4-2 only
with the addition of the three new bi ogenic SOA species to OMC as follows:

OMC = AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI +
ASOC1 + ASOC2 + ASOC3

4.3.2 Using Modeling Results to Project Changes in Visibility

Modeling results are used in a relative fashion to project future-year visibility using relative response factors (RRFs). RRFs are expressed as the ratio of the modeling results for the futureyear to the results of the base year (2018/2002) and are Class I area and PM species specific. RRFs are applied to the Baseline Condition observ ed PM species to project future-year PM levels from which visibility can be assessed us ing the IMPROVE equations listed above. The following six steps are used to project future-yea r visibility for the worst 20 percent and best 20 percent visibility days (discussion is for worst 20 percent days but also a pplies to best 20 percent days):

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE data and IMPROVE equation (either original or new IMPROVE equation) for each year from the five-year baseline period (2000-2004) to identify the worst 20 percent visibility days for each year from the five-year baseline (see Baseline Conditions discussion above).

2. Use an air quality model to simulate a base year period (ideally the five-year Baseline period of 2000-2004, but for CENRAP just th e 2002 annual period was simulated) and a future-year (e.g., 2018) and use the resulting in formation to develop Class I area-specific RRFs for each of the six components of light extinction in the IMPROVE equation (SO4, NO3, EC, OMC, Soil and CM).

3. Multiply the RRF times the measured 24-hour PM concentration data for each day from the worst 20 percent days in each year from the five-year Baseline period to obtain projected future-year 24-hour PM concentra tions for the worst 20 percent days and the five-year Baseline.

4. Compute the future-year daily extinction us ing the IMPROVE equatio n and the projected PM concentrations for each of the worst 20 percent days in the five-year baseline from Step 3.

5. For each of the worst 20 percent days within each year of the five-year baseline, convert the future-year daily extinction to deciview a nd average the daily deciview values within each of the five years separately to obtain fi ve-years (or as many years with valid data in the 2000-2004 Baseline) of aver age deciview visibility fo r the worst 20 percent days.

6. Average the five-years of averag e deciview visibility to obta in the future-year visibility Haze Index estimate that is the future-year estimated visibility.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-12 In calculating the RRFs, EPA draft guidance recommends selecting estimated PM species concentrations “near” the monitor by taking a spatia l average of PM concentrations across a grid cell resolution dependent NX by NY array of cells centered on the grid containing the monitor. The NX x NY array of cells is grid resoluti on specific with EPA recommending that NX=NY=1 for 36 km grids, NX=NY=3 for 12 km grids and NX=NY=7 for 4 km grids (EPA, 2007). For the CENRAP 2002 36 km modeling, just the model estimat es for the grid cell c ontaining the monitor was used (i.e., NX=NY=1).

4.4 EPA Default 2018 Visibility at CENRAP and Ne arby Class I areas and Comparisons to
2018 URP Goals

Using the EPA default visibility projection proc edure described in Section 4.3 and the CENRAP 2002 Typical Base G and 2018 Base Case Base G CMAQ modeling resu lts, 2018 visibility projections were made for CENRAP and near by Class I areas. Appendix D details the 2018 Base G visibility projections for each Clas s I area in the CENRAP region using the new IMPROVE equation. Results for the Caney Creek (CACR), Arkansas Class I area are discussed in Section 4.4.1 below Displays for other CE NRAP Class I areas are provided in Appendix D
and summarized in Section 4.4.2

4.4.1 Example 2018 Base G Visibility Proj ections for Caney Creek, Arkansas

The 2018 visibility projections for the Caney Cr eek (CACR), Arkansas Cl ass I area given in Figure D-1 in Appendix D are reproduced in Figure 4-3 and described below.

4.4.1.1 EPA Default 2018 Visibility Projections

The 2018 Base G visibility projection usin g the EPA default method (EPA, 2007a) and comparison with the 2018 URP point for the worst 20 percent days and th e CACR Class I area is shown in Figure 4-3a. The 2000-2004 Baseline Conditions for CACR is 26.36 dv and the 2018 URP point is 22.91 dv so that a 3.45 dv reduction in visibility for the wors t 20 percent days is needed to meet the 2018 URP point. The 2018 Ba se G CMAQ projected vi sibility is 22.48 dv so that the modeling predicts more visibility im provements (3.88 dv reduction) than required to meet the 2018 URP point (3.45 dv reduction). When looking at visibility projections across several Class I areas, it has been useful to present the 2018 visi bility projections as a percentage of meeting the 2018 URP point; where 100% is mee ting the point, greater than 100% surpassing the point (i.e., below the glidepath) and less than 100% means th at less visibility improvement is
achieved than needed to meet the 2018 URP point. For 2018 Base G CMAQ modeling at CACR, we achieve 112% of the vi sibility reduction needed to m eet the 2018 URP point. Note that meeting the 2018 URP point is not a requirement of the RHR SIPs, rather it just serves as a benchmark to compare progress toward Natural C onditions in 2064 a nd is designed to help states in selecting their 2018 RPGs. As clearly stated in EPA guidance “The glidepath is not a presumptive target, and States may establish a RPG that provides for greater, lesser, or equivalent improvement as that desc ribed by the glidepath” (EPA, 2007b).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-13 The 2018 Base G CMAQ visibility projections for th e best 20 percent days and CACR is shown in Figure 4-3b. Recall the RHR goal fo r this visibility metr ic is no worsening of the visibility for the best 20 percent days. The Ba seline Conditions for the best 20 percent days at CACR is 11.24 dv. The 2018 Base G projected visibility for the best 20 percent days is 10.35 dv, which represents a 0.89 dv visibility improvement fo r the best 20 percent days at CACR and demonstrating no worsening in visibili ty for the best 20 percent days.

Figure 4-3c displays “StackedBar Chart” plots of observed and model estimated extinction for each of the worst 20 percent days in 2002 and the 2002 Typical Base G CMAQ simulation and the average across the worst 20 percent days. Th is figure allows a comparison of how well the model is reproducing the observed extinction at CACR for the worst 20 percent days in 2002 and the breakdown of the PM components that are cont ributing to visibility impairment (more details on model performance were presented in Chapter 3). The 2002 worst 20 percent days at CACR are dominated by SO4 days (yellow), although dur ing the winter there are also three days dominated by NO3 (Julian Days 80, 320 and 341). For most of the worst 20 percent days at CACR, the model reprodu ces the observed extinction reasonab ly well, although it does tend to understate SO4 on a few days and overstate NO3 on the f our winter days. The observed average
extinction across the 2002 worst 20 percent days at CACR is 150 Mm -1 , compared to a modeled value that is 23% lower (115 Mm -1 ).

Figure 4-3d displays “Boxplots” of difference s in modeled extinction for the 2002 worst 20 percent days between the 2018 Base G and 2002 T ypical Base G CMAQ simulations. On most days SO4 is the largest component of the extincti on that is estimated to be reduced at CACR on the worst 20 percent days. The exception to this is for the winter NO3 days where NO3 is the largest component of extin ction that is reduced. The modeling results are not used directly in the visibility projections, rath er they are used to develop the PM-species specific RRFs. That is, an important attribute in Figures 4- 3c and 4-3d is the relative cha nges in the modeled PM species averaged across the worst 20 percent days that ar e represented by the last bar in each figure and provide insight into the RRFs used in the visibility projections. These results are summarized in Table 4-3 below. Table 4-3 compares the av erage extinction across the 2002 worst 20 percent days at CACR from the measured IMPROVE data , the modeled values and the modeled change
in extinction between the 2018 and 2002 emissions scenarios. Alt hough the results in Table 4-3 are not RRFs (RRFs are based on ratios of concentr ations not extinction) they do show how the RRFs may magnify or deflate the importance of a modeled PM species. For example, the model estimates that appr oximately 23% (26.66 Mm -1 ) of the visibility extinction average across the worst 20 percent days is due to NO3, whereas it is only 7% in the obs erved values (10.22 Mm -1 ).
So the modeled ~40% reduction in NO3 betwee n the 2018 and 2002 scenarios is applied to the
smaller observed NO3 value to obtain the 201 8 projected NO3 value making NO3 a smaller portion of the 2018 projected visibility than th e 2018 modeled visibility. On the other hand, the
modeled SO4 extinction is less than observed so that its importance in the 2018 projections is much greater than in the modeled 2018 SO4 values.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-14 Table 4-3. Observed and Modeled Extinction by S pecies Averaged Across the Worst 20 Percent Days in 2002 at CACR. 2002 Average Observed W20% (Mm -1 ) 2002 Average Modeled W20% (Mm -1 ) 2018-2002 Reduction (Mm -1 ) 2018-2002 Reduction (%)
bSO4 109.50 67.90 -24.47 -36% bNO3 10.22 26.66 -10.90 -41% bOMC 19.65 16.68 -2.12 -13% bEC 4.38 2.32 -0.67 -29% bSOIL 1.43 1.04 +0.21 +20% bCM 4.30 0.37 -0.01 -3%



### Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

26.36 25.38

22.91

20.45

17.99

15.52

13.06
11.58 22.48

0 5 10 15 20 25 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Figure 4-3a. 2018 Visibility Projections and 2018 URP Glidepaths in Deciview for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) days Using 2002/2018 Base G CMAQ 36 km Modeling Results.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-15 

### Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - Best 20% Days

11.24 11.24 11.24 11.24 11.24 11.24 11.24 11.24

10.35

0 2 4 6 8 10 12 14

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearHaziness Index (Deciviews)

Glide Path Natural Condition (Best Days) Observation Method 1 Prediction
Figure 4-3b. 2018 Visibility Projections and 2018 URP Glidepaths in Deciview for CACR, Arkansas and Best 20 Percent (B20%) days Using 2002/2018 Base G CMAQ 36 m Modeling
Results.

Worst 20% Obs (left) vs Typ02g (right) at CACR1

0 50 100 150 200 250 300 350 400

80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 _ _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3c. Comparison of Observed (left) and 2002 Base G Modeled (right) Daily Extinction for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) days in 2002.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-16 Bext Response (base18g - typ0 2g) at CACR1 on Worst 20% Days

-200 -150 -100 -50

0 50

80 128 143 155 170 173 188 191 200 203 215 218 221 239 242 248 251 254 257 260 296 320 341 Avg
Julian DayDelta Bext (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3d. Differences in Modeled 2002 and 2018 Base G CMAQ Results (2018-2002) Daily Extinction for Caney Creek (CACR), Arkansas and Worst 20 Percent (W20%) Days in 2002.

4.4.2 Summary 2018 Visibility Projections Across Class I Areas

Figure 4-4 displays a “DotPlot” of 2018 visib ility projections using the 2002 Typical and 2018 base case Base G CMAQ 36 km modeling result s. DotPlots present the 2018 visibility projections as a percentage of meeting the 2018 URP point. For example, at CACR the 2018 Base G modeling achieved 112% of the visibility reduction needed to meet the 2018 URP point so the dot under CACR is plotted at 112%. Cl ass I areas’ with dots above 100% surpass the 2018 URP point (i.e., are below th e glidepath), whereas Class I areas’ with dots that are under 100% fail to meet the 2018 URP point. Figure 4- 4 summarizes the 2018 visibility projections using the EPA default “Regular RRF” and the two alternatives where CM is assumed to be natural (CM RRF=1) and both CM and Soil are a ssumed to be natural (CM&SOIL RRF=1).
When CM or CM&SOIL are assumed to be natural that means that we assume the same CM or CM&SOIL occurs in the 2018 future-year as in the 2000-2004 Baseline Conditions. For the CENRAP sites, the EPA default and alternative projection, assuming CM alone or CM and Soil are natural, techniques produced similar results.

