# Appendix K

Recommended ImprovementsAppendix K - 1Appendix K - 2

The Regional Haze Rule requires states to conduct a mid course review in 2012 of progress toward reasonable progress goals for 2018. There a re several technical improvements that are recommended in the emissions inventory and air qual ity models that are used to support regulatory decisions for regional haze. These techn ical improvements are also important for states’ efforts to demonstration attainment of the ozone and PM2.5 health standards.

1) Emissions inventory Continued improvements in emission inventories for regulatory sources, particularly emissions profiles for the composition of PM2.5 emissions is necessary.

2) Continued improvements are needed in the integrated one-atmosphere air quality models that are used to project air quality responses to emissi ons reductions. The interactions among primary and secondary pollutants in the atmosphere affect the levels of ozone, PM2.5, and haze in the atmosphere. As understanding of partit ioning between gaseous and aerosol phases improves, models reflecting this understandi ng need to be developed.

3) Sulfate performance for the CMAQ regional air quality model is good ov erall. Sulfate deposition is frequently overestimated in the model s, particularly in the summer months. Overestimation of precipitation volume from convect ive storms is one possible explanation. Improvements in treatment of convective systems in the meteorological models would improve projections of pollutant deposition concent rations. At the coastal sites, when winds are blowing from the Gulf of Mexico or Atlantic Oce an, CMAQ under estimates measured sulfate at the monitors. Improvements to emissions inventories for commercial shipping in the Gulf and Atlantic were made in 2006; additional refinements to emissions estimates are needed. As well CMAQ’s processes should be reviewe d for sulfate formation over water, including availability of precursor species (hydrog en peroxide and ozone), dispersal patterns for moving and fixed SO2 emission sources, and vert ical and horizontal within clouds over water. The accuracy of meteorological modeling of flux at the land-water interface is also an area for additional attention.

4) Nitrate is overestimated by the CMAQ model in the winter a nd underestimated in the summer, although summer monitored values of nitrate are very low. In the southeastern US, nitrate formation is driven largely by level of amm onia available in the atmosphere. Current understanding of seasonal variation in ammonia emis sions from agricultural sources has improved; additional improvements in seasonal alloc ation of annual inventories would further improve model estimates of ammonium nitrate formation.

5) Organic carbon is generally underestimated in the CMAQ in the sum mer months. Improvement in the characterization of primary carb on emissions and partitioning between gas and aerosol phase and formation of secondary or ganic aerosols is needed.

6) Better tools for organic carbon source apportionment need to be developed. In aged aerosol most of primary aerosol has reacted to form some se condary organic aerosol. Improved emissions profiles and stable molecular makers for anthropogenic and biogenic sources and fossil and modern carbon are critical.Appendix K - 3

7) Consistent measurement techniques are needed betwee n rural and urban monitoring networks so that concentrations of organic carbon and elemen tal carbon can be compared between sites.

8) To improve understanding of the contribution of fir e from natural forest fires, prescribed burning, land clearing, and agricultural burning, s tates need to improve record keeping. Records should include acres burned, dates, locatio n (latitude/longitude), fuel type, and intensity of burn.

9) Additional improvements to international emissions inventory are needed to improve understanding of boundary conditions for modeling d omains and of the contributions from international emissions to pollutant concentrations at the VISTAS Class I areas. Currently monthly average inventories are used for non-US sou rces. Even when highly resolved meteorological data are used, these monthly average inventories do not reflect the episodic variation in emissions, particularly for fire event s, that are important at the receptors. In the current modeling we cannot distinguish US from non- US sources of pollutant concentrations at the boundaries of the model domain. States and RPOs need to improve their ability to trace emissions that originate within US as they ar e transported outside modeling grids so that when they re-circulate back into the modeling domain they retain their identify as US emissions rather than non-US boundary conditions.

10) The effect of meteorological model performance on a ir quality model performance needs additional attention.

11) Values of continuous monitoring need to be captured by evaluating model for the diurnal and spatial patterns seen in the monitored data.

12) Confidence in trajectory model for periods greater than 72 hours also needs to be improved. Currently trajectories of longer duration have such large bands of uncertainty as to reduce any conclusions than can be drawn for transport gre ater than 72 hours.International Corporation Air Sciences

Final Report

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

Prepared for:

Pat Brewer VISTAS Technical Coordinator 2090 U.S. 70 Highway Swannanoa, NC 28778

Prepared by: Ralph E. Morris Bonyoung Koo Piti Piyachaturawat ENVIRON International Corporation

773 San Marin Drive, Suite 2115 Novato, CA 94998

Gregory Stella Dennis McNally

Cyndi Loomis Alpine Geophysics, LLC 7341 Poppy Way Arvada, CO 80007

Cha-Jung Chien Gail Tonnesen University of California at Riverside

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

March 11, 2009

773 San Marin Drive, Suite 2115, Novato, CA 94998 415.899.0700March 2009

vi TABLE OF CONTENTS

Page

ACCRONYMS ...................................................................................................................... ......... i

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

1.1 Background................................................................................................................. ..... 1-1 1.2 VISTAS Modeling Approach .......................................................................................... 1-3 1.2.2 VISTAS Two-Phased Approach.......................................................................... 1-3 1.3 Overview of 2002 Annual Emissions and Air Quality Modeling Approach ................................................................................................... 1-6 1.3.1 Modeling Protocol ............................................................................................... 1-6 1.3.2 Quality Assurance Project Plan (QAPP).............................................................. 1-7 1.3.3 Model Selection ................................................................................................... 1-7 1.3.4 Horizontal Modeling Domains .......................................................................... 1-18 1.3.5 Vertical Domain Definition ............................................................................... 1-23 1.3.6 2002 Calendar Year Selection ........................................................................... 1-24 1.3.7 Initial Concentrations and Boundary Conditions............................................... 1-25 1.3.8 Emissions Input Preparation .............................................................................. 1-26 1.3.9 Meteorological Input Preparation ...................................................................... 1-27 1.4 Known Errors in the Base G2 and G4 Modeling........................................................... 1-33

2. DEVELOPMENT OF EMISSION INPUTS..................................................................... 2-1

2.1 Emissions Modeling Methodology.................................................................................. 2-2 2.2 Set-up of SMOKE Over the VISTAS Domain................................................................ 2-2 2.2.1 Processing of Point Source Emissions................................................................. 2-5 2.2.2 Processing of Area and Non-Road Source Emissions ......................................... 2-6 2.2.3 Processing of On-Road Mobile Source Emissions .............................................. 2-7 2.2.4 Processing of Biogenic Source Emissions........................................................... 2-8 2.2.5 Processing of Wildfires and Prescribed Burns..................................................... 2-8 2.2.6 Windblown Dust .................................................................................................. 2-9 2.2.7 Sea Salt................................................................................................................ . 2-9 2.3 SMOKE-Specific Processi ng Characteristics.................................................................. 2-9 2.3.1 Temporal Allocation .......................................................................................... 2-10 2.3.2 Speciation........................................................................................................... 2-2 7 2.3.3 Spatial Allocation................................................................................................2.27 2.4 Development of Modeling Input Inventories/Run Scripts/ASSIGNS Files............................................................................................ 2-29 2.5 Products of the Emissions Inventory Development Process for QA......................................................................................................... 2-29 2.6 2009 and 2018 Base G4 Best and Final Inventories................................................ 2-32 2.7 Uncertainties and Caveats........................................................................................ 2-36 2.7.1 Updated Inventories........................................................................................... 2-36 2.7.2 Low Level Fires ................................................................................................. 2-36 2.7.3 MMS Offshore Shipping.................................................................................... 2-36 2.7.4 Incorrect Location Parameters ........................................................................... 2-37 2.7.5 SMOKE/IOAPI Coordinate Transformation Error............................................ 2-37March 2009

vii 3. MODEL PERFORMANCE EVALUATION.................................................................... 3-1

3.1 Introduction............................................................................................................... ....... 3-1 3.2 CMAQ Evaluation Methodology..................................................................................... 3-2 3.2.1 Evaluation Approach ........................................................................................... 3-2 3.2.2 Particulate Matter a nd Component Species ......................................................... 3-3 3.2.3 Ambient Air Quality Data for VISTAS Model Evaluation ................................. 3-3 3.3 Model Performance Statistics and Goals ......................................................................... 3-5 3.4 Regional Model Performance Evaluation........................................................................ 3-6 3.4.1 Sulfate (SO4) Model Performance....................................................................... 3-6 3.4.2 Nitrate (NO3) Modeling Performance............................................................... 3-10 3.4.3 Organic Carbon Mass (OCM) Performance ...................................................... 3-13 3.4.4 Elemental Carbon (EC) Performance ................................................................ 3-16 3.4.5 Other PM 2.5 or Soil Performance ....................................................................... 3-19 3.4.6 Coarse Mass (CM) Performance........................................................................ 3-22 3.4.7 Fine Particulate (PM 2.5 ) Performance ................................................................ 3-25 3.5 Evaluation at VISTAS and Nearby Class I Areas ......................................................... 3-28 3.5.1 Example Spatial Distribution Model Performance............................................ 3-28 3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 Percent Days Performance................................................................................. 3-33 3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 Percent Days Performance................................................................................. 3-33 3.5.4 Everglades (EVER), Florid a Best and Worst 20 Percent Days Performance.............................................................................................. 3-33 3.5.5 St. Marks (SAMA), Florida Best and Worst 20 Percent Days Performance.............................................................................................. 3-40 3.5.6 Cohutta (COHU), Georgia Best and Worst 20 Percent Days Performance.............................................................................................. 3-40 3.5.7 Okefenokee (OKEF), Georgia Best and Worst 20 Percent Days Performance.............................................................................................. 3-40 3.5.8 Mammoth Cave (MACA), Kentucky Best and Worst 20 Percent Days Performance]................................................................................ 3-40 3.5.9 Linville Gorge (LIGO), Nort h Carolina Best and Worst 20 Percent Days Performance................................................................................. 3-49 3.5.10 Shining Rock (SHRO), North Carolina Best and Worst 20 Percent Days Performance................................................................................. 3-49 3.5.11 Swanquarter (SWAN), North Carolina Best and Worst 20 Percent Days Performance................................................................................. 3-49 3.5.12 Cape Romain (ROMA), South Carolina Best and Worst 20 Percent Days Performance..................................................................................3.49 3.5.13 Great Smoky Mountains (GRS M), Tennessee-North Carolina Best and Worst 20 Percent Days Performance .................................................. 3-49 3.5.14 James River Face Wilderness Area (JARI), Virginia Best and Worst 20 Percent Days Performance .................................................. 3-60 3.5.15 Shenandoah (SHEN), Virginia Best and Worst 20 Percent Days Performance................................................................................. 3-60 3.5.16 Dolly Sods (DOSO), West Virginia Best and Worst 20 Percent Days Performance................................................................................. 3-60March 2009

viii 3.5.17 Breton Island (BRET), Louisiana Best and Worst 20 Percent Days Performance................................................................................. 3-60
3.5.18 Caney Creek (CACR) and Upper Buffalo (UPBU), Arkansas Best and Worst 20 Percent Days Performance .................................. 3-60 3.5.19 Mingo (MING) and Hercules Glade (HEGL), Missouri Best and Worst 20 Percent Days Performance................................... 3-73 3.5.20 Brigantine (BRIG), New Jersey Best and Worst 20 Percent Days Performance................................................................................. 3-73 3.6 Conclusions of Model Performance Evaluation ............................................................ 3-80

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

4.1 Guidance for Visibility Projections ................................................................................. 4-1 4.2 Calculation of Visibility a nd 2018 Uniform Rate of Progress from IMPROVE Measurements ................................................................................ 4-2 4.2.1 Calculation of Visibility fr om IMPROVE PM Measurements............................ 4-3 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 ............................................................................. 4-9 4.2.4 Natural Conditions............................................................................................... 4-9 4.2.5 2018 Uniform Rate of Progress ........................................................................... 4-9 4.3 EPA Default Approach for Visibility Projections ......................................................... 4-10 4.3.3 Mapping of Modeling Results to the
IMPROVE Measurements ................................................................................. 4-10 4.3.4 Using Modeling Results to Pr oject Changes in Visibility ................................. 4-11 4.4 EPA Default 2018 Visibility Projections at VISTAS and Nearby Class I areas and Comparisons to 2018 Uniform Rate of Progress ........................................................................................ 4-12 4.4.1 Example 2018 Base G2 Visibility Projections for Worst 20% Days Sipsey, Alabama .................................................................... 4-13 4.4.2 Summary 2018 Visibility Projections for Worst 20 Percent Days across Class I Areas..................................................................... 4-17 4.4.3 2018 Visibility Projections for the Best 20 Percent Days.................................. 4-23 4.5 2018 Visibility Projections using the 2018 Best and Final Emissions .......................... 4-29

5. ADDITIONAL SUPPORTING ANALYSIS ......................................................................5.1

5.1 Comparison of VISTAS 2018 Visibility Projections with Other Groups........................ 5-1 5.1.1 Comparison of CENR AP, VISTAS, MRPO and MANE-VU Visibility Projections........................................................................ 5-1 5.2 Alternative 2018 Visibility Projection Software.............................................................. 5-3 5.3 Alternative Visibility Projection Procedures ................................................................... 5-5 5.3.1 Use of Original IMPROVE Equation .................................................................. 5-5 5.3.2 Treatment of Coarse Mass and Soil as Natural.................................................... 5-8 5.3.3 Accounting for Model Performan ce in Visibility Projections ........................... 5-10 5.3.4 Use of Classification and Regre ssion Tree (CART) Weighted RRFs ............... 5-13 5.4 Effects of International Transpor t on 2018 Visibility Projections................................. 5-15 5.5 Visibility Trends ......................................................................................................... ... 5-24 5.6 Geographic Areas of Influence of Visibi lity Impairment for Class I Areas.................. 5-42March 2009

ix 5.6.1 Back Trajectory Analyses..................................................................................... 5-42 5.6.2 Residence Time Plots............................................................................................ 5-43 5.6.3 SO2 Areas of Influence...................................................................................... 5-44 5.6.4 Emissions Sources within SO2 Areas of Influence ........................................... 5-45 5.6.5 Specific Source Types in the Areas of Influence............................................... 5-46 5.6.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each Area of Influence..................................................................... 5-49 5.7 Area of Influence Analyses Results fo r VISTAS and Nearby Class I Areas ................ 5-52

6. REFERENCES.................................................................................................................. ... 6-1

APPENDICES

Appendix A: Emission Processing Sel ected QA Summary Reports and Figures

Appendix B: Air Quality Model Perf ormance Evaluation: Regional Model Evaluation across the VISTAS Region

Appendix C: Air Quality Model Perf ormance Evaluation: Evaluation at VISTAS and Nearby Class I Areas

Appendix D: Air Quality Model Performance Evaluation: Spatial Maps of Model Performance on the Best and Worst 20 Percent Days at VISTAS and Nearby Class I Areas

Appendix E: 2018 Visibility Project s at VISTAS and Nearby Class I Areas for the Worst 20 Percent Days using EPA Default Approach and Comparison with 2018 Uniform Rate of Progress

Appendix F: 2018 Visibility Projectio ns at VISTAS and Nearby Class I Areas for the Best 20 Percent Days using EPA Default Approach

Appendix G: Evaluation of the 2002 GEOS-CHEM Global Chemistry Model Used for Boundary Conditions in th e VISTAS Regional Haze Modeling

Appendix H: Based on “VISTAS Area of Influence Analyses for Sulfur Dioxide” by Archuleta, A dhloch, Mansell, Stella and Brewer (February 28, 2007) and “Procedures for Developing and Displaying AoI Back-trajectories Residence Time GIS Data by Mansell (August 29, 2007)March 2009

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc i ACCRONYMS

ACM Asymmetric Convective Mixing AIRS Aerometric Information Retrieval System Ap Accuracy of paired peak AQS Air Quality System ARS Air Resource Specialists
ASCII American Standard Code for Information Interchange
ASIP Association of the Sout heast for Integrated Planning
BART Best Available Retrofit Technology Base02a 2002 Base Case, version a BCs Boundary Conditions BEIS3 Biogenic Emission Inve ntory System, version 3 BELD Biogenic Emissions Landcover Database
bext Extinction coefficient
BF Bias Factor BRAVO Big Bend Regional Aerosol and Visibility Observations Study BRET Breton Island National Wildlife Refuge BRIG Brigantine Wildlife Refuge
CAAA Clean Air Act Amendments CACR Caney Creek Wilderness Area
CAIR Clean Air Interstate Rule CAMx Comprehensive Air Quality Model with extensions
CARB California Air Resources Board
CASTNet Clean Air Status and Trends Network
CB-IV Carbon Bond IV
CBM-IV Carbon Bond Mechanism IV CCRS+CPRM Coarse matter (coarse crustal & coarse primary) CEM Continuous Emissions Monitoring Data CENRAP Central Regional Air Planning Association CFR Code of Federal Regulations CHAS Chassahowitzka Wildlife Refuge CHIEF Clearinghouse for Inventories and Emissions Factors

CM Coarse Mass CMAQ Community Multiscale Ai r Quality modeling system CMU Carnegie Mellon University CNG Compressed Natural Gas
CO Carbon Monoxide
COHU Cohutta Wilderness Area DDM Decoupled direct method DOSO Dolly Sods Wilderness AreaMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc ii

dv deciview
EBI Euler Backward Iterative
EC Elemental Carbon EFIG Emissions Factors and Inventory Group EGAS Economic Growth Analysis System EGUs Electrical Generating Units
EMFAC California Air Resources Boar d mobile source emissions model EPA U.S. Environmental Protection Agency
EPM Emission Production Model
EPRI Electric Power Research Institute ERG Energy Resources Group Eta Eta model - a hydrostatic mesoscale model. EVER Everglades National Park

FCRS+FPRM Fine Particulate Matter (fine crustal & fine primary)

FDDA Four Dimensional Data Assimilation

FE Fractional Gross Error

FIP Federal Implementation Plan

FLM Federal Land Managers

GA DNR Georgia Department of Natural Resources

GE Goddard Earth Observing System
GEOS-CHEM Goddard Earth Observ ing Systems – Chemistry model

GIS Geographic Information System

GMAO Global Modeling and Assimilation Office

GMT Greenwich Mean Time

GRSM Great Smoky Mountains National Park

HEGL Hercules-Glades Wilderness Area

HI Haze index

HNO3 Nitric acid

ICs Initial concentrations

IDA Inventory Data Analyzer

IMPROVE Interagency Monitoring of Prot ected Visual Environments

IPM Integrated Planning Model

ISHALLO= No Shallow Convection ISORROPIA ISORROPIA aerosol equilibrium model. JARI James River Face Wilderness

JPROC Models 3 Photolysis Rates processors

Kh Horizontal diffusivity coefficient

km Kilometer

Kzmin Minimum vertical diffusivity coefficient

LAC Light Absorbing Carbon

LIGO Linville Gorge Wilderness Area
LITTLE_R MM5 meteorology processor

LPG Liquefied petroleum gasMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc iii

LSM Land-surface model

MACA Mammoth Cave National Park

MAGE Mean Absolute Gross Error

MANE-VU Mid-Atlantic/Northeast Visibility Union

MARAMA Mid-Atlantic Air Mana gement Association

MATS EPA’s Modeled Attainment Test Software

MB Mean Bias

MCIP Meteorological Chemistry Interface Processor

MEGAN Model of emissions of gases and aerosols from nature

MFB Mean Fractionalized Bias

MIMS Multimedia Integrated Modeling System

MING Mingo Wilderness Area

Mm-1 Inverse megameters

MM5 Mesoscale Meteorological Model

MMS Minerals Management Service

MNB Mean Normalized Bias

MNGE Mean Normalized Gross Error MOBILE6 EPA’s latest computer program fo r compiling emissions from mobile sources

MPE Model performance evaluation

MPI Message passing interface

MRPO Midwest Regional Planning Organization

MWSS Monday-weekday-Saturday-Sunday
NAAQS National Ambient Air Quality Standards

NADP National Atmospheric Deposition Program

NARSTO North American Research Strategy for Tropospheric Ozone

NCEP National Centers for Environmental Prediction

NEI National Emissions Inventory

NH3 Ammonia

NIA New IMPROVE Algorithm

NIF National Emission Inventory Input Format

NO3 Nitrate

NMB Normalized Mean Bias

NME Normalized Mean Error

NO Nitrogen Oxide

NO2 Nitrogen dioxide

NOAA National Oceanic & Atmo spheric Administration

NOx Nitrogen Oxides

O3 Ozone
OAQPS Office of Air Quality and Planning Standards

OIA Old IMPROVE Algorithm

OKEF Okefenokee Wildlife Refuge

OMC Organic mass carbonMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc iv

PA Process Analysis

PBL Planetary Boundary Layer

PEC Primary elemental carbon

PinG Plume-in-Grid

PM Particulate matter

PM2.5 Particulate matter of 2.5 microns and less

PMC Particulate Matter Coarse

PNO3 Particulate nitrate

POA Primary Organic Aerosol

POC Primary Organic carbon

POG Policy Oversight Group

PPM Piecewise Parabolic Method

PSAT PM Source Apportionment Technology

PSO4 Particulate sulfate

PSU/NCAR Pennsylvania State Univer sity/National Center for Atmospheric Research

P-X Pleim-Xiu
QA Quality Assurance
QAPP Quality Assurance Project Plan

r2 Coefficient of determination

RADM Regional Acid Deposition Model

RAMS Regional Atmospheric Modeling System

RHR Regional Haze Rule

RICE MACT Reciprocating Internal Combustion Engine Maximum Av ailable Control Technology

RMSE Root Mean Square Error

ROG Reactive Organic Gas

ROMA Cape Romain Wildlife Refuge

RPGs Reasonable Progress Goals

RPOs Regional Planning Organizations

RRFs Relative Response Factors

RRTM Rapid Radiative Transfer Model

SA Source Apportionment

SAMA Saint Marks Wildlife Refuge

SCC Source Classification Code
SEARCH Southeastern Aerosol Research and Characterization

SECA Sulphur Emissions Control Area

SHEN Shenandoah National Park

SHRO Shining Rock Wilderness Area

SIC Source Industrial Classification

SIP State Implementation Plan

SIPS Sipsey Wilderness Area

SMOKE Sparse Matrix Operator Kernel EmissionsMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Accronyms4.doc v

SOA Secondary Organic Aerosol SOAA Secondary Organic Aerosol from Anthropogenic Sources

SOAB Secondary organic Aaerosol from biogenic sources

SORGAM Secondary Organic Aerosol Model

SST Sea Surface Temperature

STI Sonoma Technology, Inc.

STN Speciation Trends Network

SO4 Sulfate

SWAN Swanquarter Wildlife Refuge

TIPs Tribal Implementation Plans

TOG Total Organic Gas

TOMS Total Ozone Mapping Spectrometer

TPY Tons Per Year

TSD Technical Support Document

TSS Technical Support System TUV Tropospheric Ultraviolet a nd Visible Radiation Model Typ02G Base G Typical Version 2

UCR University of California at Riverside

UH University of Houston

UPBU Upper Buffalo Wilderness Area

URP Uniform Rate of Progress

VIEWS Visibility Information Exchange Web Site VISTAS Visibility Improvements State a nd Tribal Associati on of the Southeast

VMT Vehicle Miles Traveled

VOC Volatile Organic Compounds

VR Visual Range

WRAP Western Regional Air Partnership

μ g/m 3 Micrograms per cubic meter (concentration)March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-1 1.0 INTRODUCTION

This is the final version of the Technica l Support Document (TSD) for the Visibility Improvements for States and Trib al Associations in the South east (VISTAS) regional emissions and air quality modeling to support the southeas tern states Regional Ha ze State Implementation Plans (SIPs). The VISTAS 2002 annual emissions and air quality modeling was performed by
the contractor team of ENVI RON International Corporation (ENVIRON), Alpine Geophysics, LLC (Alpine) and the University of California at Riverside (UCR). This final version of the TSD updates the previous draft TSD dated Nove mber 2007 through inclusion of 2018 visibility projections for the 2018 Base G4 Best a nd Final (BaF) emissions scenario.

1.1 Background

The 1977 Clean Air Act Amendments (CAAA) adde d a new Section 169A for the protection of visibility in federally mandatory Class I area s (specific national parks, wilderness areas and wildlife refuges). Sec tion 169A(a)(1) of the CAAA establishe d the national goal for visibility protection: “Congress hereby declares as a nationa l goal the prevention of any future, and the remedying of any existing, impairment of visibili ty in mandatory class I Federal areas which impairment results from manmade air pollution.” The CAAA requires States to submit State Implementation Plans (SIPs) containing em ission limits, schedule of compliance and “promulgate regulations to assure reasonabl e progress toward meet ing the national goal” (Section 169A(a)(4)). In response to these mand ates EPA promulgated the Regional Haze Rule (RHR) on July 1, 1999 that requires States to “establish goals (e xpressed in deciviews) that provide for reasonable progress towa rds achieving natural visibility conditions” at Class I areas. The States’ RHR SIPs were due December 17, 2007 and an important component of the SIP are the 2018 Reasonable Progress Goals toward achiev ing natural cond itions in 2064. Regional air quality models are used to project visibility to 2018 to determine th e level of visibility improvement that is expected to be achieved that can be used by the states to assist in setting their 2018 Reasonable Progress Goals.

VISTAS is one of five Regiona l Planning Organizations (RPOs) that have responsibility for coordinating development of State Implementation Plans (SIPs) and Tribal Implementation Plans (TIPs) in selected areas of the U.S. to addr ess the requirements of the RHR. VISTAS is a regional partnership of states, tribes, federa l agencies, stakeholders and citizen groups established to initiate and coordinate activities associated with the management of regional haze and other air quality issues within the VISTAS region. The VISTAS region includes the states of Alabama, Florida, Georgi a, Kentucky, Mississippi, Nort h Carolina, South Carolina, Tennessee, Virginia and West Virginia. Th e VISTAS Emissions and Air Quality Modeling Team is comprised of staff from ENVIRON, Alpine and UCR, w ith assistance and coordination from the VISTAS states, federal agencies an d stakeholders. The VISTAS Modeling Team performs the emissions and air quality modeli ng simulations for the VISTAS region, providing analytical results used in developing state implementation plans under the RHR. Figure 1-1
identifies the states included in each of the fi ve Regional Planning Orga nizations in the U.S, including VISTAS. The VISTAS States and Cla ss I areas within the VISTAS region are shown in Figure 1-2.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-2 VISTAS is performing emissions and air quality modeling to project visibility to 2018 to determine the level of visibility improveme nts expected in 2018 under various emission strategies to assist states to determine their 2018 RPG toward achieving natural conditions in
2064.

The VISTAS Modeling Team is funded through the Southeastern States Ai r Resource Managers (SESARM) and has implemented a regional air qual ity planning process to provide the necessary technical and policy tools needed by states to comply with the Section 308 of the RHR requirements. In March 2003, VISTAS contracted with ENVIRON, with Alpine and UCR as Subcontractors, to be the VIST AS Modeling Team to perform the emissions and air quality modeling needed to develop the t echnical basis for the RHR SIPs.

Figure 1-1. Regional Planning Organizations engaged in regional haze modeling.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-3 Figure 1-2. VISTAS states and Class I areas within the VISTAS region.

1.2 VISTAS Modeling Approach

The VISTAS 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 effects on visibility in Class I Areas in the southeastern U.S. A key element of this work includes the integration of emissions inventories and m odels with regional transport models. The general services provided by the VISTAS 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.

1.2.2 VISTAS Two-Phased Approach

The VISTAS Emissions and Air Quality Modeling activities were performed in two Phases. Phase I, which occurred primarily during the 2003 calendar year, consisted of emissions and regional haze modeling for three episodes to id entify the optimal model configuration(s) for simulating regional haze in the southeastern U.S. Phase II, initiated in 2004, consists ofMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-4 operating the emissions and air quality models fo r the 2002 calendar year to develop the regional haze modeling databases needed to address th e requirements of the Section 308 RHR SIPs.

1.2.2.1 VISTAS Phase I

The objective of VISTAS Phase I was to determ ine the optimal modeling configuration for use in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a comprehensive literature review of recent rele vant visibility studies usi ng various photochemical/aerosol modeling platforms in order to assess and identif y appropriate model configurations, data bases, and model testing methodologies that were appr opriate for use in conducting the VISTAS Phase I emissions and PM modeling assessment. Key elements of Phase I included:

• Review all relevant air quality model simula tions that have been completed related to regional haze and PM2.5 modeling and documen t the relevant sensitivity analyses, model configuration testing, and performan ce evaluations that have been performed (ENVIRON, 2003b);

• Review the current science in regiona l emissions modeling (e.g., EPS, EMS and SMOKE) and PM air quality modeling (e.g., CMAQ, CMAQ-MADRID, CMAQ-AIM, REMSAD, UAM-V/PM, CAMx and PMCAMx) to determine the most appropriate model(s) for use by VISTAS (ENVIRON, 2003b);

• Review available ambient data for eval uating one-atmosphere PM/ozone models (ENVIRON, 2003c);

• Develop and implement a plan or Modeli ng Protocol for testing and evaluating alternative science configurat ions of the recommended Phas e I model(s) and document the results (ENVIRON, 2003a); and

• Prepare a Task 6 report prescribing the model set-up, data base development, performance testing, and control strategy eval uation procedures to be implemented in VISTAS Phase II (ENVIRON, 2004a).

The VISTAS Technical Analysis Workgroup provided oversight for the comprehensive model evaluation. In Phase II the m odeling system (models and database s) identified and tested in Phase I was implemented following the procedures set forth in the Phase II Modeling Protocol (ENVIRON, 2004a).

For the meteorological component of the Phas e I modeling, SESARM contracted with Baron Advanced Meteorological Systems (BAMS) to apply the PSU/NCAR Mesoscale Model (MM5) in multiple configurations and to evaluate its performance against surface and aloft meteorological observations (Ole rud, 2003a-f). The emissions modeling component of VISTAS Phase I was carried out by the research team of ENVIRON/Alpine/UCR w ith staff at Alpine Geophysics taking the lead role in setting up, tes ting, and applying the emissions modeling system. The air quality modeling compone nt was performed by the team at the ENVIRON/Alpine/UCR modeling cente rs. A dominant theme during Phase I was the exchange of modeling codes, databases, a nd evaluation software between th e three modeling centers as the air quality modeling was carried out.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-5 1.2.2.2 VISTAS Phase II

The VISTAS Phase II modeling includes annual PM /regional haze simulations with the objective of demonstrating model performance for the sele cted modeling year, 2002. After detailed model review, testing and performance evaluation, the m odeling system was exercised with a variety of emissions scenarios to enable VISTAS to assess the effects of future year emissions on visibility impairment at Class I areas in the VISTAS a nd nearby states. The modeling system allows VISTAS to estimate 2018 visibility to assist in tracking progress toward regional haze goals. The VISTAS Phase II program applied the SM OKE emissions and CMAQ air quality modeling systems for calendar year 2002 over the same 36/12 km horizontal grid system used in Phase I to estimate 2018 visibility improvements at VISTAS a nd nearby Class I areas. A number of annual model simulations were performe d that included the following:

3⁄4 2002 Initial Annual “Actual” Emissions Runs

. Initial 2002 annual model simulations and performance evaluations using the 2002 inventory for VIST AS and non-VISTAS states, Canada and Mexico were performed to confirm the appropriateness of the model science configuration(s) recommended by the Phase I work, to eval uate updates to the model and model inputs and to refine model performance. The 2002 Actual emission scenarios used day-specific fire emissions and emissions from El ectric Generating Units (EGUs) using continuous emissions monito ring (CEM) data. The initial CMAQ 2002 36/12 km Base A Actual base case simula tion used CMAQ Version 4.4beta and was performed during the Summer of 2004. The results and model performance evaluation are documented in Morris et al., (2004a).

3⁄4 2002 Revised Annual “Actual” Emissions Runs

. The 2002 emissions inventories for VISTAS and non-VISTAS stat es, Canada and Mexico we re updated with improved assumptions (MACTEC, 2006). The CMAQ m odel was updated to version 4.5 and a modified CMAQ module for Secondary Orga nics Aerosols (SOA) was developed and tested. Additional annual 2002 simulations were performed using the revised 2002 modeling inventories and revised CMAQ configur ations to evaluate model performance . The model performance evaluation for the final VISTAS 2002 Actual base case CMAQ simulation for the Base G2 emissions is disc ussed in Chapter 3 and Appendices B, C and
D.

3⁄4 2002 Annual Run with “Typical Year” EGU/Fire Inventory. Typical year emissions inventories were developed fo r EGU and fire to avoid atyp ical EGU outages or atypical fire activity in the 2002 modeling year from influencing the assumptions used in the projection year. Continuous emissions monitoring (CEM) data for the 2000-2004 baseline period for EGU were used to de velop the 2002 typical EGU inventories. Available fire activity data in the period 2000-2004 was used to develop 2002 typical fire
emissions. All other sectors used the sa me 2002 actual inventory for the 2002 typical inventory The 2002 typical inventories pr ovide the baseline modeled air quality condition against which future year modeling runs were compared to develop relative response factors (RRFs) for each pollutant specie s used in the visibility projections. This is further discussed in Chapter 4.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-6 3⁄4 Future Year Annual Base Case Runs. Future year simulations for 2018 were based on were performed that included a projected base case inventory of typical EGU emissions and current year baseline typi cal fire emissions for 2018. The objective of the future year model base case runs was to project 2018 visi bility conditions under conditions of growth and current (on-the-books) regulated contro l measures. These 2018 projections were compared to the Uniform Rate of Progress for 2018 (see Chapter 4) as one part of VISTAS states’ determination of reasonabl e progress goals for 2018. The 2018 visibility projections provided the modeled air quality basis against which alternative emissions assumptions were evaluated.

3⁄4 Future Year Emission Sensitivity Simulations. Future year sensitivity simulations were performed to assess the effects alternative future year emission assumptions and potential control measures would ha ve on visibility in Class I areas.

This report documents the 2002 and 2018 emissi ons and air quality modeling methods, 2002 model performance, and 2018 model projections for the Base G2 emissions inventory. The VISTAS 2018 Best and Final was delivered by the VISTAS states to the modeling team on October 1, 2007 (MACTEC, 2008). Final 2018 emissions and air quality modeling for the Best and Final inventory were provided in fall 2007 a nd the 2018 visibility proj ections using the 2018 Base G4 Best and Final inventories ar e presented at the end of Chapter 4.

1.3 Overview of 2002 Annual Emissions and Air Quality Modeling Approach

The VISTAS Phase II annual 2002 emissions and air quality modeling was initiated in 2004 and involved the preparation of nume rous databases, model simulati ons, presentations and reports. Much of the modeling analyses have been posted to the VISTAS modeling website at: http://pah.cert.ucr.edu/vistas/ . There were numerous versions a nd iterations of the modeling with interim results. The results presented in this TSD focus on the final Base G2 modeling results, for key findings in their development the reader is referred 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 VISTAS emissions and air quality and a form of communication of the modeling plans to the VISTAS particip ants (ENVIRON, Alpine, UCR and UCD, 2004). The Modeling Protocol was prepared following EPA guidance at the time it was prepared (EPA, 1991; 1999, 2001) and the modeling needs of the RHR SIPs. Th e first version of the Modeling Protocol was released in March 2004. Based on comments r eceived from VISTAS, the Modeling Protocol was updated to the current version that was date d May 6, 2004. This Modeling Protocol can be found on the VISTAS modeling website at:

http://pah.cert.ucr.edu/vistas/vistas2 /reports/vistasII_ Final_Protocol_05_10_2004.pdf

Under a separate EPA grant and contract for the Association for Southeastern Integrated Planning (ASIP), the modeling team is also pe rforming emissions and air quality modeling for 2009 to support southeastern states’ demonstr ation of attainment of the ozone and PM 2.5 nationalMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-7 ambient air quality standards. The same model configur ation and methods and the 2002 modeling results that are used for VISTAS are al so being used for the 2009 ASIP modeling. The ASIP Modeling Protocol dated January 31, 2006 (Morris et al ., 2006) updates some of the
methods described in the VI STAS Modeling Protocol.

1.3.2 Quality Assurance Project Plan (QAPP)

A Quality Assurance Project Plan (QAPP) was prepared for the VISTAS annual emissions and air quality modeling study that described the quality manageme nt functions performed by the modeling team. The QAPP was finalized Novemb er 17, 2004 (Morris, Tesche and Tonnesen, 2004; see: http://pah.cert.ucr.edu/vistas /vistas2/reports/VIS TAS_QAPP_Nov_17_2004.pdf). The QAPP was based on the national consensus standards for quality assurance (ANSI/ASQC, 1994), followed EPA’s guidelines for quality assu rance project plans for modeling (EPA, 2002), for QAPPs (EPA, 2001) and EPA modeling guid elines (EPA, 1991; 1999; 2001; 2007) and took
into account the recommendations from the No rth American Research Strategy for Tropospheric Ozone (NARSTO) Quality Handbook for modeli ng projects (NARSTO, 1998). The EPA and NARSTO guidance documents were developed speci fically for modeling projects, which have different quality assurance concer ns than environmental monitoring data collection projects. The work performed in this project involves m odeling at the basic research level and for regulatory/policy applications. In order to utilize model outputs for these purposes, it must be established that each model is scientifically sound, robust, and defensible. This is accomplished by following a project planning process that incor porates the following elements as described in the EPA guidance document for modeling:

• 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, and parameterization that is detailed enough so others can under stand the model output;

• Input data and parameters that are accura te and appropriate fo r the problem; and

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

A key component of the VISTAS 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 VISTAS modeling Website that posted displays of products (e.g., emi ssions plots, model outputs, etc.) for review by the VISTAS modeling team, workgroups and others . This Website can be found at: http://pah.cert.ucr.edu/vistas/vistas2/index.shtml.

1.3.3 Model Selection

The selection of the meteorologi cal, emissions and ai r quality models for the VISTAS annual regional haze modeling was based on EPA guidance (EPA, 1991; 1999; 2001; 2005b; 2007), a review of previous modeling studies and the VISTAS Phase I episodic model testing and evaluation (ENVIRON, Alpine and UCR, 2003a,b,c; 2004). The VI STAS annual emissions andMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-8 air quality Modeling Protocol (Morris, Tesche and Tonnese n, 2004) provides details on the justification for model selection and the formul ation of the different models. Based on the VISTAS Phase I comprehensive model testing a nd evaluation and other work, VISTAS selected the following models for use in modeling particul ate matter (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 Model (MM5 Version 3.6 MPP) is a nonhydrostatic, prognostic meteorological model rou tinely used for urban- and regional-scale photochemical, fine particulate, and regional haze regulato ry modeling studies (Anthes and Warner, 1978; Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 2000).

3⁄4 SMOKE : The Sparse Matrix Oper ator Kernel Emissions (S MOKE) modeling system is an emissions modeling system that generate s hourly gridded speciat ed emission inputs of mobile, non-road, area, point, fi re and biogenic emission sour ces 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 simulate d from ‘first principles’. This means that, with the exception of m obile and biogenic sources, it s purpose is to provide an efficient, modern tool for converting an exis ting base emissions inventory data into the hourly gridded speciated formatted emission files required by an air quality simulation
model.

3⁄4 CMAQ: EPA’s Models-3/Community Multis cale Air Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid de position at regional scale for periods up to one year (Dennis, et al., 1996; Byun et al., 1998a; Byun and Ching, 1999, Pleim et al., 2003).

1.3.3.1 MM5 Meteorological Model Configur ation for VISTAS Annual Modeling

Application of the MM5 for the 2002 annual modeling on a 36 km gr id for the continental U.S. and a 12 km grid for the eastern U.S. was pe rformed by Barons Advanced Meteorological Systems (BAMS). As part of Phase I of VISTAS, BAMS performed numerous MM5 simulations to identify the optim al configuration for simulating meteorology to support regional PM and visibility modeling of the eastern U.S. (Olerud, 2003a,b,c,d). Details of the 2002 36/12 km MM5 model application and ev aluation are available on the BAMS VISTAS project website (www.baronams.com/projects/VISTAS).

Based on the extensive sensitivity testing carr ied out by Olerud and Sims (2003) as part of VISTAS, the MM5 (Version 3.6, MMP) configurati on used by BAMS modelers for the VISTAS Phase II modeling consisted of the following (see Table 1-1 or www.baronams.com/projects/VISTAS for more details):

• Nested 36/12 km grids, with 34 vertical layers;

• Two way nesting, no feedback;

• Initialization and boundary conditions from Eta analysis fields;

• Pleim-Xiu (P-X) land soil model (LSM);March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-9 • Asymmetric Convective Mixing (ACM) planetary boundary layer (PBL) model;

• Kain-Fritsch 2 cumulus parameterization;

• Mixed phase (Reisner 1) cloud microphysics;

• Raptid Radiative Transfer Model (RRTM) radiation;

• Snow effect turned on;

• ETA model sea surface temperature;

• 24-category USGS vegetation data sets;

• Thermal roughness by the Garratt method; and

• Standard FDDA analysis nudging on 36 km and 12 km grid nests.

The emissions and air quality modeling team al so performed their own independent evaluation and quality assurance (Morris et al., 2004b) to a ssure that the MM5 modeling fields prepared by BAMS were transferred and processed correctly for input into CMAQ us ing the MeteorologicalChemical transport model Interface Program (MCIP). MCIP Version 3.0 was used in the processing which invoked the Ple im M3DRY dry deposition scheme in CMAQ that uses the MM5 P-X LSM parameters. MCIP Version 2 in cluded a significant upda te from previous versions in the calculation of layer collapsing. MCIP was also run using the option to specify land-use dependent minimum vertical turbulent exch ange coefficients (Kz) that ranged from 0.1
to 2.0 m 2 /s.

Table 1-1. MM5 Meteorological Model Configuration for VISTAS (Olerud, 2003a,b,c,d). Science Options Configuration Details/Comments Model Code MM5 Version 3.6 (MPP) Grell et al., 1994
Horizontal Grid Mesh 36/12 km 36 km grid 164 x 128 cells Continental U.S. 12 km grid 180 x 189 cells Eastern U.S. Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coord.
Grid Interaction No Feedback IFEED=0 Initialization Eta first guess fields/LittleR Boundary Conditions Eta first guess fields/LittleR Microphysics Reisner I Mixed Ice Look up table Cumulus Scheme Kain-Fritsch 2 On both 36/12 Grids Planetary Boundary Layer ACM PBL
Radiation RRTM Vegetation Data USGS 24 Category Scheme Land Surface Model P-X Land Surface Model (LSM) Cold restart every 5.5 days
Shallow Convection None Sea Surface Temperature Eta Skin Spatially varying Thermal Roughness Garratt
Snow Cover Effects None 4D Data Assimilation Analysis Nudging on 36/12 Integration Time Step 90 seconds
Simulation Periods Annual 2002
Platform Linux Cluster Done at BAMSMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-10 1.3.3.2 SMOKE Emissions Model Configuration for VISTAS Annual Modeling

SMOKE supports area, mobile, fire and point source emission processi ng and also includes biogenic emissions modeling thr ough a rewrite of the Biogenic Emission Inventory System, version 3 (BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis ). SMOKE has been available since 1996, and it has been used for em issions processing in a number of regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with the support of the U.S. Envir onmental Protection Agency (EPA), for use with EPA's Models3/CMAQ ( http://www.epa.gov/asmdnerl/models3 ) and is currently maintained and available from the CMAS center (www.cmascenter.org).

As an emissions processing system, SMOKE has far fewer ‘science c onfiguration’ options compared with the MM5 and CMAQ models. Ta ble 1-2 summarizes the version of the SMOKE system to be used and the sour ces of data to be employed in constructing the required modeling inventories. Emissions for the VISTAS stat es for processing by SMOKE were provided by MACTEC (2006; 2008). Details on the SMOKE emissions modeling for the VISTAS 2002 annual modeling is presented in Chapter 2 w ith emissions summary reports available in Appendix A.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-11 Table 1-2. SMOKE Emissions Model Configuration for VISTAS. Emissions Component Configuration Details/Comments

Emissions Model SMOKE Version 2.1 Errors in SMOKE Version 2.1 conversion of lat/lon coordinates to Lambert Confo rmal modeling grid resulted in displacement of sources and receptors. Discovered in Aug 2007. Receptor locations corre cted for Base G2 results. Horizontal Grid Mesh 36/12 km 36 km grid 148 x 112 cells RPO Unified Grid 12 km grid 168 x 177 cells VISTAS/ASIP eastern US 12 km grid

Area Source Emissions VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction MRPO Base K, CENRAP Base F, MANE-VU Version 3.1 and WRAP Plan02b

Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates

On-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction MRPO Base K, CENRAP Base F, MANE-VU Version 3.1 and WRAP Plan02b Mexico/Canada Emissions: 1999 Mexico from BRAVO. 2000 Canada

Point Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states and stakeholders Last update Base G2.

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction MRPO Base K, CENRAP Base F, MANE-VU Version 3.1 and WRAP Plan02b

Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates

Off-Road Mobile Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states Last update Base F

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction MRPO Base K, CENRAP Base F, MANE-VU Version 3.1 and WRAP Plan02b

Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates

Biogenic Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states
Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction

Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates

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

Chemical Speciation Revised CB4 Chemical Speciation Revised in 2004

Gridding Revised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/

Growth and Controls MACTEC (2006) MRPO Base K, CENRAP Base F, MANE-VU Version 3.1 and WRAP Plan02b Quality Assurance QA Tools in SMOKE 2.1 Indepe ndent QA with AG's Established Protocol
Simulation Periods Annual 2002

Platform Dual Athelon 2600 \+ Local 1.8 terabyte Ultra 320 RAID 5 system processing and storageMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-12 1.3.3.3 CMAQ Air Quality Model Configurat ion for VISTAS Annual Modeling

VISTAS used Version 4.51 of the Community Multi-scale Air Qual ity (CMAQ) modeling system with an enhanced secondary organic aerosol (SOA) module (SOAmods) using the model configuration shown in Table 1-3. The mode l was set up and exercised on the 36 km grid continental U.S. Inter-RPO modeling domain that was also used by WRAP and CENRAP. VISTAS also performed annual 2002 12 km modeli ng for an eastern U.S. modeling domain that included all of the VISTAS states as well as adjacent states to the west (CENRAP), in the Midwest (MRPO) and the Northeast (MANE-VU).

Initial CMAQ 2002 simulations performed by VISTAS found that the model greatly underestimate Organic Carbon Mass (OCM) concentra tions, especially in the summer (Morris et al., 2004b). A review of the CMAQ formulation f ound that it failed to treat Secondary Organic Aerosol (SOA) formation from sesquiterpenes and isoprene and also failed to account for polymerization of SOA so that it is no longer vo latile and stays in the particle form. The standard versions of CMAQ V4.51 assume that SOA is volatile so that once an aerosol is formed it can evaporate from particle to gaseous form depending on atmos pheric conditions (e.g., temperature and humidity). After a detailed li terature review, VISTAS updated the CMAQ SOA module to include these missing processes and found that the updated CMAQ V4.5 SOAmods produced much better OCM model performance in the summer (Morris et al., 2006c).

1.3.3.3.1 CMAQ V4.5 SOAmods Enhancement

The formulation of the CMAQ standard SOA module is described in Binkowski and Roselle (2003). In the CMAQ standard SOA module, S OA is formed primarily from aromatic VOCs and biogenic terpenes. The biogenic SOA precur sor emissions were modeled with the Biogenic Emissions Information System – Version 3 (B EIS3) model (Pierce et al., 2002). BEIS3 generates three biogenic VOC species: isopren e (ISOP), monoterpenes (TERP) and other biogenic VOC (OVOC). After testing of alterna tive gas-phase chemical mechanisms available in CMAQ (SAPRC99 and CBM-IV; ENVIRON, Alpi ne and UCR, 2003c)), VISTAS selected the Carbon Bond IV photochemical mechanism (G ery et al., 1987) for use in the annual air quality modeling since it produced essentially identical model performance as SAPRC99 and was more computationally effici ent. CBM-IV represents VOC co mpounds based on their carbon bond structure. The BEIS3 ISOP, TERP and OVOC species are speciated into the CB4 species for photochemical modeling in CMAQ as follows (molar speciation):

• ISOP = ISOP (isoprene is an explicit species)

• ALD2 = 1.5 x TERP

• OLE = 0.5 x TERP

• PAR = 6.0 x TERP

• NR = 0.5 x OVOC

• OLE = 0.5 x OVOC

• PAR = 8.5 x OVOC

• TERPB = TERPMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-13 Here, ALD2, OLE, PAR and NR are the CBM-IV chemical mechanism representations of the biogenic VOC emissions as high molecular we ight aldehydes, olefinic carbon bond, paraffin carbon bond and non-reactive functional groups. In CMAQ, the TERPB spec ies is specified in the emissions inputs, along with its CBM-IV re presentation of ALD2, OLE and PAR, but does not participate in the photochemi cal mechanism and is only used in the SOA formation module. The TERPB species forms a SGTOT species base d on oxidation parameters extracted from the photochemical module. SGTOT consists of the combined gaseous condensable gas (CG) plus particle SOA that are assumed to be in equili brium. CMAQ transports the SGTOT species and splits it to a CG gaseous and particle SOA for output.

The CMAQ TERB SOA formation rate is base d on a two-product fit to smog chamber data collected at the California In stitute of Technology for several biogenic monoterpene species (Binkowski and Roselle, 2003). A review of recent literature of biogenic SOA measurements identified several processes that may be important to biogenic SOA forma tion that are not treated by the BEIS3 biogenic emissions mo del and the CMAQ SOA module:

Polymerization : Recent measurements indicate that some SOA species may polymerize, resulting in species that are no longer volatile and ca nnot evaporate back to a CG. In this case, the equilibrium assumption between the CG and SOA will understate the amount of particle SOA present in the atmosphere (Kalberer et al., 2004; Jang et al., 2002).

Sesquiterpenes : Sesquiterpenes are not accounted for in the BEIS3/CMAQ SOA modeling system (Guenther et al ., 2000; Vizuete et al., 2004).

Isoprene : More recent evidence suggests that isoprene can also form particle SOA compounds that are not accounted for in CMAQ (Claeys et al., 2004; Matsunaga et al., 2003; 2005).

Acid Catalyzation : Recent literature al so suggests that some SOA formation may have acid catalyzed reactions (Claeys et al., 2004; Jang et al., 2005).

Heterogeneous Reactions : Recent evidence suggests th at some SOA formation may occur during heterogeneous aqueous-phase chemical reactions (Yu et al., 2005).

An enhanced SOA module was added to CMAQ th at accounted for the first three processes listed above. The last two processes were not in cluded in this work because there are not enough quantitative experimental data ye t to establish a parameterization. Modules were added to the CMAQ SOA module under the following constraints:

• The existing CMAQ SOA module for m onoterpenes would remain unchanged;

• The same CMAQ model inputs would be used; and

• The basic CMAQ model formulation would re main unchanged, modules would be added to account for polymerization and SOA from sesquiterpenes and isoprene.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-14 Figure 1-3 displays how the enhanced SOA modul e represented the new processes that were added to the CMAQSOA module to represent S OA polymerization and SOA formation from sesquiterpenes and isoprene along with the ex isting CMAQ SOA module st ructure and inputs. The new components of the SO A module are indicated in bol d italic, whereas the existing CMAQ SOA components (Binkowski and Roselle, 2002 ) use a regular font. There are several parameters that must be defined in the new elements of the enhanced SOA module: emission factors (EF), canopy escape efficiencies for ga ses (EEG) and aerosols (EEA) and SOA yields (Y). Based on an analysis of recent measurem ents, primarily from a recent biogenic emissions field study in Duke Forest, North Carolina (Stro ud et al., 2005; Matsunaga et al., 2005), a range of values for the factors in Figure 1-3 were de veloped as shown in Table 1-4. The enhanced SOA module used the mid-point of the range va lues for the factors from the measurements (Table 1-4), which represent the best estimates of these parameters given the information when they were developed. No attempt was made to optimize the parameters in Table 1-5 for OC/TCM model performance.

The emission factors, EF1 and EF2, relate the monoterpene emissions estimated by BEIS3 to emissions of monoterpenes, EF1 (e.g., α -pinene), and sesquiterpenes (EF2). Table 1-4 displays the range of EF1 and EF2 factors based on recent field study data (Stroud et al., 2005). Using the midpoint of the range results in emission factors of 0.7 fo r EF1 and 0.4 for EF2. EF1 is assigned a value of 0.7 based on field observations that indicate that the BEIS3 terpene emission factors are likely overestimated due to a tendency of earlier measurements approaches to artificially increase the emissions due to dist urbance when leaves were enclosed in the measurement system. Sesquiterp ene emissions were included us ing an EF2 value of 0.4 based on the ratio of the observed ses quiterpene emission from the Duke Forest field study to terpene emissions that are provided by BEIS2 TERP species (Stroud et al, 2005). Th e net result is that BEIS3 TERP emissions are increased by 10% a nd split 64% as monoterpenes and 36% as sesquiterpenes. The CG yields from the sesquite rpenes are assumed to partly condense into a non-volatile SOA particle that is modeled in CMAQ using the new secondary organic carbon species (SOC2) species and only some of the gas and aerosol species associated with sesquiterpenes are assumed to escape from the canopy using the mid-range of the Escape Efficiencies (EE) estimated by St roud et al. (2005). The fracti on of BEIS3 TERP emissions that are assumed to be monoterpenes (i.e., 64% of the emissions) are treated with the standard CMAQ two-product SOA module (Binkowski and Ro selle, 2003) assuming equilibrium between the CG and SOA with the SOA output in the st andard AORGB species (B inkowski and Roselle, 2003). The isoprene SOA formation pathway forms a CG using the mid-poi nt yield rate based on the range of recent measurements (Stroud et al., 2005) and a CG/SOA partitioning rate based on the mid-point of measurements from Matsunaga et al. (2003, 2005) (Table 1-4). The isoprene SOA is assumed to be volatile and is modeled as a new secondary organic carbon species in CMAQ SOAmods (SOC3). Finally, all SOA speci es, with the exception of the already nonvolatile SOC1 (polymerized SOA) and SOC2 (s esquiterpene product) species, are assumed to partially polymerize into non-volatile particles th at are stored in the SOC1 species. The polymerization rate is based on th e results of Kalberer et al (2004) who found that 50% of the SOA polymerized in 20 hours.

Several levels of Quality Assurance and Qualit y Control of the enhanced SOAmods module in the CMAQ model were conducted as follows.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-15 QA/QC of SOAmods Coding : The SOAmods implemen tation was conducted at ENVIRON. Staff at the University of Ca lifornia at Riverside performed independent QA/QC of the SOAmods code implementati on and independent te sting and evaluation.

QA of SOAmods Formulation : The new processes being added to the CMAQ SOA module were discussed with researchers at EPA’s Office of Research and Development (ORD). Although they have not completed all the laboratory tests, the inclusion of SOA from sesquiterpene and isoprene has b een observed and are supported by their preliminary measurements.

Peer Review of SOAmods : The formulation of the SOAmods enhancement to the CMAQ SOA module was documented, reviewed by many parties an d comments were provided. The results were also published in a peer-reviewed journal (Morris et al., 2006c).

Model Performance Evaluation of SOAmods : The final level of QA of the SOAmods was comparisons of CMAQ V4.4 model perfo rmance with and without including the SOAmods enhancement. Table 1-5 displays fractional bias error for Organic Carbon Mass (OCM) IMPROVE and STN monitoring sites in the VISTAS, MRPO, MANE-VU and CENRAP states using th e standard CMAQ Version 4.4 (V4.4) and then CMAQ V4.4 with the SOAmods enhancement. Here the original IMPROVE equation was used to adjust the IMPROVE measured OC to OC M (i.e., OCM = 1.4 x OC). Whereas the standard CMAQ V4.4 underestimates OCM acr oss IMPROVE sites from –76% (MRPO) to –102% (VISTAS), with the SOAmods enha ncement the fractional biases centered on zero and ranged from –14% to +8%. Simila r results are seen for OCM fractional bias across the more urban STN sites where the CM AQ V4.4 exhibits an un derestimation bias of –67% to –105%, when using SOAmods the under-prediction bias is –27% to –44%.
Note that the continued underestimation of OCM across the urban STN sites is likely due to missing primary OCM emissions and uncerta inties in the STN OCM measurements. Also note that a 1.4 multiple was used to co nvert the measured OC to OCM, if a 1.8 factors was used for such conversion, as gi ven in the new IMPROVE equation, than the OCM underprediction bias would be even greater.

With the release of CMAQ Versions 4.5 in October 2005, the SOAmods enhancement was added to the AERO3 aerosol module in CMAQ Version 4.5 that was compared against the standard CMAQ Versions 4.5 and SOAmods was found to pr oduce similar improvements in OCM model performance as seen with CMAQ Versions 4.4. In March 2006 Version 4.51 of CMAQ was released that included active Sea Salt chemistry and Sea Salt emissions. Given that many of the VISTAS Class I areas are located in the coasta l environment then Sea Salt may be important. Consequently, the SOAmods enhancement was implemented in the AERO4 (AE4) aerosol module in CMAQ V4.51 and that was the vers ion used in the fina l 2002 and 2018 Base G2 modeling.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-16 Table 1-3. CMAQ Air Quality Model Configuration for VISTAS. Science Options Configuration Details/Comments

Model Code CMAQ Version 4.51 with SOAmods enhancement Pleim et al., (2005); Morris et al., (2006c)

Horizontal Grid Mesh 36/12 km 36 km covering cont. U.S; 12 km covering eastern US 36 km grid 148 x 112 cells RPO National Grid 12 km grid 168 x 177 cells VISTAS 12 km EUSA 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 3-hourly GEOS-CHEM annual run Day-specific 3-hour BCs from Global Climate Model
Emissions Baseline Emissions Processing SMOKE (Ver 2.2) MM5 Meteorology input to SMOKE, CMAQ

Dust Transport Fraction Applied in emissions before SMOKE Tom Pace updates

NH3 Inventory Adjustment Applied in emissions before SMOKE Sub-grid-scale Plumes No Plume-in-Grid (PinG) Chemistry Gas Phase Chemistry CBM-IV Aerosol Chemistry AE4/ISORROPIA Includes active Sea Salt

Secondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) w/ SOAmods enhancement Schell et al., (2001); Morris et al, (2006c)
Aerosol Mass Conservation Patch Yes Georgia Institute of Technology Update Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processes N2O5 Reaction Probability 0.01 – 0.001

Meteorological Processor MCIP Version 3.0 Includes dry deposition for Sea Salt and percent urban minimum Kz (PURB) Horizontal Transport

Eddy Diffusivity Scheme K-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approach Vertical Transport Advection Scheme Yamertino V4.5 Mass Conservation (Yamertino) Eddy Diffusivity Scheme K-theory Diffusivity Lower Limit Kzmin = 0.1 to 2.0 PURB option in Ver 4.5 Planetary Boundary Layer No Patch

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

Gas Phase Chemistry Solver Euler Backward Iterative (EBI) solver Hertel et al (1993) EBI solver ~ 2x faster than MEBI

Horizontal Advection Scheme Piecewise Parabolic Method (PPM) scheme Simulation Periods Annual 2002 With~15 day spin-up in December 2001 Integration Time Step Determined by met conditions 15 minute coupling time step Platform Athlon MP 2600+ MPI using 6 processors per quarterMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-17 Figure 1-3. Schematic describing the addition of new SOAmods processes (bold italic) to the existing CMAQ SOA module (regular font) to treat polymerization and SOA formation from sesquiterpenes and isoprene (see Table 1-4 for parameters).

Table 1-4. Parameters use in SOAmods module to enhance CMAQ (see Figure 1-3).
Parameter Mid-Point Range
EF1 0.7 0.4 ~ 1.0 EF2 0.4 0.2 ~ 0.6 EEG1 0.325 0.2 ~ 0.45 EEA1 0.2 0.05 ~ 0.35

Y2 0.875 0.75 ~ 1.0 Y1 0.11 0.06 ~ 0.16 P1 0.45 0.15 ~ 0.75 EF1 = emission factor of monoterpenes to the TERP emissions estimated by BEIS3 EF2 = emission factor of sesquiterpenes relative to the TERP emissions estimated by BEIS3 EEG1 = escape efficiency of gas phase precursor of sesquiterpenes from canopy EEA1 = escape efficiency of SOA from sesquiterpenes from canopy Y1 = SOA yield of oxidated isoprenes Y2 = SOA yield of sesquiterpenesBiogenic emissions



### TERP ISOP ISPD Y1

P1



### PAR
OLE
ALD2

TERPRXN A_TRP1 G_TRP1

A_TRP2 G_TRP2 ISPRXN SGTOT_ISP G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) 

### AORGB

ASOC2 ASOC3

* Y3

* Y4 O

O

O: Oxidation reaction with OH, O3, and NO3
ISPRXN: The amount of reacted ISPD with oxidants
TERPRXN: The amount of reacted TERP with oxidants Dotted box: Not implemented in the initial test Bold italic: New processes 

### ASOC1 Polymerization 

### All SOAs except SOC1 & SOC2 FPOLY EF1Biogenicemissions 

### TERP ISOP

OVOC



### PAR
OLE
NR ISPD Y1

P1



### PAR
OLE
ALD2 SGTOT

_ TRP1

TERPRXN SGTOT
_TRP2 ISPRXN SGTOT_ISP G_ISP

A_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) 

### ASOC2 ASOC3

*

* O O

O

O: Oxidation reaction with OH, O3, and NO3
ISPRXN: The amount of reacted ISPD with oxidants
TERPRXN: The amount of reacted TERP with oxidants Dotted box: Not implemented in the initial test Bold italic: New processes 

### ASOC1 Polymerization 

### All SOAs except SOC1 & SOC2 FPOLY EF1March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-18 Table 1-5. Results of initial evaluation of the SOAmods enhancement comparison of fractional bias performance metric for Organic Carbon Mass (OCM) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAm ods enhancement (observed OCM = 1.4 x OC). IMPROVE OCM STN OCM July 2002 Fractional Bias V4.4 SOAmods V4.4 SOAmods

Southeastern U.S. - 102% -2% -105% -32% Midwestern U.S. -76% +12% -67% -24% Northeast U.S. -82% -14% -95% -44% Central U.S. -98% +8% -81% -27%

1.3.4 Horizontal Modeling Domains

VISTAS used the same 36 km continental U.S. and 12 km eastern U.S. modeling domains in the Phase I and II modeling. The 36 km Inter-RPO coarse grid continental Unite d States horizontal modeling domain is the same domain used for CENRAP and WRAP modeling. The CMAQ domain is nested in the MM5 domain. The selec tion of the MM5 domain is described in the VISTAS MM5 modeling protocol (Olerud, 2003a,b,c,d). Figure 1-4 displays the MM5 horizontal domain as the outer bluest gri d. Also shown in Figure 1-4 is th e CMAQ 36 km domain nested in the MM5 domain. To achieve finer spatial resoluti on in the eastern U.S., including the VISTAS region, VISTAS also used a one-way nested higher resolution grid with a 12 km grid resolution. Figure 1-5 displays the 36 km CMAQ continental grid and the high resolution, nested 12 km grid for the eastern U.S. Figure 1-6 shows in more detail the 12 km grid for the eastern U.S. region that is the focus of VISTAS.

Both MM5 and CMAQ employ the Regional Pl anning Organization (RPO) unified grid definition for the 36 km continental domain. Th e RPO unified grid consists of a LambertConformal map projection using the map proj ections parameters listed in Table 1-6.

Table 1-6. RPO Lambert Conformal Projection (LCP) unified grid definition.
PARAMETER VALUE projection Lambert-conformal alpha 33 degrees beta 45 degrees x center 97 degrees y center 40 degrees

The MM5 36 km grid includes 164 cells in the east- west dimension and by 128 cells in the northsouth dimension. The CMAQ 36 km grid includes 148 cells in the east-west dimension and 112
cells in the north-south dimension. Because the MM5 model is also nested in the Eta model, there is a possibility of boundary effects n ear the MM5 boundary that occur as the Eta meteorological variables are being simulated by MM5 and must come into dynamic balance with MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid cells around each boundary of the CMAQ 36 km do main. This is designed to eliminate any errors in the meteorology from boundary effects in the MM5 simulation at the interface of the MM5 and Eta models. The buffer region used here exceeds the EPA suggestio n of at least 5 grid cell buffer at each boundary.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-19 Table 1-7 lists the number of rows and columns a nd the definition of the X and Y origin (i.e., the southwest corner) for the 36 km and 12 km gr ids for both MM5 and CMAQ. Note that the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are reversed. In Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells while “cross” refers to the grid mesh defined by the grid cell centers. Thus, th e dimension of the dot mesh is equal to the cross mesh plus one. Finally, we note that th e grid definition for the CMAQ Meteorology Chemistry Interface Processor (MCIP), SMOKE emissions model and CMAQ Chemical Transport Model (CCTM) are identical.

Table 1-7. Grid definitions for MM5 and CMAQ using the RPO LCP unified grid coordinate system (Table 1-7).

MODEL COLUMNS DOT(CROSS) ROWS DOT(CROSS) XORIGIN YORIGIN MM5 36km 129 (128) 165 (164) -2952000 -2304000 CMAQ 36km 149 (148) 113 (112) -2736000 -2088000

MM5 12km 190 (189) 181 (180) 7200 -1656000 CMAQ 12km 169 (168) 178 (177) 108000 -1620000

In Aug 2007 it was discovered that the SMOKE ve rsion 2.1 provided incorrect projections from lat/long coordinates to Lambert Conformal coordina tes. This error results in a displacement of stack locations of approximately 2-3 km, so point sources in the Base G2 2002 and 2018 modeling inventories could be displaced spatially by one 12 km or 36 km grid cell to another. In general, this displacement was of a few kilometers so had a minimal effect on the location of grid cells for the point sources in the VISTAS 36/12 km modeling. This error has not been corrected. The location of the receptor monitors has been corrected in the Base G2 2018 visibility projections.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-20 Figure 1-4 . Nesting of VISTAS 36-km CMAQ grid in the MM5 36-km grid.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-21 .

Figure 1-5. Nesting of CMAQ 12-km grid in the CMAQ 36 km grid.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-22 Figure 1-6. Domain definition for the VISTAS higher resolution CMAQ 12 km eastern U.S. gridMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-23 1.3.5 Vertical Domain Definition

The CMAQ vertical layer structure is primarily defined by the vertical grid used in the MM5 modeling. The MM5 model employed a terrain follo wing coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. Table 1-8 lists the layer definitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the computational cost of the CMAQ simulations. The effects of layer averag ing were evaluated in the VISTAS Phase I modeling effort and found to have a relatively minor effect on the model performance metrics when both the 34 layer an d a 19 layer CMAQ models were compared to ambient monitoring data (ENVIRO N, Alpine and UCR, 2003c).

Table 1-8. Vertical layer definition for the VISTAS MM5 simulations (left most columns) using 34 layers and approach for reducing CMAQ layers by collapsing multiple MM5 layers (right columns). MM5 CMAQ 19L

Layer Sigma Pressure (mb) Height (m) Depth (m) Layer Sigma Pressure (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 1.000 1000 0 0March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-24 1.3.6 2002 Calendar Year Selection

The calendar year 2002 was selected for VISTAS regional haze annual mode ling as described in the VISTAS Modeling Protocol prepared in 2003 (ENVIRON, Alpine and UCR, 2003d). EPA’s applicable guidance on PM 2.5 /Regional Haze modeling at th at time (EPA, 2001) identifies specific goals to consider when selecting mode ling periods for use in demonstrating reasonable progress in attaining the regional haze goals. Howe ver, 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 guidance addresses all thr ee in a common document. 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 as in interim place holder until the final guidance was published in April 200 7 (EPA, 2007a) as part of the PM 2.5 /regional haze NAAQS implementation process. The interim EPA m odeling guidance for episode selection (EPA, 2001;Timin, 2002) was consistent with the final guidance (EPA, 2007a).

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

• A variety of meteorological c onditions should be covered that includes the types of meteorological conditions that produce the Wo rst 20% and Best 20% visibility days at Class I areas in the VISTAS states during the 2000-2004 baseline period;

• 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

• 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 over the 20% worst and 20% best visibility days determined for each Class I ar e based on monitoring data from the 2000 – 2004 baseline period. Additional EPA guidance (Timin, 2002) suggests that states should model at least 10 worst and 10 best visibility days at each Class 1 area. EPA also lists several ‘other considerations’ to bear in mind when choosing potential PM/regional h aze episodes including: (a) choose periods which have already been modeled, (b) choose periods which are drawn from the years upon which the current design values are based, (c) include we ekend days among those chosen, and (d) choose modeling periods that meet as many episode se lection criteria as possible in the maximum
number of nonattainment or Class I areas as possible.

VISTAS elected to model a complete single Calendar Year (CY) to assure that at least one year of worst 20% and best 20% visibility days would be available at each VI STAS Class I area (i.e., 24 worst 20% and 24 best 20% days assuming full IM PROVE data capture for the selected year). The RHR uses the five-year baseline of 2000-2004 as the starting point for projecting future-year visibility. Thus, the modeling year should be se lected 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:March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-25 • Based on available information, appears to be a fairly typical year in terms of meteorology for the 5-year Baseline period of 2000-2004;

• 2003 appeared to be colder and wette r than typical in the eastern US;

• The enhanced IMPROVE and IMPROVE Protoc ol and Supersite PM monitoring data were fully operational by 2002 with much le ss IMPROVE monitoring data available during 2000-2001;

• IMPROVE data for 2003 and 2004 were not yet available at the time the VISTAS annual modeling was initiated; and

• 2002 was being considered by the other RPOs.

1.3.7 Initial Concentrations and Boundary Conditions

The CMAQ model was operated separately for each of four quarters of the 2002 year using a ~15 day spin up period (i.e., the model was started approximately 15 days before the first day of interest in each quarter in order to limit the influence of the assumed initial concentrations, e.g., start June 15 for quarter 3 whose first day of interest is July 1). Sensitivity simulations demonstrated that with ~15 initialization days, the influence of initial concentrations (ICs) was minimal using the 36 km Inter-RPO continental U.S. modeling domain. Consequently, clean ICs were specified in the CMAQ 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 1-4) 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 Assimila tion Office. It is applied by research groups around the world to a wide range of atmos pheric composition 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 coordinated by VISTAS in which Harvard University applied the GEOS-CHEM global model for the 2002 calenda r year (Jacob, Park and Logan, 2005). The University of Houston (UH) was retained 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 year 2002. Th ese were obtained from 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 upda ted GEOS-4 assimilation, with horizontal
resolution of 1 o x1.25 o and 48 vertical layers (note 1 o latitude is equally to approximately 110 km). The horizontal resolution was 4 ° x5 ° . The emissions used in GEOS-CHEM included U.S. anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; international emissions for the most recent year available, a nd forest fire information specific to 2002. Emissions were aggregated to monthly average in acknowledgement that greater accuracy is not feasible for international emissions. This GEOS-CHEM run included the secondary organic aerosol (SOA) formation mechanism from Chu ng and Seinfeld (2002), prototype soil dust and sea salt simulations, and applic ation of surface emissions and dr y deposition to the entire mixed layer column rather than to just the surface layer of the model.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-26 The GEOS-CHEM output was processed by mapp ing the GEOS-CHEM chemical compounds to the species in the CBM-IV chemical mechan ism used by CMAQ and mapping the GEOS-CHEM vertical layers to the 19 laye r vertical layer structure used by CMAQ in the VISTAS modeling (Byun, 2004). The results were day-specific th ree-hourly BC inputs for the CMAQ model.
The BCs generated from the 2002 GEOS-CHEM we re subjected to QA/QC. The first QA/QC check was a range check to assure reasonable values. The BCs were compared against the GEOS-CHEM outputs to assure th e mapping and interpolation wa s performed correctly. The code used to map the GEOS-CHEM output to the CMAQ BC format was obtained from UH, reviewed and the BC generation duplicat ed for several time periods during 2002.

1.3.8 Emissions Input Preparation

Emissions for the CMAQ model were prepared using the SMOKE emissions modeling system with the configuration given in Table 1-2. Four types of emissions inventories were prepared:

• 2002 Actual Base Case;

• 2002 Typical Base Case;

• 2018 Base Case; and

• 2018 Sensitivity Case.

The differences between the 2002 Actual and Typical Base Cases were emissions from Electrical Generating Units (EGUs) and fires. For the 2002 Actual Base Case day-specific emissions forthe EGUs based on measurements from continuous emissions monitoring (CEM) data and dayspecific fire emissions were us ed. For the 2002 Typical Base Ca se the EGU and fire emissions are representative of the 2000-2004 5-year Base line period. The 2002 Actual Base Case was used when comparing the CMAQ modeling resu lts to the 2002 measurements in the model performance evaluation, whereas the 2002 Typical Base Case results were used with the 2018 modeling results to make visibility projections.

The base emissions for the VISTAS states were provided by the VISTAS emissions contractor (MACTEC, 2006; 2008). Emissions for Non-VISTAS states were obtained from the other RPOs during late 2006. These data were either provided in the SMOKE IDA format or in the case of the MRPO converted to the IDA format for i nput into SMOKE. Emissi ons for Mexico were based on the 1999 BRAVO inventory and a year 2000 inventory was used for Canada. Emissions for stationary point, ar ea, on-road mobile, non-road mob ile, fires and biogenic sources were processed. Day-specific biogenic emi ssions were generated using the SMOKE-BEIS3 program and the hourly 2002 36/12 km MM5 data discussed previousl y. In order to account for seasonal and day-of-week effects, on-road mobile sources were simulated for a week from each month of 2002 using the SMOKE-M OBILE6 module and the MM5 mete orological data as input. Holidays were modeled as a Sunday. Similarly, ar ea and non-road emissions were modeled for a Thursday, Friday, Saturday, Sunday and Monday from each month with emissions for Thursday used to represent Tuesday and Wednesday and Holidays used Sunday emissions. More details on the emissions modeling is provided in Chapter 2.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-27 1.3.9 Meteorological Input Preparation

The VISTAS 2002 36/12 km MM5 modeling was conducted by Barons Advanced Meteorological Systems (BAMS) as described previously (Olerud, 2003a,b,c,d). The VISTAS annual emissions and air quality modeling team processed the 2002 36/12 km MM5 data using the MCIP processor version 3.0 for input into CMAQ and conducte d QA/QC on the CMAQ meteorological input files.