At the four eastern CENRAP Class I area site s close to the Mississippi River (CACR, UPBU, HEGL and MING), the 2018 visibilit y projections meet (HEGL) or surpass the 2018 URP point. Breton Island Class I area (BR ET) comes up 6% short of meeti ng the 2018 URP point (i.e., 94% of the URP point). Wichita Mountains Class I area (WIMO) comes up approximately 40% short of the 2018 URP point. The two northern Class I areas (BOWA a nd VOYA) also come up about 40% short of meeting the 2018 URP point (i.e ., achieve 69% and 53% of the visibility improvement needed to meet th e 2018 URP point). The two Texa s Class I areas only achieveSeptember 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-17 26% (BIBE) and 34% (GUMO) of the visibil ity improvement needed to meet the 2018 URP point for the worst 20 percent days . As discussed in more detail in Chapter 5, much of the difficulty for the Texas and some of the othe r CENRAP Class I areas in meeting the 2018 URP point is due to large co ntributions due to international tran sport, much of which (e.g., Mexico and global transport) is assumed to remain unchanged from 2002 to 2018.

CMAQ BaseGa EPA Default Visibili ty Projections fo r CENRAP+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Regular RRF

CM RRF=1

CM&SOIL RRFs=1

CENRAP non-CENRAP Figure 4-4. 2018 Base G CMAQ Visibility Projections for CENRAP and Nearby Class I areas Using DotPlots that Express 2018 Visibility as a Percentage of Meeting the 2018 URP Point On the Deciview Linear Glidepath.

Figure 4-5 displays the model estimated absolute change in extinction (Mm -1 ) averaged across the 2002 worst 20 percent days at Class I areas in and near the CENRAP region. The largest
modeled reductions are in SO4 extinction. Figure 4-6 displays the percent change in the projected PM extinction by PM species for each CENRAP and nearby Class I area average across the worst 20 percent days (i.e., the relative modeled change). The four CENRAP Class I areas that meet the 2018 URP point (CACR, UPBU, HEGL and MING) are characterized by large SO4, NO3 and EC extinction reductions (30-40 %) with small Soil increases. At the other CENRAP Class I areas, however, there are lo wer levels of SO4, NO3 and EC extinction
reductions and even some NO3 in creases (BIBE). At the non-CE NRAP Class I areas, the two VISTAS Class I areas (MACA and SIPS) have large reductions in SO4 extinction (~50%),
whereas the WRAP Class I areas SO4 ex tinction reductions are much smaller.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-18 Average change in extinction components from 2002 baseline to 2018 projected at CENRAP sites using base18g/typ02g RRFs

-60.0 -50.0 -40.0 -30.0 -20.0 -10.0

0.0 10.0

BIBE1 GUMO1 WIMO1 CACR1 UPBU1 HEGL1 MING1 BRET1 VOYA2 BOWA1

IMPROVE site[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinc tion components from 2002 baseline to 2018 projected at non-CENRAP sites using base18g/typ02g RRFs

-120.0 -100.0 -80.0 -60.0 -40.0 -20.0

0.0 20.0

MACA1 SIPS1 ISLE1 SACR1 WHIT1 WHPE1 GRSA1 ROMO1 WICA1 BADL1 THRO1 LOST1

IMPROVE site[2018 project Bext - (2000-2004 baseline Bext)] (1/Mm)

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5 . Absolute Model Estimated Changes in Extinction (Mm -1 ) by PM Species for Class I Areas in the CENRAP region (top) and Near the CENRAP region (bottom).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-19 Percent change in extinction components from 2002 baseline to 2018 projected at CENRAP sites using base18g/typ02g RRFs

-60% -50% -40% -30% -20% -10%

0% 10% 20% 30%

BIBE1 GUMO1 WIMO1 CACR1 UPBU1 HEGL1 MING1 BRET1 VOYA2 BOWA1

IMPROVE sitePercent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Percent change in extinction components from 2002 baseline to 2018 projected at non-CENRAP sites using base18g/typ02g RRFs

-60% -50% -40% -30% -20% -10%

0% 10% 20% 30%

MACA1 SIPS1 ISLE1 SACR1 WHIT1 WHPE1 GRSA1 ROMO1 WICA1 BADL1 THRO1 LOST1

IMPROVE sitePercent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 Worst 20 Percent Days for Class I areas in the CENRAP region (top) and Near the CENRAP region (bottom).September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-20 4.5 2018 Visibility Projections for Base G C1 Control Scenario

The 2018 visibility projections based on the CMAQ simulations for the 2018 Base G C1 Control Strategy simulations are presented in this section. The C1 Contro l Strategy results in reductions mainly in SO2 and NOx emissions from point so urces in the CENRAP states. Consequently, PM improvements are limited to mainly SO4 and NO3 concentration reductions in the CENRAP states. Figure 4-7 displays the differences in CMAQ-estimated annual average SO4 and NO3
concentrations between the 2018 Base G base case and the 2018 Base G C1 Control Strategy case; the differences in all othe r PM species (with the exception of NH4) were negligible (see: http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml#base18gc1vsbase18g ). Annual average SO4 concentration reductions of over a quarter of a μ g/m 3 are estimated to occur in northeast Texas, east Oklahoma, Missouri, northeast Arkansas and up into Iowa and Illinois. There are much
lower reductions in NO3 that cover a similar area.

Figure 4-7. CMAQ-Estimated Reductions in Annual Average SO4 (left) and NO3 (right) Fine Particle Concentrations Between the 2018 Base G Base Case and 2018 Base G C1 Control Strategy Case.

Figure 4-8 displays the DotPlot comparisons of the 2018 visibility projections for 2018 Base G and 2018 Base G C1 Control Strategy emission scen arios. The additional controls in the C1 Control Strategy are projected to result in visibility improvements for the worst 20 percent days at Class I areas throughout and near the CENRAP region. Sites ar e closer to being on the glide path by 10 to 30 percent. For Breton Island th is makes a difference of not meeting the 2018 URP point in 2018 Base G (94%) to surpassing the URP point in the C1 Control Strategy (106%).

Table 4-4 presents a tabular summary of the in formation presented in Figure 4-8, including the Baseline, 2018 URP point, and 2018 projected visibi lity for the Base G and C1 Control Strategy
simulations.September 2007

F:\CENRAP_Modeling\TSD\draft# 3\Chapter_4_VisProj3.doc 4-21 CMAQ BaseGc1 vs BaseG Method 1 predictions for CENRAP+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved BaseGc1 BaseG

CENRAP non-CENRAP Figure 4-8. 2018 Visibility Projections as a Percentage of Meeting the 2018 URP Point (i.e., DotPlot) for the 2018 Base G and 2018 Base G C1 Control Strategy Emission Scenarios.September 2007F:\CENRAP_Modeling\TSD\draft#3\Chapter_4_VisProj3.doc4-22Table 4-4.2000-2004 Baseline, 2018 URP Point, and Projected 2018 Visibility and Percent of Meeting the 2018 URP Point for the2018 Base G and 2018 C1 Control Strategy CMAQ Simulations.Class I Area NameStateIDLat.Lon.00/04BaselineCondit.2018URPPoint2018 Base GBase Case2018 Base GC1 ControlStrategy(deg) (deg)(dv) (dv)(dv) (%)(dv)(%)Badlands NP SD BADL1 43.81 -102.3617.14 15.02 16.53 29% 16.31 39%Big Bend NP TX BIBE1 29.33 -103.3117.30 14.93 16.69 26% 16.43 37%Boundary Waters Canoe Area MN BOWA1 48.06 -91.4319.58 17.72 18.30 69% 17.84 93%Breton LA BRET1 29.87 -88.8225.73 22.51 22.72 94% 22.34 106%Caney Creek Wilderness AR CACR1 34.41 -94.0826.36 22.91 22.48 112%21.48 142%Great Sand Dunes NM CO GRSA1 37.77 -105.5712.78 11.35 12.53 18% 12.49 20%Guadalupe Mountains NP TX GUMO1 31.91 -104.8517.19 14.74 16.35 34% 16.09 45%Hercules-Glades Wilderness MO HEGL1 36.68 -92.926.75 23.14 23.06 102% 22.09 129%Isle Royale NP MI ISLE1 48.01 -88.8320.74 18.78 19.36 71% 19.05 87%Lostwood ND LOST1 48.59 -102.4619.57 16.87 19.27 11% 19.26 12%Mammoth Cave NP KY MACA1 37.20 -86.1531.37 26.64 25.60 122%25.23 130%Mingo MO MING1 37.00 -90.1928.02 24.37 23.71 118%23.21 132%Rocky Mountain NP CO ROMO1 40.35 -105.713.83 12.29 13.17 43% 13.14 45%Salt Creek NM SACR1 33.6 -104.4118.03 15.41 17.25 30% 17.10 36%Sipsey Wilderness AL SIPS1 34.32 -87.4429.03 24.82 23.57 130%23.42 133%Theodore Roosevelt NPND THRO1 46.96 -103.4617.74 15.42 17.40 15% 17.34 17%Upper Buffalo Wilderness AR UPBU1 36.17 -92.4126.27 22.84 22.52 109%21.61 136%Voyageurs NP MN VOYA2 48.47 -92.819.27 17.58 18.37 53% 18.10 69%White Mountain Wilderness NM WHIT1 33.48 -105.8513.70 12.11 13.14 35% 12.89 51%Wheeler Peak Wilderness NM WHPE1 36.57 -105.410.41 9.49 10.34 8% 10.30 13%Wind Cave NP SD WICA1 43.58 -103.4715.84 13.94 15.39 24% 15.26 30%Wichita Mountains OK WIMO1 34.75 -98.6523.81 20.01 21.47 61% 20.72 81%September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-1 5.0 ADDITIONAL SUPPORTING ANALYSIS

This Chapter presents additional supporting analys is to the modeled 2018 visibility projections provided in Chapter 4. This supporting analysis may be used by the states in their RHR SIPs, along with their factor analysis, to assist in setting th eir 2018 RPGs for the worst 20 percent days and best 20 percent days.

5.1 Comparison of CENRAP 2018 Visibility Projections with Other Groups

2018 visibility projections for CENR AP and nearby Class I area have also been performed by the other RPOs. Thus, it is useful to compare the CENRAP 2018 visibility projections with those from the other RPOs as a quality assurance (QA) check and to foster confidence in the CENRAP modeling results.

5.1.1 Comparison of CENRAP, VISTAS, MRPO and WRAP Visibility Projections

The CENRAP 2018 Base G visibility projections were compared to the following other RPO visibility projections:

• VISTAS 2018 visibility pr ojections based on their CMAQ 12 km 2002 annual modeling
results for the 2002 Base G and 2018 Base G2a emissions scenarios.

• MRPO 2018 visibility project ions based on their CAMx 36 km 2002 annual modeling for the Run 4 Scenario 1a (R4S1a) emissions scenario.

• WRAP 2018 visibility results based on their Plan02b and Base18b CMAQ 36 km modeling of the 2002 calendar year.