1.3.10 Photolysis Rates Model Inputs

Several chemical reactions in the atmosphere ar e initiated by the photo dissociation of various trace gases. To accurately represent the comple x chemical transformations in the atmosphere, accurate estimates of these photodi ssociation rates must be made. 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 measured 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 a nd was obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . Dayspecific TOMS data was used in the CMAQ radi ation model (JPROC) to calculate photolysis rates. The TOMS data were missing or bad for several periods in 2002: August 2-12; June 10; and November 18-19. Thus, the TOMS data fo r August 1, 2002 was used for August 2-7 and TOMS data for August 13 was used for August 8-12. Similarly, TOMS data for June 9 was used for June 10 and data for August 17 was used for August 18-19. Note that the total column of ozone in the atmosphere is dominated by stratosp heric ozone which has very little day-to-day variability so the use of TOMS data within a week or two of an actua l 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.

1.3.11 Air Quality Input Preparation

Air quality data used with the CMAQ modeling syst em include: (1) Initial Concentrations (ICs)
that are the assumed three-dimensional concentr ations through the modeling domain at the very start of the simulation; (2) the Boundary Conditions (BCs) that are the concentrations assumed along the lateral edges of the 36 km inter-RPO continental U.S. modeling domain; and (3) airMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-28 quality observations that are used in the model performance evalua tion discussed in Section 3 of this TSD.

The CMAQ default clean Initial Concentrations (I Cs) were used along with an approximately 15 day spin up (initialization) period to elimin ate any significant influence of the ICs on the modeled concentrations for the days of interest. That is, the model is started approximately 15 days prior to the first day of interest (e .g., started on around December 16, 2001 for January 1, 2002) to allow concentrations to build up in the model to real levels in the atmosphere. CMAQ was run for each quarter of the year so was initia lized 15 days prior to th e first day of interest (e.g., for quarter 3, Jul-Aug-Sep, the model wa s initialized on June 15, 2002 with the first modeling day of interest July 1, 2002).

The CMAQ Boundary Conditions (BCs) for the In ter-RPO 36 km continental U.S. grid were based on day-specific 3-hour averages from th e output of the GEOS-C HEM global simulation model of 2002 (Jacob, Park and Logan, 2005). The 2002 GEOS-CHEM output was mapped to the species and vertical layer structure of CMAQ and interpolated to the lateral boundaries of the 36 km grid shown in Figure 1-4 (Byun, 2004).

1.3.12 2002 Base Case Modeling and Model Performance Evaluation

The CMAQ model was evaluated against ambien t measurements of PM species, gas-phase species and wet deposition within and outside of the VISTAS region. Figure 1- 7 displays the locations of monitoring sites within and nearby the VISTAS region. Ambient measurements from the IMPROVE, STN, CASTNet, NADP, SE ARCH and AQS monitoring networks were used in the VISTAS CMAQ model performance ev aluation (note that the locations of the AQS sites are not shown in Figure 1-7 because thei r shear number would obscure the figure). In general, the model performance of the CMAQ model for sulfate (SO4) and elemental carbon (EC) was good, performance for nitrate (NO3) wa s variable with a summer underestimation and winter overestimation bias. Model performance for organic carbon mass (OCM) was also variable, whereas performance fo r Soil and coarse mass (CM) was generally poor. More details on the model performance evaluation can be found in Chapter 3.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-29 Figure 1-7. Locations of monitoring sites within and nearby the VISTAS region (locations of AQS sites not shown).

1.3.13 2018 Modeling and Visibility Projections

Emissions for 2018 base case were generated follo wing the procedures discussed in Chapter 2 and in the Emissions Inventor y report by MACTEC (2008). 2018 emissions for Electrical Generating Units (EGUs) were based on simulati ons of the Integrated Planning Model (IPM)
that took into account the effect s of the Clean Air In terstate Rule (CAIR) on emissions from EGUs in CAIR states using an IPM realization of a CAIR cap a nd trade program. In some cases the IPM projected EGU emissions did not agr ee with the state projections so those EGU emissions were adjusted to match state exp ectations (MACTEC, 2008). Emissions for on-road and non-road mobile sources were based on activity growth and emissions factors from the EPA MOBILE6 and NONROAD models, re spectively. Area sources and non-EGU point sources were grown to 2018 levels using assumptions from EPA and DOE. The Canadian year 2000 emissions inventory was replaced by a Cana dian 2020 emissions inventory for the 2018 CMAQ simulations and emissions in Mexico were held constant. The following controls were assumed in the 2018 Base G modeling scenarios: 250 500 750 1000 1250 1500 1750 2000 -1500 -1250 -1000

-750 -500 -250 0 250 500

IMPROVE

CASTNet

SEARCH

STN

NADP

A QSMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-30 • Clean Air Interstate Rule (CAIR) and Clean Air Mercury Rule (CAMR) for EGUs using IPM output.

• NOx SIP Call.

• North Carolina Clean Smokestack Act.

• Various Consent Agreements (e.g., TECO, VEPCO, Gulf Power Crist 7).

• 1-Hour Ozone Sips (Atlanta / Birmingham / Northern Kentucky).

• NOx RACT.

• Heavy Duty Diesel Rule.

• Tier 2 Tailpipe Rule.

• Large Spark Ignition and Recreat ional Vehicle Nonroad Rule.

• Nonroad Diesel Rule.

• Industrial Boiler/Proces s Heater/RICE MACT.

• Combustion Turbine MACT.

• VOC 2-, 4-, and 10-year MACT Standards.

The following sources were assumed to remain unchanged between the 2002 and 2018 base case
simulations:

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

• 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.

The results from the 2002 Typical Base G2 and 2018 Base G2a CMAQ simula tions were used to project 2018 PM levels from which 2018 visibil ity estimates were obtained. The 2002 and 2018 modeling results were used in a relative sense to scale the observed PM concentrations from the 2000-2004 Baseline and the IMPROVE monitoring network to obtain the 2018 PM projections. The 2002/2018 modeled scaling factors are called Re lative Response Factors (RRFs) and consist of the ratio of modeling result s of the 2018 model simulation to the 2002 model simulation. The two important regional haze metrics are the av erage visibility for th e worst 20% (W20%) and best 20% (B20%) days from the 2000-2004 five -year Baseline. For the 2018 W20% days projections, EPA guidance recommends developi ng RRFs specific to each PM component and each Class I area based on the ratio of the average modeled PM species concentrations for the 2018 to 2002 model simulations. The results of the VISTAS 2018 visibility projections following EPA guidance procedures are provided in Chapter 4. VISTAS has also developed alternative procedures for visibility projec tions that are discussed in Chapter 5.

The 2018 visibility projections for the W20% da ys are compared against the 2018 Uniform Rate of Progress. The 2018 Uniform Rate of Progress is obtained by constructi ng a linear visibility Glidepath in deciview from the observed 2000-2004 Baseline for the W20% days to Natural Conditions in 2064. Where the lin ear Glidepath crosses the year 2018 is the 2018 Uniform Rate of Progress. States evaluate the modeled 2018 vi sibility projection compare to the 2018 Uniform Rate of Progress as one part of their evaluatio n to define Reasonable Progress Goals for their RHR SIPs. However, as noted in EPA’s RPG guidance “The glidepath is not a presumptiveMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-31 target, and States may establish a RPG that provides for greater, lesser, or equivalent visibility improvement as that described by the glidepath” (EPA, 2007b).

Figure 1-8 displays the VISTAS 2018 Base G2 visibility proj ections using the EPA guidance (EPA, 2007a), the new IMPROVE equation for conve rting PM concentrations to visibility extinction and the VISTAS 2002 Typical and 2018 BaseG2a 12 km CMAQ modeling results. Class I areas in the interior of the VISTAS re gion, including those in th e Appalachian Mountains as well as Sipsey and Mammoth Cave, achieve the 2018 Unifor m Rate of Progress by a fair margin. The Swanquarter and Cape Romain Cl ass I areas on the coas t of South and North Carolina, respectively, also achieve the 2018 Unif orm Rate of Progress. The St. Marks and Chassahowitzka, Florida Class I areas are near the 2018 Uniform Rate of Progress, whereas Okefenokee Georgia and Everglades Florida fall slightly less than the 2018 Uniform Rate of Progress.

For the Class I areas near the VISTAS states, the four CENRAP Class I areas in Missouri and Arkansas (CACR, UPBU, MING and HEGL) and th e Brigantine Class I area on the coast of New Jersey north of the VISTAS region are projected to meet the 2018 Uniform Rate of Progress. The Breton Island Class I area off the s outhern coast of Louisiana is projected to fall short of the 2018 Uniform Rate of Progress.

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm

VISTAS non-VISTAS Figure 1-8. 2018 visibility projections expressed as a percent of achieving the 2018 URP goal for the 2018 BaseG2a CMAQ 12 km base case simulation using the EPA default (EPA, 2007) visibility projection procedures and the new IMPROVE equation.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-32 The 2018 visibility projections in Figure 1-8 were based on the Base G2 emissions inventory that was released in July 2007 (MACTEC, 2008). Sin ce then, several VISTAS States have updated their point source emissions to reflect improve d knowledge of EGU emissi ons and controls in 2018. Some of the VISTAS States have also specified changes to non-EGU point sources to reflect new information on anticipated controls and shutdowns. The new 2018 inventory is referred to as the 2018 Best and Final (BaF) in ventory, which is also called the 2018 Base G4 inventory. There were both incr eases and decreases in emissions between the 2018 Base G2 and Base G4 inventories with a net change of -1% and +1% for, respectively, SO2 and NOx emission across the VISTAS States. Figure 1-9 compares the 2018 visibility proj ections using the 2018 G2a and 2018 Base G4/BaF 12 km CMAQ modeling results.

CMAQ 2018g4a vs 2018g2b Me thod1 Prediction at VISTAS+ sites on W20% Days

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 2018g4a 12k

2018g2b 12k

VISTAS non-VISTAS Figure 1-9. 2018 visibility projections expressed as a percent of achieving the 2018 URP goal for the 2018 BaseG2a and 2018 Base G4 (BaF) CMAQ 12 km base case simulation using the EPA default (EPA, 2007) visibility projection procedures and the new and original (old) IMPROVE equation.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-33 1.3.14 Additional Supporting Analysis

VISTAS perform additional supporting analysis of its modeling results and visibility projections to help verify the validity of th e 2018 visibility projec tions that are discussed in Chapter 5 and included the following:

• Verification of the VISTAS 2018 visibility projections using the new EPA Modeled Attainment Test Software (MATS).

• Comparison of the VISTAS 2018 visibility projections with those from CENRAP, MRPO and MANE-VU.

• Analysis of alternative visibility projections:

o Use of the original IMPROVE equation.

o Sensitivity analysis assuming Coarse Ma ss (CM) and CM and Soil are natural.

o Model projections accounting for NO3 and SO4 performance issues.

o Use of CART weights in the develop of RRFs.

o Accounting for International Tr ansport in the projections.

• Analysis of observed trends in visibility ac ross the VISTAS and nearby Class I areas for the worst 20 percent days, best 20 per cent days and all IMPROVE sampled days.

• Area of Influence Analysis to examine SO2 sources most likely to contribute to elevated SO4 concentrations at a Class I ar ea during the worst 20 percent days.

1.4 Known Errors in the Base G2 and G4 Modeling

In addition to the uncertainties associated with the emissions, meteorological and air quality modeling and associated procedures for projec ting future year visibi lity discussed throughout this document, there are several known errors in the Base G2 and Base G4 (BaF) modeling that are listed below most of which are discu ssed in more detailed in Section 2.7:

• Errors in Point Source Locati on Coordinate Transformations : The SMOKE Version 2.1 emissions modeling system used in the VIST AS modeling uses an I/O API routine for converting point source latitude/longitude coordinates to th e Lambert Conformal Projection (LCP) coordinate system used by MM5/CMAQ in the VISTAS modeling. Although SMOKE calls the I/O API routine with the correct parameters to use the MM5/CMAQ LCP coordinate system, the I/O API coordinate tran sformation routine ignores the correct LCP parameters and uses its default parameters. The result is that the locations of point sources are off by approximately 2-3 km in the Base G2 modeling. In most cases this would not have an effect on the modeling as the point source would be in the same 36 km or 12 km grid cell, but in some cases it could cause a point source location to be located in an adjacent grid cell to the cell it should reside.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_1_Intro3_updated.doc 1-34 • Incorrect Point Source Location Parameters : As part of the continuing quality assurance of the Base G2 modeling, point sources were screened to see whethe r they were within 100 km of the centroid of the county where they were suppose be located. A number of point sources were so identified and many of them were valid locations within larger counties. Some of the points so identified did have invalid coordi nates; however these turned out to be small point sources with em issions of any single pollutant less than 80 tons per year.

• Missing Low Level Fires : During the Base G2 emissions modeling emissions from a few low level fires were inadvert ently dropped. These dropped fire emissions were for a few prescribed burns in six counties in Kentucky and one county each in Tennessee and West Virginia.

• Double Counting Off-Shore Marin Em issions in the Gulf of Mexico : VISTAS developed an off-shore marine vessel emissions inventory for the Pa cific and Atlantic Oceans and the Gulf of Mexico. When updating the emi ssions from Base D to Base G, VISTAS received a new off-shore oil and gas productio n inventory for the Gulf of Mexico from the MMS. Previously inventories from MMS included emissions from platform oil and gas production and support vessels and aircraft . However, this time the MMS inventory also included off-shore marine vessels whic h resulted in the double counting of emissions from off-shore marine vessels in the oil and gas development zones in the Gulf of
Mexico.

• 2018 Gasoline RVP Error : The Summer/Winter MOBI LE6 emission inputs were inadvertently switched for the 2018 SMOK E-MOBILE6 on-road mobile sources emissions modeling resulting in higher gasoli ne RVP in the summer than there should which produces higher VOC and CO emissions from on-road gasoline vehicles. To investigate this, VISTAS reran the SMOKE -MOBILE6 and CMAQ m odels for the 2018 emissions scenario using the corrected gasoline RVP for July only and compared the 2018 visibility projections for the original Ba se G2 or Base G2a simulation to the one with the corrected RVP in July (BaseG2b). The 2018 visibility projections for the two runs with the different ga soline RVP were identical.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-1 2.0 DEVELOPMENT OF EMISSION INPUTS

The base year emissions inventory for Base G2 of the VISTAS modelin g is founded on revised 2002 emissions developed by VISTAS emission i nventory contractors in NEI Input Format (NIF) 3.0. These revised BaseG/G2 emissions we re reviewed by VISTAS stakeholders and considered complete in March of 2007. Control a ssumptions, growth rates, and other inputs used to develop the 2018 forecasts were initially de veloped in 2004, many of them carried forward into these documented inventories. Additional growth and control modifications have since been identified or analyzed by VISTAS and its particip ating States or contractors, but for reasons of maintaining consistency between the various s cenarios, have not been included in these
documented files and associated runs. The de velopment of the 2002 Base G2 inventory is provided in MACTEC (2008).

Non-VISTAS state emissions for the 2002 episode y ear are based on inventor ies obtained by the Study Team during late 2006. Emission inventorie s for 2002 and 2018 for each of the other four RPOs (MANE-VU, MRPO, CENRAP, and WRAP) we re directly acquired from the RPOs and either converted from their nati ve format into structures or temporal basis consistent with VISTAS modeling or used dir ectly in the SMOKE IDA format s provided (see Table 1-2 for versions). The exception was for MRPO’s 2018 base case, which was not av ailable in an annual format, and therefore was prepared using the late st available 2002 base y ear data (Base K) and growth and control factors (Round IV) as provided by MRPO staff.

Mexican and Canadian emissions used for 2002 ar e based on the latest available inventories obtainable by the Study Team in formats lendi ng themselves to emissions modeling. Mexican
emissions are based on the EPA Phase II 1999 Mexican National Emission Inventory (MNEI) 1 and 2000 and 2020 (representing 2018) Canadian em issions are based on EPA releases of Environment Canada invent ories in SMOKE IDA or preprocessed CMAQ formats 2 . These inventories were collected in the December 2006 timeframe.

For purposes of air quality m odel validation, actual 2002 calenda r year emissions for EGU and fire activity were used within the VISTAS domain, while during strategy and future year emission runs, “typical year” emissions for th ese categories were processed. Outside of the VISTAS RPO, 2002 emissions obtained from the non-VISTAS RPOs were used in an “as-is” format with no modification made for actual or typical operation.

All emissions were converted to Inventory Data Analyzer (IDA) format and the data were processed for air quality modeling using Version 2.1 (except for elevated fires where Version 2.0 was used) of the Sparse Matrix Operating Kernel Emissions (SMOKE) mode l. Included in these runs is the temporal, spatial, and speciation prof iles and cross-reference data currently provided with the 2.0 release of the model augmented with recommended and ap proved emission profile data provided by the VISTAS stake holders or emissions inventory contractor or obtained from EPA prior to initial emissions modeling. The pro cessing was adjusted for each run to account for the specific air qua lity model (AQM) input required by CMAQ.

1 http://www.epa.gov/ttn/chief/net/mexico.html 2 http://www.epa.gov/ttn/chief/net/canada.htmlMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-2 2.1 Emissions Modeling Methodology

Emissions inventory development for photochemi cal modeling must address several source categories including: (a) stationa ry point sources, (b) area sour ces, (c) on-road mobile sources, (d) non-road mobile sources, and (e) biogenic s ources. For this analysis, inventories were developed for 2002 actual, 2002 typical, and 2018.

Development of an emissions inventory customi zed for the VISTAS region requires a merging of: (a) the most recent pertinent regional inve ntory and (b) available high-resolution, localespecific emissions estimated by local, state, and regional agencies in the VISTAS region. Local air regulatory and transportation planning agencies are generally the best sources of domain specific activity and control factors to use in developing the base year emissions. Often, these
local emissions data sets come from a variety of sources, frequently in different formats.

Contacts with VISTAS’ emission inventory cont ractors and the RPOs were established and formal requests made for inventory corrections , updates and ancillary da ta pertinent to the modeling of emissions in their jurisdictions. Where feasible and consistent with project resources and schedule, these update d data sets were acquired and used to create day-specific modeling inventories specific to the VISTAS domain for the base year modeled.

2.2 Set-up of SMOKE Over the VISTAS Domain

SMOKE was configured to generate point, area, nonroad, highway, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs were maintained in separate source category files in order to allow maximum flexibility in producing alternate strategies. Settings for each of the source categories are disc ussed in relevant sections below. With the exception of biogenic and highway mobile sour ce emissions that are generated using the, respectively, BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions were processed using the month, day, and hour specific temporal (for point sources with CEM data) profiles of the SMOKE model.

To produce an emissions inventory to support a nnual modeling, representative time periods were selected and modeled. Area, nonr oad, and point sources were modeled as a block of Thursday, Friday, Saturday, Sunday, Monday one per month (total of 60 days modeled). On-Road motor vehicles were represented by an entire single w eek for each month. This selection criterion allows for the representation of day-of-the-week variability in the on-r oad motor vehicles, and models a representation of the meteorological variability in each month. EGU sources which mapped to continuous emissions monitoring (C EM) data were processed using CEM-based temporal profiles. Holidays were modeled as Sund ays. A list of modeled holidays is provided in Table 2-1. The biogenic emissions were mode led on a day specific basis (365 days).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-3 Table 2-1. SMOKE Modeled Holidays.

Population was used as a gridding default for all source categorie s when the assigned surrogate would cause SMOKE to drop emissions. This can be a case when the county-level emission inventories are prepared using surrogates other than those av ailable for modeling purposes.

The domain for the Phase II episode is based on the EPA’s 36-km national CMAQ domain, illustrated in Figure 2-1 below.

Figure 2-1 . EPA 36-km National CMAQ Domain.

Date Julian Day Holiday Description January 1, 2002 2002001 New Year's Day March 29, 2002 2002089 Good Friday May 27, 2002 2002147 Memorial Day July 4, 2002 2002185 July 4th September 2, 2002 2002245 Labor Day November 28, 2002 2002332 Thanksgiving Thurs November 29, 2002 2002333 Thanksgiving Fri December 24, 2002 2002358 Christmas Eve December 25, 2002 2002359 Christmas DayMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-4 The parameters for the SMOKE runs are as follows:

Episodes : 2002 Calendar Base Year (Actual and Typical)

Future Year: 2018.

Output Time Zone: Greenwich Mean Time (zone 0)

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

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

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

Layer Structure: The CMAQ layer structure is 19 layers, with specific layer positi ons defined in the meteorology files (see Table 1-8).

CMAQ Model Species: The CMAQ initial configuration is for th e CB-IV chemical mechanism with PM. The model species in the emissi on input files are: CO, NO, NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO3, POA, PSO4, and PMC.

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

Elevated Sources: All sources were treated by SMOKE as potenti ally elevated. No plume-in-grid sources were modeled. Where day specific and locatio n specific wildfire data was provided by states and Federal Land Managers, wildfire emissions were handled as point sources.

Producing 365 day-specific input fi les for all source categories places a burden on available computing facilities, data management systems, and would have adversely affected the modeling schedule. Selecting representative model days fo r some or all of the source categories reducesMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-5 the processing and file handling re quirements to a more manageable level and in most cases does not compromise the accuracy of the emissions files.

Other current or recent projec ts undertaken by EPA, WRAP and LADCo have used a selection approach for all of the source categories (ex cept biogenics) that use a representative weekday/Saturday/Sunday either for each month or each season to model all of the emissions files. In an attempt to better represent the level of temporal and spatial detail available for each source category, we have developed and implemented a more detailed strategy.

Biogenic emissions were modeled for each epis ode day, using the daily meteorology. Point sources, including CEM and elevated fire emissions were modeled for each episode day to take advantage of the available day-specific emissi ons and meteorology. Area sources, including non-road mobile, low level fires and dust emissions do not utilize meteor ological data, and are temporally allocated by monthly, daily and hourly profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved by selecting representative Thursday, Friday, Saturday, Sunday, and Monday profiles for each month.

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

On-Road Mobile Sources Repres ented by the Following Weeks:

January 13-19 February 10-16 March 10-16 April14-20 May 12-18 June 9-15 July 14-20 August 11-17 September 15-21 October 13-19 November 10-16 December 15-21

2.2.1 Processing of Point Source Emissions

Stack parameters are often more important to the reliability of the air quality modeling results than the emissions rates themselves. Stack paramete r data are frequently incorrect, especially in some of the current regional modeling inventories and careful QA is require d to assure that the point source emissions are properly located bot h horizontally and vertically on the modeling grid. To screen for simple, but potentially serious inventory errors such as these, the study teamMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-6 has modified procedures or iginally developed by EPA 3 to quality assure, augment, and where necessary, revise, stack parameters to examine the accuracy of the point source emissions, as well as standardize procedures to identify and correct stack data errors. These procedures were implemented in the NIF to IDA conversion step of the inventory development. Additionally, SMOKE has a number of built-in QA procedures de signed to catch missing or out-of-range stack parameters. These procedures were also invoke d in the processing of the point source data.

For the final baseline modeling, we separated th e point source emissions into EGU and non-EGU categories. The non-EGU categor y was not processed using any day or hour-specific emissions inputs. All non-EGU point source emissions were temporally a llocated to month, day, and hours using annual emissions and source category code (SCC) based allocation factors. These factors were based on the cross-referen ce and profile data supplied w ith the utilized SMOKE version and were supplemented with relevant data provided to the study team by VISTAS and its

contractors.

For EGU sources with EPA reported continuous emissions monitoring (CEM) data for 2000- 2004 or with hourly emissions provided by stakehol ders, actual hourly data were used. For those sources where EPA CEM data are utilized, NOx, SO2, and he at input-based hour-specific profiles were developed and applied to NOx, SO 2, and all other emissions, respectively. This ensured that the annual emission values provide d by the EI contractor were maintained, but distributed using hourly to annual profiles. For sources providing hour-specific data and where they were approved by the State in which they operated, those data were substituted for EPA CEM-based emissions and distributions.

To temporally allocate the remaining EGU point sources (those which do not report under the CEM program), the NOx, SO2, and heat input data were collected from the 2000-2004 CEM datasets, and used to develop un it-level typical tempor al distributions. CEM data from 2002 were used to develop comparable profiles and emi ssion distributions during the actual 2002 model validation runs. The hour, day of week, and monthly specific tem poral profiles were used in conjunction with the EI supplied emissions data to calculate hourly EGU emissions by unit.

All point sources were spatially allocated in the domain based on the stationary source geographic coordinates. If a poi nt source was missing its latitude /longitude coordinates and data could not be found to properly site the unit with in the domain, the source was placed in the center of its reported county.

2.2.2 Processing of Area and Non-Road Source Emissions

All area and non-road source emissions were te mporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allocation factors. These factors were based on the cross-referen ce and profile data supplied w ith the utilized SMOKE version and were supplemented with relevant data provided to the study team by VISTAS or its contractors. Area and non-road sources were sp atially allocated in the domain based on SCC-

3 ftp://ftp.epa.gov/EmisInventory/2002finalnei/documenta tion/point/augmentation_point/2002nei_qa_augmentation_r eport0206.pdfMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-7 matched spatial allocation factor files. If an area or non-road source SCC did not have an existing cross-reference profile assigned to it, the county-leve l emissions were allocated by population density in the respective county.

A county-specific crustal PM transport factor was applied to fugitive dust emission sources that had been identified in U.S. EPA modeling to have only a portion of its mass transportable from the source of the emission generation. These EPA’s studies 4 indicate that 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simulation. For this reason, the modeling team applied county-specific fugitive dust emissions transport factors to thes e sources to adjust PM emissions during the conversion of emission input files from the raw mass emissions. These adjusted PM emissions are reported in summary emissions tables.

2.2.3 Processing of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE was used to deve lop the base year on-road mobile source emissions estimates for CO, NH3 NOx, PM a nd VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates were combined with gridded, episode-specific temperature data to calculate th e gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link loca tion and subsequently summed to the grid cell level, the off-network em issions estimates are spatially allocated based on a combination of the FHWA version 2.0 high way networks and population. For the VISTAS 36/12 km modeling, no link based data was used. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by the meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:

• Hourly and daily minimum/maximum temperatures;

• Facility speeds;

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

• Adjustments for running losses;

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

• VMT, fleet turnover, and changes in fuel composition and Reid vapor pressure (RVP).

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and main tenance program in effect, type of oxygenated fuel program in effect, alternat ive vehicle mix profiles, RVP of in-use fuel, operating mode) that
direct the calculation of the MOBILE6 emissions factors.

For the production of the MOBILE6 emission factors SMOKE was run using the hourly episodespecific meteorological data for temperature an d humidity. SMOKE produces emissions factors for state and county groups that are selected fo r regional similarities and consistent MOBILE6 option requirements (i.e. I/M programs, RVP, fuel programs). The hourly average temperature and humidity are calculated from the hourly temper atures in each grid cel l in the state/county

groups.

4 http://www.epa.gov/ttn/chief /emch/invent/index.html#dustMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-8 SMOKE was run using the daily average speed opti on for all states except North Carolina. The daily average speed was provided based on stat e, county, and roadway type. North Carolina provided hourly average speeds, also base d on state, county and roadway type.

2.2.4 Processing of Biog enic Source Emissions

A revised version of a commonl y used biogenic emissions model, the Biogenic Emissions Inventory System (BEIS), was developed and tested by EPA over two separate modeling domains/episodes. This version of the mode l (BEIS-3, v.0.9) contains several changes over BEIS-2, including the following:

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

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

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

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

For this particular application of BEIS-3, version 0.9 as curren tly incorporated in the SMOKE processor was used. This means that: 1) soil NO emissions shall be prepared without the input of specific soil moisture and precipitation data and 2) MEOH emissions will not be modeled explicitly.

The BELD-3 landuse data on a Lambert conformal grid at 1-km resolution have already been developed, are available, and were used to estimate biogenic emi ssions in this study. The BEIS model also requires as input hourl y, gridded temperature and sola r radiation data to estimate biogenic emissions, and these data were derived from the MM5 predictions.

2.2.5 Processing of Wildfi res and Prescribed Burns

Wildfire, agricultural, and prescribed burn emissi ons were handled separately from the standard area source input files. We us ed day specific or monthly estim ates of fire emissions from VISTAS, which include burn acreag e and biomass loading information for the VISTAS states. Depending on the completeness and quality of the data received, VISTAS-specific calculations were made for spatial and temporal distributions of the fire emissions, rather than relying on standard distribution profiles. We calculated vertical distribution of the fire emissions, based on fire size and biomass involveme nt. SMOKE v2.0 can model fire pl ume rise when provided with the following variables:

• PTOP – Top of the fire plume profile (meters above ground level)

• PBOT – Bottom of the fire plume pr ofile (meters above ground level)March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-9 • Lay1 – The percent of the emissions en trained in the first modeling layer

For those elevated fires having the necessary data elements to site these files with distinct time and space coordinates, these variables were prepar ed and included in the modeling files used to process this emission source type. For low level fires where these discrete spatial and temporal data were not available, we distributed emissions in the lowest modeled vertical layer using the month, day of week and diurnal temporal profile s calculated from the total ASIP state fire
distribution files.

The WRAP Fire Emissions Joint Forum Emissions Inventory Report 5 has documented an approach for calculating these pl ume descriptors. In this me thod, which was used in VISTAS modeling, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to calculate representative hourly plume profiles. These profiles are then used by SMOKE 2.0 to distribute the vertical emissions for the fires. To successfully model fires as el evated point sources, th e data included both the day or days on which the fire occurs, and a spatial identifier of the fire location.

2.2.6 Windblown Dust

PM10 and PM2.5 emissions from wind erosion of natural geogenic sources (SCCs 2730100000 [total] and 2730100001 [dust devils]) were excluded from the resu lting modeling files as these meteorological and episode specific categories could not be accurately reflected using the precalculated emission estimates and temporal profiles available at th e time of the modeling.

2.2.7 Sea Salt

In March 2006 Version 4.51 of CMAQ was released that includes Sea Salt emissions and active Sea Salt chemistry. Given the number of coastal VISTAS Class I areas this could be important so VISTAS updated the CMAQ V4.51 with the SOAmods enhancement and it was used for the final Base G2 simulations. CMAQ V4.51 internal ly generates Sea Salt emissions using a user supplied OCEAN file. Thus, Sea Salt emissions were not addressed in the SMOKE processing.

2.3 SMOKE-Specific Proce ssing Characteristics

The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridde d speciated emission inputs of mobile, nonroad mobile, area, point, fire and biogenic emission s ources for photochemical grid models. SMOKE
is one of the fastest em issions processing tools currently available to the air quality modeling community. The sparse matrix approach util ized throughout SMOKE permits both rapid and flexible processing of emissions data. The pro cessing is rapid because SMOKE utilizes a series
of matrix calculations instead of less efficien t algorithms used in previous systems. The processing is flexible because the processing st eps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allo cation have been separated into independent

5 http://www.wrapair.org/forums/fejf/documen ts/WRAP_2002_PhII_EI_Report_20050722.pdfMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-10 operations wherever possible. The results from these steps are merged together at a final stage of processing. Each of these emissions processing steps is detailed below.

2.3.1 Temporal Allocation

VISTAS 2002 and 2018 annual emi ssions modeling were configured to generate point, area, nonroad mobile, on-road mobile, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs were main tained in separate source category files in order to allow maximum flexibility in producing alternate strategies. With the exception of biogenic
and on-road mobile source emissions that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions we re processed using the month, day, and hour specific temporal profiles of the SMOKE model. Area and nonroad sources were modeled as a block of Thursday, Friday, Saturday, Sunday, and Monday one per month (total of 60 days modeled). On-road motor vehicle sources were modeled for one seven-day week per month. Point sources and biogenics were modeled for each day of the annual period.

VISTAS based its temporal profiles and sour ce category cross-reference files on the EPA CAIR/CAMR/CAVR modeling platform with f iles located on EPA’s CAIR file transfer website 6 . Modifications were made to reflect State specific profiles or updated state of knowledge application of these profiles. Some of these changes included the reallocation of North Carolina NONROAD generated emission categor ies to a regional set of temporal profiles more consistent with the operation of these so urce types in the State. Additionally, EGU CEMbased temporal profiles and onroad emissions modeling were prepared in manners deviating from EPA’s original CAIR platform.

As noted previously, on-road mobile modeling in SMOKE was done for selected weeks (seven days) of each month - using these days as a “r epresentative week” of the entire month. This selection allows for the representation of day- of-the-week variability in the on-road motor vehicles, and models a representation of the me teorological variability in each month. VISTAS executed sensitivity tests to examine this “representative week” methodology versus an everyday on-road mobile modeling method 7 . VISTAS determined that the use of representative week onroad mobile emissions produced ozone and partic ulate matter concentrations (and thus regional haze) that were nearly indi stinguishable from the “everyday” mobile method. VISTAS
determined that the difference in the modeled air quality - resulting from the on-road mobile modeling methods - was insignificant.

2.3.1.1 CEM-Based Temporal Profile Development and Application

Two sets of monthly profiles were developed for processing EGU emissions with CEM data:

1. Profiles based solely on actual 2002 CEM-base d data at the state level. The 2002-only profiles are intended to be used by VISTAS in developing model performance evaluation metrics necessary for configuring air quality mo dels in attainment de monstration analyses.

6 ftp://www.airmodelingftp.com/ 7 http://www.epa.gov/ttn/chief/confere nce/ei15/session9/abraczinskas.pdfMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-11 2. Profiles based on historical averages of 2000 through 2004 CEM-based data. These historical 2000-2004 average profiles were developed and ar e recommended to be used to represent consistent “typical” operating condi tions at EGUs in the VISTAS domain for the base year and future year emission estimates.

Analyses conducted by the modeling team 8 indicate an added benefit to the modeling results with the application of CEM-based day-of-week and diurnal profiles, in a ddition to the monthly profiles for each state. For the majority of E GU units processed in the VISTAS domain, unit specific monthly, weekly, and diurnal profiles we re developed and applied using historical CEM averages of heat input and emissions and as furthe r described below. As an additional part of this analysis, specific day-of-week (Monday, Tuesda y, Wednesday, etc.) and diurnal profiles were also developed for each month and State to better represent operating conditions at units within a State. The State specific day of week and di urnal profiles were develo ped from averages of CEM-based emissions and heat i nput activity occu rring on that day-of-weekor during that hourof-day across all units within that State. Thes e profiles were then appl ied to units were CEM specific matches could not be made to VISTAS emission inventories.

Data Obtained

Five years (2000 through 2004) of hourly CEM information from EPA = s CAMD website were obtained for each unit in the VISTAS states 9 . The “Prepackaged Data” option allows the download of files containing emissions data for a specific state, quarter or month, and year. Each prepackaged data file is in .csv (comma deli mited) format and contains the following fields: State, Facility Name, Facility ID (ORISPL), Unit ID, Date, Hour, SO 2 Emissions (lbs), CO 2 Emissions (tons), NO
x Emissions Rate (lb/mmBtu), NO
x Emissions (lbs), H eat Input (mmBtu), Operating Time (hours), Gross Load (MW), and St eam Load (1000 lb/hr). Fo r this analysis, we obtained the prepackaged monthly unit-level hour ly emissions data by state and year. Using these data, we reformatted the files and quality assured for applicability to this analysis.

File Contents

The reformatted files were prepared as identified in Table 2-2.

Quality Control / Quality Assurance

Each file was reviewed to dete rmine if NOx, SO2 and heat input values were represented for each hour of every day for each unit in the obtained data. Zero values were considered to be valid if operating time identifiers indicated no operation during that hour (e.g., data value of zero but operating hours grea ter than zero).

Using the measurement flags and field values in the reformatted files, numerous spot checks were made of anomalous or missing variable da ta to ensure that da ta corruption was not impacting the statistical analyses. Additionally, each year’s hourly total of NOx, SO2, and heat input (per state) were summed and compared to EPA annual summaries of the same data
elements.

8 http://www.epa.gov/ttn/chief/confe rence/ei14/session11/stella.pdf 9 http://cfpub.epa.gov/gdm/index.cfm?fuseaction=prepackaged.selectMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-12 When there were facilities or units with no emissions data or unit ch aracteristics, we verified that these sources are not required to report emissions data or had not yet reported emissions data to EPA. In some cases, certain months or quarters of the year were blank for individual units or facilities and using EPA data caveat reports, we ve rified these units were not in operation during
those times.

Inventory Matching

Prior to the development of the unit-specific SO2, NOx, and heat input ratios for each hour, the step of matching CEM units to the VISTAS 20 02 modeling inventory st arted. Because naming convention and facility or unit numbering can be unique at the Federal, State, local, or facility level, the step of matching exis ting units from an emissions inventory to the CEM data base proved to be more complicated than anticipated.