Figure 5-1 displays a DotPlot comp arison of the four RPO visibil ity projections expressed as a percentage of achieving the 2018 UR P point at CENRAP and nearby Class I areas. For the four CENRAP Class I areas just west of the Missis sippi River in Arkansas and Missouri (CACR, UPBU, HEGL and MING), 2018 visibility projectio ns are available from the CENRAP, VISTAS and MRPO RPOs. At HEGL, the three RPOs 2018 visibility projec tions are in close agreement with each other (estimated to achieve 99%, 101% and 95% of the 2018 URP point). The
CENRAP and VISTAS 2018 visibili ty projections are also very close at the other three
Arkansas-Missouri CENRAP Class I areas: CA CR (112% and 116%), UPBU (109% and 112%) and MING (118% and 114%). But the MRPO 2018 visibility projections are approximately 12 to 25 percentage points lower than the CENRAP and VISTAS projections at these three Class I areas, with values of 97% to 100 %. The reasons why the MRPO 2018 visibility projections are less optimistic than CENRAP and VISTAS ar e unclear. However, the MRPO focused on visibility projections at their northern Class I areas and likely did not use the latest CENRAP emission estimates. In addition, the CENRAP 2018 visibility projections included BART
controls on several sources in CENRAP states not included in the MRPO projections. Such BART controls are even more important in those states not covered by CAIR.

For the Breton Island (BRET) Class I area, 2018 visibility projections are available from
CENRAP and VISTAS. CENRAP estimates that BRET will achieve 94% of the URP point andSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-2 VISTAS is slightly less optimistic with an 84% va lue. One potential contri butor to this is that
emissions from off-shore marine vessel emissions in the oil and ga s production areas of the Gulf
of Mexico are double counted in the VISTAS Base G modeling. As these emissions were assumed to remain unchanged between 2002 an d 2018, the double counting of their emissions
will result in stiffer RRFs than th ere should be and consequently less visibility benefits in 2018. This double counting also occurred in the CENRAP Base F modeling but wa s corrected in Base G. The double counting occurred because off-shore marine vessels were present in both the MMS off-shore oil/gas developmen t inventory for the Gulf of Mexico and the VISTAS off-shore marine vessel inventory for the Pacific and Atla nta Oceans and the Gulf of Mexico. VISTAS
intends to correct this double count ing in their next round of modeling.

At the two northern Minnesota Class I areas (BOWA and VOYA), the MRPO 2018 visibility projections (93% and 92%) exhi bit more visibility improveme nts than CENRAP’s (69% and 53%). This is believed to be due to higher contributions to visibi lity impairment from Canada in the CENRAP modeling. Figure 5-2 displays the CENRAP 2002 Base F to tal SO2 emissions and
their differences with the 2018 Base F SO2 emi ssions. The SO2 emissions in Alberta Canada appear to be much higher and more wide spr ead when compared to the other provinces in
Canada and emissions in the U.S. states. Also , there is a very large SO2 source in northern Manitoba (> 10 5 tons/year). The Alberta SO2 emissions may be overstated in the CENRAP modeling, which would overstate the Canadian c ontribution to visibility impairment. The western boundary of the MRPO modeling domain was east of the Rocky Mountains so did not
include Alberta. CENRAP confirmed that the Alberta emissions and the source in Manitoba were present in the emissions provided by Ca nada. Air parcels from Canada are generally associated with clean visibility conditions at th e northern Minnesota Class I areas with the worst 20 percent days generally occu rring under conditions with a southerly wind component. However, in 2002 some of the worst 20 percent days did occur with transport out of Canada. For example, Figure 5-3 displays back trajectories of f of the VIEWS website for two of the worst 20 percent days at Voyageurs National Park (Jul ian Days 347 and 332). Th ese back trajectories suggest that the potentially ov erstated emissions in Alberta would have an impact at VOYA during the worst 20 pe rcent days in 2002.

At the VISTAS Mammoth Cave (MACA), Kent ucky Class I area, VISTAS, CENRAP and the MRPO estimated that 2018 visibility for the wo rst 20 percent days will achieve, respectively, 122%, 123% and 102% of the 2018 URP point. The close agreement between the VISTAS (122%) and CENRAP (123%) 2018 visibility pr ojections for MACA is encouraging. Why MRPO is 20 percentage points lower is unclear, but may be due to using earlier versions of the
VISTAS and CENRAP emissions. The 2018 visibili ty projections at Sipsey (SIPS), Alabama estimated by VISTAS (127%) and CENR AP (130%) are also extremely close.

Both the CENRAP and WRAP 2018 vi sibility projections agree that the WRAP Class I areas fail to achieve the 2018 URP point by a wide margin, with values achie ving only ~40% or less of the 2018 URP point. The CENRAP 2018 visibility projec tions agrees well with the WRAP values at Great Sands (GRSA), Colorado (18% vs. 15%), Badlands (BADL), South Dakota (24% vs. 31%), Theodore Roosevelt, North Dakota (15% vs. 11%) and Lostwood (LOST), Montana (11% vs. 14%). There is also reasonable agreemen t between CENRAP and WRAP 2018 visibility projections at Salt Creek (SACR), New Mexi co (30% vs. 12%), Rocky Mountain (ROMO), Colorado (43% vs. 30%), and Wind Cave (WICA) , South Dakota (24% vs. 6%). There are two WRAP Class I areas, White Mountains (WHIT) and Wheeler Peak (WEPE), where the WRAPSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-3 2018 visibility projections estimate that visibility will degrade for the worst 20 percent days (i.e., negative percent of achieving the 2018 URP point), whereas CE NRAP estimates visibility improvements. The reasons for these differences are unclear.

CMAQ Method 1 predictions with new IMPROVE algorithm at CENRAP+ sites Across RPOs

0% 20% 40% 60% 80% 100% 120% 140%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved CENRAP 36k Base18g/Typ02g

VISTAS 12k 2018g2b/2002gt2a

WRAP 36k Base18b/Plan02b corrected

MwRPO 2018 R4S1a

CENRAP non-CENRAP Figure 5-1. DotPlot comparing the CENRAP, VI STAS, MRPO and WRAP 2018 visibility projections expressed as a percentage of achieving the 2018 URP goal.

Figure 5-2. 2002 Base F SO2 emissions (left) as LOG10(tons/year) and differences in 2018 and 2002 Base F SO2 emissions (tons/year).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-4 Figure 5-3. Exampled back trajectories to Voyageurs National Park for two of the worst 20 percent days from 2002: December 13, 2002 (Julian Day 347) and November 28, 2002 (Julian Day 332).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-5 5.2 Extinction and PM Species Specific Visibility Projectio ns and Comparisons to 2018
URP Point

It is useful to examine 2018 visibility projec tions by PM species to determine how each PM component of visibility is changi ng as both a diagnostic analysis of the visibility projections as well as whether species that are associated more with anthropogenic emissions (e.g., SO4 and NO3) are being reduced substa ntially compared to those that are less influenced by anthropogenic emissions (e.g., Soil and CM). However, because deci view is the natural logarithm of total extinction, such comparisons can not be made using the deciview scale and must be made using extinction. The linear glidepath from which the 2018 URP points are derived are based on deciview, thus to examine corresponding glidepath using extinction the curvature associated with the logarithmic tran sformation of the linear deciview glidepath to extinction must be accounted for in the extinction glidepath.

5.2.1 Total Extinction Glidepaths

Figure 5-4 displays a total extinction based glid epath for Caney Creek that is based on the EPA
default deciview linear glidepath counterpart show n in Figure 4-3a. That is, the deciview linear glidepath defined by the line connecting the 26.36 dv Baseline Conditions at 2004 to the 11.58 dv Natural Conditions in 2064. The glidepath points in 2008, 2018, 2028, etc. from the linear deciview glidepath (Figure 4-3a ) are turned into extinction (Bext) [Bext = 10 exp(dv/10)] to create the curved extinction glid epath that exactly match the linear deciview glidepath points.
Note that the 2000-2004 Baseline usi ng the curved extinction glidepat h is slightly different than if you just converted the deciview baseline to extinction because the logarithm relationship is performed before the averaging, but they are extremely close. Using the extinction curved glidepath, the 2018 URP point is a reduction of the Baseline 145.10 Mm -1 to 98.88 Mm -1 (a
46.22 Mm -1 reduction). The modeled 2018 visibility projection in ex tinction is 97.54 Mm -1 , a 47.56 Mm -1 reduction, which achieves 103% of the re duction needed to achieve the 2018 URP point. Note that this comp ares with achieving 112% of th e 2018 URP reduction point when using the deciview linear glidepath. The pe rcent of achieving the 20 18 URP point using the linear deciview and curved extinction glidepaths will rarely be the same due to the logarithmic relationship between the two visibi lity metrics and the fact that averaging within and across years
in the deciview calculations occur after the lo garithms have been applied. The greater the difference in extinction across the worst 20 percent days in a year and av eraged across the years in the 2000-2004 Baseline and the greater nu mber of years available from the 2000-2004 Baseline may result in greater differences in the 2018 URP points using th e linear deciview and the curved extinction glidepaths.

Appendix F contains total extinc tion curved glidepaths for all the CENRAP Class I areas and Figure 5-5 contains a DotPlot th at compares the percent of ach ieving the 2018 URP point at each CENRAP Class I area using the 2018 Base G modeling results and the linear deciview and curved extinction glidep aths. At most CENRAP Class I ar eas the ability of the 2018 modeling results to achieve the 2018 URP point is the sa me using either the deciview or extinction glidepaths. There are some differences at GUMO, BOWA and VOYA Cl ass I areas which are due to these Class I areas having more comple te data during the 2000-2004 Baseline period and therefore more years in the Baseline than other Class I areas as well as having variations in extinction across the worst 20 per cent days and years (Appendix F). In any event, the closeness of the ability of the model to achieve the 2018 UR P point using either the extinction or deciviewSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-6 glidepath verifies the validity of the extinction based glidepaths and allows for the construction of PM species specific glidepat hs in extinction to gain insight into how each component of extinction is being reduced to achieve a uniform rate of pr ogress toward natu ral conditions in
2064.

Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

145.10

126.50

98.88

77.29

60.41

47.22

36.91 32.16 97.54

0 20 40 60 80 100 120 140 160 180

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearBext (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Figure 5-4. 2018 Visibility Projections and 2018 URP Glidepaths in extinction (Mm -1 ) for Caney Creek (CACR), Arkansas and Worst 20% (W20%) days using 2002/2018 Base G CMAQ 36 km modeling results.

CMAQ BaseG Method 1 pred ictions for CENRAP+ sites

-100% -80% -60% -40% -20%

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Deciview

Extinction

CENRAP non-CENRAP Figure 5-5. CMAQ 2018 Base G visibility projections and comparison of ability to achieve the 2018 URP point using the EPA default deciview and alternative total extinction Glidepaths.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-7 5.2.2 PM Species specific Glidepaths

The VIEWS website ( http://vista.cira.colostate.edu/views/ ) has posted PM species specific
Natural Conditions based on the new IMPROVE e quation. Using these PM species specific Natural Conditions and the curved extinction g lidepaths we can evaluate how well visibility extinction achieves the 2018 URP po int on a species-by-species basis. The PM species specific glidepaths are constructing starting with a Baseli ne at 2004 averaging the extinction for each PM species measured using the 2000-2004 IMPROVE observations and ending with the Natural
Conditions in 2064 from the VIEWS website. Points in the glidepath for the years in between
2004 and 2064 are constructed based on the relative differences in the 2004 Baseline and 2064 Natural Conditions PM species extinction such that the total extinction due to all PM species at each interim year adds up to th e same as the total extinction on the extinction-based glidepath (e.g., Figure 5-3). For example, for the CACR SO4 extinction glidepat h the 2018 URP point is generated from the 2004 and 2064 SO4 extinction (BSO4) and the 2004, 2018 and 2064 total extinction (BTOT) as follows:

BSO4_2018 = BSO4_2004 – [(BSO4_2004 – BSO4_2064)/ (BTOT_2004- BTOT_2064)] x (BTOT_2004 – BTOT_2018) = 87.05 –[(87.05 – 3.20)/(145.10 – 32.16)] x (145.10 – 98.88)
= 52.73 Mm -1

Note that the SO4 2018 URP point in Figure 5-5 and F-1b (52.77 Mm-1) does not exactly match the
52.73 Mm -1 calculated due to round off error in the above calculation that only used numbers with precision to the nearest hundredth.