The VISTAS EGU emission inventory accounted fo r approximately 3.7 million tons of SO2 and 1.5 million tons of NOx in calendar year 2002. There were 861 units reporting to the CEM database in 2002 for the ten VISTAS States. Th e primary objective of the inventory matching steps was to account for as many units and t ons as possible allowing for the unit-specific application of hourly tempor al distribution profiles.

Table 2-2. CEM data file format.

Column Description

State State in which the facility is located. Facility Name The name given by the owners and operators to a facility.

Facility ID (ORISPL) The unique six-digit facility identification num ber, also called an ORISPL, assigned by the Energy Information Administration, a co mponent of the Department of Energy.

Unit ID Each unit at a facility has a unique identification number. It is alphanumeric and may be from one to six characters in length. For utility unit s and other units that generate energy for sale, the unit ID used for Part 75 reporting is the same unit ID that appears in the National Allowance Database (NADB) (for Acid Rain Pr ogram units) or in the State's allowance allocation list. Day Day on which a unit was operating. Hour Hour on which a unit was operating. Operating Hours Percent of hour in which a unit was operating. Gross Load (MW) Gross load is the output of the unit as measured in megawatts. Steam Load (1000 lb/hr) Steam load is the output of the unit as measured in 1000 lb/hr of steam.

SO 2 Mass (lbs) SO 2 released for the hour in pounds.

SO 2 Mass Measurement Flag Indicates whether the value for SO 2 mass was measured or derived due to missing data.

SO 2 Rate (lbs/mmBtu) SO 2 emissions rate in pounds per millio n British thermal units (lbs/mmBtu).

SO 2 Rate Measurement Flag Indicates whether the value for SO 2 rate was measured or derived due to missing data.

NO xRate (lb/mmBtu) NOx emissions rate in pounds per million British thermal units (lbs/mmBtu).

NO xRate Measurement Flag Indicates whether the value for NO xrate was measured or derived due to missing data.

NO xMass (lbs) NO xreleased for the hour in pounds.

NO xMass Measurement Flag Indicates whether the value for NO xmass was measured or derived due to missing data.

CO 2 Mass (lbs) CO 2 released for the hour in pounds.

CO 2 Mass Measurement Flag Indicates whether the value for CO 2 mass was measured or derived due to missing data.

CO 2 Rate (lbs/mmBtu) CO 2 emissions rate in pounds per million British thermal units (lbs/mmBtu).

CO 2 Rate Measurement Flag Indicates whether the value for CO 2 rate was measured or derived due to missing data. Heat Input (mmBtu) Heat per hour as calculated by mult iplying the quantity of fuel by the fuel's heat content.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-13 Under the direction of VISTAS, emissions inventor y contract staff prepared comparisons of the VISTAS 2002 emission inventory of EGU sources to that of CEM-based emissions, heat input, and operating characteristics. For each unit iden tified as an EGU source in the VISTAS inventory, an attempt was made to match it to a CEM unit and associated data.

Automated facility (ORIS) and unit identificat ion was made for a majority of units who maintained the same numbering and nomenclature between the two data sets. This first computerized step captured the majority of emi ssions by matching some of the largest units in the VISTAS domain. The remaining steps were followed in order to match the outstanding
facilities and emissions as reported by VISTAS States in the 2002 emission inventory.

Inventory contractors developed county-level reports of the rema ining unmatched facilities and units from the VISTAS inventory and made co mparisons of annual emissions of SO2 and NOx to the CEM-based SO2 and NOx for sources also id entified within the same county. This step of the matching process allowed an incremental amount of emissions and units to be accounted for and assigned unit-specific profiles for model performance evaluation.

Finally, remaining VISTAS inventory and CEM s ources were manually compared to each other
in an effort to determine if reporting errors in State or county codes or facility or unit
identification codes accounted for this reminder of unmatched sources. These manual matches were confirmed or revised with VISTAS State a nd stakeholder participati on and input. With this step, a few sources were identified to have faci lity identification changes or misreported county codes preventing automated matching from occurri ng and corrected for the final application of
factors.

Once all methods of comparison were exhauste d, the remaining unmatched VISTAS emission inventory of EGU sources was excluded from the unit-specific profile assignment steps and was allocated more generalized facility or State temporal profiles as described in the following
section.

This inventory comparison process allowed for the match of over 650 of the 861 CEM identified units (76%) to the VISTAS EGU emission inve ntory for 2002. More importantly, however, was the match of 99.95 percent of the SO2 emissions and over 99.4 percent of the NOx emissions from these sources in the VISTAS domain.

Profile Calculations

Two sets of profile types have been develope d for modeling EGU emissions within the VISTAS domain. The first set are to be applied to indivi dual units able to be matched to CEM data, the second are to be applied to EGU sources within the VISTAS domain wh ere CEM-based matches
could not be identified.

The first set of temporal profile s have been developed for specif ic hour-of-date periods based on historical actual 2002 or average NOx, SO2, and heat input data for sources reporting under EPA’s CEM program between 2000 and 2004. Thes e profiles are based on the actual or statistical average of the CEM da ta variables (NOx, SO2, and heat input) for each hour-of-date (e.g., Hour 12 of March 3) during the year. In the typical profile cal culation, variables areMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-14 calculated for each hour when the operating time of the CEM is greater than 0 (e.g., the unit is in operation during that hour). In the case of 2002-only calculat ions, all reported NOx, SO2, and heat input data were used in the averagi ng, including those identifie d as non-operating hours. This allowed for the best representation of actu al 2002 conditions for the expected use of these profiles for model validation studies.

In the second set of profiles, NOx, SO2, and heat input values were averaged over each unit to allow for the calculation of Stat e level monthly, day-of-week, and diurnal profiles for VISTAS
States.

For the 2000-2004 averaging period, representation of typical operating c onditions was desired, so in the averaging calculation only valid opera ting hour NOx, SO2, and heat input values were used. This prevented the introduction of equipm ent shutdown because of power outages, control installation, or planned maintenance in to the temporal profile calculation.

Actual 2002 Profiles

Through the EPA’s Clean Air Market’s Data a nd Maps website, quarte rly unit-level hourly emissions data by State and calendar year 20 02 were obtained for purposes of developing temporal allocation factors applicable to EGU s ources within the VISTAS domain. Key elements in these data sets include the State where the unit is located, facility name, facility identification (ORISPL) code (assigned by the Department of Energy at the Energy Information Administration), unit identificati on code, date of record, hour of record, SO2, CO2, and NOx mass (in lbs per hour), heat i nput (million British thermal units [MMBtu]), and NOx emission rate (lbs/MMBtu).

SO2 and NOx mass and heat input values were summed for each unit to an annual level to allow
for the calculation of an hour of date-to-annu al ratio estimation. Equation 2-1 provides this calculation for heat input. Tabl e 2-2 provides an example resu lt of the ratio calculation.

Equation (2-1) 

# ∑

= 1

31 , , , / Jan

Dec date hr date hr ratio hi hi hi

where hi = heat input (MMBtu)

Since it was assumed that all sources in the VI STAS EGU inventory woul d not be matched to individual CEM-based units, the same calculati ons were performed for each State so that a hierarchical application of ratios (unit first, State sec ond) could be assigned as necessary. Table 2-3 shows example ratios calculated for each mont h by State. Table 2-4 reflects an example of the State-month-day of week ra tio calculation and Table 2-5 s hows a State-month-diurnal ratio calculation example. Each of these ratios were ca lculated for each State in the VISTAS domain and used in instances where CEM unit matches c ould not be made to the VISTAS base year emissions inventory.

Three parameter values (SO2 mass, NOx mass, h eat input) were calculated at each aggregation as NOx and SO2 emissions vary due to fuel blend, sulfur content, or seas onal control and are not necessarily representative of the other variables’ seasonal, daily, or even hourly variation. AsMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-15 seen in Figure 2-2, when view ed on a VISTAS-domain total, the monthly variation in relative distribution of SO2, NOx, and heat input differs enough to jus tify calculating each parameter value set of temporal profiles with CEM data.

Table 2-3. Application of Calculated Ratios for Actual 2002 by Unit.

Table 2-4. Application of Calculated Ratios for Actual 2002 by Example State and Month. Actual Reported Values [2002] Calculated Ratios

ORISPL UnitID Date Hour SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input

3 2 01-01-2002 0 15.563 10.294 13.3 1.417E-06 1.915E-06 1.372E-06

3 2 01-01-2002 1 14.977 8.338 12.5 1.364E-06 1.551E-06 1.289E-06

3 2 01-01-2002 2 14.93 9.286 12.6 1.360E-06 1.728E-06 1.300E-06

3 2 01-01-2002 3 14.774 9.677 12.8 1.346E-06 1.800E-06 1.320E-06

... ... ... ... ... ... ... ... ... ... 3 2 07-01-2002 0 1084.017 717.467 995.1 9.873E-05 1.335E-04 1.026E-04

3 2 07-01-2002 1 1102.47 750.04 1012.2 1.004E-04 1.395E-04 1.044E-04

3 2 07-01-2002 2 1109.41 768.55 1016.6 1.010E-04 1.430E-04 1.049E-04

3 2 07-01-2002 3 1102.598 772.614 1012.6 1.004E-04 1.437E-04 1.044E-04

3 2 07-01-2002 4 1087.909 736.967 998.6 9.909E-05 1.371E-04 1.030E-04

3 2 07-01-2002 5 1099.375 731.888 1009.5 1.001E-04 1.362E-04 1.041E-04

3 2 07-01-2002 6 1127.007 693.779 1026.3 1.026E-04 1.291E-04 1.059E-04

3 2 07-01-2002 7 1203.814 644.008 1114.2 1.096E-04 1.198E-04 1.149E-04

... ... ... ... ... ... ... ... ... ... 3 2 12-31-2002 21 712.26 503.505 835 6.487E-05 9.367E-05 8.612E-05

3 2 12-31-2002 22 716.983 587.419 850.1 6.530E-05 1.093E-04 8.768E-05

3 2 12-31-2002 23 521.311 430.787 647.8 4.748E-05 8.014E-05 6.681E-05

3954 3 Annual Sum 10979533.36 5375215.80 9695608.12 1.000 1.000 1.000

Actual Reported Values [2002] Calculated Ratios

State Month SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input
FL Jan 67,755,539 42,004,513 113,531,981 0.072 6 0.0813 0.0733
FL Feb 56,516,278 34,145,451 91,969,840 0.060 5 0.0661 0.0594
FL Mar 69,997,283 39,244,669 107,685,763 0.075 0 0.0759 0.0695 FL Apr 73,678,638 40,824,242 118,170,997 0.078 9 0.0790 0.0763 FL May 88,889,603 48,974,695 142,351,045 0.095 2 0.0948 0.0919
FL Jun 79,736,153 44,027,147 138,648,667 0.085 4 0.0852 0.0895
FL Jul 94,401,559 50,007,339 157,075,598 0.101 1 0.0968 0.1014 FL Aug 93,041,423 50,077,048 160,601,359 0.099 6 0.0969 0.1037 FL Sep 93,349,234 49,183,990 155,433,110 0.100 0 0.0952 0.1003 FL Oct 84,214,449 46,837,495 146,347,289 0.090 2 0.0906 0.0945
FL Nov 60,374,969 33,098,684 105,854,682 0.064 7 0.0641 0.0683
FL Dec 71,853,245 38,331,463 111,702,695 0.076 9 0.0742 0.0721 FL Total 933,808,373 516,756,735 1, 549,373,024 1.000 0 1.0000 1.0000March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-16 Table 2-5. Application of Calculated Ratios for Actual 2002 by Example State and Month and Day of Week.

Table 2-6. Application of Calculated Ratios for Actual 2002 by Example State and Month and Hour of Day. Actual Reported Values [2002] Calculated Ratios

State Month Day of Week SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input
GA Mar Sun 13,057,959 4,005,097 10,089,522 0.146 7 0.1437 0.1458
GA Mar Mon 11,937,355 3,841,1 72 9,564,295 0.134 1 0.1378 0.1382
GA Mar Tue 11,860,749 3,766,3 17 9,351,652 0.133 2 0.1351 0.1352
GA Mar Wed 12,020,458 3,764,6 53 9,232,574 0.135 0 0.1351 0.1334
GA Mar Thu 11,560,778 3,677,1 00 9,056,011 0.129 9 0.1319 0.1309
GA Mar Fri 14,572,757 4,616,042 11,368,579 0.163 7 0.1656 0.1643
GA Mar Sat 14,005,730 4,197,929 10,522,180 0.157 3 0.1506 0.1521
GA Mar Total 89,015,786 27,868,31 1 69,184,812 1.000 0 1.0000 1.0000

Actual Reported Values [2002] Calculated Ratios

State Month Hour SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input SC Dec 0 1,356,270 556,321 1, 309,476 0.0399 0.0399 0.0398 SC Dec 1 1,332,499 540,268 1, 279,485 0.0392 0.0387 0.0389 SC Dec 2 1,324,618 536,330 1, 275,732 0.0389 0.0384 0.0388 SC Dec 3 1,330,924 538,908 1, 284,514 0.0391 0.0386 0.0391 SC Dec 4 1,335,158 545,819 1, 296,880 0.0392 0.0391 0.0394 SC Dec 5 1,385,906 565,695 1, 340,759 0.0407 0.0405 0.0408 SC Dec 6 1,436,829 586,536 1, 387,329 0.0422 0.0420 0.0422 SC Dec 7 1,488,961 611,648 1, 440,753 0.0438 0.0438 0.0438 SC Dec 8 1,491,509 613,176 1, 444,956 0.0438 0.0440 0.0440 SC Dec 9 1,501,425 618,516 1, 447,916 0.0441 0.0443 0.0440 SC Dec 10 1,484,685 610,879 1, 431,441 0.0436 0.0438 0.0435 SC Dec 11 1,459,697 593,638 1, 395,938 0.0429 0.0426 0.0425 SC Dec 12 1,423,246 578,669 1, 365,957 0.0418 0.0415 0.0415 SC Dec 13 1,391,851 570,939 1, 345,091 0.0409 0.0409 0.0409 SC Dec 14 1,352,161 557,078 1, 319,068 0.0397 0.0399 0.0401 SC Dec 15 1,344,643 551,497 1, 312,670 0.0395 0.0395 0.0399 SC Dec 16 1,369,024 559,569 1, 333,589 0.0402 0.0401 0.0406 SC Dec 17 1,449,587 595,765 1, 398,917 0.0426 0.0427 0.0426 SC Dec 18 1,493,742 621,423 1, 438,833 0.0439 0.0445 0.0438 SC Dec 19 1,473,502 611,050 1, 427,712 0.0433 0.0438 0.0434 SC Dec 20 1,479,504 608,223 1, 424,664 0.0435 0.0436 0.0433 SC Dec 21 1,475,680 608,049 1, 421,202 0.0434 0.0436 0.0432 SC Dec 22 1,450,119 597,906 1, 401,208 0.0426 0.0429 0.0426 SC Dec 23 1,391,087 573,310 1, 351,539 0.0409 0.0411 0.0411 SC Dec Daily 34,022,628 13,951,21 0 32,875,627 1.000 0 1.0000 1.0000March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-17 0.060 0.065 0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecRelative Distribution Heat Input NOx SO2

Figure 2-2. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass for VISTAS domain.

When viewed on a State by State basis, the diffe rences in monthly variation are even more pronounced as individual facilities within each State may be aff ected during any calendar year by extreme temperature variation, shutdowns, or re gular maintenance or installation of equipment. As an example, Figure 2-3 represents CEM data from the State of Mississippi during calendar year 2002 and reveals that SO2 emissions incr ease throughout the year, NOx emissions stay relatively high during the summer months, and heat input peaks during the month of July. Although Figures 2-2 and 2-3 are roughly comparab le in shape and monthly distribution, the relative distribution of these values is quite different. In Mississippi’s case, close to thirteen percent of the State’s CEM-base d heat input occurs in July. This compares to the VISTAS average of just over ten percent of CEM-based heat input in July.

Finally, when these data are reviewed at a unit le vel, the differences become incrementally more distinct due to the unique na ture of individual facilities, their operating schedules, pollution regulation, fuel charact eristics, and applied technologies. Fo r example, a facility that is complying with summertime NOx regulation may have selective catalytic reduction (SCR) installed on its boiler(s) which in practice may on ly be run during ozone season months. During this period of time, heat input and SO2 emissions may remain cons istent with State or regional monthly profiles, but the NOx emi ssions may drop significantly relative to the rest of the year.

Figure 2-4 represents an extreme unit-specific case for monthly differe nces from State or regional temporal allocation. The unit presented is a Mississippi baseload coal-fired boiler which in 2002 emitted over 4,000 tons of NOx and over 11, 000 tons of SO2. This unit would typically run at consistent levels during the entire period, but due to a planned maintenance outage was not in operation in late January th rough the middle of April in 2002. Given the unique operation ofMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-18 this boiler during this year, th e use of a regional or even State-level monthly temporal distribution would introduce sign ificant inaccuracy to air qualit y modeling in the immediate or downwind area associated with this facility. While this may not be significant at great distance downwind of the source or for annual concentrati on estimates, more locally, and especially over shorter time scales (daily or weekly), such simp lifications would have a noticeable effect on air quality model predictions.

0.020 0.040 0.060 0.080 0.100 0.120 0.140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecRelative Distribution Heat Input NOx SO2

Figure 2-3. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass for Mississippi.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-19 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecRelative Distribution Heat Input NOx SO2

Figure 2-4. Monthly variation in 2002 of CEM reported heat input, NOx mass, and SO2 mass for specific baseload coal-fired unit in Mississippi with planned outage in late January through mid April.

Thus, while improving the representativeness of unit-specific monthly temporal profiles is desirable, providing day and hour-s pecific values are clearly bette r. For this reason, during the model performance evaluation process in the VI STAS modeling, hour-sp ecific temporal ratios were developed for every CEM reporting unit in the VISTAS domain. These ratios allowed for the hour-by-hour accounting of emissions released at each unit at each facility within the VISTAS domain that reported ou tput under the CEM guidelines.

Figure 2-5 represents the actual daily distribution of SO2 and NOx emissions and heat input from the Mississippi baseload unit from the above example. As can been seen in this figure, not only is the planned January thr ough April outage represented co rrectly, there are significant peaks and valleys throughout the calendar year which could not be accurately re presented with the application of average monthl y, day-of-week, or hourly distri bution factors. In reality, only the actual operating characteristics of this unit could capture the differences from hour to hour which are potentially quite important in terms of correctly modeling the imp act of the source on downwind oxidant and fine particulate concentrations 10

.

10 http://www.epa.gov/ttn/chief/confe rence/ei14/session11/stella.pdfMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-20 0 10 20 30 40 50 60 70

01-Jan-02 01-F eb

-02

0 1- M

ar -02 01- Ap

r -02 01-May-02 01-Jun-02

01 -J ul-02 01- A ug-0

2 01- S ep-

02 01-Oct-02 01-Nov-02 01-De c -02 31-D e c-02Emissions (Tons)

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000Heat Input (MMBtu) SO2

NOx
Heat Input

Figure 2-5. Actual daily unit-specific 2002 SO2 (tons), NOx, (tons), and heat input (MMBtu) distribution from CEM data.

Typical EGU Profiles

Hour of day of month specific temporal profile s were developed by calculating the arithmetic mean of each unit’s NOx, SO2, and heat input by specific hour of day per month (e.g., Hour 21 of Wednesdays in July) from the data obtai ned from 2000 through 2004. In order to accomplish this calculation, each record of CEM data was fi rst assigned a day of week. This assignment was based on the actual CEM’s date of record and day of week of that record. An example of this assignment is shown in Table 2-7.

Table 2-7. Example Day-of-Week per Month Assignment.

Once days of week were assigned to each record in the CEM data base, the arithmetic mean of each unit’s NOx, SO2, and heat input were calculated for the ORISPL-UNITID-MONTH-DAY OF WEEK-HOUR combination. Only records wher e the CEMs were operating for more than half the recorded hour (OPTIME > 0.5) were used in the averaging calculation. An example of the averaged results can be seen in Table 2-8.

Date Day of Week 08/01/02 Thu 08/02/02 Fri 08/03/02 Sat 08/04/02 Sun 08/05/02 Mon 08/06/02 Tue 08/07/02 WedMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-21 Table 2-8. Arithmetic Mean of CEM-based Variables for Temporal Profile Calculation.

These values were then applie d to each unit and hour based on the 2002 calendar to match the meteorological data used in the emissions processi ng. An example of this application can be seen in Table 2-9. The date specific hourly averages were then summed to a unit summer (May – Sept) and winter months total and ratios we re developed based on each daily hour’s average value divided by the average sum total depending on the season of the day. This permitted the appropriate allocation of summertime NOx (as forecasted by IPM) when summer control only was predicted. Using the annual average ratios instead of the seasonal distributions would produce summertime emissions different th an what was output from the model. Calculated Average Values [2000 – 2004]

State Facility ORISPL UnitID Month Day of Week Hour SO2 Mass NOx Mass Heat Input

WV Mount Storm Power Station 3954 3 1 Tue 0 406.0526 3384.074 5196.11

WV Mount Storm Power Station 3954 3 1 Tue 1 389.6474 3287.845 5103.06

WV Mount Storm Power Station 3954 3 1 Tue 2 395.2737 3342.848 5175.95

... ... ... ... ... ... ... ... ... ... WV Mount Storm Power Station 3954 3 7 Mon 4 524.7864 2505.9391 4654.34

WV Mount Storm Power Station 3954 3 7 Mon 5 690.5636 2602.9887 4795.64

WV Mount Storm Power Station 3954 3 7 Mon 6 912.4227 2572.0275 4727.08

WV Mount Storm Power Station 395 4 3 7 Mon 7 1060.3 2664.8686 4914.25

WV Mount Storm Power Station 3954 3 7 Mon 8 850.2364 2678.231 5029.58

WV Mount Storm Power Station 395 4 3 7 Mon 9 415.3455 2716.8 5042.55

WV Mount Storm Power Station 3954 3 7 Mon 10 408.8591 2876.5008 5123.71

WV Mount Storm Power Station 3954 3 7 Mon 11 371.9909 2776.0361 5147.85

WV Mount Storm Power Station 3954 3 7 Mon 12 327.2045 2785.5325 5129.66

WV Mount Storm Power Station 3954 3 7 Mon 13 316.0364 2826.901 5172.29

WV Mount Storm Power Station 3954 3 7 Mon 14 317.1136 2816.1328 5146.07

WV Mount Storm Power Station 3954 3 7 Mon 15 329.6455 2789.0962 5121.75

WV Mount Storm Power Station 3954 3 7 Mon 16 332.7773 2818.5379 5147.05

... ... ... ... ... ... ... ... ... ... WV Mount Storm Power Station 395 4 3 12 Tue 21 806.7 3432.001 5375.49

WV Mount Storm Power Station 3954 3 12 Tue 22 806.5778 3447.709 5377.68

WV Mount Storm Power Station 3954 3 12 Tue 23 795.4667 3419.069 5359.43March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-22 Table 2-9. Application of Calculated Ratios to Day of Year by Unit.

Equation 2-2 reflects th is calculation for heat input for a summer hour. Ratios were calculated for NOx, SO2, and heat input values. These rati os were then applied to each unit’s seasonal (summer or winter) emission value for NOx, SO 2, and all other pollutants, respectively.

Equation (2-2) 

## ∑

= 1

30 , , , , , / May

Sep sum date hr sum date hr ratio hi hi hi

where hi = heat input (MMBtu)

The actual hour-of-day-of-month averages calculate d from the CEM data we re not used directly as emissions for that hour, but were used only in the calculation of the rati os to be applied to a pre-calculated seasonal (summer or winter) emi ssion value. This allowed for the retention of emission estimates calculated using means other than CEM data, if a State or local agency found them to be more appropriate or if it were derived by other means (e.g., IPM) but an improved
distribution of emissions using CEM-based ratios.

As in the actual 2002 profiles ca lculations, these same calculati ons were additionally performed for each State so that a hierarchical applicati on of ratios (unit first, State second) could be assigned as necessary. Instead of having variables at the unit level, however, State level values were used. These State value ca lculations were based on the sum of the unit-level variable averages to the level of aggregation required by the calculation (e.g., State-month. State-monthday-of-week, or State-month-hour). Table 2-10 shows example ratio s calculated for each month by State. Table 2-11 reflects an example of the State-month-day of week ratio calculation and Table 2-12 shows a State-month-diurnal ratio calcu lation example. Each of these ratios were Calculated Average Values [2000 – 2004] Calculated Ratios

ORISPL UnitID Date Day of Week Hour SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input

3797 4 09/30/02 Mon 19 2591.95 381.78 1527.26 2.899E-04 2.396E-04 2.863E-04

3797 4 09/30/02 Mon 20 2596.81 379.88 1525.60 2.904E-04 2.385E-04 2.860E-04

3797 4 09/30/02 Mon 21 2569.03 370.50 1506.74 2.873E-04 2.326E-04 2.824E-04

3797 4 09/30/02 Mon 22 2547.62 367.24 1498.66 2.849E-04 2.305E-04 2.809E-04

3797 4 09/30/02 Mon 23 2483.88 360.66 1465.68 2.778E-04 2.264E-04 2.747E-04

3797 4 10/01/02 Tue 0 1968.94 478.47 1170.76 1.587E-04 1.517E-04 1.604E-04

3797 4 10/01/02 Tue 1 1942.47 480.28 1160.68 1.565E-04 1.522E-04 1.590E-04

3797 4 10/01/02 Tue 2 1858.54 462.44 1122.29 1.498E-04 1.466E-04 1.537E-04

3797 4 10/01/02 Tue 3 1988.43 486.07 1187.56 1.602E-04 1.541E-04 1.627E-04

3797 4 10/01/02 Tue 4 2125.96 528.59 1263.00 1.713E-04 1.676E-04 1.730E-04

3797 4 10/01/02 Tue 5 2255.22 562.18 1325.40 1.818E-04 1.782E-04 1.815E-04

3797 4 10/01/02 Tue 6 2267.27 558.77 1337.81 1.827E-04 1.771E-04 1.832E-04

3797 4 10/01/02 Tue 7 2313.00 579.94 1370.73 1.864E-04 1.838E-04 1.878E-04

3797 4 Summer 8941480.78 1593123.80 5334723.17

3797 4 Winter 12408352.17 3154758.40 7300596.69

3797 4 Annual Sum 21349832.95 4747882.21 12635319.86March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-23 calculated for each State in the VISTAS domain and used in instances where CEM unit matches could not be made to the VISTAS base year emissions inventory.

Again, three parameter values (SO2 mass, NOx mass, heat input) were calculated at each aggregation as NOx and SO2 emissions vary due to fuel blend, sulfur content, or seasonal control and are not necessarily re presentative of the other variables’ seasonal, daily, or even hourly variation.

Table 2-10. Application of Calculated Ratios for Typical Operation by Example State and Month.

Table 2-11. Application of Calculated Ratios for Typical Operation by Example State and Month and Day of Week. Calculated Average Values [2000 – 2004] Calculated Ratios

State Month SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input
FL Jan 116,011,253 62,989,958 198,751,048 0.085 8 0.0875 0.0831
FL Feb 93,958,786 50,831,818 164,949,702 0.069 5 0.0706 0.0690
FL Mar 111,505,553 60,285,972 196,705,697 0.082 4 0.0838 0.0822 FL Apr 107,015,438 59,071,792 195,338,204 0.079 1 0.0821 0.0817 FL May 118,589,361 61,811,604 207,803,996 0.087 7 0.0859 0.0869
FL Jun 116,068,987 59,801,640 202,716,214 0.085 8 0.0831 0.0848
FL Jul 123,868,749 62,500,169 212,478,437 0.091 6 0.0868 0.0888 FL Aug 125,384,940 64,572,843 214,637,218 0.092 7 0.0897 0.0897 FL Sep 113,080,789 59,913,723 206,712,956 0.083 6 0.0832 0.0864 FL Oct 109,960,828 61,551,310 206,924,170 0.081 3 0.0855 0.0865
FL Nov 101,781,383 55,861,718 186,984,665 0.075 2 0.0776 0.0782
FL Dec 115,588,740 60,566,444 197,592,133 0.085 4 0.0841 0.0826 FL Total 1,352,814,807 719,758,990 2,391,594,43 9 1.0000 1.0000 1.0000

Calculated Average Values [2000 – 2004] Calculated Ratios

State Month Day of Week SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input
GA Mar Sun 15,495,089 4,641,883 12,167,666 0.145 5 0.1403 0.1319
GA Mar Mon 14,334,901 4,513,847 13,362,335 0.134 6 0.1365 0.1449
GA Mar Tue 14,420,895 4,532,750 13,463,600 0.135 4 0.1370 0.1460
GA Mar Wed 14,170,345 4,489,531 13,057,182 0.133 1 0.1357 0.1416
GA Mar Thu 14,004,649 4,446,853 12,249,119 0.131 5 0.1344 0.1328
GA Mar Fri 17,177,952 5,357,920 14,842,639 0.161 3 0.1620 0.1609
GA Mar Sat 16,881,455 5,096,281 13,100,433 0.158 5 0.1541 0.1420
GA Mar Total 106,485,284 33,079,06 5 92,242,973 1.000 0 1.0000 1.0000March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-24 Table 2-12. Application of Calculated Ratios for Typical Operation by Example State and Month and Hour of Day.

Application of Factors

VISTAS chose to prepare its air quality modeli ng inventories with Version 2.1 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. For this reason, all emissions were required to be converted to SMOKE’s data formats. In particular, because hour specific temporal profiles for each day of a year are not accepted dire ctly by the model, it was necessary to develop a set of hourly emissions inputs to circumvent th is limitation. These were generated in the EMS PTHOUR format as described in SMOKE input file documentation 11

.

The CEM format for individual hour-specific data files as available in SMOKE was not utilized for VISTAS emissions processing as the emissions allowable by hour would have been limited to NOx, SO2, and CO2. If this file format and opt ional run configuration were exercised, the NOx, SO2, and CO2 emissions processed by the model would have been accurate for CEM reported emissions, but the remaining pollutants coupled with each CEM unit would have received the

11 University of North Carolina at Chapel Hill, Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System

, http://cf.unc.edu/cep/empd/products/smoke/index.cfm

. Calculated Average Values [2000 – 2004] Calculated Ratios

State Month Hour SO2 Mass NOx Mass Heat Input SO2 NOx Heat Input SC Dec 0 1,790,705 674,127 1, 908,284 0.0401 0.0395 0.0375 SC Dec 1 1,760,847 659,279 1, 877,399 0.0395 0.0386 0.0369 SC Dec 2 1,766,498 660,719 1, 890,957 0.0396 0.0387 0.0372 SC Dec 3 1,766,455 664,407 1, 922,535 0.0396 0.0389 0.0378 SC Dec 4 1,788,023 677,882 2, 005,041 0.0401 0.0397 0.0394 SC Dec 5 1,839,136 708,796 2, 193,779 0.0412 0.0415 0.0431 SC Dec 6 1,903,431 736,781 2, 379,535 0.0427 0.0432 0.0468 SC Dec 7 1,957,422 760,608 2, 504,498 0.0439 0.0446 0.0492 SC Dec 8 1,958,923 768,669 2, 515,860 0.0439 0.0450 0.0494 SC Dec 9 1,974,624 767,392 2, 419,052 0.0443 0.0450 0.0475 SC Dec 10 1,944,825 751,207 2, 264,252 0.0436 0.0440 0.0445 SC Dec 11 1,888,552 723,857 2, 140,166 0.0423 0.0424 0.0421 SC Dec 12 1,833,408 694,261 2, 022,036 0.0411 0.0407 0.0397 SC Dec 13 1,781,162 673,316 1, 936,841 0.0399 0.0395 0.0381 SC Dec 14 1,755,403 663,791 1, 911,001 0.0393 0.0389 0.0376 SC Dec 15 1,743,443 660,042 1, 897,088 0.0391 0.0387 0.0373 SC Dec 16 1,775,717 669,264 1, 937,000 0.0398 0.0392 0.0381 SC Dec 17 1,877,548 713,920 2, 099,275 0.0421 0.0418 0.0413 SC Dec 18 1,948,165 753,627 2, 255,923 0.0437 0.0442 0.0443 SC Dec 19 1,940,185 753,123 2, 258,417 0.0435 0.0441 0.0444 SC Dec 20 1,941,859 750,942 2, 221,568 0.0435 0.0440 0.0437 SC Dec 21 1,930,605 743,880 2, 182,689 0.0433 0.0436 0.0429 SC Dec 22 1,909,077 732,480 2, 140,416 0.0428 0.0429 0.0421 SC Dec 23 1,841,457 702,464 2, 003,560 0.0413 0.0412 0.0394 SC Dec Daily 44,617,469 17,064,83 3 50,887,170 1.000 0 1.0000 1.0000March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-25 monthly, daily, and diurnal tem poral profiles associated with th e source category codes from the unit. This could lead to potentially displaced em issions if a unit were ope rating at different times than the default profiles indicated. Additionally , in cases where States may not have reported annual emission estimates directly based on CEMs, these emissions would be slightly different that the original annual inventory.

In VISTAS modeling, for those EGU sources wh ere CEM data were utilized, NOx, SO2, and heat input-based hour-specific profiles were de veloped and applied to annual NOx, SO2, and all other emissions, respectively, for both the act ual and typical 2002 modeling. Heat input was chosen as a surrogate for non-CEM reported pollu tants as the majority of remaining compounds are not as significantly impacted by controls or fuel content, yet the distribution of these emissions would occur during the same times CEM reported pollutants were emitted.

The application of hourly ratios to annual emissions ensured that the annual values provided by States under the CERR were maintained, but dist ributed using actual hourl y to annual profiles. Additionally, for stakeh older sources providing hour-specifi c data approved by the State in which they operated, data were substitute d for State provided em issions and CEM-based
distributions.

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input data were collected from the 2002 or 2000-2004 CEM da tasets, and used to develop State-level temporal distributions. These m onth-specific hour and day of week temporal profiles were used in conjunction with the emissi ons inventory to calculate hourly EGU emissions by unit.

Although not as accurate a distribution as the un it-specific factors, the State-based temporal distribution provided improved result s to the default profiles provided with the emissions model. Figure 2-6 represents the monthl y distribution comparis ons of VISTAS State heat input to the default monthly distribution from Version 2. 0 of SMOKE for source category code (SCC) 10100201, representing External Combus tion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal), a relatively common boiler type and fuel configuration in the VISTAS domain. This example is for the actual 2002 modeling exercise.

Much like the distinction in mont h to month variation of the prof iles, day of week and diurnal patterns based on CEM data vary from unit to unit. Again, if one were to assign the same day of week or diurnal profile to ev ery unit in the inventory, emi ssions from these sources would inappropriately be distributed during the episode of interest. In addition to the unique distribution provided by the unit-specific f actors based on CEM data, aggregat e State level daily and diurnal temporal distribution factors were developed an d applied during this process. Figure 2-7 shows the variance in diurnal distri bution from Tennessee’s average CEM-based NOx emissions data for each of the twelve months of calendar y ear 2002 as would have been applied to units unmatched to CEM sources.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-26 0.040 0.050 0.060 0.070 0.080 0.090 0.100 0.110 0.120 0.130

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecRelative Distribution

AL FL GA KY MS NC SC TN VA WV SMOKE

Figure 2-6. Relative distribution of actual 2002 monthly VISTAS State CEM-based heat input.

0.0370 0.0380 0.0390 0.0400 0.0410 0.0420 0.0430 0.0440

hr1 hr2 hr3 hr4 hr5 hr6 hr7 hr8 hr9 hr10 hr11 hr12 hr13 hr14 hr15 hr16 hr17 hr18 hr19 hr20 hr21 hr22 hr23 hr24Relative Distribution

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Figure 2-7. Relative distribution of diurnal actual 2002 CEM-based NOx emissions for Tennessee.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-27 The work conducted in this process had the main objective of developing temporal profiles for VISTAS EGUs necessary to apply in the gene ration of SMOKE PTHOUR formatted emissions. Additionally, State-level monthl y, day-of-week, and diurnal profiles were developed for application to non-CEM matched units in the VI STAS emissions inventory. These temporal distributions represent a significant improvement over the EPA defaults.

2.3.2 Speciation

Speciation is the process of disaggregating invent ory pollutants into individual chemical species components or groups of species. The need fo r speciation is determined by the inventory purpose. Inventory applications th at require detailed speciation include photochemical modeling, air toxics inventories, chemical mass ba lance modeling, and visibility modeling.

Depending on the purpose of a part icular emissions inventory, th e inventory may include TOG, NOx, sulfur oxides (SOx), CO, total suspended pa rticulate matter (TSP), particulate matter less than 10 micrometers in aerodynamic diameter (PM10), or ammonia (NH3). However, modeling inventories may require these emissions to be expressed in terms of other pollutants. Additionally, for some models, NOx emissions ma y need to be specified as NO and NO2. Also, PM may need to be separated into various fr actions, such as PM10 and PM less than 2.5 micrometers in aerodynamic diameter (PM 2.5 ).

SMOKE was configured to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM -IV, CB-IV or CB4). The SMOKE model
reformats the emissions estimates for use in CMAQ modeling based on source category code (SCC) and speciation profile cros s-reference files. The speciation profiles and source category cross-references use in VISTAS modeling are based on EPA’s CAIR /CAVR/CAMR modeling platform with files located on EPA’s CAIR file transfer website (ftp://www.airmodelingftp.com/). Minor modificati ons were made to reflect State specific profiles or updated state of knowle dge application of these profile s. One major change made in the VISTAS modeling was the modification of co al combustion cross-reference from speciation profile “NCOAL” to profile “22001.”

2.3.3 Spatial Allocation

Because air quality modeling strives to replicate the actual physical and chemical processes that occur in an inventory domain, it is important that the physical location of emissions be determined as accurately as possible. In an ideal situation, the physical location of all emissions would be known exactly. In reality, however, the spatial allocation of em issions in a modeling inventory only approximates th e actual location of emissions.

Gridding surrogates are used to spatially allocate emission sources from a coarse geographic area to finer grid cells used for modeling. There can be hundreds of unique s ource categories in an emissions inventory, which is typically developed for counties, states, or other areas. The exact location of most major emission sources is known and their geographic coordinates are usually contained in the inventory. These usually are referred to as major point sources and include electric utilities and major indus trial facilities. However, other emission sources are estimated forMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-28 the entire county or other area as an aggregat e since the exact locations of each source are not included in the modeling inventor y. Surrogates are human activitie s or land use information that are used to represent a more precise locati on of emission source category groups. A gridded surrogate ratio is the ratio of th e amount of a surrogate in a mode ling grid cell to the total amount of that surrogate in a county. Grid cell emissi ons are calculated by multipl ying the cell’s gridded surrogate ratio by the county emissions.

These surrogates and their associated SCC cro ss-references were originally developed by EPA (http://www.epa.gov/ttn/chief/em ch/spatial/newsurrogate.html) a nd converted to the gridded
domain definitions of the VISTAS model requirements.

2.3.3.1 Development of Gridded Surrogate Files

The general process for creating the SMOKE-rea dy gridded surrogate files from the ArcGis shape files is as follows:

1. Overlay the grid on the surrogates. Ge nerate the grid polygons (36/12/4km) with the specifications of the VISTAS domain and spatially overlay (intersect) the grid onto the surrogate area polygons or points. The resulting geodatabase contains, for each surrogate, the county FIPS code, the grid column and row numbe r, and the amount (area, miles or count) of the county’s portion of the surrogate in that cell.

2. Extract and convert each geod atabase table to a useful data set. Each table contains the gridded area, miles or count in each county for a specific su rrogate. The variables include FIPS code, column number, row num ber and area, miles or count.

3. Calculate surrogate ratios. Surrogate ratios are calculated for each surrogate using a series of program files. The programs sum the surroga tes for each county and calculate each ratio by dividing the county cell surrogate value by th e total county surrogate value. Combination surrogates where both are of the same type (i.e., Heavy and High Tech Industrial are both area data) were summed prior to calculating the ratio. Combination surrogates with unlike data (i.e., 3/4 Roadway Miles plus 1/4 Populatio n are line and area data) were summed after calculating the ratios and then normalized. Th e surrogate cross-reference code was also assigned here.

4. Gap-fill surrogates for counties missing data. There will be many instances where inventory emissions exist for a pa rticular county but th ere is no spatial data , for that county, for the surrogate assigned. For example, a coun ty with class 1 locomotive emissions may not have data for the class 1 railroad surrogate. In this case we have selected to incorporate, within the assigned surrogate, a secondary sour ce of data (a different surrogate) for that particular county. We incorporate secondary su rrogates even if there is no emission source that requires it for that particular county. We denote this process as “gap-filling.” All surrogates resulting from the gap-filling process have ratios for all counties.

For each primary spatial surrogate, we assign a secondary or tertiary spatial surrogate where needed for gap filling. For the class 1 railro ad example mentioned above, we chose total railroads as the secondary spa tial surrogate since emissions ha d been estimated for class 1March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-29 railroads but no class 1 spatial surrogate was avai lable in that county from our coverage files. The secondary or tertiary spatial surrogate chosen is the same across all counties for a particular primary spatial surrogate and applies to all SCCs that use the particular primary surrogate. We pull in and substitute the second ary surrogate for counties where the primary surrogate is missing to allow for an otherwise omitted spatial distribution of a pre-calculated emission value. Tertiary surrogates are assign ed to those counties th at are still without surrogates.

For identified counties having no values for each surrogate, we assign the data based on the appropriate secondary or tertiary surrogate to these counties. Checks to see that surrogate ratios for each county sum to approximately 1.00 were also performed in our surrogate development. Ratios will not always sum exac tly to 1.00 due to rounding. However, SMOKE will normalize surrogates greater than 1.00.

5. Create SMOKE-formatted spatial surrogate file s. The resulting data from the previous steps is then reconfigured into SMOKE-ready format and used in the spatial allocation

process.

2.4 Development of Modeling Input Inve ntories/Run Scripts/ASSIGNS Files

The VISTAS State emissions inventories modeled for the Base G2 runs were obtained from the emissions inventory contractor in NIF 3.0 format (exception of fires and onroad mobile inputs). These files were converted to SMOKE IDA format using procedures previously applied for the VISTAS Phase I and earlier Phase II modeling an alysis and allocated to subcategory major source level (EGU, non-EGU point, ar ea, nonroad, onroad, fires) for ease of processing and data management. Most of the 2002 and 2018 modeling files for non-VISTAS St ates were obtained directly from the other RPOs in SMOKE fo rmat (MANE-VU, CENRAP , WRAP) or converted from NIF 3.0 (MRPO) as necessary.

Additionally, with the annual episode required for modeling, certain source categories were prepared in a monthly format and the SMOKE model was run for each month of the year. For this reason, individual run sc ripts and ASSIGNS files were pr epared and executed during each SMOKE year/scenario run. An example of the c onfigurations used can be seen in Table 2-13 below. This particular example is for point source processing of the typical 2002 emissions episode. Comparable configura tions were developed for stat ionary area, nonroad mobile, elevated fire, low-level fire, onroad mobile, a nd fugitive dust sources for 2002 typical and actual and 2009 and 2018 episodes.

2.5 Products of the Emissions Inventory Development Process for QA

In addition to the CMAQ-ready input files genera ted for each hour of the days modeled in the annual runs, a number of quality assurance (QA) f iles will be prepared and used to check for gross errors in the emissions inputs. Importi ng the model-ready emissions into PAVE and looking at both the spatial and temporal distri bution of the emission provides insight into the quality and accuracy of the emissions inputs.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-30 • Visualizing the model-ready emissions with the scale of the plots set to a very low value, we can determine whether there are areas omitted from the raw inventory or if emissions sources are erroneously located in water cells.

• Spot-check the holiday emissions files to conf irm that they are temporally allocated like Sundays.

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

• Spot check vertical allocation of point sources using PAVE.

We used state inventory summaries prepared prior to the emissions processing to compare against SMOKE output report totals generated after each major step of the emissions generation

process.

To check the chemical speciation of the emissions to CB-IV terms and the vertical allocation of the emissions, we compared reports generated w ith SMOKE reports to target these specific areas of the processing. For speciation, we compared the inventory import state totals versus the same state totals with the speciation matrix applied.

For checking the vert ical allocation of the emissions, we created reports by source, hour, and layer for randomly selected states in the domain. We created these reports for a representative weekday in each of the episodes for e ach of these selected states.March 2009F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc2-31Table 2-13.Example SMOKE Setup for 2002 Typical Point Source Processing.Run Script: /scripts/run_rev/run_pnt_XXk_allmet.typical.bat (XX replace with 12 or 36 for desired grid)ASSIGNS File: /assigns_pII_point/ASSIGNS.vistas.cmaq.cb4p25.rpoXX.phaseIIrev.agLogical File NameDirectory NameActual File NameCommentsPTINV $SMKDAT/inventory/2002typ/point ptinv_2002typ_date.idadatetypically indicates date of filepreparationPTHOUR $SMKDAT/inventory/2002typ/point pthour.typ.NNN.lstNNN indicates the month abbreviation.The list file points to"pthour_rev2002typ_NNN_12nov04.ems"PTPRO$SMKDAT/inventory/2002typ /pointamptpro_typ_us_can_NNN_vistas_date.txtdatetypically indicates date of filepreparationPTREF$SMKDAT/inventory/2002typ /pointptref_2002_us_can_vistas_date.txtdatetypically indicates date of filepreparationPELVCONFIG$SMKDAT/inventory/2002typ /pointpelvconfig.top50.txtREPCONFIGPSTK$SMKDAT/ge_datpstk.m3.txtINVTABLE$SMKDAT/inventory/2002typ /otherinvtable_onroad.cb4.120202.txt.agGRIDDESC$SMKDAT/ge_datGRIDDESCCOSTCY$SMKDAT/ge_datcostcy.txtHOLIDAYS$SMKDAT/ge_datholidays.txtSCCDESC$SMKDAT/ge_datscc_desc.txtSRGDESC$SMKDAT/ge_datsrg_desc.us36.txtORISDESC$SMKDAT/ge_datoris_info.txtGSCNV$SMKDAT/ge_datgscnv.txtGSREF$SMKDAT/ge_datgsref.cmaq.cb4p25.txtGSPRO$SMKDAT/ge_datgspro.cmaq.cb4p25.txtMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-32 The quantitative QA analyses often reveal significant deficiencies in the input data or the model setup. It was necessary to tailor these proc edures to track down the source of each major problem. As such, we only outline the basic quantitative QA steps that we performed in an attempt to reveal the underlying problems with the inventories or processing. Following are some of the reports that were generated to review the processed emissions:

• State and county totals from inve ntory for each source category

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

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

• State and county totals by model species after chemical speci ation for each source category

• State and county model-ready totals (after sp atial allocation, tem poral allocation, and chemical speciation) for each source categ ory and for all source categories combined

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

sources

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

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

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

• Totals by speciation profile code fo r area, mobile, and point sources

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

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

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

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

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

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

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

• PAVE plots of gridded invent ory pollutants for all pollutant s for area, mobile, and point

sources

Examples of some of these reports are lo cated in the Appendix A of this document.

2.6 2009 and 2018 Base G4 Best and Final Inventories

The current version of the VISTAS emissions inventory is referred to as the Best and Final (BaF) or Base G4 inventory. After the release of the 2009 and 2018 Base G2 inventory in July 2007, states specified additional changes to the point source inventory to reflect improved information on controls and shutdowns in the 2009 and 2018 future y ear point source emissions. No changes to the other source sectors (e.g., on-road mobile, non-road mobile, area fires, etc.) were made in going from the 2009 and 2018 Base G2 to the Base G4/BaF emission inventories. The 2018 Base G4/BaF inventory was released in Oct ober 2007, and the 2009 Base G4/BaF inventory was released in December 2007. The developm ent of the 2002 Base G2 and 2009 and 2018 Base G4/BaF inventories is given in MACTEC (2008).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-33 Table 2-14 displays the point source SO2 and NOx emissions by VISTAS State and VISTAS region for the 2002 Base G2 and 2009 and 2018 Base G2 and Base G4 (BaF) emission scenarios. The largest changes in future-year EGU emissions between Base G2 and Base G4 occurred in Florida where SO2 EGU emissions were incr eased by 57% (2009) and 40% (2018) and NOx emissions were increased by 54% (2009) and 17% (2018). In the other VISTAS States there were mostly small reductions or no change in EGU SO2 emissions. EGU NOx emissions were also increased in Mississippi.

With the exception of a 27% and 16% redu ction in the 2018 Tennessee and Virginia, respectively, non-EGU SO2 emissions, the non-E GU SO2 and NOx emissions exhibited no or small (<±10%) changes between the future y ear Base G2 and Base G4 inventories.

Across all VISTAS States, the total change in point source SO2 emissions between Base G2 and Base G4 was +1% and -1% for 2009 and 2018, respec tively. There were la rger changes in the future year Base G2 and Base G4 NOx point s ource inventories across the VISTAS States that were increased by 5% in 2009 and 1% in 2018.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-34 Table 2-14a. Point source SO2 emissions (tons per year) by VISTAS State for the 2002 Base G2 and the 2009 and 2018 Base G2 and Base G4/BaF inventories and the differences between the 2009 and 2018 Base G2 and Base G4/BaF inventories.

EGU Point Sources

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 2018 G4-G2 AL 447,194 378,052 378,052 0 0% 135,851 135,851 0 0% FL 453,631 186,055 291,831 105,77 6 57% 138,340 194,028 55,688 40% GA 514,952 417,449 408,679 -8,770 -2% 79,430 68,515 -10,915 -14% KY 484,057 290,193 271,669 -18,524 -6% 226,062 222,102 -3,960 -2% MS 67,429 76,579 76,646 67 0% 15,146 15,213 67 0% NC 477,990 242,286 242,286 0 0% 114,771 120,165 5,394 5% SC 206,399 124,608 129,122 4, 514 4% 93,274 95,377 2,103 2% TN 334,151 255,410 255,410 0 0% 112,672 112,672 0 0% VA 241,204 193,112 174,777 -18,335 -9% 114,255 98,988 -15,267 -13% WV 516,084 277,489 268,952 -8, 537 -3% 105,935 106,199 264 0% Total 3,743,091 2,441,233 2,497,424 56, 191 2% 1,135,736 1,169,110 33,374 3%
Non-EGU Point Sources

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 % AL 96,481 101,246 101,246 0 0% 113,224 103,303 -9,921 -9% FL 65,090 65,511 62,651 -2,860 -4% 75,047 71,810 -3,237 -4% GA 53,778 53,987 53,987 0 0% 59,349 59,349 0 0% KY 34,029 36,418 36,418 0 0% 40,682 40,682 0 0% MS 35,960 25,564 25,564 0 0% 26,678 25,674 -1,004 -4% NC 44,123 42,536 42,536 0 0% 46,314 46,314 0 0% SC 53,518 48,324 47,193 -1,131 -2% 53,577 52,410 -1,167 -2% TN 79,604 70,678 64,964 -5,714 -8 % 77,247 56,682 -20,565 -27% VA 63,903 62,560 58,039 -4,521 -7% 68,909 57,790 -11,119 -16% WV 54,070 55,973 55,598 -375 -1% 62,193 61,702 -491 -1% Total 580,556 562,797 548,196 -14,601 -3% 623,220 575,716 -47,504 -8% Total Point Sources (EGU plus non-EGU)

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 % AL 543,675 479,298 479,298 0 0% 249,075 239,154 -9,921 -4% FL 518,721 251,566 354,482 102,91 6 41% 213,387 265,838 52,451 25% GA 568,730 471,436 462,666 -8,770 -2% 138,779 127,864 -10,915 -8% KY 518,086 326,611 308,087 -18,524 -6% 266,744 262,784 -3,960 -1% MS 103,389 102,143 102,210 67 0% 41,824 40,887 -937 -2% NC 522,113 284,822 284,822 0 0% 161,085 166,479 5,394 3% SC 259,917 172,932 176,315 3, 383 2% 146,851 147,787 936 1% TN 413,755 326,088 320,374 -5,714 -2 % 189,919 169,354 -20,565 -11% VA 305,107 255,672 232,816 -22,856 -9 % 183,164 156,778 -26,386 -14% WV 570,154 333,462 324,550 -8, 912 -3% 168,128 167,901 -227 0% Total 4,323,647 3,004,030 3,045,620 41, 590 1% 1,758,956 1,744,826 -14,130 -1%March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-35 Table 2-14b. Point source NOx emissions (tons per year) by VISTAS State for the 2002 Base G2 and the 2009 and 2018 Base G2 and Base G4/BaF inventories and the differences between the 2009 and 2018 Base G2 and Base G4/BaF inventories.

EGU Point Sources

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 State AL 161,038 82,305 82,305 0 0% 64,358 64,358 0 0% FL 257,677 86,165 132,535 46,370 54% 74,640 87,645 13,005 17% GA 147,517 98,497 98,497 0 0% 75,717 69,856 -5,861 -8% KY 198,817 92,021 97,263 5,242 6% 64,378 64,378 0 0% MS 43,135 36,011 47,276 11, 265 31% 10,271 21,535 11,264 110% NC 151,853 66,522 66,521 -1 0% 62,353 61,110 -1,243 -2% SC 88,241 46,915 48,668 1,753 4% 51,456 51,751 295 1% TN 157,307 66,405 66,405 0 0% 31,715 31,715 0 0% VA 86,886 62,547 64,358 1,811 3% 66,074 64,344 -1,730 -3% WV 230,977 86,328 85,476 -852 -1% 51,241 51,474 233 0% Total 1,523,448 723,716 789,304 65,588 9% 552,203 568,166 15,963 3%
Non-EGU Point Sources

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 State AL 83,310 69,409 69,409 0 0% 78,318 77,960 -358 0% FL 45,156 46,020 47,125 1,105 2% 51,902 52,959 1,057 2% GA 49,251 50,353 50,353 0 0% 55,824 55,824 0 0% KY 38,392 37,758 37,758 0 0% 41,034 41,034 0 0% MS 61,526 56,397 56,398 1 0% 61,533 61,252 -281 0% NC 44,929 34,767 34,768 1 0% 37,801 37,802 1 0% SC 42,153 40,019 39,368 -651 -2% 44,021 43,331 -690 -2% TN 64,344 57,883 57,514 -369 -1% 63,453 62,519 -934 -1% VA 60,415 51,046 51,001 -45 0% 55,945 55,734 -211 0% WV 46,612 38,031 38,023 -8 0% 43,359 43,280 -79 0% Total 536,088 481,683 481,717 34 0% 533,190 531,695 -1,495 0% Total Point Sources (EGU plus non-EGU)

State 2002 Base G2 2009 Base G2 2009 Base G4 2009 G4-G2 2009 G4-G2 2018 Base G2 2018 Base G4 2018 G4-G2 State AL 244,348 151,714 151,714 0 0% 142,676 142,318 -358 0% FL 302,833 132,185 179,660 47,475 36% 126,542 140,604 14,062 11% GA 196,768 148,850 148,850 0 0% 131,541 125,680 -5,861 -4% KY 237,209 129,779 135,021 5,242 4% 105,412 105,412 0 0% MS 104,661 92,408 103,674 11,266 12% 71,804 82,787 10,983 15% NC 196,782 101,289 101,289 0 0% 100,154 98,912 -1,242 -1% SC 130,394 86,934 88,036 1,102 1% 95,477 95,082 -395 0% TN 221,651 124,288 123,919 -369 0% 95,168 94,234 -934 -1% VA 147,301 113,593 115,359 1,766 2% 122,019 120,078 -1,941 -2% WV 277,589 124,359 123,499 -860 -1% 94,600 94,754 154 0% Total 2,059,536 1,205,399 1,271,021 65,622 5% 1,085,393 1,099,861 14,468 1%March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-36 2.7 Uncertainties and Caveats

Following the completion of VISTAS Base G2 m odeling, a number of issues were identified with the input emission inventories and emission pr ocessing modeling system that are identified here. Total impact to the overall processed resu lts has not been quantified for these issues although individually each issue is be lieved to have minimal impact on regional haze or visibility results domain wide.

2.7.1 Updated Inventories

As in the normal process among emissions and air quality analysis, non-VISTAS RPOs have continued to update and revise th e base year and future year em ission inventories used in the development of regional haze and visibility m odeling simulations. The non-VISTAS RPO data obtained for this analysis were gathered dur ing the November-December 2006 timeframe and it is recognized that each of the four sister RPOs to VISTAS has since modified either their 2002 base year and/or their 2018 ba se case projection inventories.

2.7.2 Low Level Fires

During the Base G2 modeling, a series of low level fire emissions were inadvertently omitted from the processing stream. Thes e fires include emissions for si x counties in Kentucky, and one county each in Tennessee and West Virginia for prescribed burning. Th ese fires are data for prescribed burning that were only received from the USFS during the initia l file development for these counties. No State data was received for this category in these c ounties and thus the NEI values from EPA’s inventory were maintained for this fire type consistent with the replacement scheme developed when Base D inventories were prepared. During the update process of fire emissions from Base D to Base G2, this file wa s not remerged with the other updated fire types.

2.7.3 MMS Offshore Shipping

During the update of emission inventories from Base D to Base G, VISTAS obtained from MMS a set of updated 2000 emission files (GOADS 12 ) which included off-shore marine vessel emissions in the Gulf of Mexico oil/gas deve lopment area and platform and platform support equipment that has always been included in past MMS inventories. When VISTAS overlaid recently obtained offshore emission estimates from shipping lanes, we double counted the offshore marine emissions in the area.

12 http://www.gomr.mms.gov/homepg/regula te/environ/airquality/gulfwide_emi ssion_inventory/2000GulfwideEmissio nInventory.htmlMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_2_Emissions4.doc 2-37 2.7.4 Incorrect Location Parameters

During the quality assurance steps of the Base G2 emission inventories in preparation for VISTAS’ best and final modeling, a number of emission release poi nts were identified to have stack location parameters that we re of distances greater than 100 miles from the county centroid that the stack’s FIPS codes indi cated it was situated. Although ma ny of these release points were sited at valid locations (in counties with outer boundaries greater than 1 00 miles from centroid), those that were incorrectly sited had emission values of very low (less than 80 tons per year) of any single pollutant.

2.7.5 SMOKE/IOAPI Coordinate Transformation Error

During emissions processing of in ventories for an unassociated project, the modeling team became aware of an issue 13 with one of the IOAPI libraries necessary to convert input file coordinates into output file locations. This i ssue occurs with the preco mpiled version of SMOKE utilized by the modeling team in VISTAS emi ssions processing. During the processing step of point source emissions, the latit ude/longitude coordinates of the i nput files are transformed into Lambert conformal projection, consistent with the CMAQ setup. The error identified in the IOAPI library shifted this loca tion by several kilometers. Although this error was corrected in subsequent versions of SMOKE and the IOAPI libra ries, no corrections have been made to Base G2 modeling or earlier simulations.

13 http://bugz.unc.edu/show_bug.cgi?id=511March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-1 3.0 MODEL PERFORMANCE EVALUATION

3.1 Introduction

The discussion of model performance evaluation is summarized in this Chapter with details provided in Appendices B, C and D. The model performance evaluation focuses on the evaluation of the 2002 Base G2 Actual base case modeling results from the Community Multiscale Air Quality (CMAQ) modeling system Version 4.51 with SOAmods enhancement. This evaluation focuses on monitoring sites within the VISTAS states and VISTAS 12 km grid. The CMAQ evaluation of the 2002 Base G2 Ac tual base case is the product of numerous evaluations of predecessor CMAQ base case simulati ons using interim versions of the base case modeling inputs and CMAQ model (Morris et al., 2004b,c ; 2005; 2006). With the exception of Organic Carbon Mass (OCM), the basic model perf ormance evaluation features have remained unchanged from the initial evaluations in Ph ase I and II of VISTAS (e.g., Morris et al., 2004a,b,c). As noted previously, during the course of the VIST AS Phase II modeling VISTAS updated the secondary organic aerosol (SOA) module in CMAQ to include important SOA processes missing in the standard CMAQ model that significantly improved the OCM performance in the summe r (Morris et al., 2006).

The CMAQ results are compared with observational data from the I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE), S peciated T rends N etwork (STN) 1

, C lean A ir S tatus T rends Net work (CASTNet), F ederal R eference M ethod (FRM) PM 2.5 mass, N ational
A cid D eposition P rogram (NADP) and S outh E ast A erosol R esearch and CH aracterization (SEARCH) study monitoring networks. The ev aluation focuses primarily on the operational model evaluation of the air quali ty model’s performance with resp ect to individual components of fine particulate matter (PM 2.5 ), as good model performance of the PM component species will dictate good model performance of total or reconstituted fine particulate matter from which visibility is based. The FRM networks only collects total PM 2.5 mass so are not as relevant for judging how well the model is pr edicting the components of light extinction. However, model performance of the total fine particulate matter and light extinction will also be provided as a means to discuss the overall model performance. We have also performed a diagnostic evaluation analyzing the ability of the model to reproduce gaseous PM pr ecursor (e.g., SO2 and NOx) and product (e.g., HNO3) species.

This chapter presents a brief summary of th e CMAQ Base G2 model performance evaluation with details provided in three Appendices:

• Appendix B presents the regional model pe rformance evaluation of the CMAQ modeling system across PM monitoring networks in the VISTAS states and VISTAS 12 km grid as well as the diagnostic evaluation for PM precursor and product species.

• Appendix C contains a detail ed evaluation of the CMAQ m odel performance at Class I areas within and nearby the VISTAS region w ith particular focus on the best and worst 20 percent days during 2002 that are used in the 2018 visibility projections.

1 The STN network is now referred to as the Chemical Speciation Network (CSN)March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-2 • Appendix D contains spatial maps that co mpare predicted and observed PM species concentrations for each day from 2002 that was either a best or worst 20 percent day at a Class I area within or nearby the VISTAS region.

3.2 CMAQ 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 testi ng, consisting of the four major components: operational, diagnostic, mechanistic (or sc ientific) and probabilistic (EPA, 2007a).

3.2.1 Evaluation Approach

The CMAQ model performance evaluation effort focused on the first two components of the EPA’s recommended evaluation approach, 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, that is, su lfate, nitrate, 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).

The diagnostic evaluation also includes the perf ormance of diagnostic tests to better understand model performance and identify potential flaws in the modeling system that can be corrected. The diagnostic evaluation may al so includes the use of “probing tools” to understand why the model obtains a given prediction; probing tools in clude Process Analysis (PA), decoupled direct method (DDM) and source apportionment (SA).

In this final model performa nce evaluation for the VISTAS 2002 Actual Base G2 CMAQ 36/12 km base case simulation, the operational evaluati on has been given the gr eatest attention since this is the primarily thrust of EPA’s modeling guidance. However, we have also examined certain diagnostic features dealing with the model’s ability to simulate sub-regional and monthly/diurnal gas phase and aero sol concentration distributions. In the course of the VISTAS study numerous diagnostic sensitiv ity tests were performed to investigate and improve model performance and test the model a ssumptions that are available on the VISTAS modeling website: http://pah.cert.ucr.edu/vistas/vistas2/ and reported by Morris and co-workers (2004a,b,c; 2005; 2006).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-3 3.2.2 Particulate Matter and Component Species

Regional haze is calculated by estimating li ght scattering and absorption by components of
PM 2.5 . Regional haze is measured by an extinction coefficient (bext), which represents the light attenuation resulting from scattering and absorpti on of light from ambient particulate matter plus scattering of light due to gas molecules in the air (i.e., Raylei gh scattering).

Some scattering does occur from air molecules; however, the bulk of light extinction is caused by the presence of suspended particles, partic ularly fine particles (aerodynamic mass <2.5 μ m). Fine particulate matter can be composed of varying amounts of several different species, including:

• Sulfate (SO4)

• Nitrate (NO3)

• Ammonium (NH4)

• Organic Carbon Matter (OCM)

• Elemental Carbon (EC)

• Coarse particulate matter or Coarse Mass (i.e., PM 10 – PM 2.5 or CM).

• Soil (also known as crustal material, fine soil , major metal oxides, i norganic particulates, or other PM 2.5 )

• Sea Salt (NaCl)

3.2.3 Ambient Air Quality Data for VISTAS Model Evaluation

A ground-level model evaluation database for 2002 was compiled by the modeling team using several routine and research-gra de databases. The focus of the VISTAS evaluation of the CMAQ model was for use in simulating visibility in the southeastern is on the PM components that can cause visibility impairme nt. The primary monitoring networ ks available to evaluate this component of the CMAQ are: (a) Interagency M onitoring of Protected Visual Environments (IMPROVE); (b) Clean Air Status and Trends Network (CASTNET ); (c) Southeastern Aerosol Research and Characterization (SEARCH); (d) EPA Federal Reference Method PM 2.5 and PM 10 Mass Networks (EPA-FRM); (e) EPA Speciation Trends Network (STN) of PM 2.5 species; and (f) National Acid Deposition Network (NADP). These PM monitoring networks may also provide ozone and other gas phase precursors and product species, and visibility measurements at some sites. There are also gas-phase criter ia pollutant measures from the EPA’s Air Quality System (AQS) that are available for use. The VISTAS 2002 36/12 km Actual base case simulation was also used by the Association for Southeastern Integrated Planning (ASIP) who are analyzing 8-hour ozone and PM 2.5 attainment issues mainly in urban areas. ASIP performed a detailed focused evaluation of the CMAQ 36/12 km Base G2 Actual model performance for ozone and PM 2.5 using mainly the AQS, STN and FRM ur ban monitoring networks. The ASIP evaluation of the CMAQ base case modeling for urban areas can be found at on the VISTAS modeling website ( http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml ) and is not repeated here. Table 3-1 and Figure 3-1 summari zes the species collected and locations of the monitoring sites for the IMPROVE, STN, CASTNet, NADP and SEARCH monitoring ne tworks use in the VISTAS model evaluation.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-4 For Organic Carbon Mass (OCM), the monitoring networks measure just Organic Carbon (OC) and don’t measure the other elements (e.g., oxyge n) associated with OCM. Thus, the OC measurement needs to be converted to OCM in the model performance evaluation. OCM/OC ratios are generally believe to vary between 1.2 and 2.4 (Tur pin and Lim, 2001), with lower OCM/OC ratios for fresh OCM emissions and hi gher OCM/OC ratios for aged OCM that has undergone photochemical processi ng. VISTAS used an OCM/OC ratio of 1.4 for all the networks. It should be noted that the STN OC measurements have artifac ts and are less accurate than the IMPROVE OC measurements (Solomon et al., 2004;2008). The inaccuracies and artifacts of the STN OC measurements is one of the reasons that the SANDWICH method that uses the STN PM speciation measurements to speciate FRM PM 2.5 mass doesn’t directly use the STN OC measurement but instead obtains OC through difference between the PM 2.5 mass and the remainder of the PM 2.5 species components (Frank, 2005).

Table 3-1. Ambient monitoring data available in the VISTAS region during 2002.

Monitoring Network Chemical Species Measured Sampling Frequency; Duration
IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week

SEARCH 24-hr PM 25 (FRM Mass, OC, BC, SO 4 , NO 3 , NH 4 , Elem.); 24-hr PM coarse (SO 4 , NO 3 , NH 4 , elements); Hourly PM 2.5 (Mass, SO 4 , NO 3 , NH 4 , EC, TC); and Hourly gases (O 3 , NO, NO 2 , NO y , HNO 3 , SO 2 , CO) Daily, Hourly;
NADP WSO 4 , WNO 3 , WNH 4 Weekly
EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr EPA-STN Speciated PM 2.5 Varies; Varies AIRS/AQS CO, NO, NO 2 , NO x , O 3 Hourly; HourlyMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-5 

### 250 500 750 1000 12 50 1500 1750 2000 -1500 -1250 -1000

-750 -500 -250 0 250 500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

Figure 3-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the VISTAS region and the VISTAS 12 km grid (locations of AQS sites not shown due to their large number).

3.3 Model Performance Statistics and Goals

To quantify model performance, several statisti cal measures were calculated and evaluated for all the IMPROVE, STN, CASTNet, SEARCH and NADP monitors within the VISTAS region or VISTAS 12 km domain and individually for each IMPROVE monitor associated with a VISTAS or nearby Class I areas. The statistical meas ures selected were ba sed on the recommendations outlined in section 18.4 of the USEPA’s Guidance On The Use Of Models And Other Analyses for Demonstrating Attainment of Air Qua lity Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007a).

In 2004, VISTAS established model performance goals and criteria for components of fine particle mass (Table 3-2) base d on previous model performan ce for ozone and fine particles (e.g., Morris et al., 2004a,b,c). EPA modeling gui dance for fine particulate matter at the time noted that PM models might not be able to ac hieve the same level of performance as ozone models. VISTAS’ evaluation considered several statistical performance measures and displays.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-6 Table 3-2. Model performance goals and criteria for components of fine particle mass.

Fractional Bias Fractional Error Comment

< 15% < 35% Goal for PM model performance based on ozone model performance, considered excellent performance

< 30% < 50% Goal for PM model performance, considered good performance

< 60% < 75% Criteria for PM model performance, considered average performance. Exceeding this level of performance indicates fundamental concerns with the modeling system and triggers diagnostic evaluation.

The statistical measures were calculated for al l of the component species of particulate matter responsible for light extinction, and for total PM 2.5 , PM 10 and light extinction. For convenience, these statistical measures or me trics, along with a variety of a dditional statistical measures, are summarized in Table C-3 in Appendix C. Appendix C also contains summary tables of all of the performance statistics and each PM component acr oss the VISTAS region and at each individual VISTAS and nearby Class I area.

3.4 Regional Model Performance Evaluation

Details on the regional model performance ev aluation across the VISTAS region and the VISTAS 12 km grid are provided in Appendix B. Below we su mmarize model performance for each PM species using monthly fr actional bias and fractional perf ormance statistics and monthly time series plots and Bugle Plots. The reader is referred to Appendix B for more details and additional summary performance displays (e.g., scatter plots and quant ile-quantile plots).

3.4.1 Sulfate (SO4) Model Performance

Figure 3-2 compares the monthly SO4 concentration fractional bias and error across the VISTAS region for the IMPROVE, STN, CASTNet, SEAR CH Daily and SEARCH Hourly monitoring networks. Note that these monthly model perform ance statistics were calculated across monitors within the VISTAS 12 km grid as compared to just monitors in the VISTAS states that is used in the monthly performance evaluation scatter plots and statistics given in Figures B-2 through B-5 in Appendix B. The underpredicti on bias in SO4 concentrations is clearly evident across most of the year and monitoring networks with the best (i.e., lowest) SO4 bias occurring in October. However, this underprediction bias is not seve re usually not going lower than -20% and never going lower than -25% and achieves the goal fo r PM model performance (Table 3-2). The exception to this is the SEARCH Hourly network that exhibits a positive overprediction SO4 bas for several months.

The SO4 monthly fractional error values are usually under 40% and, with one exception, always under 50% thereby achieving the PM performan ce goal (Figure 3-2). The exception is the SEARCH Hourly network with error values ra nging from 50% to 70%. The SEARCH Hourly SO4 measurements are based on research grad e monitoring equipment and likely have higher uncertainties and artifacts than the other monitoring networks.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-7 Figure 3-3 displays Bugle Plots of monthly SO4 fractional bias a nd error. Bugle Plots compare the monthly bias and error values with a perf ormance goal and performa nce criteria and accounts for the concept that better perf ormance is needed (i.e., more stringent model performance goals and criteria) for species with higher concentrati ons/extinctions and that performance goals and criteria can be relaxed as the species concentr ations become low and are no longer an important component of the PM or extinction budget. The Bugle Plots in Figure 3-3 show that SO4 model performance tends to be good across all months and, with one exception, always achieves the PM model performance goal for both bias and erro r and frequently achieves the more stringent ozone performance goal. The one exception is fo r fractional error across the SEARCH Hourly network that is above the perfor mance goal, but still ac hieves the performan ce criteria (Figure 3- 3, bottom).

As shown in Figures B-2 through B-5, wet deposition of SO4 tends to be overstated, particularly in the summer. This is due in part to overstated convective precipitation from the MM5 simulation (Olerud, 2003c,d). Duri ng the winter, when precipitatio n is dominated by large scale synoptic events, the SO4 wet deposition bias is near zero (e.g., Figure B-2). Thus, some of the SO4 underprediction bias is due to overstated we t scavenging that is likely due to overstated MM5 convective precipitation. Th e SO4 underprediction bias is al so due to a general tendency of the model to understate SO4 at the coastal si tes. For example, during July the CMAQ model tended to underestimate the observed SO4 at the Breton Island and Everglades IMPROVE monitors by approximately - 100% and underestimated SO4 at the other coastal sites (e.g., SAMA, ROMA, SWAN) by -20% to - 40% with the fractional bias at the inland sites closer to zero. For EVER and BRET, part of the SO4 unde restimation bias is likely due to missing emissions from the Caribbean (e.g., Cuba) and for all coastal sites there could be some issues associated with the characterizat ion of the 36/12 km grid cell containing the monitors as water when the monitor itself is clearly on land. In addition, the strength and penetration of the sea breeze is critical on some days to define whether the coastal mon itor is affected more by dirtier air coming off of land or the cleaner air coming of the sea, which is not well simulated by MM5 using a coarse grid resolution.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-8 

### VISTAS CMAQ 2002 Actual Base G2 12km
SO4

-40 -30 -20 -10

0 10 20 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] CASTNET IMPROVE STN SEARCH SEARCH_H

0 10 20 30 40 50 60 70 80

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-2. Monthly SO4 fractional bias (top) and fractional gross error (bottom) statistical measures for CASTNet, IMPROVE, STN and SEA RCH (daily and hourly) monitoring sites in the VISTAS 12 km grid.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-9 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot SO4

-200 -150 -100

-50

0 50 100 150 200

0 4 8 12 16 20
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria blank

CASTNET IMPROVE STN SEARCH SEARCH_H

0 50 100 150 200

0 4 8 12 16 20
Average Concentr ation (ug/m3)Fractional Error (%)

Figure 3-3 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for SO4 in the VISTAS 12 km grid and comparison to model perfo rmance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-10 3.4.2 Nitrate (NO3) Modeling Performance

The monthly fractional bias time series plot for NO3 clearly show s a seasonal dependence of this performance measure with a severe summer underp rediction bias of -100% to -150% and bias values generally within ±50% in the winter (F igure 3-4). The time seri es of fractional error values also exhibit a seasonal dependence with an 80% error in the winter peaking to a 160% error in the summer with a bell-like distribution. At first glance it appears that the summer underprediction bias would adversely affect the re gional haze modeling as that is when a vast majority of the worst 20 percent days occur at Class I areas in the VISTAS region. However, as shown in the Bugle Plots in Fi gure 3-5, these large summer unde rprediction bias values occur when NO3 concentrations are extremely low (< 1.0 μ g/m 3 ) and in fact when the average observed NO3 concentration exceeds 1 μ g/m 3 the model mostly achieves the model performance goal and almost always achieves the model perform ance criteria for bias (Figure 3-5, top). The model performance goal and criteria is achieved less often for NO3 fracti onal error (Figure 3-5, bottom), but the goal is always achieved across the CASTNet and usually achieved across the IMPROVE monitoring networks.