As there are larger differences between the Base line and Natural PM species extinction for some species, then the rate of improvement to achieve a species specific 2018 URP point will vary across PM species. For example, current Baseline extinction values for Soil and CM tend to be
closer to Natural Conditions than extinction du e to SO4 and NO3. Consequently the rate of progress to achieve the 2018 URP point for So il and CM will be less than for SO4 and NO3.

Appendix F contains the PM spec ies specific glidepaths compares them to the modeled 2018 projections for all CENRAP Class I areas. The species specific results for the CACR Class I area in Figure F-1 are reproduced in Figure 5-6. The modeled rate of SO4 and NO3 extinction reduction is greater than the PM species specific glidepaths and both achieve the species specific 2018 URP point by achieving 111% and 104% of th e reduction needed to achieve the 2018 URP point. The modeled rate of exti nction improvement at CACR for EC and OC is less than the species specific glidepath achieving only 65% and 75% of the reduction needed to achieve the species specific 2018 URP point. The PM species sp ecific glidepath for So il is flat because the
Baseline and Natura l Conditions (1.12 Mm -1 ) are the same. This does not mean that anthropogenic emissions of Soil do not contribute on worst 20 per cent days at CACR. It just points to a mismatch between th e current set of worst 20 per cent days and those in 2064 under Natural Conditions. The worst 20 percent days in 2064 under Natural Conditions will be dominated by wind blown dust days when Soil and CM may be higher than during the current set of worst 20 percent days that are dominated by SO4, NO3 and OMC. Thus, the Soil and CM glidepaths tend to be flatter and in some cases may even have an upward trend for some Class I areas (see Appendix F). Soil is projected to in crease at CACR in 2018 so does not achieve its species specific URP point. Little reduction in CM is also seen by 2018. As discussedSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-8 previously, this is due in part to incompatibil ities between the measured Soil and CM values at the IMPROVE monitor and the modeled Soil and CM species. In the model, a large component of the Soil and CM in the inventory is due to paved and unpaved road dust. These emissions are directly related to Vehicles Miles Traveled (VMT). VMT is projected to increase in future-years resulting in increases in road dust emissions. At the IMPROVE monitor, much of the measured Soil and CM is likely due to local dust events th at are not simulated by the model using a 36 km grid resolution. Thus, the 2018 projections for Soil and CM are likely applying modeled changes
due to road dust to local Soil and CM concentrati ons that in reality are likely natural and should remain unchanged in the future year. This is why altern ative 2018 modeled projection approaches have been developed that assume th at CM and CM and Soil are natural so remain unchanged in the future-year (see Section 5.5).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-9 Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

87.05

73.26

52.77

36.75

24.23

14.45

6.80 3.20 49.17

0 20 40 60 80 100 120

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbSO4 (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

13.78

11.69

8.58

6.14

4.24

2.76

1.60 1.05 8.36

0 2 4 6 8 10 12 14 16 18

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbNO3 (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

4.80

4.06

2.97

2.11

1.44

0.92

0.51 0.32 3.42

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbEC (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

23.44

21.56

18.78

16.60

14.90 13.57 12.53 12.04 20.42

0 5 10 15 20 25 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbOC (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.35

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbSOIL (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Uniform Rate of Reasonable Progress Glide Path Caney Creek Wilderness - 20% Data Days

3.73 3.63 3.49 3.38 3.30 3.23 3.17 3.15 3.64

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearbCM (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Figure 5-6. 2018 Visibility Projections and 2018 URP Glidepaths for SO4 (top left), NO3 (top right), EC (middle left), OMC (middle right), Soil (bottom left) and CM (bottom right) in extinction (Mm -1 ) for Caney Creek (CACR), Arkansas and Worst 20 Percent Days using 2002/2018 Base G CMAQ 36 km modeling results.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-10 Figure 5-7 displays a DotPlot th at compares the 2018 projected to tal and PM species specific extinction with the 2018 URP points. These results show that SO4 is most frequently achieving its 2018 URP point at those Clas s I areas that achieve the deci view URP point. Reductions in
NO3 and EC also sometimes achieve their species specific URP point.

There are some anomalies in the species specific projections and glidepat hs that bear mention and point to areas where better estimates of emi ssions growth and Natural Conditions are needed
needed. The increase in 2018 Soil projections is not an isolated incident at CACR and occurs at
other CENRAP Class I areas. There are three CENRAP Class I areas that “achieve” the Soil specific 2018 URP point (HEGL, BOWA and VOYA). An examination of these glidepaths and visibility projections (Figures F-4f, F-5f and F- 6f) reveals that the curr ent Baseline Conditions
Soil at these three Class I areas is actually less than the 2064 Natural Conditions so that the glidepath is an accent rather than reduction (Figur es F-4g, F-5g and F-6g). In these three cases to “achieve” the 2018 URP point the modeling results must increase the projected Soil extinction, which is why these three Class I ar eas “achieve” their 2018 URP point for Soil. Clearly, the 2018 URP point for Soil is not very meaningful under these conditions. The current
Baseline Conditions for OMC at BRET and BOWA is also less than the Natural Conditions resulting in anomalous glidepat hs (Figure F-3e and F-4e).

CMAQ BaseG Method 1 predictions for CENRAP+ sites

-200% -180% -160% -140% -120% -100%

-80% -60% -40% -20%

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Bext

bSO4

bNO3

bOC

bEC

bSOIL

bCM

CENRAP non-CENRAP Figure 5-7. Ability of total and species specific 2018 visibility projections to achieve 2018 URP points.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-11 5.3 Alternative 2018 Visibility Projection Software

The CENRAP 2018 visibility projections were ma de using software developed by the CENRAP modeling team. PM concentrations in the 36 km grid cells containing each of the Class I area IMPROVE monitoring sites were ex tracted using the UCR Analysis Tool. These modeling data were then ported into Excel spreadsheets that also include the filled RHR IMPROVE database available from the VIEWS website along with the EPA default Natural Conditions (EPA, 2003b). Excel macros are then used to perform the visibility projections using the EPA default procedures described in Chapte r 4 and alternative procedures described in this Chapter.

EPA is developing a Modeled Atta inment Test Software (MATS) program that codifies the 8- hour ozone, PM 2.5 and visibility projection procedures given in EPA’s latest air quality modeling guidance (EPA, 2007a). The June 2007 release of the beta version of MATS is capable of performing 8-hour ozone and visib ility projections; MATS is sti ll under development for making
PM 2.5 projections. The June 2007 be ta versions of MATS was a pplied to the CENRAP 2002 and 2018 Base G 36 km CMAQ results and the resultant 2018 visibility project ions were compared with the CENRAP values using the EPA defau lt projection approach (see Chapter 4) at CENRAP and nearby Class I areas. The projected 2018 visibility estimates using the CENRAP
and EPA MATS software are shown in Table 5- 1. The biggest differences in the two 2018 visibility projections are for the Boundary Wa ters (BOWA). Breton Is land (BRET), and Mingo (MING) Class I areas where MATS produces no 2018 visibility projections. This is because there is insufficient capture of valid IMPR OVE PM measurements within the 2000-2004 fiveyear baseline to generate three ye ars of annual visibility estimates that is the minimum needed to develop the Baseline Conditions following EPA’ s guidance (EPA, 2003a). For the CENRAP projections, data filling was used to fill out the IMPROVE measurements with sufficient data so
that Baseline Conditions could be calculated at th ese three Class I areas. At 14 of the remaining 17 Class I areas, the CENRAP and MATS 2018 visi bility projections agree exactly to within a
hundredth of a deciview. At the three sites that are different (BIBE, GUMO and ISLE) the difference is 0.01 dv, which is 0.06 percent or less. These differences are likely due to round off errors in the calculations and are not significant. These results verify the consistency with the CENRAP spreadsheet based and EPA MATS soft ware for projecting future-year visibility
estimates.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-12 Table 5-1. Comparison of CENRAP and EPA MATS 2018 visibility projections at CENRAP and nearby Class I areas.

2018 Visibility

Projections 2000-2004 Baseline Conditions

Site MATS (dv) CENRAP (dv) MATS (dv) CENRAP (dv)

BADL 16.53 16.53 17.14 17.14

BIBE 16.70 16.69 17.30 17.30
BOWA NA 18.30 NA 19.58

BRET NA 22.72 NA 25.73
CACR 22.48 22.48 26.36 26.36
GRSA 12.53 12.53 12.78 12.78
GUMO 16.36 16.35 17.19 17.19
HEGL 23.06 23.06 26.75 26.75
ISLE 19.35 19.36 20.74 20.74
LOST 19.27 19.27 19.57 19.57
MACA 25.60 25.60 31.37 31.37
MING NA 23.71 NA 28.02
ROMO 13.17 13.17 13.83 13.83
SACR 17.25 17.25 18.03 18.03
SIPS 23.57 23.57 29.03 29.03
THRO 17.40 17.40 17.74 17.74

UPBU 22.52 22.52 26.27 26.27
VOYA 18.37 18.37 19.27 19.27

WHIT 13.14 13.14 13.70 13.70

WHPE 10.34 10.34 10.41 10.41
WICA 15.39 15.39 15.84 15.84
WIMO 21.47 21.47 23.81 23.81
NA = Not Available

5.4 PM Source Apportionment Modeling

The PM Source Apportionment Technology ( PSAT) was used to obtain PM source apportionment by geographic regions and majo r source category for the CENRAP 2002 and
2018 Base E base case conditions. PSAT uses reactive tracers that operated in parallel to the CAMx host model using the same emissions, tran sport, chemical transformation and deposition
rates as the host model to account for the contri butions of user specified source regions and categories to PM concentrations throughout the modeling domain. Details on the formulation of the CAMx PSAT source apportionment can be f ound in the CAMx user’s guidance (ENVIRON, 2006; www.camx.com).

5.4.1 Definition of CENRAP 2002 and 2018 PM Source Apportionment Modeling

PSAT calculated PM source apportionment for user defined source groups. Source groups are usually defined by specifying a source region map of geographic regions where source contributions are desired and providing source categories as input so that source group wouldSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-13 consist of a geographic region plus source category (e.g., on-road mobile source emissions from Oklahoma). Although other source group configurations and even individual sources may be specified. For the CENRAP PSAT application, a source region map was used that divided up the modeling domain into 30 geographic source region s as shown in Figure 5-8. The 2002 and 2018
emissions inventories were divide d into six source categories. The 30 geographic source regions consisted of CENRAP and nearby states, with Texas divided into 3 regions, remainder of the western and eastern States, Gulf of Mexico, Cana da and Mexico. The original intent of the CENRAP PSAT analysis was to obtain separate contributions due to on-road mobile, non-road mobile, area, natural, EGU point and non-EGU poi nt sources. However, the CAMx emissions for the PSAT runs were based on the CMAQ pre- merged 3-D emission files. Since all point
sources were contained in a si ngle CMAQ pre-merged emissions fi le, then the separate source apportionment modeling of EGU an d non-EGU point sources was not possible. The six source categories that were separately tracked in th e PSAT PM source apportionment modeling were:

• Elevated point sources;

• Low-level point sources (i.e., point source emissions emitted into layer 1 of the model);

• On-Road Mobile Sources;

• Non-Road Mobile Sources;

• Area Sources; and

• Natural Sources.

Natural Sources included biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model, emissions from wildfires and emissions from wind blown dust due to non-agriculture land use types.

PM source apportionment in PSAT is available for fi ve families of PM trac ers: (1) Sulfate; (2) Nitrate and Ammonium; (3) Secondary Organi c Aerosols (SOA); (4) Primary PM; and (5) mercury. The CENRAP PSAT 2002 and 2018 applica tions used three of the PSAT families of tracers and did not use the SOA and mercury fa milies. For SOA, the standard CAMx model output was used that partitions SOA into an anthropogenic (SOAA) and biogenic (SOAB) components.

The PSAT results were extracted at the CENRAP and nearby Class I areas and the contributions for the average of the worst 20 percent and best 20 percent days were processed. A PSAT Visualization Tool was developed that can be used by States, Tr ibes and others to generate displays of the contributions of source regions and ca tegories to visibility impairment for the average of the worst 20 percent and best 20 percent days at each CENRAP and nearby Class I

areas.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-14 Figure 5-8. 30 source regions used in the CENRAP 2002 and 2018 CAMx PSAT PM source apportionment modeling.

5.4.2 CENRAP PSAT Visualization Tool

The PSAT Visualization Tool allows CENRAP St ates, Tribes and others to visualize the CENRAP 2002 and 2018 PSAT modeling results a nd identify which source regions, categories and PM species are contributing to visibility impairment at Cla ss I areas for the average of the worst 20 percent and best 20 per cent visibility days. The Visu alization Tool is currently available on the CENRAP website ( http://www.cenrap.org ) under Projects. The Tool can generate bar charts of source contributions at Cla ss I areas. It can be run in a receptor oriented
mode where it identifies the contributions of PM species and source regi ons and categories to visibility impairment on the worst and best 20 pe rcent days. It can also be run in a source
oriented mode to examine an individual source region’s (State’s) cont ribution to visibility impairment at downwind Class I areas on the wors t and best 20% days. The original IMPROVE equation is used to convert the PM species concentrati ons to extinction.

There are 14 air quality analysis metrics in the Tool:

W20% Modeled Bext : The source region, source category and PM species contributions to the extinction (Bext) at a Class I area es timated by the model averaged across the worst 20 percent days in 2002. -273 6 -241 2 -208 8 -176 4 -144 0 -111 6 -792 -46 8 -144 180 50 4 82 8 115 2 147 6 180 0 212 4 244 8 -2088 -1872 -1656 -1440 -1224 -1008

-792 -576 -360 -144

72 288 504 720 936 1152 1368 1584 1800September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-15 W20% Projected Bext : The source region, source categor y and PM species contributions to the extinction (Bext) at a Class I area projected by the model averaged across the worst 20 percent days in the 2000-2004 Baseline.

W20% Modeled USAnthro : The source region, source category and PM species
contributions to the extincti on (Bext) at a Class I area for just U.S. anthropogenic emission source categories estimated by the mo del averaged across the worst 20 percent days in 2002.

W20% Projected USAnthro : The source region, source category and PM species
contributions to the extincti on (Bext) at a Class I area for just U.S. anthropogenic emission source categories projected by the model averaged across the worst 20 percent days in the 2000-2004 Baseline.

Emissions : Emissions by source region, source cat egory and PM precursor. Precursors include SOx, NOx, primary organic aerosol (POA), primary elemental carbon (PEC) other primary fine particulate (FCRS+F PRM) and coarse mass (CCRS+CPRM). Emissions for four days have been ex tracted and implemented in the Tool.

Control Effectiveness : Control effectiveness is define d as the PM contribution divided by the emissions of the primary precursor. For example the SO4 contribution divided by
the SO2 emissions.

Visualization Tool results are available for visi bility contributions on both an absolute (Mm -1 ) and percentage basis. When look ing at contributions at a given Cl ass I area, contributions can be examined in terms of PM species, source regions and/or source categories. Results are available for both the current year (2002 modeled or 20 00-2004 projected) and future year (2018). The “2002 W20% Project Bext” metric applies the 2002 PSAT modeled source apportionment to the observed 2000-2004 Baseline extinction keeping the relative contributions of source groups to each PM species (e.g., SO4, NO3, etc.) the same av eraged across the 2002 worst 20 percent days but scaling their magnitudes up or down base d on the ratio of the 2000-2004 Baseline to the 2002 modeling results. Similarly, the “2018 W 20% Projected” metric uses the relative contributions of the 2018 PSAT results from each source group and scales them according to the differences in the 2018 projected PM species to the 2018 modeled PM species for the average of the worst 20 percent days. The US Anthropogeni c metrics just include source groups associated with U.S. man-made emissions (i.e., non-Natura l source categories from states and Gulf of Mexico source regions) so excludes contri butions from Canada and Mexico, Boundary Conditions, SOA from biogenic sources and th e natural source category (biogenic NOx, wildfires and wind blown dust).

5.4.3 Source Contributions to Visibility Impairment at Class I Areas

Appendix E displays example contributions of PM species, source regions and source categories to visibility impairment for the worst and best 20 percent days at the CENRAP Class I areas. Some of the results from Figure E-1 for the CACR Class I area are reproduced in Figures 5-9, 5-
10 and 5-11 below.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-16 5.4.3.1 Caney Creek (CACR) Arkansas

2002 visibility impairment for the worst 20 percent days at CACR is primarily due to SO4 from elevated point sources that contributes over half (66.3 Mm -1 ) of the total extinction of 118.8
Mm -1 (Figure E-1a and 5-8 left). By 2018, the tota l extinction at CACR for the worst 20 percent days is reduced by approximately one third (38.5 Mm -1 ) which is primarily due to reductions in
SO4 extinction from elevated point sources (f rom 66.3 to 37.3 Mm -1 ) as well as reductions in visibility impairment from on-road and non-road mobile sources. Even with such large reductions in SO4 from point sources in 2018, extinc tion due to elevated point sources is still the highest contributor to visibility impairment on the worst 20 per cent days contributing over half (41.8 Mm -1 ) of the total extinction in 2018 of 80.3 Mm -1 , with area sources the next most important source category contributing 16.0 Mm -1 (~20%).

The geographic source apportionment for the worst 20 percent says at CACR is shown in Figures 5-10, E-1c and E-1d. Elevated po int sources from the eastern source region is the largest
contributor in 2002 cont ributing almost 18 Mm -1 that is reduced by over a factor of three in 2018 to approximately 5 Mm -1 . By 2018, Arkansas is the larges t contributor to extinction at CACR for the 20 percent worst days followed by East Te xas, the large Eastern U.S. region and then SOA due to biogenic sources. Figures E-1e ranks the source group contributions to extinction on the worst 20 percent days at CACR with Elevat ed Point Sources from East Texas being the highest contributor to total extinction, similar results are seen when examining extinction at CACR for the worst 20 percen t days due to just SO4 a nd NO3 (Figure E-1f).

For the best 20 percent days at CACR (Figures 5- 11, E-1g-j), SO4 is still a major contributor but no where near as dominate as seen for the worst 20 percent days, but elevated point is still the largest contributing source category Local contri butions from within Arka nsas contribute the most to the average of extinction acro ss the best 20 percent days at CACR.

Figure 5-9. PSAT source category by PM species contributions to the average 2000-2004 Baseline and 2018 projected extinction (Mm -1 ) for the worst 20 percent visibility days at Caney Creek (CACR), Arkansas.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-17 Figure 5-10. PSAT source region by source category contributions to the average 2000-2004 Baseline and 2018 projected extinction (Mm -1 ) for the worst 20 percent visibility days at Caney Creek (CACR), Arkansas.

Figure 5-11. PSAT source category by PM species contributions to the average 2000-2004 Baseline and 2018 projected extinction (Mm -1 ) for the best 20 percent visibility days at Caney Creek (CACR), Arkansas.

5.4.3.2 Upper Buffalo (UPBU) Arkansas

The contributions to extinction on the worst 20 per cent days at UPBU (Figure E-2) is similar to CACR only with less contributions from East Texas and more from Missouri, Illinois and Indiana. By 2018, the t op five highest contribut ing source groups to the average extinction on the worst 20 percent days are as follows: Arka nsas Elevated Point; SOA from biogenics; Boundary Conditions, East Elevated Points, and Illinois Elevated Points (Figure E-2e). On the best 20 percent days at UPBU vi sibility impairment is primarily due to Arkansas and adjacent states Oklahoma, Missouri, and Kansas).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-18 5.4.3.3 Breton Island (BRET) Missouri

Visibility impairment for the worst 20 percent da ys at Breton Island is primarily (69%) due to elevated point sources that contribute 77.7 Mm -1 out of a total of 122.2 Mm -1 (Figure E-3a). Although the contribution of elevat ed point sources is reduced s ubstantially by 2018, they still
contribute over half of th e total extinction (101.1 Mm -1 ) on the worst 20 percent days at BRET (Figure E-3b). The top five contributing source groups to 2018 visibility impairment at BRET for the worst 20 percent days are: Louisiana El evated Point Sources; Boundary Conditions; East Elevated Point Sources; Gulf of Mexico Area S ources and Louisiana Area Sources. Gulf of Mexico Area sources includes off shore shippi ng and oil and gas development emissions; note
that for the PSAT simulation the off-shore ma rine shipping emissions were double counted
which was corrected in the Base G emission scen arios used in the 2018 visibility projections discussed in Chapter 4.

5.4.3.4 Boundary Waters (BOWA) Minnesota

As seen for the other Class I areas, elevated point sources contribute the largest amount (47%) to visibility impairment at BOWA for the worst 20 percent days in 2002 (Fi gure E-4a). However, unlike many of the other Class I areas, there is little reductions (~10%) in the elevated point source contributions going from 2002 (29.0 Mm -1 ) to 2018 (26.2 Mm -1 ) (Figures E-4a and E-4b). This is because there is a slight increase in the contributions of elevated point sources in Minnesota from 2002 to 2018 (Figures E-4c and E-4d) that is the highest contributing source group (Figure E-4e). Note that the 2018 emission scenario includ es growth and CAIR controls but no BART controls. For the best 20 percent days, the largest contributing source group by far is Boundary Conditions (i.e., global transport) followed by Minnesota and Canada (Figures E-4g-j).

5.4.3.5 Voyageurs (VOYA) Minnesota

Results for VOYA are similar to BOWA with Minnesota, Canada and Boundary Conditions contributing the most to visibility impairment on the worst and best 20 percent days (Figure E-5).