The summer NO3 underprediction bi as is likely due in part to the overstated MM5 convective precipitation discussed previously, which is some what confirmed by a -20% fractional bias for wet NO3 deposition in July (Figure B-9). Like we saw for SO4, the NO3 performance at coastal sites tends to be worse with near zero NO3 pred ictions at many of the sites during the summer and many days with very low values in the wint er. At the inland site s the NO3 winter model performance is better. For example, the NO3 fr actional bias in January at the coastal VISTAS IMPROVE sites is approximately -80%, whereas at the inland IMPROVE sites in ranges from 0% (36 km) to -20% (12 km).

The modeled NO3 concentrations are highly sens itive to ammonia (NH3) emissions as whether oxidized NOx is gaseous nitric acid (HNO3) or particulate NO3 depends in part on the availability of NH3 or other basic compound to buffer the NO3. Although VISTAS spent considerable effort updating and improving the a mmonia emissions, ammonia emissions are still highly uncertain. NH3 is emitted from a variety of sources quite unique from the other precursor emission sources (e.g., livestock, soil, etc.) and th e sampling of these emission rates is extremely difficult, thus uncertainties in the ammonia emi ssions will likely persist. Another potential reason NO3 is underestimated by CMAQ that is partic ularly important for th e coastal sites is the fact that the CMAQ model appears to greatly understate Sea Salt. As shown in Figure B-37 and the spatial maps in Appendix D (example given in lower left panel of Figure 3-16b), the CMAQ model is estimating near zero Sea Sa lt when the observed values are frequently a few tenths of a

μ g/m 3 or even up to one μ g/m 3 . The sodium (Na) in the Sea Salt can also act as a buffering agent for HNO3 to form particulate NO3 and it appear s that Sea Salt is und erstated in the CMAQ modeling. Note that this issue is somewhat comp licated by the fact that sodium nitrate (NaNO 3 ) would likely be a coarse particle so would not even appear in the IMPROVE speciated NO3 measurement and instead be embedded in the IMPROVE CM measurement.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-11 

### VISTAS CMAQ 2002 Actual Base G2 12km
NO3

-200 -150 -100

-50

0 50 100 150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] CASTNET IMPROVE STN SEARCH SEARCH_H

0 20 40 60 80 100 120 140 160 180 200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-4. Monthly NO3 fractional bias (top) and fractional gross error (bottom) statistical measures for CASTNet, IMPROVE, STN and SEAR CH daily and hourly monitoring sites in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-12 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot
NO3

-200 -150 -100

-50

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria blank

CASTNET IMPROVE STN SEARCH SEARCH_H

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Error (%)

Figure 3-5 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for NO3 in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-13 3.4.3 Organic Carbon Mass (OCM) Performance

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation OCM performance given in Figure 3-6 displays fairly good performance across the IMPROVE monitors with bias usually wi thin ±15% and errors between 40% and 50%. However, at the urban STN sites the model exhibits a large OCM unde rprediction bias of -60% to -90%. Part of the underprediction bias may be due to measuremen t uncertainties and artifacts and part may be due to the model over diluting the urban OCM emi ssions through the coarse 36/12 km grid. The underprediction of urban OCM is a common problem in PM modeling and likely also points to uncertainties in the OCM and SOA precursor emission inventories.

One source of uncertainty in the OCM measuremen ts is the fact that OCM is actually derived from OC measurements. The amount of additiona l elements (e.g., oxygen) attached to the OC to form OCM varies with the age and level of ph otochemical processing of the organic particles with OCM/OC ratios typi cally ranging from 1.2 to 2.4 for fresh to aged processed OCM. For the model performance evaluation we used a 1.4 OCM/OC ratio that is consistent with the original IMPROVE equation and based on measurements from Los Angeles. The new IMPROVE equation uses a higher 1.8 OCM/OC ratio reflecting the fact that OCM that reaches the mostly rural Class I areas will be aged and subject to photochemical pro cessing. There really is no one right OCM/OC factor to use in all cases. Howe ver, in interpreting the model performance it is important to know which OCM/OC ratio was used and recognize that selec tion of another ratio could make a 30% or so differen ce in the OCM measurements.

Unlike the IMPROVE OC measurements, the STN OC measurements are not blank corrected, which is believed to result in an approximate 0.5 μ g/m 3 positive artifact in the STN OC measurement (which would be 0.5-0.9 positive artifact in OCM depending on which OCM/OC factor is used). As seen in the scatter plot s in Figures B-12 through B-15 in Appendix B, the subtraction of 0.5-0.9 μ g/m 3 from the measured OCM value would greatly improve the OCM model performance across the STN network and bring the CMAQ OCM bias closer to zero.

The Bugle Plots of monthly frac tional bias and gross error (Figure 3-7) c oncurs that the OCM performance across the IMPROVE monitoring sites is fairly good achieving the model performance goals. But the OCM underprediction bias for the STN monitors is significant enough so that it always falls out of the model performance goals and is right at the model performance criteria. However, as noted above th is is partly due to m easurement artifacts. The OCM performance across the SEARCH networ k is mixed usually achieving the model performance goal and always achievi ng the model performance criteria.

In the preliminary evaluation of the CMAQ m odel using early VISTAS databases, the OCM performance was a great cause of concern due to a large summer OCM underprediction bias (Morris et al.., 2004b,c). This resulted in VI STAS conducting a focused research study on the reasons for the summer OCM unde rprediction, the identification of processes missing in the CMAQ model and the development of the SOAm ods enhancement to CMAQ (see Section 1.3.3.3.1) that greatly improved the CMAQ OCM model performance (Morris et al., 2006).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-14 

### VISTAS CMAQ 2002 Actual Base G2 12km
OC

-100

-90 -80 -70 -60 -50 -40 -30 -20 -10

0 10 20 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] IMPROVE STN SEARCH

0 10 20 30 40 50 60 70 80 90 100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-6. Monthly OCM fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-15 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot OC

-200 -150 -100

-50

0 50 100 150 200

0 4 8 12 16 20
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria

IMPROVE STN SEARCH

0 50 100 150 200

0 4 8 12 16 20
Average Concentr ation (ug/m3)Fractional Error (%)

Figure 3-7 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OCM in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-16 3.4.4 Elemental Carbon (EC) Performance

The monthly bias and error perfor mance statistics for EC across the VISTAS 12 km domain are shown in Figure 3-8. Bias and e rror are lower in the first and last quarters of the year with EC performance across the IMPROVE netw orks exhibiting a large underpredi ction bias (-40% to -80%) that peaks in June. The EC performance across the SEARCH network is somewhat similar to IMPROVE only the underprediction bi as in the summer is not as great. The STN network has fairly low EC fraction bias during qu arters 1, 3 and 4 (achieving the most stringent < ±15% ozone goal), but has larger underprediction bias during quart er 2 (-20% to -40%). The STN network very low EC bias during most of the year suggests that the anthropogenic EC emissions inventory may be adequate ly characterized in urban areas.

The Bugle Plot in Figure 3-9 shows that although the EC underprediction produces large fractional bias values, because the EC concentr ations are low the EC performance achieves the model performance goal for all months and networks since it is in the flar ed horn section of the Bugle Plot.

The scatter plots of EC performance in Fi gures B-17 through B-20 in Appendix B provide additional insight into the EC model performance. Across the urban STN sites the EC underprediction bias is much less using the CM AQ 12 km modeling results compared to the 36 km modeling results. Thus, for at least the ST N sites and probably the urban SEARCH sites as well, some of the EC underprediction bias is likel y due to the coarse grid spacing used. Across the more rural IMPROVE sites the opposite is ge nerally true with th e underprediction bias greater with the 12 km results than 36 km results, although the di fferences are not as pronounced as across the STN monitoring network. One reason for the EC underprediction across the IMPROVE monitoring network is because the m odel fails to capture a few isolated high observed EC observations. For example, in Ja nuary 2002 most of the observed and modeled EC values are less than 1 μ g/m 3 but there are two observed va lues that are at 2 and 3 μ g/m 3 that are not captured by the model (Figure B-17). These high values could be due to fires or other biomass burning that is not capt ured by the model. In the summer, however, the model EC underprediction bias appears to be more system atic. But again both the modeled and observed values are mostly less than 0.5 μ g/m 3 , so EC is a minor component of the visibility and PM 2.5 budget.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-17 

### VISTAS CMAQ 2002 Actual Base G2 12km
EC

-80 -60 -40 -20

0 20 40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] IMPROVE STN SEARCH SEARCH_H

0 10 20 30 40 50 60 70 80 90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-8. Monthly EC fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE, STN and SEARCH daily and hourly monitoring sites in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-18 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot EC

-200 -150 -100

-50

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria

IMPROVE STN SEARCH SEARCH_H

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Error (%)

Figure 3-9 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for EC in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-19 3.4.5 Other PM 2.5 or Soil Performance

The seasonal dependence of the Soil model performance across the IMPROVE and STN monitors in the VISTAS region is clearly evident in the monthly bias time series plot in Figure 3- 10 that show a large (> 100%) ove rprediction bias in the winter and near zero (IMPROVE) or much lower (SEARCH) bias in th e summer. As seen in the scat ter plots in Figures B-22 through B-25 in Appendix B, the modeled Soil values tend to always be between 0 and 2 μ g/m 3 year round, however the observed values are much lower in the winter (0 to 0.5 μ g/m 3 ) and comparable to the modeled values in the summer. These results suggest that the poor Soil model performance in the winter, spring and fall is likely due to incorrect emission temporal adjustment factors. For example, the effect of wetted surf aces that suppresses fugitive dust emissions may not be properly characterized in the seasona l adjustments to the emissions inventory.

The Bugle plots of bias and error (Figure 3-11) also display the seasonal dependence of the Soil model performance with the m odel achieving the model performance goals for Soil at the IMPROVE monitoring network for approximately ha lf the year centered on the summer and not achieving both the model performance goals and cr iteria at the IMPROVE network for the other half centered on the winter. Performance for Soil across the SEARCH network is worse with the model not achieving either the performa nce goals or criteria for most months.

The Soil model performance is confounded by the incommensurability between the modeled and measured Soil species. The IMPROVE observed Soil is built up off of measured elements (see Table 4-1). The modeled “Soil”, on the other hand, is fine particulate matter emissions that have not been explicitly identified and speciated as SO4, NO3, EC and OC in the PM speciation profiles used in the SMOKE emissions modeli ng. The emissions PM speciation profiles may have unidentified PM that is lumped in the othe r PM category that are not Soil. The measured Soil will typically be associated with fugitive dust emissions, so the separate tracking of fugitive dust may be an approach to separate the true Soil component from the other PM 2.5 component. VISTAS investigated this issue with an in ert (no chemistry) CMAQ sensitivity run that separately tracked just fugitive dust emissions and found improved Soil m odel performance with lower bias when just other PM 2.5 concentrations due to just fugitive just emissions were mapped to the Soil species.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-20 

### VISTAS CMAQ 2002 Actual Base G2 12km
SOIL

-50

0 50 100 150 200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] IMPROVE SEARCH

0 20 40 60 80 100 120 140 160 180

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-10. Monthly Soil fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE and SEARCH Daily mo nitoring sites in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-21 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot SOIL

-200 -150 -100

-50

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria

Series6 IMPROVE SEARCH Series7

0 50 100 150 200

012345678
Average Concentration (ug/m3)Fractional Error (%)

Figure 3-11 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for Soil in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-22 3.4.6 Coarse Mass (CM) Performance

Observed Coarse Mass (CM) is defined as the difference between the PM 10 mass and PM 2.5 mass measurements. Since IMPROVE network only speciates the PM 2.5 fraction of PM, then any coarse SO4 or NO3 would be in the CM measurem ent. In the model, on the other hand, CM is due solely to primary PM emissions that have been speciated to the coarse fraction (PM 2.5-10 ). Thus, there are potential incompatibility issues with the measured and modeled CM concentrations.

In addition, CM has a much shorter transport di stance than fine PM. In the modeling this is accounted for though fugitive dust transport fact ors (FDTF) that adjust the fugitive dust emissions downward to account for the fact that these emissions are deposited locally and are not transported out of the grid cell where they ar e emitted. VISTAS used county-specific FDTF provided by EPA that ranged from 0.03 to 0.87 (i.e ., reduced the fugitive dust emissions from 97% to 13%). The level of th e FDTF depends on land cover, areas with dense canopy vegetation have higher deposition rates (lower FDTF) than a desert area w ith no vegetation. Across the VISTAS domain, the FDTF typically ranged from 0.1 to 0.4 (i.e., 60% to 90% deposit out before being transported).

Although use of the FDTF is appropriate for modeling transport of fugitive dust over long distances, the local fugitive dust emissions can impact an IMPROVE monitor. Thus, much of the measured CM at the IMPROVE monitors is likely local in nature and subgrid-scale to the model. In this case we would expect the mo del to underestimate the measured CM which is what is seen.

Figure 3-12 displays the monthly fractional bi as and gross error valu es for CM across the IMPROVE monitors in the VISTAS region. The underpre diction bias ranges from -60% to -120% with errors of 80% to 140%. The Bugle Pl ot (Figure 3-13) shows that these performance statistics are either right at or exceeds the performance criteria indicating poor CM model performance.

The poor CM performance is not as bad of a prob lem as indicated in Figures 3-12 and 3-13 since it has the lowest extinction effi ciency and is usually not a ma jor component of visibility impairment at VISTAS Class I areas. It also has no effect on the PM 2.5 concentrations so would not affect the ASIP PM 2.5 modeling and projectionsMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-23 

### VISTAS CMAQ 2002 Actual Base G2 12km
CM

-140 -120 -100

-80 -60 -40 -20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] IMPROVE

0 20 40 60 80 100 120 140 160

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-12. Monthly coarse mass (CM) fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE monitoring sites in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-24 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot CM

-200 -150 -100

-50

0 50 100 150 200

0 2 4 6 8 10
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria

Series6 IMPROVE Series5 Series7

0 50 100 150 200

0 2 4 6 8 10
Average Concentr ation (ug/m3)Fractional Error (%)

Figure 3-13 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for coarse mass in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-25 3.4.7 Fine Particulate (PM 2.5 ) Performance

Figure 3-14 displays the monthly time seri es of fractional bias and error for PM 2.5 mass concentrations for the IMPROVE, STN, SEARCH Daily, SEARCH Hourly and FRM networks in the VISTAS region. The model genera lly is exhibiting good performance for PM 2.5 concentrations. For the first three and last thr ee months of the year the model performs well for
PM 2.5 with low bias (<±20%) and error (<40%, except for SEARCH Hourly). During April through July the underprediction bias becomes larger and is in the -20% to -45% range and the errors are also larger. This is driven in part by the SO4 underpre diction bias discussed previously as SO4 is a major component of PM 2.5 in the summer and the VISTAS region.

The Bugle Plots confirm the good performance for PM 2.5 mass concentrations that achieves the model performance goals most of the time and always achieves the model performance criteria (Figure 3-15).

The scatter plots in Figures B-32 through B-35 in Appendix B confirm that the performance for total PM 2.5 mass is fairly good, albeit with a summer underestimation bias.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-26 

### VISTAS CMAQ 2002 Actual Base G2 12km
PM25

-50 -40 -30 -20 -10

0 10 20 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Bias [%] IMPROVE STN SEARCH SEARCH_H FRM

0 10 20 30 40 50 60 70

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFractional Error [%]

Figure 3-14. Monthly PM 2.5 fractional bias (top) and fractional gross error (bottom) statistical measures in the VISTAS region.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-27 

## VISTAS CMAQ 2002 Actual Base 12km Bugle Plot
PM25

-200 -150 -100

-50

0 50 100 150 200

0 5 10 15 20 25 30 35 40
Average Concentration (ug/m3)Fractional Bias (%) (+) Goal (-) Goal (+) Criteria (-) Criteria blank

IMPROVE STN SEARCH SEARCH_H FRM

0 50 100 150 200

0 5 10 15 20 25 30 35 40
Average Concentr ation (ug/m3)Fractional Error (%)

Figure 3-15 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for PM 2.5 in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-28 3.5 Evaluation at VISTAS a nd Nearby Class I Areas

Projections of light extinction fo r the evaluation of regional haze are based on a subset of days for which there is monitored data. IMPROVE m onitors collect 24-hour PM samples every third day. Observations of component species on these days are used to calculate light extinction, which is the basis on which these days are ranke d. These rankings are then used to identify the 20 percent most impaired days, or “worst 20 per cent days”, and the 20 percent least impaired days, or “best 20 percent days”. EPA guidance recommends using the modeling results for the worst 20 percent and best 20 per cent days from 2002 to make 2018 visibility projections (EPA, 2007a). Thus, the model performance of the visi bility impairment pollutants for the best and worst 20 percent days at the Class I areas is critically important for making reliable 2018 visibility projections in the VISTAS modeling. Table C-1 in Appendix C lists the best 20 percent and worst 20 percent days for all of Class I areas within and nearby the VISTAS region. Appendix C provides details on the CMAQ 2002 Actu al Base G2 base case model performance evaluation at each Class I area within and near the VISTAS region for the worst and best 20 percent days including model performance statistics, scatter plots, time series plots and other summary performance evaluation measures. Appendi x D displays the spatia l distribution of the predicted and observed PM species concentrati ons for each day from 2002 that is a best and worst 20 percent day for a VISTAS or nearby Cla ss I areas (i.e., days in Table C-1 in Appendix C). Below in Section 3.5.1 we provide an exampl e of the spatial distribu tion evaluation plots for one day (July 10, 2002) that are reproduced from A ppendix D, the reader is referred to Appendix D for the spatial plots for the remainder of the best and worst 20 percent days during 2002. In sections 3.5.2 through 3.5.22 we summarize the CMAQ 12 km PM species model performance across the best and worst 20 percent days separate ly for each Class I area in or near the VISTAS region. This summary model performance uses Bugle Plots. Additional summaries of model performance for each Class I area and the best and worst 20 percent days are provided in Appendix C that includes summary tables of a co mplete set of model performance statistics and scatter plots of predicted and observed PM spec ies concentrations for the best and worst 20 percent days and time series of predicted and obs erved concentrations across all monitoring days in 2002.

3.5.1 Example Spatial Distribution Model Performance

Table 3-2 and Figure 3-16 reproduce the spatia l model performance plots for one of the approximately 180 best or worst 20 percent da ys from Appendix D for July 10, 2002. For each of the worst or best 20 percen t days in Appendix D, a table lik e the one shown in Table 3-2 for July 10, 2002 is presented that list s which Class I areas th e day represents eith er a worst or best 20 percent day. In the case of Ju ly 10, 2002, it is a wors t 20 percent day for 13 Class I areas that are generally inland (exception for BRIG) and a be st 20 percent day for 5 Class I area that tend to be in the southern (e.g., Fl orida) or coastal (e.g., ROMA) ar eas of the VISTAS region (Table 3-2).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-29 Table 3-2. Class I areas with worst or best 20 percent days from 2002 occurring on July 10, 2002. Date Julian Day Type Class I Areas Affected

07/10/02 191 W20% LIGO, SHRO, GRSM, JARI, SIPS, CACR, DOSO, HEGL, COHU, MACA, UPBU, MING, BRIG 07/10/02 191 B20% OKEF, BRET, CHAS, EVER, ROMA

Figure 3-16 compares the spatial distributions of the predicted and observed PM species (Figures 3-16a and 3-16b) and then total PM 2.5 mass (Figure 3-16c) concentrations. In this case, the model is capturing the very high sulfate concen trations across the Appa lachian Mountains and interior part of the VISTAS regi on well (Figure 3-16a, top left). It is also reproducing the much lower SO4 conditions in s outhern Georgia, Florida and the co astal regions of South Carolina and North Carolina. The low NO3 c onditions are also reproduced we ll by the model and even the spatial distribution of OCM is reproduced well (F igure 3-16a). The spatia l plot of the modeled and observed NH4 concentrations suggests that the model is underestimating the observed NH4 (Figure 3-16a, bottom left). However, the IMPROVE monitoring network doesn’t measure NH4 and the “observed” NH4 in Figure 3-16a is act ually derived NH4 assuming that the SO4 and NO3 are completely neutralized by NH4. If SO4 is not complete ly neutralized by NH4 then the modeled NH4 should be less than the derived NH4 values which is the case. Previous VISTAS sensitivity analyses have found that modeled SO4 is not completely neutralized by NH4 all the time, especially in the summer.

Both the modeled and observed EC levels ar e low and the model does a good job in reproducing the region’s lower less than 0.25 μ g/m 3 on the coast versus areas with more elevated (0.5-1.5

μ g/m 3 ) EC levels inland (Figure 3-16b, top left). The model does a good job in reproducing the Soil levels in the eastern part of the VISTAS domain, but fails to re produce the higher values toward the western part of the VISTAS regi on and on into the CENR AP region. Although the model reproduces the CM levels over Missouri and adjacent regions, it is too clean in the VISTAS region where CM is underestimated.

The Sea Salt performance looks poor with th e model greatly underestimating the elevated observed values at the ROMA an d EVER coastal sites. However, care must be taken in the interpretation of the Sea Salt performance. The new IMPROVE equation recommendation is to represent Sea Salt as 1.8 times the Chloride measurement and if the Chloride measurement is missing or below detection to substitute the Chlo rine ion for the Chloride. However, Chloride was almost always missing so the Sea Salt eval uation is based on the Chlorine measurement. Another approach to obtain measured “Sea Salt” is to multiple the observed Sodium (Na) particle concentrations by 2.5. However, this was not recommended for the new IMPROVE equation since there are other sources of Na (e.g., fugitiv e dust). However, the Na measurement appears more stable than Chloride and perhaps futu re Sea Salt evaluations should use the Na measurement at coastal sites as well. Although the model Sea Salt is not used in the visibility projections (the new IMPROVE equation uses the measured Sea Salt and holds it constant between the Baseline and 2018), th e Sea Salt underprediction may affect the NO3 performance as discussed previously.

Figure 3-16c displays the model and IMPR OVE measured spatial distribution of PM 2.5 mass across the VISTAS 12 km mode ling domain. The model does an excellent job in reproducing the spatial distribution of the observed total PM 2.5 concentrations on th is day, which is notMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-30 surprising since it is dominated by SO4 for which th e model performs well. The gradients in the observed PM 2.5 concentrations between the higher va lues across the southern Appalachian Mountains to the lower values at the coast and to the south is reprodu ced well by the model along with the higher values in the CENRAP region. The elevated observed PM 2.5 concentrations in the northern Appalachian Mo untains and across to BRIG are underrepresented by the model.

Appendix D contains similar spatial evaluations for all of the worst and best 20 percent days during 2002 at VISTAS and nearby Class I areas. The reader is referred to Appendix D for further analysis of the spatial model performance of the CMAQ 2002 Base G2 Actual base case simulation.

Figure 3-16a. Predicted and observed spatial maps of daily average sulfate (SO4) (top left), nitrate (NO3) (top right), ammonium (NH4) (bottom left), and Organic Carbon Mass (OCM) concentrations ( μ g/m 3 ) for July 10, 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-31 Figure 3-16b. Predicted and observed spatial maps of daily average elemental carbon (EC) (top left), coarse mass (CM) (top right), sea salt (NaCl) (bottom left), and Soil (bottom right) concentrations ( μ g/m 3 ) for July 10, 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-32 Figure 3-16c. Predicted and observed spatial maps of daily average total fine particulate matter (PM 2.5 ) concentration ( μ g/m 3 ) for July 10, 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-33 3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 Percent Days Performance

Figure 3-17a and 3-17b display B ugle Plots of fractional bias a nd gross error by PM species for the SIPS Class I area and the, respectively, best 20 percent and worst 20 percent days in 2002. For the best 20 percent days, OCM is the best performing species followed by NO3 and EC which all achieve the model performance goal. SO4 and Soil achieve the performance criteria but not the performance goal, and CM performan ce is the worst not achi eving the performance criteria for the best 20 percent days.

On the worst 20 percent days, EC, Soil and OC M all achieve the performance goal, although because of the low concentrations of the EC a nd Soil they benefit from being in the flared portion of the Bugle Plot. NO3, SO4 and CM fail to achieve both th e performance goal and criteria. Due to its high concen trations and high extinction effici ency SO4 is the most important species and it is encouraging that it is right at the performance criteria for the worst 20 percent days at Sipsey.

Additional model performance statistics for Sipsey and the best and wors t 20 percent days are provided in Tables C-4t and C- 5t in Appendix C. Also in Appendix C are scatter plots of predicted and observed extinction by species for the best and worst 20 percent days at Sipsey (Figure C-3). Finally, time series of pr edicted and observed PM component species concentrations for all days during 2002 and Sipsey are provided in Figure C-7.

3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 Percent Days Performance

The performance of the CMAQ model for the best and worst 20 percent days at the CHAS Class I area is shown in Figures 3-18 and C-8 through C-12. For the be st 20 percent days, EC, Soil and SO4 achieve the model performance goa l and OCM achieves the model performance criteria. However, NO3 and CM fall outside of the performance criteria. CM has the highest concentrations for the best 20 percent days at CHAS that is over twice as high as the other species and just barely fails to achieve the performance criteria. NO3 has the lowest concentrations of all the species.

On the worst 20 percent days (Figure 3-18b), EC and CM achieve the performance goal and OCM is right at the goal. SO4 achieves the perf ormance criteria but not the performance goal and the Soil overprediction bias and error makes it fall outside of the performance criteria range.

3.5.4 Everglades (EVER), Florida Best and Worst 20 Percent Days Performance

EC, Soil and OC achieves the performance goal fo r the best 20 percent days at EVER, SO4 is right at the criteria and CM and NO3 fall out of the criteria range (Figure 3-19a). In fact NO3 bias and error are pegged at -200% and 200% which suggests that it is near zero most of the time for the best 20 percent of the days at EVER. For the worst 20 percent days at EVER, NO3 and CM are again the poorest performing species falli ng out of the performan ce criteria range with SO4 just falling out of the range for bias, but with in the criteria range for error. Again NO3 bias and error are almost at -200% and 200% indica ting near zero concentrations. As noted in Chapters 4 and 5, the CMAQ 12 km near zero NO3 concentrations at EVER does affect the 2018 visibility projections.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-34 Figure 3-17a. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, Alabama and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-35 Figure 3-17b. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, Alabama and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-36 Figure 3-18a. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-37 Figure 3-18b. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-38 Figure 3-19a. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-39 Figure 3-19b. Bugle Plots of Fractional Bias a nd Fractional Gross Error for EVER and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-40 3.5.5 St. Marks (SAMA), Florida Best and Worst 20 Percent Days Performance

The SAMA model performance for th e best/worst 20 percent days is similar to the other Class I areas with CM being the biggest performance problem followed by NO3 and the remainder of the species performing fairly we ll. NO3 has worse performance statistics than SO4 but much lower mass so is less of a concern.

3.5.6 Cohutta (COHU), Georgia Best and Worst 20 Percent Days Performance

Better performance is seen at the COHU Class I area for the best and worst 20 percent days (Figure 3-21). For the best 20 percent days a ll PM species achieve the model performance goal, except CM that achieves the performance criteria but not the goal. For th e worst 20 percent days at COHU, EC, NO3, Soil and OC achieve the performance goal, SO4 achieves the performance criteria and CM achieves the criter ia for bias but not for error.

3.5.7 Okefenokee (OKEF), Georgia Best a nd Worst 20 Percent Days Performance

Performance for the PM species and the best a nd worst 20 percent days mostly achieves the model performance goals and/or criteria fo r all species by CM (Figure 3-22). The SO4 performance on the worst 20 percent days achi eves the performance criteria but not the performance goal.

3.5.8 Mammoth Cave (MACA), Kentucky Best an d Worst 20 Percent Days Performance]

MACA has a several species right at the performance goal or criter ia level for the best 20 percent days, with Soil barely exceeding the criteria fo r bias and CM barely exceeding in for error (Figure 3-23). For the worst 20 percent days, EC is the only species that clearly achieves the performance goal with Soil and SO4 right at the performance goal and OCM right at the performance criteria.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-41 Figure 3-20a. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-42 Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-43 Figure 3-21a. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-44 Figure 3-21b. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-45 Figure 3-22a. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-46 Figure 3-22b. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-47 Figure 3-23a. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-48 Figure 3-23b. Bugle Plots of Fractional Bias an d Fractional Gross Error for MACA and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-49 3.5.9 Linville Gorge (LIGO), North Carolina Best and Worst 20 Percent Days Performance

Performance for the best 20 percen t days at LIGO is fairly good with all specie s achieving the performance goal except CM that does not even ach ieve the performance criteria (Figure 3-24). Similar good performance is seen for the worst 20 percent days at LIGO with most species achieving the performance goal except SO4 that ach ieves the performance criteria and CM that does not meet either the pe rformance goal or criteria.

3.5.10 Shining Rock (SHRO), North Carolina Best and Worst 20 Percent Days Performance

With the exception of CM, fairly good model perf ormance for the best and worst 20 percent days is also seen at SHRO (Figure 3-25). On th e best days all species except CM achieve the performance criteria and on the worst days there ar e similar results only SO4 just barely achieves the performance criteria.

3.5.11 Swanquarter (SWAN), North Carolina Be st and Worst 20 Percent Days Performance

CM is the poorest performance species at SWAN , although NO3 performance also falls out of the criteria range for the worst 20 percent days (Figure 3-26). SO4 does exceed the performance goal range but achieves the performance criteria.

3.5.12 Cape Romain (ROMA), South Carolina Best and Worst 20 Percent Days Performance

For the best 20 percent days at ROMA, only EC and Soil are clearly achieving the performance goal with NO3, OC, SO4 and CM be tween the performance goal and criteria (Figure 3-27). On the worst 20 percent days, EC Soil and CM achieve the performance goal and OC and SO4 achieve the performance criteria. But NO3 falls out of the performance criteria range, but has low concentrations.

3.5.13 Great Smoky Mountains (GRSM), Tennesse e-North Carolina Best and Worst 20 Percent Days Performance

Performance for the best 20 percent days at GRSM is fairly good with Soil and CM the only species exhibiting performance issues (Figure 3- 28). For the worst 20 pe rcent days, CM is the only species that falls outside of the criteria rang e, with SO4 just at the criteria and other species achieving the performance goal.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-50

Figure 3-24a. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-51 Figure 3-24b. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-52 Figure 3-25a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-53 Figure 3-25b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-54 Figure 3-26a. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-55 Figure 3-26b. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-56 Figure 3-27a. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-57 Figure 3-27b. Bugle Plots of Fractional Bias an d Fractional Gross Error for ROMA and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-58 Figure 3-28a. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-59 Figure 3-28b. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-60 3.5.14 James River Face Wilderness Area (JARI), Virginia Best and Worst 20 Percent Days Performance

As seen for the other Class I areas, CM is the only clear performance problem at JARI for the best and worst 20 percent days with all other species achieving the performance goals except SO4 for the best and SO4 and OCM for the wo rst 20 percent days that just achieve the performance criteria (Figure 3-29).

3.5.15 Shenandoah (SHEN), Virginia Best and Worst 20 Percent Days Performance

The NO3 performance at SHEN for the best 20 pe rcent days is a cause for concern, with most other species achieving or nearly achieving the performance crit eria (Figure 3-30). On the worst 20 percent days CM is the only sp ecies that is clearly performi ng poorly with the other species except SO4 achieving the performance goal with SO4 falling between the performance goal and criteria.

3.5.16 Dolly Sods (DOSO), West Virginia Best and Worst 20 Percent Days Performance

Performance at DOSO is fairly good for the best and worst 20 percent da ys with most species achieving the performance goal and almost all speci es achieving the perfor mance criteria (Figure 3-31). The exception is CM for the worst 20 percen t days that is underpred icted by over -100%.

3.5.17 Breton Island (BRET), Louisiana Best and Worst 20 Percent Days Performance

The performance at BRET is a cause for concern on the worst 20 percent days since the SO4, OC and CM all fail to achieve the pe rformance criteria due to large underpredicti on biases (Figure 3- 32). Better performance is seen on the best 20 percent days.

3.5.18 Caney Creek (CACR) and Upper Buffalo (UPBU), Arkansas Best and Worst 20 Percent Days Performance

Performance at CACR (Figure 3-33) and UPBU (Fi gure 3-34) is similar. For the best days only CM is a problem species. And on the worst days CM is the main problem species but SO4 is also underestimated, although it still achieves the performance criteria.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-61 Figure 3-29a. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-62 Figure 3-29b. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-63 Figure 3-30a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-64 Figure 3-30b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-65 Figure 3-31a. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-66 Figure 3-31b. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-67 Figure 3-32a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-68 Figure 3-32b. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-69 Figure 3-33a. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-70 Figure 3-33b. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-71 Figure 3-34a. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-72 Figure 3-34b. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-73 3.5.19 Mingo (MING) and Hercules Glade (HEG L), Missouri Best and Worst 20 Percent Days Performance

At the two Missouri Class I areas for the best 20 percent days, the Soil performance exhibits a large overprediction bias that ex ceeds all goals and criteria and the CM performance is right at the goal or between the goal and criteria. Perfor mance for the remainder of the species achieves the performance goal (Fi gures 3-25 and 3-36). The overpredict ion of Soil is also a problem for the worst 20 percent days at th ese two sites. Alth ough SO4 performs fairly well at MING for the worst 20 percent days (Figure 3- 35b), it is underestimated at HEGL resulting in a fractional bias that is right at the perfor mance criteria (Figure 3-36b).

3.5.20 Brigantine (BRIG), New Jersey Best a nd Worst 20 Percent Days Performance

With the exception of CM for both the best and worst 20 percent days and NO3 error for the worst 20 percent days, all PM species achieve the performance criteria for the best and worst 20 percent days at BRIG. The SO4 performance is al so a concern as the underprediction is severe enough so that it falls between the perfo rmance goal and criteria (Figure 3-37).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-74 Figure 3-35a. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-75 Figure 3-35b. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-76 Figure 3-36a. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-77 Figure 3-36b. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-78 Figure 3-37a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRIG and the Best 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-79 Figure 3-37b. Bugle Plots of Fractional Bias and Fractional Gross Error for BGIG and the Worst 20 Percent Days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-80 3.6 Conclusions of Model Performance Evaluation

As noted previously, the CMAQ mo deling results for the best and worst 20 percent days are used to project the observed PM concentrations from the 2000-2004 Baseline to 2018 which the 2018 visibility projections are based on. Thus, model performance for these days is fairly critical. There are several common themes in the model performance of the CMAQ model for the best and worst 20 percent days at Class I ar eas within and near the VISTAS region:

• Poor CM Performance : Coarse Matter is consistently underestimated by the model for the best and worst 20 percent days such that is rarely achieves the model performance goal or criteria. As noted prev iously, this is likely partially due to local fugitive dust impacts that are subgrid-scale to the model. CM has a low extinction efficiency and is a minor contributor to visibility impairment in the VISTAS region, so the CM performance issues are probably not a big issue for the wors t 20 percent days. Fo r the best 20 percent days CM is a larger component of the extinction budget. If in fact CM at the IMPROVE monitors is local fugitive dust impacts (e.g., wind blown dust), then it may be appropriate to hold the CM constant between the 2000-2004 Baseline and 2018. This issue is addressed in Chapter 5 in a sensitivity anal ysis that assumes CM alone and both CM and Soil are natural and are held c onstant in the 2018 vi sibility projections for the worst 20 percent days. This sensitivity analysis found essentially no difference in the 2018 visibility projections at VI STAS Class I areas when CM alone and both CM and Soil were assumed natural and held constant between 2000-2004 and 2018. This was because there is very little change in the CM emissions between the 2002 and 2018 emission scenarios so that the RRFs are near unity a nyway. Thus, the CM performance issues are not a cause for concern in the VISTAS regional haze modeling.