5.4.3.6 Hercules Glade (HEGL) Missouri

Elevated point sources contribute over half to the total extinction for the worst 20 percent days at HEGL in 2002 (Figures E-6a and E-6b). Goi ng from 2002 to 2018 the contributions due to elevated point sources, on-road mobile and non -road mobile are reduced substantially, but the
contributions due to the other sources remain unchanged. The largest source group contributing to visibility impairment on the worst 20 percents days is area sources from Missouri in both 2002 and 2018 (Figures E-6c and E-6d). Since area emissions are not reduced much between 2002 and 2018 and Missouri elevated point sources are mostly unchanged because the IPM model assumed Missouri CAIR sources would buy credit s, then the Missouri contributions is only reduced a little going from 2002 to 2018 (from ~18 Mm-1 to ~16 Mm-1). However, the contributions due to the Eastern U.S., Illinois an d Indiana are reduced subs tantially. Missouri is by far the largest contribution to visibility impairment at UPBU on the best 20 percent days asSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-19 well with area sources from Missouri being the largest source category (Figures E-6h through E6j).

5.4.3.7 Mingo (MING) Missouri

The substantial improvements in visibility impa irment at MING for the worst 20 percent days from 2002 (141 Mm -1 ) to 2018 (96 Mm -1 ) is primarily due to reductions in SO4 from nonMissouri elevated point sources (Figures E-7a th rough E-7d). Even so, with the exception of the top contributing Missouri area s ources the largest contributing s ource groups to 2018 visibility impairment for the worst 20 percent days are s till elevated point sources from several CAIR states (Illinois, Indiana, Missouri, East; Figure E-7e). Missouri is the largest contributor to visibility on the best 20 percent days followe d by Boundary Conditions and Illinois (Figure E-7i-j).

5.4.3.8 Wichita Mountains (WIMO) Oklahoma

Elevated point sources are the largest contributo rs to visibility impairment on the worst 20 percent days at WIMO in both 2002 and 2018 (Fi gures E-8a and E-8b). East Texas followed closely by Oklahoma are the largest contributing source regions in 2002, but by 2018 the reverse is true (Figures E-8c and E- 8d). By 2018 the largest contribu ting source group to visibility impairment on the worst 20 percent days at WIMO is global transport (i.e., boundary conditions) followed by Oklahoma Area Sources and East Texa s Elevated Point sources (Figure E-8e). Oklahoma Area Sources is the largest contributor to visibility impairment on the best 20 percent days at WIMO (Figures E-8g-j).

5.4.3.9 Big Bend (BIBE) Texas

Elevated point sources (~17 Mm -1 ) followed by Boundary Conditions (~12 Mm 1) are the largest contributions to total extinction (46 Mm -1 ) on the worst 20 percent days at BIBE in 2002 (Figure E-9a). In 2018 there is very little (~2 Mm -1 ) reduction in the contribu tions of elevated point sources and no reductions in global transport result ing in little reductions (~7%) in visibility impairment on the worst 20 percent days from 2002 (46 Mm -1 ) to 2018 (43 Mm -1 ). This is due to the extremely large contributions of emissions from Mexico in both 2002 (Figure E-9c) and 2018 (Figure E-9d). In fact , the four highest contri buting source groups to vi sibility impairment at BIBE for the worst 20 percent days are assume d to be unchanged from 2002 to 2018: Boundary Conditions, Mexico Elevated Points , West Texas Natural and Mexico Natural (Figure E-9e). For the best 20 percent days at BIBE, West Texas, Mexico and Boundary Conditions are the highest three contributors to visibility impairment (Figures E-9g-j).

5.4.3.10 Guadalupe Mountains (GUMO) Texas

The large contribution of CM to visibility impair ment at GUMO is clearly evident in the source apportionment modeling results (Figures E-10a-b). These sources are about evenly divided in the modeling between natural sources and area s ources. Since these source categories are not reduced in the future year then there is little reduction in extinction from 2002 to 2018 (50 to 45September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-20 Mm -1 ) and what reductions there are come from Elevat ed Point Sources. S ources in West Texas, Mexico, Boundary Conditions and New Mexico are the largest contributing source regions for both the worst 20 percent days (Figure E-10c-e) a nd best 20 percent days (Figures E-10g-j).

5.5 Alternative Visibility Projection Procedures

In this section we analyze seve ral alternative visibility project ion procedures from the EPA’s default approach (EPA, 2007a) used in Chapter 4.

5.5.1 Treatment of Coarse Mass and Soil

As noted previously, much of the coarse mass (C M) and, to a lesser extent, Soil measured at the IMPROVE monitor is likely due to local wind bl own dust that is natural in origin and not captured by the model. Consequent ly, even using the modeling resu lts in a relative sense with the RRFs may not be appropriate fo r projecting CM and Soil. If CM and Soil are in fact local impacts due to wind blown dust from natural lands, then it would be appropriate to assume they are natural and remain unchanged from the 2000- 2004 Baseline to 2018. This is probably certainly appropriate for CM because CM is primarily due to fugitive dust and it has a very short transport distance that is subgrid-s cale to the model. In fact the model evaluation discussed in Chapter 3 and Appendix C clearly shows a large underp rediction bias for CM that is likely due to local fugitive dust impacts at the IM PROVE monitor. For Soil this is less clear as fine particles can be transported over longer distances and is produced by anth ropogenic sources, such as combustion and road dust, as well as natural so urces. We initially performed two CM and Soil sensitivity tests, the first assumed CM was all natural so remains unchanged from the 2000-2004 Baseline to 2018 (i.e., set the RRF for CM equal to 1.0). The second sensitivity test assumed
both CM and Soil were natural so set RRFs for both of them to 1.0. A comment from an FLM noted that we know some of the Soil is likely anthropogenic in origin. So it was suggested to
subtract the 2002 base case modeled Soil from th e observed values for the 2002 worst 20 percent days and assume that the remainder (if any) was natural so hold the rest of the Soil constant in
2018 and add to the 2018 modeled Soil values.

The results of the CM and Soil visibility projecti on sensitivity analysis are shown in the DotPlot in Figure 5-12. The CM and Soil visibility project ion sensitivity analysis has little effect on the 2018 visibility projections at the CENRAP Class I areas. Even GUMO, which has a large CM and Soil component, shows very litt le sensitivity. This is proba bly because the CM at GUMO is likely dominated by wind blown dust that was a ssumed constant from 2002 to 2018 so the RRF calculated using the default EPA method is near 1.0 anyway. Some larger sensitivity is seen at
several WRAP Class I areas. It is encouraging that CENRAP 2018 visibility projections are not sensitive to the CM and Soil components of the modeling which are highly uncertain.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-21

CMAQ BaseG Method 1 predictions for CENRAP+ sites

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140% 150% 160%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Regular RRF

CM RRF=1

CM&SOIL RRFs=1
RRF CM&rest(SOIL)=1

CENRAP non-CENRAP Figure 5-12. Sensitivity of 2018 visibility projections to various methods that assume all CM, all CM and Soil and all CM and part of the Soil is natural.

5.6 Alternative Model

The CAMx model was also run for a 2002 and 2018 ba se case scenarios with earlier versions of the CENRAP emissions (Base E modified to eliminate double counting of some area fire emissions) than the final CMAQ 2002 Base G modeling. The CAMx 2002 and 2018 output was processed the same way that th e CMAQ results were to generate 2018 visibility projections at the CENRAP and nearby Class I areas that we re compared with the 2018 URP point. Figure 5- 13 summarizes the CAMx 2018 visibility projec tions using the new IM PROVE algorithm (NIA) in a DotPlot and compares them with the CM AQ 2018 Base G results (fro m Figure 5-12). The CMAQ and CAMx 2018 visibility pr ojections are remarkably similar. The four Arkansas and Missouri Class I areas are projected to achieve the 2018 URP point by almost the exact same amount by the two models. The two Texas Class I areas are projected to come up short of achieving the 2018 URP point by the same amount by the two models. The largest differences are seen at BRET, and to a lesser extent BOWA and VOYA. At BRET the CAMx 2018 visibility projections are much less optimistic (< 80%) in achieving the 2018 URP point than CMAQ (> 90%). And CMAQ is slightly less op timistic than CAMx in achieving the 2018 URP point for the two northern Minnesota Class I areas. The reasons for these differences are unclear but could be partially due to the emissions upda tes in the final CMAQ Base G run that included eliminating the double counting of off-shore marine emissions in the Gulf of Mexico that was present in the CAMx simulation, which makes it more difficult to get visibility improvements at BRET since it is influenced by sources in the Gulf. Corrections to stack parameters for Canadian point sources were also made for the final Base G. The general close agreement of the CAMx 2018 visibility projections to the final CM AQ values is encouraging and good QA check.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-22 CMAQ BaseG vs CAMx BaseE Method 1 predictions for CENRAP+ sites On Worst 20% Days

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved CMAQ BaseG NIA

CAMx BaseE NIA

CENRAP non-CENRAP Figure 5-13. Comparison of CAMx 2018 visibility projections with 2018 URP points for CENRAP and nearby Class I areas.

5.7 Effects of International Transp ort on 2018 Visibility Projections

As seen in the PM source apportionment modeli ng discussed in Section 5.4, there is significant
contributions of international sources to visibil ity impairment at many CE NRAP Class I areas for the worst 20 percent days. W ith the exception of Canada, wher e we used a year 2000 inventory for the 2002 base case modeling and a 2020 inve ntory for the 2018 inventory, international
sources were assumed to be constant betwee n 2002 and 2018. Thus, Cl ass I areas that are heavily impacted by contributions of internatio nal transport will have a difficult time achieving the 2018 URP point since international sources ar e assumed to remain constant. The CAMx PSAT runs discussed previously provide a framework for quantitatively assessing the contributions of international transport to th e visibility projections and whether reasonable progress toward natural conditions is being achieved in the 2018 modeling.

There are several source regions (Figure 5-8) and source categor ies in the PSAT modeling that
include international sources:

• Mexico Anthropogenic Sources (a ssumed all international);

• Canada Anthropogenic Sources (a ssumed all international);

• Gulf of Mexico (assumed all U.S. sources);

• Pacific and Atlanta Ocean (assumed all U.S. sources); and

• Boundary Conditions (assumed half international and half natural sources).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-23 Although it can be argued that Mexico and Canada are not truly internationa l due to the presence of numerous U.S. corporations in Mexico along with free trad e among the two countries, states and federal government have no jurisdiction to regul ate industry in these two countries so they
are considered international in these calculations. The Gulf of Mexico includes off-shore oil and gas production facilities, support vess els and aircraft and off-shore marine shipping. Given that emissions from the oil and gas production can be regulated by the U.S., then the Gulf of Mexico
is not considered an internati onal source. Emissions from off- shore shipping in the Pacific and
Atlantic Oceans are also curren tly not regulated by th e U.S. government. However, there are current efforts to apply some regul ations to these emissions so fo r these calculations they were
not assumed to be international sources. Finally, the Boundary Conditions (BCs) for the CENRAP modeling were generated from a 2002 simulation of the GEOS-CHEM global chemistry model and held constant in 2018. These BCs would include contributions from international sources as well as natural sources, so need to be split. For the sensitivity
calculations discussed below we a ssumed that the BCs were half due to natural and half due to
international sources. This results in inte rnational sources bein g defined as follows:

International Contribution = Mexico Anthro + Canada Anthro + 1⁄2 BCs

Two methods were examined to see what the eff ects of international s ources on 2018 visibility projections and a Class I areas ab ility to achieve the 2018 URP point:

Elimination of International Contribu tions to 2018 Visibility Projections : In this method
the contribution of international emissions is taken out of the 2018 vi sibility projections and examined to see whether the new visibility projection achieves th e URP point. If so, then international sources are hindering a Class I area in achieving the 2018 URP point, which suggests that the 2018 URP point is not a reasonable value for an RPG.