• Nitrate Underprediction for Worst 20 Percent Days : NO3 is frequently underpredicted at Class I areas for the worst 20 percent days. Even though the NO3 concentrations are low so that their performance sta tistics are in the flared hor n of the Bugle Plots, they frequently still fail to achieve the performan ce goal or criteria. However, NO3 is usually a minor contributor to visibility impairment at VISTAS Class I areas for the worst 20 percent days and most of the time for the best 20 percent days as well. An examination of the extinction budgets for the worst and best 20 percent days (see Stacked Bar Charts in Appendix E and F) reveals that there are occasionally high NO3 days. For the worst 20 percent days most days are summer SO 4 days where the observed and modeled NO3 concentrations are extremely low, although occasionally there are non-summer NO3 days as well. In this case, th e NO3 RRFs used in the 2018 proj ections will be dominated by the few NO3 days. Given the low contributio n of NO3 to visibility impairment in the VISTAS region at this time (as SO2 is cont rolled NO3 may become a larger component of the extinction budget), then the NO3 perfor mance is not a big cause for concern. The exception to this is the situation at EVER where the CMAQ 12 km modeled NO3 concentrations are near zero fo r all of the worst 20 percent days. We suspect that model noise may be affecting the NO3 RRFs and the CMAQ 12 km 2018 visibility projections presented in Chapter 4. This is addressed with a 2018 visibility projection sensitivity analysis that holds the NO3 constant between the 2000-2004 Baseline and 2018 if the 2002 modeled average NO3 concentr ations across the worst 20 per cent days is less than a tenth of a μ g/m 3 which is presented in Chapter 5. Th e NO3 sensitivity analysis did show that the CMAQ 12 km 2018 visibility proj ections are affected by the low NO3 concentrations at EVER.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chap_3_MPE4.doc 3-81 • Sulfate Underprediction Bias for Worst 20 Percent Days : SO4 is consistently underpredicted across the best and worst 20 percent days and frequently exceeded the model performance goal and sometimes even the performance criteria. This is especially important for the worst 20 percent days when SO4 is highest and dominates the visibility impairment at most VISTAS Class I areas. It is also important because most of the visibility improvements between the 2000- 2004 Baseline and 2018 are due to reductions in SO4. If the model is estimating much cleaner SO4 conditions in 2002 for the worst 20 percent days than it should, the SO4 RRFs will be stiffer than they should be (i.e., show less reductions) because backgr ound SO4 is a higher fraction of the total SO4. An examination of the Stacked Bar Charts of predicted and observed visibility extinction for the worst 20 percent days in Appendix E reveals that most of the SO4 underprediction bias on the worst 20 percent da ys is driven by the model pr edicting low SO4 on a few of the high observed SO4 days, while predicti ng higher SO4 on others. As the modeled SO4 RRFs that are used to project the 2000-2004 Baseline to 2018 are based on the average of the SO4 concentrations across the worst/best 20 percent days, then the RRFs will in fact be dominated by the higher mode led SO4 days where the model is actually performing reasonably well. The inclusion of the low modeled SO4 days in the RRFs will make them slightly stiffer than they should be and this would add an element of conservatism in the analysis (i.e., 2018 visibility projections that show less visibility improvements then will really occur). This issue is also addressed in the Additional Analysis presented in Chapter 5 where the SO4 RRFs are based on only those worst 20 percent days in which the modeled and observe d SO4 concentrations are within a factor of 2 of each other. Again, the sensitivity an alysis demonstrates that the low modeled SO4 concentrations on the wo rst 20 percent days did not significantly affect the 2018 visibility projections.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-1 4.0 VISIBILITY PROJECTIONS

This section presents the future-year visibility projections for Class I areas within and near the VISTAS region and their comparison with the 2018 Un iform Rate of Progress. Most of the work on the visibility projections for the VISTAS Cl ass I areas was performed using the CMAQ 36/12 km modeling results for the 2002 and 2012 Base G2 emission scenarios whose emissions were released in July 2007. Since then several VISTAS States have made revisions to emissions for some point sources based on improved informati on on controls and shut downs to generate a 2018 Base G4 Best and Final (BaF) emissions inve ntory that was released in October 2007. The change in total VISTAS region point source emissions between the 2018 Base G2 and 2018 Base G4/BaF was very small (~1%), as noted in Table 2-14 in Section 2.6. The 2018 visibility projections for the 2018 Base G4 Best and Final scen ario are presented at th e end of this Chapter.

As noted in Chapter 1, the Regi onal Haze Rule (RHR) requires states with Class I areas to develop State Implementation Plans (SIPs) that include reasonable progr ess goals 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 required to adopt reasonable progress goals for improving visibility from observed Baseline C onditions. The 2000-2004 five-year period is used to define Baseline Conditions and the first future progress period is 2018. A state is required to set reasonable progress goals for each Class I area in the state for two visibility metrics:

• Provide for an improvement in visibility for th e most impaired visibili ty 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 reasonable progress goals are to provide for a rate of improvement sufficient to be on a course to attain “Natural Cond itions” by 2064. States ar e to define controls to meet reasonable progress goals every 10 years, starting in 2018, which defines progress periods ending in 2018, 2028, 2038, 2048, 2058 and finally 2064. Reas onable progress goals are defined by the states to include all reasonable control measures for each review period. States will determine whether they are meeting their goals by comparing visibility conditions from one five-year period to another (e.g., 2000-2004 to 2013-2017). As stated in 40 CFR 51.308 (d) (1), baseline visibility conditions, reasonable progress goals, and changes in visibility mu st be expressed in terms of deciview (dv) units. The haze index (HI) metric of visibility impairme nt, 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.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-2 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 reasonable progress goals:

“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).

“Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” (EPA, 2007b).

The first EPA modeling guidance document list ed above (EPA, 2007a) di scusses the use of modeling results to project fu ture-year visibility. The se cond 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 gui dance document (EPA, 2003b) discusses procedures for defining Natural Conditions for a Class I area. Natural Conditions are the visibility goal for 2064. Although states may propose alternative appr oaches for defining Natural Conditions, in this section we use the EPA default Natural Conditions at Class I ar eas updated to the new IMPROVE equation as available on the VIEW S website. The final EPA guidance document discusses how states should define their reasona ble progress goals and their relationship to the 2018 Uniform Rate of Progress.

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 VIST AS visibility projec tions are based on the new IMPROVE equation, although projections ba sed on the original IMPROVE equation are also presented as an alternative approach in Chapter 5.

4.2 Calculation of Visibility and 2018 Unif orm Rate of Progress 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 to the base-year modeling results. The RRFs are applied to the baseline visibility conditions to project future-year visibility. The major features of EPA’s recommended visibility projection ap proach are as follows (EPA, 2003a, b; 2007a):

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

• Monitored concentrations of PM 10 are divided into six major components (note that Sea Salt is also included in the new IM PROVE equation and held constant betweenMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-3 the current and future years), the firs t 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 carbon mass (OCM) 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.

• 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 Uniform Rate of Progress 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 Uniform Rate of Progress glidepath and 2018 visi bility projections. Th e baseline data are available on the VIEWS website ( http://vista.cira.colostate.edu/views/ ).

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 (EPA, 2003b) that have been updated for the new IMPROVE equation on the VIEWS website and are us ed in this section.

2018 Uniform Rate of Progress : The 2018 Uniform Rate of Progress is defined by constructing a linear glidepath in deciview s starting with the observed 2000-2004 Baseline Conditions in 2004 and ending at Natural Conditi ons in 2064 for each Class I area. Where the linear glidepath passes through 2018 is the 2018 Uniform Rate of Pr ogress in deciviews.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-4 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 documents (EPA, 2003a; 2007a) and fine and coarse particulate matter concentrations measured at IMPROVE monitors (Malm et al., 2000; Debell et al., 2006). Currently, each Class I area in the VISTAS region 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:

• Sulfate [(NH 4 ) 2 SO 4 ];

• Particulate Nitrate [(NH 4 NO 3 ];

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

• 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 - OCM 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

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).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-5 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 Carbon Mass (OCM) using the orig inal 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 OCM be sides Carbon (e.g., oxygen). IMPROVE also uses the term Particulate Organi c Matter (POM) to describe OCM.

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

• 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 whose difference gives CM.

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 OCM may also ha ve hygroscopic properties, but the IMPROVE equations assume OCM is non-hygroscopic.

There are currently two IMPROVE e quations that are used to convert PM concentrations to light extinction, the original (or old) and the new (or revised) 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 (Malm et al., 2000):

b Sulfate = 3 x f(RH) x [Sulfate] b Nitrate = 3 x f(RH) x [Nitrate] b EC = 10 x [EC] b OCM = 4 x [OCM] 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/

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-6 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 OCM + 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 ,

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 OCM concentrations accounting for the different light scattering efficiency characteri stics as a function of concentrations for these three species (DeBell et al., 2006). 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 OCM = 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 OCM are each spli t into two fractions, representing small and large size distributions of those co mponents. As noted in Table 4-1, the OCM 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 and possibl y other areas) and for light absorption by NO2 (only used where NO2 observati ons are available). Consiste nt with IMPROVE guidance, VISTAS used Cl - mass from ion chromatography to define sea salt concentrations. As NO2 is rarely measured at IMPROVE monitoring sites and, except for conditions of plume blight has little effect on regional haze, this component of the new IMPROVE equations was not used. Site-specific Rayleigh scattering for each IMPR OVE monitoring site is used in the new IMPROVE equation, as compar ed to a constant 10 Mm -1 value assumed in the original IMPROVE equation.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-7 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 OCM 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 and
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 OCM + b Soil + b CM + b NaCl + b NO2

The Haze Index (HI) and Visual Range (VR) are calculated from the total 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 VISTAS Great Smoky Mountains Class I area IMPROVE monitor. The original IMPROVE equation has a bias towa rd 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 VISTAS states have elected to perform their primary visibility projections using the new IMPROVE equation. Further justification of the new IMPROVE equation is provided in other VISTAS documents by Dr. Ivar Tombach and Air Resource Specialists available on the VIST AS website (http://www.vistassesarm.org/documents/FinalDocs.asp). Result s using the original IMPROVE equation are presented in Section 5 as an alternative approach.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-8 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.

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 the 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 measurements and either the original or new IMPROVE equation. Convert th e daily extinction 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.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-9 4.2.3 Data Filling for Sites with Insufficient Va lid Data to Calculate Baseline Conditions

Several Class I areas within and near the VI STAS region did not cont ain sufficient IMPROVE observations during the five-year 2000-2004 Baseline to have thr ee valid years of data from which Baseline Conditions could be constructed: Chassahowitzka (CHAS), Florida, Cohutta (COHU), Georgia, St. Marks (SAMA), Florida, Shining Rock (SHRO), North Carolina, and Swan Quarter (SWAN), North Carolina within th e VISTAS region, plus Breton Island (BRET), Louisiana, and Mingo (MING), Mi ssouri in the CENRAP region. For these Class I areas, Air Resource Specialists developed subst ituted data sets so that at least three years of valid data were available from which valid Baseline Conditions could be calculated using EPA’s minimal valid data requirement (EPA, 2003a). These substitute d IMPROVE databases are also 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 using the old IMPROVE equation (EPA, 2003b). These EPA default worst 20 percent Natural Conditions we re updated to the new IMPROVE equation and are available on the VIEWS website ( http://vista.cira.colostate.edu/views/) . The default Natural Conditions were used in constructing the linear glidepaths to gene rate the 2018 Uniform Rate of Progress. These EPA default Natural Conditions are used as the an chor point for the glidepaths in 2064 that are provided in Appendix E for the VI STAS and nearby Class I areas.

4.2.5 2018 Uniform Rate of Progress

The Uniform Rate of Progress glidepath is c onstructed by generating a linear glidepath in deciviews from the Baseline Conditions in 2004 to Natural Conditions in 2064. Where the linear glidepath crosses 2018 is the 2018 Uniform Rate of Progress. Figure 4- 2 displays an example linear glidepath for the Great Smoky Mountains Class I area in Tennessee-North Carolina. There are five years of sufficient valid IM PROVE data during the 2000-2004 Baseline with annual average worst 20 percent values of 30.71, 30.60, 29.94, 30.51 and 29.62 dv (blue diamonds) resulting in worst 20 percent Baselin e Conditions of 30.28 dv that is placed as the starting point in 2004 for the glidepath. The ending point for the glidepath is 11.05 dv which is the default Natural Conditions for the worst 20 pe rcent days using the new IMPROVE equation. The linear glidepath crosses 2018 at 25.79 dv which becomes the 2018 Uniform Rate of Progress.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-10 Uniform Rate of Progress Glide Path Great Smoky Mountains - 20% Worst Days

30.28 28.99

25.79

22.59

19.38

16.18

12.97
11.05

0 5 10 15 20 25 30 35

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. Uniform Rate of Progress glidepath for Great Smoky Mountains (GRSM), Tennessee-North Carolina Class I area that linearly connects the 30.28 dv Baseline Conditions in 2004 with the 11.05 dv Natural Conditions in 2064 resulting in a 2018 Uniform Rate of Progress of 25.79 dv.

4.3 EPA Default Approach for Visibility Projections

For VISTAS model application for a single ye ar (2002), EPA’s regional haze modeling guidance recommends developing Class I area-specific and PM species-specific RRFs based on the average concentrations for the worst 20 percent da ys from 2002 (EPA, 2007a). Thus, this is the methodology used to project 2018 visibility estimates 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

4.3.3 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 speci es do not exactly match up with the IMPROVE PM species, thus assumptions must be made to map the m odeled 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. In contrast the IMPROVE equations assume sulfate is fully neutralized by ammonium. For the CMAQ Version 4.5 SOAmods model, the mapping of modeled species to IMPROVE equation PM species is listed in Table 4-2.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-11 Table 4-2. Mapping of CMAQ V4.5 SOAmods modeled species concentrations to IMPROVE PM components. IMPROVE Component CMAQ V4.3 Species

Sulfate ASO4J + ASO4I Nitrate ANO3J + ANO3I

LAC AECJ + AECI OCM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI + ASOC1J + ASOC1I + ASOC2J + ASOC2I + ASOC3J + ASOC3I Soil A25J + A25I CM ACORS + ASOIL + ANAK + ACLK + ASO4K + ANO3K

For the VISTAS visibility projections using the 2002 Typical and 2018 base case Base G2 emission scenarios, the secondary organic aeroso l (SOA) module in the CMAQ V4.5 released in September 2005 was enhanced (SOAmods) to in clude additional processes related to the generation of SOA from biogenic emissions. In particular, thre e new species have been added that represent SOA products from biogenic em ission compounds that are not included in the standard version of CMAQ V4.5 (Morris et al., 2006):

• 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.

Thus, the species mapping for Organic Car bon Mass (OCM) and the CMAQ V4.5 SOAmods version of the model used in VIST AS visibility projections given in Table 4-2 has the addition of the three new biogenic SOA species not in th e standard version of CMAQ V5.4 (ASOC1J +
ASOC1I + ASOC2J + ASOC2I + ASOC3J + ASOC3I).

4.3.4 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 ar ea 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 observations and the IMPROVE equation (either original or new IMPROVE equation)March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-12 for each year from the five-year baseline period (2000-2004) to identify the worst 20 percent visibility days for each year (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 VISTAS 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, OCM, 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.

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 cell containing the monitor. The NX x NY array of cells is grid resolution dependent 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, 2007a). VISTAS performed modeling using both a 36 km and 12 km grids, so used the grid cell containing the monitor or the 9 gr id cells centered in the monito ring site when constructing the RRFs using the, respectively, 36 km a nd 12 km CMAQ modeling results.

4.4 EPA Default 2018 Visibility Projections at VISTAS and Nearby Class I areas and Comparisons to 2018 Uniform Rate of Progress

Using the EPA default visibility projection pro cedure described in Section 4.3 and the VISTAS 2002 Typical Base G2 and 2018 Base G2 36 km and 12 km CMAQ modeling results, 2018 visibility projections were made for VISTAS and nearby Class I areas. Appendix E details the 2018 visibility projections for the worst 20 percen t days and each Class I area in and nearby the VISTAS region using the new IMPROVE equation. 2018 visibility projections for the best 20 percent days are given in Appendix F. Results for the Sipsey (SIPS) Alabama Class I area and the worst 20 percent days area are discussed in Section 4.4.1 below. Displays for 2018 visibility projections for the worst 20 percent days and th e other VISTAS Class I areas are provided in Appendix E and summarized in Section 4.4.2March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-13 4.4.1 Example 2018 Base G2 Visibility Projecti ons for Worst 20% Days Sipsey, Alabama

The 2018 visibility projections for the Sipsey (SIPS), Alabama Class I area and the worst 20 percent days are shown in Figure E-1 in A ppendix E and reproduced in Figure 4-3 below. Similar worst 20 percent days 2018 visibility pr ojections and glidepaths for all VISTAS and nearby Class I areas are provided in Appendix E.

4.4.1.1 EPA Default 2018 Visibility Projections

The 2018 Base G2 CMAQ 12 km visibility proj ection using the EPA default method (EPA, 2007a) new IMPROVE equation and comparison with the 2018 Uniform Rate of Progress for the worst 20 percent days for the SIPS Class I area is shown in Figure 4-3a (and Figure E-1a). The 2000-2004 Baseline Condition for SIPS is 29.03 dv a nd the 2018 Uniform Rate of Progress is 24.80 dv so that a 4.23 dv reduction in visibility for the worst 20 percent days is needed to meet the 2018 Uniform Rate of Progress. The 2018 Ba se G2 12 km CMAQ projected visibility is 23.69 dv so the modeling predicts more visibility improvement (5.34 dv reduction) than the 2018 Uniform Rate of Progress (4.23 dv reduction). Wh en looking at visibility projections across Class I areas it is useful to present the 2018 visi bility projections as a percentage of the 2018 Uniform Rate of Progress; where 100% is mee ting the 2018 Uniform Rate of Progress, greater than 100% surpassing the visibility improvement s needed for the 2018 Uniform Rate of Progress and less than 100% means that less visibility improvement is achieved than the 2018 Uniform Rate of Progress. For 2018 Base G2 12 km CMAQ modeling at SIPS, 126% of the visibility reduction needed to meet the 2018 Uniform Rate of Progress is projected. Note that meeting the 2018 Uniform Rate of Progress 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 reasonable progress go als. As clearly stated in EPA guidance “The glidepath is not a presumptive target, and Stat es may establish a reasonable progress goal that provides for greater, lesser, or equivalent im provement as that desc ribed by the glidepath” (EPA, 2007b).

Figure 4-3b 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 G2 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 SIPS for the worst 20 percent days in 2002 (more details on model performance were presen ted in Chapter 3 and Appendices B, C and D that evaluated the 2002 Actual Base G2 CM AQ 36 and 12 km simulations). 2002 worst 20 percent days at SIPS are dominated by SO4 days (yellow) with OCM being the next most important PM component. During the winter th ere are also several days with high observed and/or modeled NO3 as well (Julian Days 80, 296, 338 and 341). For most of the worst 20 percent days at SIPS the model reproduces the observe d extinction reasonabl y well, although it does tend to understate SO4 and total extinc tion on several days. The observed average extinction across the 2002 worst 20 percent days (e xcluding Rayleigh and Sea Salt) at SIPS is
173 Mm -1 , compared to a modeled value that is 27% (43 Mm -1 ) lower (127 Mm -1 ). This understatement is primarily due to the mode l understating the observed SO4 extinction (130 Mm -1 ) by 42 Mm -1

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-14 Figure 4-3c displays “Boxplots” of differen ces in modeled extinction for the 2002 worst 20 percent days between the 2018 Base G2 and 2002 Typical Ba se G2 12 km CMAQ simulations. SO4 is always the largest component of the extin ction that is reduced fr om 2002 to 2018 at SIPS for the worst 20 percent days. There are several da ys that exhibit very li ttle extinction reduction going from 2002 to 2018 (Julian Days 80, 122, 260, 338, 341). These are either high NO3 days or days in which the observed SO4 extinction is greatly underestimated (s ee Figure 4-3c). On worst 20 percent days when the model grossly unde restimates the observed extinction, the model is very stiff (i.e., shows little response to reductions in emissi ons, see Figure 4-3d). However, since the modeled RRFs used to project future -year visibility are based on the average model changes between 2018 and 2002 across all the 2002 worst 20 percent days, then the average RRFs are driven more by the higher modeled days and are less affected by the days with gross under predictions, as long as there are only a few days with such poor performance.

The absolute modeling results are not used directly in the visibility projections, rather they are used to develop RRFs specific to each PM-species for each Class I area. That is, important attributes of Figures 4-3c and 4-3d are the rela tive changes in modeled PM species between the 2002 and 2018 emission scenarios aver aged across the worst 20 percen t days that are represented by the last bar in each figure. These results ar e summarized for SIPS in Table 4-2 below. Table 4-2 compares the average extinction across th e 2002 worst 20 percent days at SIPS from the measured IMPROVE data (2000-2004), the modeled 2002 values and the modeled change in extinction between the 2018 and 200 2 emissions scenarios. Although the results in Table 4-2 are not RRFs (RRFs are based on ratios of concentra tions not extinction) they do show how the RRFs may magnify or deflate th e importance of a modeled PM species when the RRFs are applied to the observed values in the projections. For example, the model estimates that CM is three times lower than observed and predicts a small increase (+10%) between 2002 to 2018 of less than 0.1 Mm -1 . However, when this small 10% increa se is applied to the higher observed CM extinction value it magnifies the modeled change s in CM in the 2018 visi bility projections.

Most of the changes in emissions from 2002 to 2018 are reductions in SO2 and NOx emissions, with very little changes in car bon, soil and coarse mass. Therefor e, visibility improvements are primarily due to reductions in SO4 and NO3, with very little change in EC, OCM, Soil and CM.

Table 4-2. Observed and Modeled Extinction by S pecies Averaged Across the Worst 20 Percent Days in 2002 at SIPS using 2002/2018 Base G2 12 km CMAQ results.
2002 Average Observed

W20% (Mm -1 ) 2002 Average Modeled

W20% (Mm -1 ) 2018 Average Modeled

W20% (Mm -1 ) 2018-2002 Reduction (Mm -1 ) 2018-2002 Reduction (%)

bSO4 130.0 88.1 40.0 -48.1 -54% bNO3 10.1 9.5 7.7 -1.7 -19% bOCM 23.3 25.6 21.6 -3.0 -12% bEC 5.9 2.6 1.8 -0.8 -31% bSOIL 1.0 1.4 1.7 +0.3 +18% bCM 3.1 0.6 0.7 +0.07 -10%March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-15 

### Uniform Rate of Progress Glide Path Sipsey - Worst 20% Days

29.03 27.52

24.80

21.78

18.76

15.74

12.72
10.90 26.43

23.69

0 5 10 15 20 25 30 35

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 Uniform Rate of Progress Glidepaths in Deciview for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days Using 2002/2018 Base G2 CMAQ 12 km Modeling Results.

Worst 20% Obs (left) vs 2002gt2a (right) at SIPS1

0 50 100 150 200 250 300 350

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 _ _ _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3b. Comparison of Observed (left) and 2002 Base G2 CMAQ 12 km Modeled (right) Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) days in 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-16 Bext Response (2018g2b - 2002gt2a) at SIPS1 on Worst 20% Days

-180 -160 -140 -120 -100 -80 -60 -40 -20

0 20

80 122 131 143 152 155 161 185 188 191 215 218 221 224 251 254 260 263 284 296 338 341 Avg
Julian DayDelta Bext (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-3c. Differences in Modeled 2002 and 2018 Base G2 12 km CMAQ Results (2018- 2002) Daily Extinction for Sipsey (SIPS), Alabama and Worst 20 Percent (W20%) Days in 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-17 4.4.2 Summary 2018 Visibility Projections for Worst 20 Percent Days Across Class I Areas

Figure 4-4 displays a “DotPlot” of 2018 visib ility projections using the 2002 Typical and 2018 Base G2 CMAQ 12 km and 36 km modeling results . DotPlots present the 2018 visibility projections as a percenta ge of achieving the 2018 Uniform Rate of Progress. For example, at SIPS the 2018 Base G2 modeling ac hieved 126% and 127% of the vi sibility reducti on needed to meet the 2018 Uniform Rate of Progress using the 12 km and 36 km CMAQ modeling results, respectively. So the dots under SIPS are plo tted at 126% and 127%, which are above the 100% line so the 2018 Base G2 base case modeling re sults achieve the 2018 Uniform Rate of Progress at SIPS. Class I areas’ with dots above 100% achieve more visibility improvements than the 2018 Uniform Rate of Progress, whereas Class I areas’ with dots that are under 100% do not meet the 2018 Uniform Rate of Progress.

CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 12km NIA

36km NIA

VISTAS non-VISTAS Figure 4-4. 2018 Base G2 CMAQ 36 km and 12 km Visibility Projections for VISTAS and Nearby Class I areas Using DotPlots that Expr ess 2018 Visibility as a Percentage of Meeting the 2018 URP Goal On the Deciview Linear Glidepath.

The Class I areas within the Appalachian Mo untains all achieve the 2018 Uniform Rate of Progress, typically by 150% or more. Essentially most of the 20% worst days at these sites are dominated by sulfate and occur during the warm er months; reductions in SO2 under the 2018 Base G2 emissions scenario results in large sulfate reductions. Mammoth Cave (MACA) and Sipsey (SIPS) in the interior of the eastern U.S. west of the A ppalachian Mountains also achieve the 2018 Uniform Rate of Progress with visibility reduc tions of approximate ly 120% to 125% ofMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-18 the Uniform Rate of Progress. The smaller average improvements at MACA and SIPS are attributed to some 20% worst days occurring in th e winter months with elevated NO3 levels (see Appendix E). These days show little change in SO4 and NO3 by 2018 so the average RRF are smaller than at those the Appalachian Mountains Class I ar eas. The Swanquarter (SWAN) coastal site in North Carolina achieves 140% of the 2018 Uniform Rate of Progress whereas the Cape Romain (ROMA) coastal site in South Carolina achieves 112% of the 2018 Uniform Rate of Progress. The Florida and Georgia Class I co astal areas are projected to be close to the 2018 Uniform Rate of Progress. Chassahowitzka (CHAS ) is estimating more visibility improvements than the 2018 Uniform Rate of Progress (113%-115%), with St . Marks (SAMA) right on the 2018 Uniform Rate of Progress (100% and 106 %). Okefenokee (OKEF) and Everglades (EVER) are estimate to achieve less visibility improvement than the 2018 Uniform Rate of Progress.

For a several sites, GRSM, SHRO , and EVER, there are fairly la rge differences between the 12 km and 36 km CMAQ 2018 visibility projections; these differences are discussed in more detail below.

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 th e VISTAS region. The largest modeled reductions are in SO4 extinction. There are some differences in the modeled extinction reductions between the 12 km grid modeling resu lts (Figure 4-5a) and the 36 km grid modeling results (Figure 4-5b). In particular, the 12 km modeling results exhibit much higher NO3 increases at several Class I ar eas including GRSM, SHRO and EV ER. Figure 4-6 displays the percent change in the projected PM extinction by PM species for each VISTAS and nearby Class I area for the worst 20 percen t days (i.e., the relative modele d change). These results are analogous to the RRFs used in the visibility projections. There are some significant differences in the relative changes in the 12 km (Figure 4- 6a) and 36 km (Figure 4-6b) modeling results between 2002 and 2018 NO3 values for several of th e Class I areas. In particular, the relative increases in NO3 in 2018 for GRSM, SHRO a nd EVER are much higher using the 12 km modeling results (> 100%) than the 36 km modeli ng results (< 100%). These higher relative NO3 increases using the 12 km modeling resu lts explain why the 12 km 2018 visibility projections are not as optimistic as the 36 km modeling results (see Figure 4-4). The modeled NO3 concentrations for the 2002 base case at thes e three Class I areas ar e near zero (see Figures E-3c, E-9c and E-12c) for the 12 km modeling re sults and lower than the 2002 base case NO3 for the 36 km results. Thus, although the model is estimating extremely small increases in NO3 in the 2018 scenario at 12 km, becau se it is starting with near zero NO3 in the 2002 base case it estimates a relatively larger percentage change in NO3 than in the 36 km scenario. This larger percentage change for the 12 km results is then applied to higher observed NO3 concentrations from the 2000-2004 Baseline in the 2018 projections . When the modeled NO3 (or other species) concentrations approach zero, there is less confidence in the modeled relative changes in concentrations as model noise may affect the resu lts. To examine this issue in more detail, in Chapter 5 we look at alternative projection tech niques in which we only use the modeled derived RRFs for NO3 if the 2002 base case NO3 concentration is greater than 0.1 μ g/m 3

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-19 Average change in extinction components from 2002 baseline to 2018 projected at VISTAS sites using 2018g2b/2002gt2a RRFs

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

0.0 20.0

C OH

U1 CADI 1 DOSO1 GRSM1
J A RI1 LIGO1 MA C A 1 SHEN1 SHR O1 SIPS1 CHAS1 EVER1 OK EF

1

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A1

S A M A1 SWA N 1

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

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected at non-VISTAS sites using 2018g2b/2002gt2a RRFs

-70.0 -60.0 -50.0 -40.0 -30.0 -20.0 -10.0

0.0 10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

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

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5a . Absolute Model Estimated Changes in Extinction (Mm -1 ) by PM Species for Class I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 12 km modeling results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-20 Average change in extinction components from 2002 baseline to 2018 projected at VISTAS sites using 2018g2a/2002gt2a RRFs

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

0.0 20.0

C O H U1 CADI 1 DOSO1

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J ARI

1
L IGO1 MAC A 1 SHEN1

S H RO1 SI PS

1 CHAS1

E V ER1 OKEF1 ROM A1

S A M A1 SWAN1

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

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Average change in extinction components from 2002 baseline to 2018 projected at non-VISTAS sites using 2018g2a/2002gt2a RRFs

-80.0 -70.0 -60.0 -50.0 -40.0 -30.0 -20.0 -10.0

0.0 10.0

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

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

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-5b . Absolute Model Estimated Changes in Extinction (Mm -1 ) by PM Species for Class I Areas in the VISTAS region (top) and Near VISTAS (bottom) using CMAQ 2002/2018 Base G2 36 km modeling results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-21 Percent change in extinction components from 2002 baseline to 2018 projected at VISTAS sites using 2018g2b/2002gt2a RRFs

-100% -50%

0% 50% 100% 150% 200%

COHU1 CADI1 DOSO1 GR SM

1 JARI 1 LIGO 1
M ACA1 SHEN1

S HR

O

1 S IP

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1 SAMA1

S WA

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IMPROVE sitePercent change in bEXT

bSO4

bNO3

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Percent change in extinction components from 2002 baseline to 2018 projected at non-VISTAS sites using 2018g2b/2002gt2a RRFs

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

0% 20% 40% 60% 80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE sitePercent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6a. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) using 2002/2018 Base G2 12 km CMAQ results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-22 Percent change in extinction components from 2002 baseline to 2018 projected at VISTAS sites using 2018g2a/2002gt2a RRFs

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

0% 20% 40% 60% 80%

COHU1 CA D I1 DOS O1 GRSM1
J ARI

1
L I G O

1 MACA1 SHEN1 SHR O

1 SIP S 1 CH AS1 EVER1 O KEF1 ROM A 1 SAMA1

S WA

N1

IMPROVE sitePercent change in bEXT

bSO4

bNO3

bOC

bEC

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bCM

Percent change in extinction components from 2002 baseline to 2018 projected at non-VISTAS sites using 2018g2a/2002gt2a RRFs

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

0% 20% 40% 60% 80%

BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1

IMPROVE sitePercent change in bEXT

bSO4

bNO3

bOC

bEC

bSOIL

bCM

Figure 4-6b. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 Worst 20 Percent Days for Class I areas in the VISTAS region (top) and near VISTAS (bottom) using 2002/2018 Base G2 36 km CMAQ results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-23 4.4.3 2018 Visibility Projections fo r the Best 20 Percent Days

Figure 4-7a displays the 2018 visibi lity projections for the Best 20 percent days at Sipsey, similar plots for other Class I areas are given in Appendi x F. Recall that the RHR goal for the Best 20 percent days is a no worsening in visibility impairment from current conditions. Thus the 2018 visibility projection (14.37 dv) is compared with the current observed visibility for the Best 20 percent days from the Baseline (16.67 dv). Also shown in Figure 4-7a is the natural conditions for the Best 20 percent days that is plotted at 2064 (5.03 dv). Figure 4-7b compares the predicted and observed daily extinction for the best 20 per cent days at Sipsey. In general the model predicts total extinction on the clean days fairly well, although the observed extinction is overstated on Julian Days 26 and 176. The differ ences in the modeled extinction between the 2018 and 2002 Base G2 emission scenarios on the be st 20 percent days at Sipsey are shown in Figure 4-7c. Visibility improvements on the best 20 percent days are also primarily due to SO4 and NO3 reductions, although it is not completely dominated by SO4 as seen for the worst 20 percent days and reductions in extinction due to EC are also evident. Although not as significant at Sipsey, at many of the Class I areas the reducti ons in SO4 is resulting in increases in NO3 that is likely due to “nitrate replacement” (see Ap pendix F). Nitrate replacement occurs when the reductions in SO4 results in the release of am monium (NH4) molecules that were bonded to the SO4 that then react with gaseous HNO3 to form particulate NO3. As SO4 can bind up to two NH4 molecules [SO 4 (NH 4 ) 2- ], than nitrate replacement could potentially form two particulate NO3 [NO 3 NH 4 ] for every one SO4 molecule reduced, how ever this is unlikely in the real atmosphere due to aerosol thermodynamics and the fact that NO3 form ation is not always completely ammonia limited. We also see occa sions when SO4 increases when NO3 is reduced even thought SO2 emissions are reduced as well . One potential chemical reason for increased SO4 when NO3 is reduced is through aqueous-pha se chemistry formation of SO4 which is pH sensitive. Reductions in NO3 will make water droplet s less acidic that in turn can result in faster conversion of SO2 to SO4 via aqueous-phase chemistry.

Figure 4-8 displays a DotPlot of 201 8 visibility projections for the Best 20 percent days using the CMAQ 12 km and 36 km Base G2 2002/2018 modeli ng results. For the Best 20 percent days 2018 projections, the DotPlots display the percent reductions from current visibility conditions for the 20 percent best days. With one excepti on, all of the 2018 visibili ty projections show improved visibility from current visibility conditi ons for the best 20 percent days. The exception is for Everglades that shows a worsening of visi bility for the best 20 percent days when the 36 km CMAQ modeling results are used, but improved visibility using the 12 km CMAQ modeling results. This issue was examined more cl osely using the side- by-side model/observed StackedBar charts and BoxPlots of extinction differences between modeled 2018 and 2002 values for the best 20 percent days at EVER (Figure 4-9). Although the 36 km and 12 km modeling results at EVER look sim ilar, the relative changes are quite different as shown by the percent changes in the average extinction across the 2002 worst 20 percent days between the CMAQ 2018 and 2002 simulation using the 36 and 12 km grids (Figure 4-10). Using the 12 km NO3 averaged across the worst 20 percent days is projected to increase approximately 30 percent from 2002 to 2018. However, when the 36 km grid is used the projected NO3 increase is approximately 550%. An examination of the resu lts more closely reveals that CMAQ 36 km and 12 km modeling both project that NO3 ex tinction increases by approximately 0.001 Mm -1 from 2002 to 2018 averaged across the best 20 percen t days. However, the CMAQ 36 km CMAQ results is starting with a ~0.001 Mm -1 NO3 extinction in 2002 compared to a ~0.01 Mm -1 NO3 extinction using the 12 km grid so the 36 km modeling results estimates a much larger relativeMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-24 change in extinction. When the modeled NO3 c oncentrations are so low, the reactive changes are less reliable which is why one alternative projection approach, is not to use the modeled RRFs when NO3 concentrations are less than 0.1 μ g/m 3

.

Reasonable Progress Assessment Base G2, new IMPROVE Algorithm Sipsey - Best 20% Days

15.57 15.57 15.57

14.37

5.03

0 5 10 15 20 25

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

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction Figure 4-7a. 2018 visibility projections for Best 20 percent days at Sipsey, Alabama using Base G2 12 km CMAQ results and comparisons with current (2000-2004) observed Best 20 percent days and natural conditions for the best days (plotted at 2064).

Best 20% Obs (left) vs 2002gt2a (right) at SIPS1

0 20 40 60 80 100 120

11 14 26 32 59 77 89 113 116 167 176 179 212 230 257 302 308 347 356 359 362 _ _ _ _ Avg
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 4-7b. Compared of predicted and observed da ily extinction for the best 20 percent days at the Sipsey, Alabama Class I area and the CMAQ 2002 Typical Base G2 12 km simulation.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-25 Bext Response (2018g2b - 2002gt2a) at SIPS1 on Best 20% Days

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Figure 4-7c. Modeled changes in daily extinction us ing CMAQ 12 km results between the 2018 and 2002 emission scenarios for the best 20 percent days at the Sipsey, Alabama Class I area.