Visibility Projections and Glidepaths Based on Cont rollable Visibility Impairment : The
second method would look at the visibility projections for ju st the U.S. controllable portion of the visibility impairment. The glidepath end point in 2064 would be to
eliminate the U.S. man-made contributions to visibility impairment on the worst 20 percent days.

Note that this analysis is performed solely for providing states and others additional information on which Class I areas the modeling suggest are unduly influenced by International Transport.

5.7.1 Elimination of International Contri butions to 2018 Visibility Projections

This method was also discussed in a recent te chnical brief prepared by the Electric Power Research Institute (EPRI), only in EPRI’s analysis they used results from a global chemistry model and VISTAS CMAQ runs with no global an thropogenic emissions (EPRI, 2007). Thus, before discussing our results of this analysis using PSAT, we discuss EPRI’s analysis.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-24 5.7.1.1 EPRI’s Analysis of Effects of International Contributions

EPRI funded Harvard University to perform annual simulations of the GEOS-Chem global chemistry model ( http://www-as.harvard.edu /chemistry/trop/geos/ ) for annual simulations with and without non-U.S. anthropogenic emissions to determine the contributions of international transport to PM and visibility. The EPRI Ha rvard GEOS-Chem simulations were performed for 2001. Figure 5-14 and 5-15 compare the annual aver age ammonium sulfate, ammonium nitrate organic mass carbon (OMC, also called OCM) and elemental ca rbon (EC) due to the GEOSChem global modeling and the CAMx PSAT source apportionment modeling. The similarity of
the results for ammonium sulfate is remarkable (Figure 5-14). Both methods estimate that the annual average ammonium sulfate contribution due to internati onal sources ranges from 0.4 to
1.0 μ g/m 3 across the Class I areas. There is le ss agreement between the two methods for ammonium nitrate due in part to a CAMx overestimation issue that is likely due in part to how ammonia emissions were classified as being an thropogenic or not in the no U.S. anthropogenic emissions simulations (Figure 5-15). Better agreement is seen between the two methods
international contributions of OMC and EC, although CAMx estimat es higher contributions than
GEOS-Chem.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-25 Figure 5-14. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and CENRAP PSAT (bottom) international source contributions to ammonium sulfate at Class I areas. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0ACADLYBRBRIGBOWAISLEBIBECACRMINGUPBUEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENAmmonium Sulfate (μg/m3)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0ACAD1LYBR1BRIG1BOWA1ISLE1BIBE1CACR1MING1UPBU1EVER1CHAS1SAMA1COHU1OKEF1ROMA1SIPS1SHRO1GRSM1LIGO1MACA1SWAN1JARI1DOSO1SHEN1Ammonium Sulfate (μg/m3)September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-26 Figure 5-15. Comparison of EPRI Harvard GEOS-Chem global chemistry (top) and CENRAP PSAT (bottom) international source contributions to ammonium nitrate, organic carbon mass (OCM or OMC) and elemental carbon (EC) at Class I areas. 0.0 0.1 0.2 0.3 0.4 0.5ACADLYBRBRIGBOWAISLEBIBECACRMINGUPBUEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENConcentration (μg/m3) Amm_NO3 OCM EC

0.0 0.1 0.2 0.3 0.4 0.5ACAD1LYBR1BRIG1BOWA1ISLE1BIBE1CACR1MING1UPBU1EVER1CHAS1SAMA1COHU1OKEF1ROMA1SIPS1SHRO1GRSM1LIGO1MACA1SWAN1JARI1DOSO1SHEN1Concentration (μg/m3) Ammonium Nitrate

OCM
ECSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-27 The EPRI technical brief used the VISTAS CM AQ runs to adjust the modeled 2018 visibility projections to eliminate the eff ect of international transport an d compared them to the 2018 URP point. For the Boundary Waters, Voyageurs, Isle Royal and Seney Class I areas the standard 2018 visibility projections did not achieve the 2 018 URP point. However, when the effect of transboundary pollutions was removed the 2018 UR P point was essentially achieved or more
than achieved at all four Class I areas.

5.7.1.2 CENRAP Results From Elimin ation International Transport

Because the elimination of the international sour ces from the 2018 visibility projections results in a portion of the total light extinction, then these comparisons with the 2018 URP points were done using extinction glidepaths and projections rather than deciview. In Section 5.2.1 we demonstrated that the level of achieving the 20 18 URP point was almost identical at CENRAP
Class I areas whether the linear deciview or curv ed extinction glidepaths were used. The PSAT source apportionment was used to determine the contribution to the proj ected extinction in 2018 due to international sources. As noted above, in ternational sources were assumed to be due to anthropogenic emissions in Mexico and Cana da and half of the Boundary Conditions.

Figure 5-16 shows the standard CAMx extinction glidepaths and 2018 visibility projections and the 2018 visibility projections wh en the contributions of interna tional sources is eliminated. CACR, which achieved the 2018 URP point by 104%, achieves it by even more when international sources are eliminated (117%). UPBU that barely achieved the 2018 URP point by 102% achieves it by 116% without international emissions.

BRET comes up short of achieving the 2018 URP point when international emission are included (76%) as well as when they are e liminated (92%), although it is mu ch closer (recal l contributions of Gulf of Mexico to visibility impairment at BRET that is assumed in this analysis to be of U.S. origin). Eliminating internati onal transport emissions makes of difference of meeting the 2018 URP point without them (120%) to not meeting it with them ( 64%) at BOWA. Similarly at VOYA the standard 2018 visibility projections do not achieve the 2018 URP point (54%), whereas it is achieved by a far margin when international sources are eliminated (132%).

HEGL comes up short achieving th e 2018 URP point when interna tional sources are included (95%), but achieves it when they are eliminated (107%). Reca ll the standard CAMx deciview visibility projections barely achieved the URP point even when international emissions are included (Figure 5-13). MING achieves the 201 8 URP point with (106%) and without (116%)
international sources. WIMO does not achie ve the 2018 URP point when international
contributions are eliminated.

International sources have by far the largest effect at BIBE. Whereas the standard 2018 visibility projections only achieved 27% of the reducti ons needed to achieve the 2018 URP point,
elimination of the international source contri butions achieves 172% of the reduction needed. GUMO comes up short in achieving the 2018 URP point when interna tional sources are included (31%), but achieves it when they are not (107%).September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-28 Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - Worst 20% Days

145.10

126.58

99.07

77.55

60.70

47.51

37.19 32.10 97.01 91.11

0 20 40 60 80 100 120 140 160

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Uniform Rate of Reasonable Progress Glide Path Upper Buffalo Wilderness - Worst 20% Days

142.95

125.43

98.34

77.10

60.44

47.39

37.15 32.10 97.31 91.19

0 20 40 60 80 100 120 140 160 180 200

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction.

Uniform Rate of Reasonable Progress Glide Path Breton - Worst 20% Days

135.47

119.88

95.83

76.61

61.24

48.95

39.13 34.21 105.54
98.97

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Uniform Rate of Reasonable Progress Glide Path Boundary Waters - Worst 20% Days

74.38

67.16

58.79

51.46

45.05

39.43
34.52 31.87 64.35

55.63

0 10 20 30 40 50 60 70 80 90 100

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction.

Uniform Rate of Reasonable Progress Glide Path Voyageurs NP - Worst 20% Days

71.99

65.48

58.17

51.68

45.91

40.79
36.23 33.75 64.59

53.78

0 10 20 30 40 50 60 70 80 90 100

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Uniform Rate of Reasonable Progress Glide Path Hercules-Glades Wilderness - Worst 20% Days

151.24

130.98

101.37

78.46

60.73

47.00

36.38 31.19 103.68 98.00

0 20 40 60 80 100 120 140 160 180

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction.

Uniform Rate of Reasonable Progress Glide Path Mingo - Worst 20% Days

172.02

148.57

114.77

88.66

68.49

52.91

40.87 35.01 111.43 105.48

0 20 40 60 80 100 120 140 160 180 200

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Uniform Rate of Reasonable Progress Glide Path Wichita Mountains - Worst 20% Days

111.18

97.06

74.06

56.51

43.12

32.90

25.11 21.35 86.71

78.06

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-29 Uniform Rate of Reasonable Progress Glide Path Big Bend NP - Worst 20% Days

57.89

52.75

44.61

37.73

31.91

26.98

22.82 20.64 54.33

35.11

0 10 20 30 40 50 60 70 80

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Uniform Rate of Reasonable Progress Glide Path Guadalupe Mountains NP - Worst 20% Days

57.87

52.04

43.75

36.78

30.92

25.99

21.85 19.69 53.53

42.80

0 10 20 30 40 50 60 70 80

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1B Prediction. Figure 5-16. Elimination of international sources from 2018 visibility projections and comparison with 2018 URP point at CENRAP Class I areas.

5.7.2 Glidepaths Based on Controllable Extinction

Another alternative glidepath that was examined using the CAMx PSAT source apportionment results was based on the U.S. anthropogenic emissi ons contributions to visibility impairment on the worst 20 percent days at the CENRAP Class I areas. The RHR strive s to achieve “natural visibility conditions” by 2064 and defines natural conditions as conditions that would exist “in the absence of human caused impairment”. As shown above, anthropogenic emissions from
international sources cont ribute significantly to visibility impairment at many of the CENRAP Class I areas making the RHR objective not practical if contributions from such sources are not reduced. Given that states and EPA have no juri sdiction over international sources, then we can not assume they will be controlled and have therefor e held most of them constant at 2002 levels.
For such Class I areas with hi gh contributions from international sources, the comparison with the 2018 URP point is not very meaningful sin ce the 2018 URP assumes such sources will be reduced. A more meaningful comparison would be to focus on the U.S. man-made contributions to visibility impairment at the Class I areas and develop a URP glidepath and 2018 URP point that is aimed at eliminating the U.S. anth ropogenic emissions contri butions to visibility impairment at Class I areas for the worst 20 percent days in 2064.

The CAMx 2002 base case PSAT PM source apporti onment results were processed to identify the portion of the 2000-2004 Baseline extinction th at was due to U.S. anthropogenic emissions (i.e., man-made sources). The contributions of source groups that includ ed on-road mobile, nonroad mobile, elevated point sources, low-level point sources and area sources from the PSAT source regions covering the U.S. states and Gulf of Mexico (Figure 5-8) were assumed to make up the U.S. anthropogenic contributions (i.e., ex cluding the Natural source category, all sources
from the Mexico and Canada source regions and boundary conditio ns). Note that off-shore
marine emissions in the Pacific and Atlantic O ceans and Gulf of Mexico were included in the U.S. anthropogenic emissions definition because th ey were in source regions associated with
states or the Gulf of Mexico. As off-shore marine emissions may not be controllable by U.S. agencies and they were assumed to remain unchanged going from 2002 to 2018, then the 2018 visibility projections for the U.S. anthropogenic component are overstated.

The 2064 objective for the U.S. anthropogenic emissi ons glidepath would be no contributions on the worst 20 percent days. This does not m ean the 2064 U.S. anthropoge nic extinction objectiveSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-30 is zero, rather the U.S. anthropogenic plus natural background is less than the Natural Conditions for the worst 20 percent days. The PSAT results were used to define the natural background contributions on the current worst 20 percent da ys which was subtracted from the EPA default Natural Conditions to obtain the 2064 objectiv e for the U.S. anthropogenic emissions
contributions. Here the PSAT derived natura l background was defined as the sum of the contributions from the Natural source category, secondary organic aerosol from biogenic sources (SOAB) and half of the boundary conditions. For ex ample, Figure 5-17 top left displays the US anthropogenic emissions glidepath for CACR. Th e PSAT natural sources contribution (=Natural Source Category + SOAB + 1⁄2 BC) is approximately 13 Mm -1 so that is subtracted from the 2064 Natural Background (~32 Mm -1 , see figure 5-16) to obtain a 2064 end point of ~19 Mm -1 for the glidepath. The 2002 PSAT results applied to th e 2000-2004 Baseline extinc tion estimates that
111 Mm -1 of the extinction is due to U.S. anthropogenic emissions which form the starting point for the glidepath. The curvature in the US an thropogenic glidepath is introduced the same way as for the extinction based glidepath to acco unt for the logarithmic relationship between
extinction and deciview.

Figure 5-17 displays the U.S. anthropogenic em issions extinction glidepaths and comparison with the 2018 visibility projec tions for extinction due to U.S. anthropogenic emissions on the worst 20 percent days. As seen by the standard linear deciview glidepaths discussed in Chapter 4, the U.S. anthropogenic emissions 2018 URP point is achieved by a wide margin at the four Class I areas in Arkansas and Missouri (C ACR, UPBU, HRGL and MING). BRET that achieved 94% of the 2018 URP point obtains si milar results using the U.S. anthropogenic emissions glidepath achieving 96% of the 2018 URP point. As discussed above, the inclusion of
the off-shore marine emissions in the U.S. an thropogenic emissions will greatly affect the BRET
Class I area so that actual reduc tion in U.S. anthropogenic emissi ons extinction would be greater and may even achieve the 2018 URP point if off-shore marine vess els were classified as not being part of the U.S..

The BOWA and VOYA northern Mi nnesota Class I areas achiev ed, respectively, 69% and 53% of the 2018 URP point using the standard EPA default deciview glidepaths and projection techniques (Figure 4-4). Us ing the U.S. anthropogenic glidepaths BOWA and VOYA achieve 92% and 86% of the 2018 point, respectively (Figur e 5-17). WIMO that came up approximately 40% short of achieving the 2018 URP point using the deciview glidepath comes up under 20% short using the U.S. anthr opogenic emissions glidepath.

The two Texas Class I areas also come up short in achieving the 2018 URP point using the U.S. anthropogenic emissions glidepaths, but not as shor t as when the linear d eciview glidepaths are
used. BIBE increases from 26% to 67% and GUMO increases from 34% to 49%. One reason these two Class I areas fail to achieve the 2018 po int for U.S. anthropogenic emissions is because of the high contributions of Soil and CM and little change in precursor emissions of these species
between 2002 and 2018.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-31 Uniform Rate of Reasonable Progress Glide Path

Caney Creek Wilderness - Worst 20% Days

111.38

96.84

75.02

57.89

44.36

33.52

24.48 19.38 67.20

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Uniform Rate of Reasonable Progress Glide Path Upper Buffalo Wilderness - Worst 20% Days

110.85

96.99

75.37

58.37

44.93

34.19

25.29 20.36 68.39

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction

Uniform Rate of Reasona ble Progress Glide Path Breton - Worst 20% Days

103.43

91.55

73.24

58.61

46.91

37.59

30.16 26.48 74.69

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Uniform Rate of Reasonable Progress Glide Path Boundary Waters - Worst 20% Days

44.12

40.11

35.80

32.09

28.93 26.30 24.22 23.25 36.50

0 10 20 30 40 50 60

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction

Uniform Rate of Reasona ble Progress Glide Path Voyageurs NP - Worst 20% Days

36.08
33.17
30.37 27.95 25.92 24.27 23.05 22.55 31.17

0 10 20 30 40 50 60

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Uniform Rate of Reasonable Progress Glide Path Hercules-Glades Wilderness - Worst 20% Days

121.44

104.95

80.70

61.90

47.26

35.76

26.46 21.42 77.22

0 20 40 60 80 100 120 140

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction

Uniform Rate of Reasonable Progress Glide Path Mingo - Worst 20% Days

143.07

123.51

95.29

73.48

56.61

43.54

33.34 28.26 86.80

0 20 40 60 80 100 120 140 160 180

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Uniform Rate of Reasonable Progress Glide Path Wichita Mountains - Worst 20% Days

77.98

67.77

50.99

38.13

28.21

20.41

13.88 9.96 55.49

0 10 20 30 40 50 60 70 80 90 100

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B PredictionSeptember 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-32 Uniform Rate of Reasonable Progress Glide Path Big Bend NP - Worst 20% Days

15.72

14.34

12.16

10.33

8.78

7.49

6.40 5.85 13.33

0 2 4 6 8 10 12 14 16 18 20

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Uniform Rate of Reasonable Progress Glide Path Guadalupe Mountains NP - Worst 20% Days

24.20

21.58

17.68

14.36

11.50

8.98

6.67
5.28 20.98

0 3 6 9 12 15 18 21 24 27 30

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearBEXT (1/Mm)

Glide Path Natural Condition (Worst Days) Observation Method 2B Prediction Figure 5-17. Glidepaths and 2018 visibility projections based on visibility due to U.S. anthropogenic emissions at CENRAP Class I areas.

5.8 Use of Original IMPROVE Equation

2018 visibility projections we re also made using the CENRAP Typ02g and Base18g CMAQ modeling results and the original (old) IMPROVE equation. Figure 5-18 di splays a DotPlot that compares the 2018 Base G visibility projectio ns using the new IMPR OVE algorithm (NIA) and the original IMPROVE algorithm (O IA). In general the new IMPR OVE equation results in more optimistic 2018 visibility projections than the original IMPROVE equation. For the Texas and WRAP Class I areas, the 2018 visibi lity projections are nearly id entical using the two IMPROVE equations. For the four Class I areas in Arkansas and Missouri the 2018 visibility projections using the new IMPROVE equation ar e from 7 to 21 percentage poi nts more optimistic than the original IMPROVE equation. In the case of UPBU, HEGL and MING the 2018 visibility projections go from not achieving to achievi ng the 29018 URP point when switching from the old to new IMPROVE equation.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-33 CMAQ BaseG Method 1 predictions fo r CENRAP+ sites on Worst 20% Days

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2BOWA1MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved BaseG NIA

BaseG OIA

CENRAP non-CENRAP Figure 5-18. Comparison of 2018 Base G visibility projections using the New (NIA) and Old (OIA) IMPROVE algorithms expressed as a percentage of achieving the 2018 URP point visibility improvements.

5.9 Visibility Trends

Figure 5-19 displays trends in visibility impairment at the CENRAP Class I areas using the period of record of measurements at the as sociated IMPROVE monitor and the new IMPROVE equation. These trends include trends for the wo rst 20 percent days, the best 20 percent days and all IMPROVE sampled days during a year. Th e EPA guidance procedur es were used to
construct the worst and be st 20 percent days that includes a minimum data capture requirement (EPA, 2003a), whereas no such minimum data ca pture was applied when looking at the “annual average” of all IMPROVE sampled days trends. So care must be taken when analyzing trends for the all sampled IMPROVE days trends as th ere could be large missing periods with high or
low extinction that are not be ing account for. The WRAP Technical Support System (TSS) website was used to calculate the visibility tren ds at the CENRAP Class I areas that includes IMPROVE data from start of recording through 2004 and includes no data filling (see: http://vista.cira.colostate.edu/TSS/Default.aspx) .

Trends in visibility at CACR has three years of data (2002- 2004) for the wors t and best 20 percent days and fives years for the IMPROVE samp led days trends. Although it is hard to come to any conclusions regarding trends with just thr ee years of data, there does seem to be a general downward trend, that is also supported by the fi ve year trend in the IMPROVE sampled days.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-34 A much longer trend plot is available for UPBU th at includes 12 years of data for the worst and best 20 percent days (Figure 5-19b ). Although there is a lot of a year-to-y ear variation in the visibility trends with cleane r years occurring in 1997, 2001 and 2004, there does app ear to be a slight trend toward impr oved visibility at UPBU.

There is insufficient data to calc ulate the worst or best 20 percent days visibi lity for any year at the BRET Class I area so only the IMPROVE sampled days trends are presented (Figure 5-19c). The trends at BRET are inconclusive and given the large amounts of missing data at this site it is difficult to interpret the results.

There is also a lot of missing years in the wors t and best 20 percent days for the BOWA Class I area making it difficult to interpre t (Figure 5-19d). But visibility appears to be more impaired in the early 1990s than in more current years so improvements have been seen. VOYA has five years of valid data and shows worsening visibi lity for 2000-2003, and then improved visibility in 2004. It is unclear whether the 2004 improved visibili ty is a trend or just due to variations in meteorology so no conclusions can be drawn.

Although a downward trend in visibility impairment appears to be occurring at the two Missouri Class I areas (Figure 5-19f-g), gi ven that there are only three years available for HEGL and lots of missing data for MING thes e trends are inconclusive.

Three years (2002-2004) of visibility trends for th e worst and best 20 percent days are available for WIMO (Figure 5-19h). The most impaired y ear from the three years for the worst 20 percent days is the most recent (2004). Again, the time pe riod is too short to dr aw any conclusions on trends in visibility at WIMO.

The two Texas Class I areas have a relatively long period of record. There is a lot of year-toyear variability in the visibil ity measurements that make interpreting the trends difficult. 1998 appears to be an anomalously high visibility impairment year at BIBE and due to the much higher OMC extinction indicates that the year wa s likely impacted by smoke from fires. GUMO has lots of year to year variability in CM and Soil which are likely due to occurrences of impacts due to wind blown dust. Even taking Soil and CM out of the interpretation it is difficult to interpret ay trend in visibility at the two Texas Class I areas. Th e higher visibility impairment in 1998 and 1999 suggests a downward trend but that may be just due to more adverse meteorological and natural emission s (e.g., wildfires) in these two years than any real long term
trend.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-35 Figure 5-19a. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Caney Creek (CACR), Arkansas for the aver age of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-36 Figure 5-19b. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Upper Buffalo (UPBU), Arkansas for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period
of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-37 Insufficient Data to Calculate Best 20 Percent days at BRET

Figure 5-19c. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Breton Island (BRET), Louisiana for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-38 Figure 5-19d. Time series of observed IMPROVE recons tructed light extinction (New IMPROVE) at Boundary Waters (BOWA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-39 Figure 5-19e. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Voyageurs (VOYA), Minnesota for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-40 Figure 5-19f. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Hercules Glade (HEGL), Missouri for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and a ll IMPROVE sampling days during the period of
record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-41 Figure 5-19g. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Mingo (MING), Missouri for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of
record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-42 Figure 5-19h. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Wichita Mountains (WIMO), Oklahoma for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-43 Figure 5-19i. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Big Bend (BIBE), Texas for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of
record

.September 2007

F:\CENRAP_Modeling\TSD\draf t#3\Chapter_5_AddAnal3.doc 5-44 Figure 5-19j. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Guadalupe Mountains (GUMO), Texas for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE sampling days during the period of record

.September 2007

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