Figure 4-8. 2018 visibility projections at VISTAS and nearby Class I areas for the Best 20 percent days using the CMAQ 2002/2018 Base G2 12 km and 36 km modeling results. CMAQ 2018g2a/2002gt2a Visibility Proj ection for VISTAS+ sites on Best 20% days -50%

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50% COHU1 DOSO1 GRSM1 JARI1 LIGO1 CADI1 MACA1 SHEN1 SHRO1 SIPS1 CHAS1 EVER1 OKEF1 ROMA1 SAMA1 SWAN1 BRET1 BRIG1 CACR1 HEGL1 MING1 UPBU1 Percent of target reduction achieved 12km

36km

VISTAS non-VISTASMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-26 Best 20% Obs (left) vs 2002gt2a (right) at EVER1

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Bext Response (2018g2b - 2002gt2a) at EVER1 on Best 20% Days

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Figure 4-9. Comparison of StackedBar predicted and observed extinction (top) and BoxPlots modeled 2018-2002 extinction difference plots for Ev erglades using the 12 km (left) and 36 km (CMAQ) modeling results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-27 Percent change in extinction components from 2002 baseline to 2018 projected at VISTAS sites using 2018g2b/2002gt2a RRFs on Best 20% days

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Figure 4-10. Percent Change In Modeled Extinction by PM Species Averaged Across the 2002 Best 20 Percent Days for Class I areas in the VISTAS region using the CMAQ 12 km (top) and 36 km (bottom) modeling results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-28 4.5 2018 Visibility Projections using the 2018 Best and Final Emissions

Figure 4-11 compares the 2018 visibility projecti ons for the worst 20 percent days for the 2018 Base G2 and 2018 Base G4 Best and Final emission scenarios using the DotPlot format that expresses them as a percentage of achieving the 2018 Uniform Rate of Progress (URP). With three notable exceptions, the 2018 visi bility projections for the Base G4 Best and Final emissions scenarios shows more visibility improvements than the 2018 Base G2 emissions scenarios. The three exceptions are the Class I areas in Florida. Table 4-3 displays the numerical values of the 200-2004 Baseline, 2018 URP and 2018 Base G2 and G4 visibility projections in deciview that shows increases in 2018 deciview at the three Florida Class I ar eas of Chassahowitzka, St. Marks and Everglades in the Best and Final scenario.

Figure 4-12 displays the 2018 visi bility projections for the Be st 20 percent days and the 2018 Base G2 and 2018 Base G4 Best and Final emissions scenarios. This figure demonstrates that both 2018 emission scenarios have no worsening in visibility for the Best 20 percent days.

CMAQ 2018g4a vs 2018g2b Method1 Predicti on at VISTAS+ sites on W20% Days

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 2018g4a 12k

2018g2b 12k

VISTAS non-VISTAS Figure 4-11. 2018 Base G4 Best and Final and Base G2 CMAQ 12 km visibility projections for VISTAS and nearby Class I areas and the Worst 20 percent days using DotPlots that express the 2018 visibility projections as a percentage of reaching the 2018 URP goal on the deciview linear glidepath to natural conditions in 2064.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_4_VisProj4.doc 4-29 Table 4-3. 200-2004 Baseline, 2018 Uniform Rate of Progress (URP) and 2018 visibility projections using the 2018 Base G2 and 2018 Best and Final (Base G4) emissions and the differences between the 2018 Base G2 and 2018 URP and 2010 Base G4 Best and Final and 2018 Base G2 (deciview).

Class I Area 2000-2004 Baseline Uniform Rate of Progress 2018 Base G2 2018 Difference G2 – URP 2018 Base G4 Best & Final 2018 Difference

G4 – G2 CHAS 25.75 22.31 21.85 -0.46 22.27 +0.42 COHU 30.25 25.71 22.84 -2.87 22.80 -0.03 DOSO 29.04 24.69 21.64 -3.04 21.50 -0.14 EVER 22.30 19.92 20.65 +0.73 20.76 +0.11 GRSM 30.28 25.79 23.66 -2.13 23.50 -0.16 JARI 29.12 24.91 22.59 -2.32 22.36 -0.24 LIGO 28.77 24.67 21.98 -2.69 21.65 -0.33 MACA 31.37 26.64 25.56 -1.07 25.40 -0.17 OKEF 27.13 23.42 23.85 +0.43 23.82 -0.03 ROMA 26.48 23.12 22.72 -0.40 22.65 -0.07 SAMA 26.31 22.89 22.89 0.00 23.01 +0.12 SHEN 29.31 25.12 22.11 -3.01 21.93 -0.18 SHRO 28.66 24.65 22.11 -3.01 21.93 -0.18 SIPS 29.03 24.80 23.69 -1.11 23,53 -0.16 SWAN 24.71 21.64 20.37 -1.27 20.26 -0.12

CMAQ 2018g4a vs 2018g2b Visibility Project ion at VISTAS+ sites on Best 20% days -50%

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50%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 12km 2018g4a

12km 2018g2b

VISTAS non-VISTAS Figure 4-12 . 2018 Base G4 Best and Final and Base G2 CMAQ 12 km visibility projections for VISTAS and nearby Class I areas and the Best 20 percent days using DotPlots that express the 2018 visibility projections as a percentage of reaching the 2018 regional haze goal of no worsening in visibility on the Best visibility days.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-1 5.0 ADDITIONAL SUPPORTING ANALYSIS

This Chapter presents additional supporting analysis to the modeled 2018 visibility projections at VISTAS and nearby Class I areas presented in Chapter 4. This supporting analysis may be used by the states in their RHR SIPs, al ong with their factor analysis, to assist in justif ying their 2018 Reasonable Progress Goals (RPGs) for the wors t 20 percent days and best 20 percent days.

5.1 Comparison of VISTAS 2018 Visibility Projections with Other Groups

2018 visibility projections for VISTAS and nearby Class I area have also been performed by the other RPOs. Thus, it is useful to compare the VISTAS 2018 visibility projections with those from the other RPOs as a quality assurance (QA) check and to corroborate the VISTAS visibility projections.

5.1.1 Comparison of CENRAP, VISTAS, MRPO and MANE-VU Visibility Projections

The VISTAS 2018 Base G2 visibility projections were compared to the following other RPO visibility projections:

• CENRAP 2018 visibility pr ojections based on their CMAQ 36 km 2002 annual modeling results for their 2002 and 2018 Base G emi ssion scenarios (ENVIRON and UCR, 2007).

• MRPO 2018 visibility project ions based on their CAMx 36 km 2002 annual modeling for Round 5.1 using Base M emissions that were presented at their October 10, 2007 meeting (www.lado.org).

• MANE-VU 2018 visibility result s based CMAQ results for the 2018 OTB/OTW scenario received October 29, 2007 (Kleim an, personal communication, 2007).

Figure 5-1 displays a DotPlot comparison of the f our RPO visibility projections at VISTAS and nearby Class I areas that express the 2018 visibility proj ections for the worst 20 percent days as a percentage of achieving the 2018 Uniform Rate of Progress. The MRPO and CENRAP have reported 2018 visibility projections for four and two of the VISTAS Class I areas, respectively. The CENRAP 2018 visibility projections at MACA and SIPS almost exactly match the VISTAS projections (CENRAP vs. VISTAS values of 122% vs. 123% at MACA and 130% vs. 126% at SIPS). The MRPO 2018 visibility projections, on the other hand, ar e less optimistic at the four common VISTAS Class I areas (DOSO, JARI , MACA and SHEN), although VISTAS and MRPO agree that more visibility improvements are projected to oc cur than necessary to meet the 2018 Uniform Rate of Progress. The reasons why the MRPO vi sibility projections are less optimistic than VISTAS and CENRAP are uncl ear. VISTAS and CENRAP may have included more controls in their states than the MRPO assumed (e.g., CENR AP assumed BART controls in several of their non-CAIR states).

For the four CENRAP Class I areas just west of the Mississippi River in Arkansas and Missouri that are the furthest right of the Class I ar eas in Figure 5-1 (CACR, UPBU, HEGL and MING), 2018 visibility projections are available from the VISTAS, CENRAP and MRPO. At HEGL, Missouri, the VISTAS and CENRAP 2018 visibility projections are in close agreement with each other with both estimating that the 2018 Uniform Ra te of Progress is just barely achieved.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-2 However, the MRPO 2018 visibility projection estimate that HEGL will only obtain 71% of visibility improvements needed to achieve th e 2018 Uniform Rate of Progress. At Ming, Missouri, again there is fairly close agreem ent between VISTAS (110%) and CENRAP (118%) 2018 visibility projections with both agreeing that MING will ach ieve the 2018 Uniform Rate of Progress. The MRPO projection, however, is less optimistic estimating MING will obtain only 91% of the visibility improvement s needed to achieve the 2018 Unif orm Rate of Progress. There is close agreement between the three RPO 2018 visibility project ions at CACR and UPBU with them essentially having the exactly the same 2018 visibility projections at CACR (112%-115%) and very close ones at UPBU (108%-120%) with both Class I areas estimated to achieve the 2018 Uniform Rate of Progress.

For the Breton Island (BRET) Cla ss I area, 2018 visibili ty projections are available from both VISTAS and CENRAP. VISTAS estimates that BRET will obtain 79% of the visibility improvements needed to achieve the 2018 Unifor m Rate of Progress, whereas CENRAP is slightly more optimistic with a 94% value. On e potential contributor to these differences at BRET is that emissions from off-shore marine vessel emissions in the oil and gas production areas of the Gulf of Mexico are double counted in the VISTAS Base G2 modeling. As these emissions were assumed to remain unchange d between 2002 and 2018, the double counting of their emissions will result in stiffer RRFs in the VISTAS modeling and consequently less visibility benefits in 2018 than the CENRAP modeling. The double counting occurred because off-shore marine vessels were present in both the MMS off-shor e oil/gas development inventory for the Gulf of Mexico and the VISTAS off-s hore marine vessel invent ory for the Pacific and Atlanta Oceans and the Gulf of Mexico.

2018 visibility projections at the BRIG Class I area in New Jersey were available from VISTAS, MRPO and MANE-VU. The VIST AS 2018 visibility projections for BRIG (111%) lie halfway between those from the MRPO (101%) and those by MANE-VU (120%). All three RPOs estimate that BRIG will achieve th e 2018 Uniform Rate of Progress.

The similarity between the VISTAS and the CENRAP and MANE -VU 2018 visibility projections provides more confidence that the VISTAS projections are correctly calculated and are likely reliable. The differences between the VISTAS and MRPO projections raise some

concerns.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-3 CMAQ Method 1 predictions with new IMPROVE algorithm at VISTAS+ sites Across RPOs

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SIPS1SHRO1SHEN1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRIG1BRET1CACR1HEGL1UPBU1MING1Percent of target reduction achieved VISTAS 12k 2018g2b/2002gt2a CENRAP 36k Base18g/Typ02g

WRAP 36k Base18b/Plan02b corrected

MRPO Round 5.1

MANU-VU 2018

Mountain Coastal non-VISTAS

Figure 5-1. DotPlot comparing the VISTAS, CENR AP, MRPO and MANE-VU 2018 visibility projections expressed as a percentage of achi eving the 2018 Uniform Rate of Progress new IMPROVE equation).

5.2 Alternative 2018 Visibility Projection Software

The VISTAS 2018 visibility projections presente d in Chapter 4 were made using software developed by the VISTAS modeling team. PM sp ecies concentrations in the 36 km and 12 km grid cells containing each of the Class I area IMPROVE monitoring sites were extracted using the UCR Analysis Tool. These modeling data were then ported into Excel spreadsheets that also include the filled Regional Haze Rule (RHR) IMPROVE database available from the VIEWS website along with the EPA default Natural Cond itions that were either available from VIEWS for the new IMPROVE equation or from EPA gu idance documents for the original IMPROVE equation (EPA, 2003b). Excel macros are then used to perform the visibi lity projections using the EPA default procedures described in Ch apter 4 and the EPA modeling guidance document (EPA, 2007a) as well as the alternative projec tion procedures described in this Chapter.

EPA has 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 beta versions of MATS was applied to the VISTAS 2002 and 2018 Base G2 12 km CMAQ results and the resultant 2018 visibility projections were compared with the VISTAS values using the EPA default projection approach (see Chapter 4) at VISTAS and nearby Class I areas. The projected 2018 vi sibility estimates using the VISTAS and EPA MATS software are shown in Table 5-1. The bi ggest differences in th e two 2018 visibilityMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-4 projection procedures are for the following five Class I areas: Breton Is land (BRET), Louisiana; Cohutta (COHU), Georgia; Mingo (M ING), Missouri; St. Marks (S AMA), Florida; and Shining Rock (SHRO), North Carolina. For these five Class I areas, the June 2007 release of MATS produces no 2018 visibility projections. This is b ecause there is insufficient capture of valid IMPROVE PM measurements within the 2000-2004 five -year baseline to gene rate at least three years of annual visibility estimates for the wors t 20 percent days, which is the minimum needed to develop the Baseline Conditions following EP A’s guidance (EPA, 2003a). For the VISTAS projections, data filling was used to fill out the IMPROVE measurements with sufficient data so that Baseline Conditions coul d be calculated at these five Class I areas.

As noted above, of the 21 VISTAS and nearby Cla ss I areas, 5 had insufficient data in the raw IMPROVE data set used in MATS to generate a valid Baseline so MA TS could not make 2018 visibility projections. Of the remaining 16 Class I areas, the VI STAS 2018 visibility projections exactly match those by MATS at 11 of the Cla ss I areas and are within 0.01 dv at two others, which could be due to just round off error in the calculations. Thus, of the 16 Class I areas with sufficient valid data, the VISTAS Excel approach and EPA’s MATS tool agree on the visibility projections at 13 of the Class I areas (i.e., > 80% of the Class I areas).

The differences in the 2018 visibility projections at the three remaining Class I areas (CHAS, GRSM and SWAN) are small, but la rger than one would expect from just round off errors. An examination of these differences found small diffe rences in the observed IMPROVE data used to define the 2000-2004 Baseline conditions (see columns 4 and 5 in Table 5-1). These small differences are due to slight changes in a few PM concentration measurements between the raw IMPROVE database included in MATS and the fi nal RHR database distributed by VIEWS.

Table 5-1. Comparison of VISTAS Excel based and EPA MATS 2018 visibility projections (deciview) at VISTAS and nearby Class I areas.
MATS VISTAS MATS VISTAS Class I

Area 2018 Projected (dv) 2018 Projected (dv) 2000-04 Baseline (dv) 2000-04 Baseline (dv)
BRET NA 23.41 NA 26.00 BRIG 24.62 24.62 29.01 29.01 CACR 22.38 22.38 26.36 26.36 CHAS 22.22 21.85 26.09 25.75 COHU NA 22.84 NA 30.25 DOSO 21.64 21.64 29.04 29.04 EVER 20.66 20.65 22.30 22.30 GRSM 23.55 23.66 30.28 30.28 HEGL 23.09 23.09 26.75 26.75 JARI 22.59 22.59 29.12 29.12 LIGO 21.98 21.98 28.77 28.77 MACA 25.56 25.56 31.37 31.37 MING NA 23.85 NA 28.05 OKEF 23.85 23.85 27.13 27.13 ROMA 22.72 22.72 26.48 26.48 SAMA NA 22.89 NA 26.31 SHEN 22.10 22.11 29.31 29.31 SHRO NA 22.11 NA 28.66 SIPS 23.69 23.69 29.03 29.03 SWAN 21.26 20.37 25.49 24.71
NA = Not Available due to insufficient data in raw IMPROVE database used in MATS to generate worst 20 percent days for at l east three years during 2000-2004 period to define Baseline visibility conditions.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-5 5.3 Alternative Visibility Projection Procedures

In this section we analyze seve ral alternative visibility projec tion procedures to EPA’s default approach where RRFs are based on the ratio of the modeled average concentrations across the worst 20 percent days for the 2018 and 2002 emissi on scenarios discussed in Chapter 4 (EPA, 2007a). Chapter 4 presented 2018 visibility proj ections at VISTAS and nearby Class I areas using the 2002 and 2018 Base G2 CMAQ 36 km and 12 km modeling results and the new IMPROVE equation. The results in Chapter 4 and Appendix E showed that a vast majority of the visibility improvements be tween the 2000-2004 Baseline and 2018 were due to reductions in sulfate (SO4). Differences were noted in the 36 km and 12 km 2018 visibility projections at Everglades that were traced to extremely lo w nitrate (NO3) predictions for the 2002 worst 20 percent days in the CMAQ 12 km modeling results resulting in m odeled RRFs that were likely affected by model noise. Thus, so me of the alternative visibil ity projection investigate their sensitivity to SO4 model performance and model noise in the NO3 predic tions. The alternative visibility projection approaches disc ussed below analyzed the following:

• Use of the original IMPROVE equation;

• Effects of assuming coarse ma ss (CM) and Soil are natural;

• Effects of model noise a nd low NO3 modeled values;

• Effects of SO4 model performance; and

• Contributions of international transport.

5.3.1 Use of Original IMPROVE Equation

The first rounds of 2018 visibility projectio ns performed by VISTAS used the original IMPROVE equation (Malm et al., 2 000) to convert PM species con centrations to light extinction and deciview. During the course of the VISTAS modeling, a new IMPROVE equation was developed to convert PM concentr ations to light extinction, whic h was subsequently adopted by VISTAS as the primary procedure for converting measured and modeled PM concentrations to light extinction. The justific ation for using the new IMPROVE equation over the original IMPROVE equation was presented previously in Section 4.2.1.1.3.

Figure 5-2 displays 2018 visib ility projections us ing the CMAQ 12 km and 36 km Base G2 modeling and the original IMPROVE equation and compares them with the projections using the new IMPROVE equation from Chapter 4. Use of the original IMPROVE equations for the 2018 visibility projections at the VI STAS mountain Class I areas resu lts in projections that still achieve the 2018 Uniform Rate of Progress, bu t by less margin than using the new IMPROVE equation. The original IMPROVE equation estimat es the VISTAS Mountain Class I areas will be from 6 to 28 percentage points less in achievi ng the 2018 Uniform Rate of Progress, but it is still achieved at th ese mountain sites.

With the exception of the Everglades (EVER), th ere is good agreement between the visibility projections at VISTAS Coastal Class I areas us ing the new and original IMPROVE equations as they are within 2 to 20 percentage points of each other in achieving the 2018 Uniform Rate of Progress. At EVER, however, the new and orig inal IMPROVE equation estimates that it would achieve, respectively, 69% and 41% of the 2018 Uniform Rate of Progress.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-6 At the non-VISTAS nearby Class I areas, the or iginal IMPROVE equation is slightly less optimistic than the new IMPROVE equation with va lues 2 to 17 percentage points lower than the new IMPROVE equation, with the largest diffe rence at BRET and the others within ~10 percentage points.

In conclusion, the original IMPROVE equation estimates lower visibility improvements between the 2000-2004 Baseline and 2018 than the new IMPR OVE equation, but the visibility projections are usually close to each other. For the VISTAS Class I areas, those areas that are estimated to achieve the 2018 Uniform Rate of Progress using the new IMPROVE equation are also estimated to achieve it with the old IMPROVE equation. And those areas that are estimated to not achieve the 2018 Uniform Rate of Progress do so using both versions of the IMPROVE equations. Finally, the SAMA area that is ri ght at the 2018 Uniform Rate of Progress is in the too close to call range using both IMPROVE equations.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-7 CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

Mountain Coastal non-VISTAS

CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

Mountain Coastal non-VISTAS
Figure 5-1. DotPlot comparing the VISTAS 2018 visibility projections using the new and original IMPROVE equations based on the 12 km (top) and 36 km (bottom) CMAQ modeling results. DotPlots express the 2018 visibility projections as a percentage of achieving the visibility reduction needed to meet the 2018 Uniform Rate of Progress.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-8 5.3.2 Treatment of Coarse Mass and Soil as Natural

Much of the coarse mass (CM) and, to a lesser extent, Soil measured at the IMPROVE monitor is likely due to local wind blown dust that is natu ral in origin and not captured by the model. Consequently, even using the m odeling results in a relative se nse with the RRFs may not be appropriate for projecting CM and So il. If CM and Soil are in fact primarily due to local impacts due to wind blown dust from natural lands, then it would be appropriate to assume they remain unchanged between the 2000-2004 Baseline and 2018. This is probably cert ainly appropriate for CM that is primarily due to fugi tive dust and has a short transport distance that is likely subgridscale to the model. In fact, in the model ev aluation discussed in Chapter 3 and Appendix C CM performance is characterized by a large underpredict ion bias that is likely due to local fugitive dust impacts at the IMPROVE monitors. For Soil this is less clear as fine particles can be transported over longer distances and are pr oduced by anthropogenic sources, such as combustion and road dust, as well as natural sources. Thus we performed three CM and Soil sensitivity tests to investigate whether assuming that all or part of them were of natural origin would affect the 2018 visibility pr ojections. The first CM/Soil sens itivity test, assumed that all CM was natural so would remain unchanged be tween the 2000-2004 Baselin e to 2018 (i.e., set the RRF for CM equal to 1.0). The second sens itivity test assumed both CM and Soil were all natural so RRFs for both of them are set to unity. In the third CM/Soil sens itivity test, part of the Soil is assumed to be natural and part of it is a ssumed to be anthropogenic in origin. This was done by subtracting the modeled 2002 base case Soil from the observed values for the worst 20 percent days and assume that the remainder (if any) was natural. This natural component of the Soil from the 2000-2004 baseline was held constant in 2018 and added to the 2018 modeled Soil values.

The results of the CM and Soil visibility proj ection sensitivity analys is using the CMAQ 12 km and 36 km modeling results are shown in the Do tPlot given in Figure 5-3. The CM and Soil visibility projection sensitivity anal ysis have little effect on the 2018 visibility projections at the VISTAS Class I areas. There are some differen ces at the non-VISTAS Class I areas in Arkansas and Missouri, but these differences are small. There are two reasons why the CM and Soil sensitivity tests have lit tle effect on the 2018 visibility projec tions: (1) unlike some of the Class I areas in the desert sout hwestern portion of the U.S., CM and Soil generally make up a small component of visibility impairment at the VIST AS Class I areas for the worst 20 percent days; and (2) there is very little ch ange in the CM and Soil emi ssions between 2002 and 2018 so the RRFs for these two species are near unity anyway.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-9 CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

RRF_CM=1

RRF_CM&SOIL=1

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Mountain Coastal non-VISTAS

CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

RRF_CM=1

RRF_CM&SOIL=1

RRF_CM&rest(SOIL)=1

Mountain Coastal non-VISTAS
Figure 5-3. Sensitivity of 2018 visibility projections to assumptions regarding whether all CM, all CM and all Soil and all CM and part of the Soil is natural using the CMAQ 12 km (top) and 36 km (bottom) modeling results.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-10 5.3.3 Accounting for Model Performanc e in Visibility Projections

In Chapter 4 we noted significant differences in the 2018 visibility projections at the Everglades (EVER) Class I area using the 36 km and 12 km CMAQ modeling results th at were traced back to the modeled NO3 RRFs (see Figure 4-4). Th e ISORROPIA aerosol equilibrium algorithm in CMAQ contains model noise that can be comparab le to modeled concentrations at very low concentrations for species aff ected by the ISORROPIA aerosol equilibrium equations (e.g., SO4, NH4 and NO3). Although this is not a big issue with SO4 since its concen trations are typically high for most worst 20 percent days in the southeastern U.S ., it may affect NO3 (note that modeled NH4 concentrations are not used in the visibility projections as the IMPROVE equations assume SO4 and NO3 are completely ne utralized by NH4). This is because NO3 is highly volatile so on hot summer days, which a ma jority of the worst 20 percent days in the VISTAS region tend to be, its c oncentrations become low. Furthermore, as shown in Chapter 3 and Appendices B, C and D, the model also tends to underestimat e the observed NO3 concentrations in the summer. In the case of EVER, this resulted in a CMAQ 12 km estimated NO3 concentration averaged across the worst 20 percent days that was extremely low resulting in very low extinction (~0.1 Mm -1 ). For the 2018 CMAQ 12 km simulation, the NO3 concentrations for the worst 20 percent days we re still very low but we re over 100% higher than the 2002 values resulting in an NO3 RRF of over 2. The 2000-2004 Base line NO3 extinction at EVER was not as low (~5 Mm -1 ) so that the modeled high RRF based on extremely small concentrations were applied to higher observe d NO3 in the Baseline which had a significant effect on the visibility projections. Given the potential for model noise in the CMAQ model at such low NO3 concentrations, we developed an alternative projection approach to hold NO3 concentrations constant from the 2000-2004 Baselin e to 2018 (i.e., set NO3_RRF=1.0) when the modeled 2002 NO3 concentrations averaged across the worst 20 percent days is extremely low:

NOxconc>0.1 : Set the NO3 RRF to 1.0 if the av erage NO3 concentration across the 2002 worst 20 percent days at a Class I area (i .e.,, the denominator of the RRF) is less than 0.1 μ g/m 3

.
Also discussed in the model performance evalua tion (Chapter 3 and Appendices) are occasions when the model greatly underestim ates the observed extinction on some of the worst 20 percent days. One concern is that if the model estimates concentrations near background on a worst 20 percent day when the observed concentrations ar e high, the model will be very stiff in response to emission reductions and provide less reductions in the RRF than there should be. Similarly, if the modeled concentrations are much higher than observed, the model may be more responsive to changes in emissions than it should be. This e ffect would be particularly important for SO4 as it is usually the highest component of light ex tinction on the worst 20 percent days at VISTAS Class I areas and is the component with th e largest reductions between the 2000-2004 Baseline and 2018. Thus, a second alternative visibility pr ojection approach was fo rmulated that only includes those worst 20 percent days in 2002 in the SO4 RRF when th e model SO4 prediction matches the observed SO4 concentrati on to within a factor of 2:

SO4_MPE : Sulfate RRFs are based on modele d 2018 and 2002 SO4 concentrations for just those 2002 worst 20 percent days when the modeled and measured SO4 are within a factor of 2 of each other.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-11 The final alternative 2018 visibility projec tion approached combined the minimum NO3 concentration check with the SO4 MPE check (NO3conc>0.1 + SO4_MPE).

Figure 5-4 shows DotPlot displays of the 2018 visi bility projections usi ng the 12 km and 36 km CMAQ results accounting for low NO3 concentrati ons and SO4 model performance in the RRFs. The NO3conc, SO4_MPE and combined alternative vi sibility projections appr oaches have little effect on the 2018 visibility proj ections at most VISTAS Class I areas. In particular, the visibility projections at the VISTAS inland mounta in Class I areas show little change due to the NO3 concentration and SO4 model performance tests. The largest differenc es in the visibility projections due to the NO3/SO4 te sts are at the EVER and BRET s outhern coastal Class I areas. In the case of EVER, the NO3conc test makes a difference in achieving sufficient visibility improvements to meet the 2018 Uniform Rate of Progress with the NO3conc>0.1 test to not achieving the 2018 Uniform Rate of Progress when the EPA default RRFs are used. At BRET, the NO3 and SO4 tests both result in more optimis tic 2018 visibility project ions such that their combined effects result in BRET achieving the 2 018 Uniform Rate of Progr ess. The remainder of the Class I areas’ 2018 visibility projections exhibit little sens itivity to the NO3 concentration and SO4 model performance tests.

Note that these results are not surprising given the model perfor mance and visibility projection discussions in, respectively, Ch apters 3 and 4 and their comp anion appendices. SO4 model performance was poorest at the EVER and BR ET Class I areas that exhibited a large underprediction bias. The SO4_MPE sensitivity test eliminated days from the SO4 RRF with large SO4 underestimation bias leaving the hi gher SO4 days so that the model was more responsive and exhibited more vi sibility improvements in 2018.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-12 CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

NO3conc>0.1

NO3conc>0.1 + SO4_MPE

SO4_MPE

Mountain Coastal non-VISTAS
CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

NO3conc>0.1

NO3conc>0.1 + SO4_MPE

SO4_MPE

Mountain Coastal non-VISTAS
Figure 5-4. Sensitivity of 2018 visibility projections to setting NO3 RRFs to 1.0 when NO3 concentrations are extremely low and/or eliminating worst 20 percent days in the SO4 RRFs when modeled/observed SO4 concentrations differ by over a factor of 2.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-13 5.3.4 Use of Classification and Regression Tree (CART) Weighted RRFs

ICF International used the Classification and Regr ession Tree (CART) procedure to classify the worst and best 20 percent days from the 2000-200 4 Baseline at each VISTAS and nearby Class I area into separate bins of days with similar conditions (Douglas, Hudischewskyj and Wei, 2007). Days from the 2000-2004 5-year baseline were classi fied into several bins based on the measured PM at and near each Class I area and meteor ological conditions at nearby meteorological stations. Using the CART classifications fo r the worst 20 percent days from the 5-year Baseline, weighting factors were developed to be applied to days fr om the 2002 modeling period to produce a weighted mix of days from 2002 th at match the mix of days within the 2000-2004 Baseline for each Class I area. Rather then us ing just the modeling days from the worst 20 percent days during 2002 in the RRFs, as is done with the EPA default projection approach, the CART weights can potentially we igh model estimates from all 365 days during the 2002 year to develop RRFs. However, in practice only about 40 days were assigned positive CART weights. Two types of CART weights were developed (see Douglas, Hudischewskyj and Wei, 2007 for more details):

CARTrev3 : Based on the total number of days a nd number of worst 20 percent days in each bin.

CARTrev3_wwtonly : Based on the number of worst 20 percent days in each bin.

The CART weights used in the alternative visibility projections were based on files received in an e-mail from Sharon Douglas of ICF Internati onal to Pat Brewer of VISTAS dated July 12,
2007.

Figure 5-5 displays the DotPlot of visibility projections using the EPA default approach for calculating RRFs (wCalculated RRF) used in Chapte r 4 and using the two sets of CART weights for calculating the RRFs. The use of CART wei ghts in the visibility projections has either essentially no change over the EPA default appro ach, or results in less visibility improvements and a lower percentage of mee ting the 2018 Uniform Rate of Progr ess. We believe the reasons for this is because the RRFs using the CART weights are weighting days in 2002 for the RRFs that have lower SO4 concentrations (e.g., no longer a worst 20 percent day) and consequently the model is more stiff and less responsive to the SO2 reductions between 2002 and 2018. For the VISTAS Class I areas, if an area achieves the 2018 Uniform Rate of Progress using the EPA default RRF approach then it also achieves it using the CART weights approach, although the degree of overachievement is less at many of the Class I areas using the CART weights approach.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-14 CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

wCalculated RRF + CARTrev

wCalculated RRF + CARTrev_wwtonly

Mountain Coastal non-VISTAS

CMAQ 2018g2a/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved wCalculated RRF

wCalculated RRF + CARTrev

wCalculated RRF + CARTrev_wwtonly

Mountain Coastal non-VISTAS
Figure 5-5. Comparison of 2018 visibility projections using RRFs based on CART weighted modeling results versus EPA default approach (wCalculated RRF).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-15 5.4 Effects of International Transpor t on 2018 Visibility Projections

VISTAS performed a focused analysis of the contributions of Intern ational Transport on visibility impairment at thei r Class I areas. These results were summarized in a document written by Dr. Ivar Tombach dated July 26, 2007 that is paraphrased below (Tombach, 2007).

Anthropogenic and natural aerosols from emissions in Mexico, Ca nada, Central America, Asia, and Africa have been shown to aff ect air quality and regional haze in the continental U.S. States may consider the fraction of the ambient aerosol th at is beyond the regulato ry control of the U.S as they evaluate emission control strategies to achieve reasonable prog ress in reducing regional haze in Class I areas. To support the VISTAS states in evaluating the contribution from international emissions, the VISTAS RPO carried out analyses to estimate how much of the haze at Class I areas in the VIST AS region in 2002 and 2018 (as projected by the CMAQ model) results from international emissions.

The VISTAS CMAQ 36 km domain includes all of the continental U.S. and portions of northern Mexico and southern Canada. Thus, the contribu tions of emissions these portions of Canada and Mexico are explicitly accounted for in the CMAQ modeling. The effects of emissions from more distant sources that are out side of the 36-km national domain were estimated using outputs from a global air chemistry model (GEOS- CHEM). The Big Bend Regional Aerosol and Visibility Observational (BRAVO) study had f ound that the performan ce of national-scale modeling was enhanced by using monthly averages of outputs from a global air pollution model, GOCART, to generate SO 2 and sulfate boundary conditions for its 36-km national modeling grid (Schichtel, et al., 2005), instead of using the EPA de fault concentrations that up to the time of the BRAVO study were primarily used. VISTAS elected to use a similar approach as BRAVO, using the outputs of the GEOS-CHEM global air pollution model, wh ich also contains a detailed representation of ozone-NO
x -VOC-PM chemistry.

Under EPRI sponsorship, Harvard University ha d already applied the GE OS-CHEM model, with
2 ° x2.5 ° horizontal resolution (approximately 200 km x 200 km), to estimate natural conditions and transboundary influences on elemental and or ganic carbon aerosols in the United States in 1998 (Park et al., 2003). After some emission inventory and model improvements, similar information was developed for sulfate and ni trate aerosols in 2001 (P ark et al., 2004).

Under contract to VISTAS (which was acting on behalf of all the RPOs), Harvard then simulated global air pollution for the year 2002 (the VISTAS modeling year) with 4 ° x5 ° horizontal resolution (approximately 400 km x 400 km) and 3-hour temporal resolution (Jacob, Park and Logan, 2005). The coarser grid scale was consid ered sufficient to pr ovide boundary conditions outside of North America, largely over th e oceans. Although the VISTAS simulation was generally similar to the 2001 EPRI simula tion mentioned above, the VISTAS modeling incorporated the following substantive changes in a ddition to the change in grid scale: (1) use of U.S. anthropogenic emissions from the EPA NEI 1999 inventory, except for ammonia; (2) use of forest fire information specific to 2002; (3) in clusion of the secondary organic aerosol (SOA) formation mechanism from Chung and Seinfeld (2002); and (4) inclusion of prototype soil dust and sea salt simulations. An additional model im provement, which has si nce been implemented in the standard version of GEOS-CHEM, was the application of surface emissions and dry deposition to the entire mixed layer column di agnosed by GEOS (the Global Earth Observation System of NASA) rather than to just the surf ace layer of the model. Details of the emissions, meteorology, model configuration, and mode l performance evaluation for the VISTASMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-16 simulations are described in the report to VISTAS (Jacob, Park and Logan, 2005) and were summarized previously in Section 1.3.7.

For North America, the VISTAS GEOS-CHEM m odeling used monthly-av erage emissions, so the model results do not reflect diurnal or weekda y-weekend variability in emissions, and also diminish the impacts of episodic emissions, such as the July 2002 wildfire in Ontario, Canada. The Mexican emissions were from the inve ntory of the BRAVO Study, while Canadian emissions for the year 2000 were used. Emissions from Caribbean islands were not included, which could impact model estimates at the Everglades.

The surface meteorological data inputs to GEOS-CHEM had 3-hour temporal resolution and the upper air meteorological inputs had 6-hour reso lution, with a horizon tal resolution of 1 ° x1 ° or better and 48 vertical layers.

The output of the GEOS-CHEM mode ling, as delivered to VISTAS, contained concentrations of all visibility-related components of particulat e matter with 3-hour temporal resolution and 4 ° x5 ° horizontal resolution over multiple layers of the atmosphere.

Even though the coarse spatial resolution degr aded the performance of the model over North America, relative to results from prior m odeling for other years using finer grids, the concentrations simulated by GEOS-CHEM maintain ed the synoptic-scale structure and did not have a continental-scale bias (S ee Appendix G). At a finer scale, the concentrations simulated by GEOS-CHEM for the surface layer in 2002 were gene rally within a factorof two of regionalaverages (~500 km scale) of measurements of the various chemical co mponents of particulate matter in the United States and, except for orga nic carbon, satisfactorily simulated the seasonal variability in the observations.

In the meantime, Harvard University had also carried out new finer-grid simulations for 2001 for EPRI, using a 1 ° x1 ° grid over North America, nested in a coarser grid that covered the globe. The final version of these simulations included modifications to the modeling grid to improve delineation between domestic US emissions a nd those of foreign sources at the national boundaries, and revision (reduction) of the Cana dian and Mexican sulfur emissions. These
GEOS-CHEM 1 ° x1 ° grid scale simulations for 2001 and th eir results are desc ribed in the paper by Park et al. (2006). Over a ppropriate geographic and temporal scales, the results of these simulations for 2001 were simila r to those of the 2002 VIST AS simulations, which gave confidence in the VISTAS GEOS-CHEM results.

The concentration fields that resulted from the GEOS-CHEM simulations were not directly usable as boundary conditions for the 36-km grid scale CMAQ simulations. GEOS-CHEM and CMAQ use different horizontal and vertical grid scales and diffe rent map projections, and have different labels for chemical constituents . The GEOS-CHEM outputs were processed by Dr. Daewon Byun at the University of Houston to adjust for these discrepancies and to develop boundary conditions for the 36-km CMAQ mode ling (Byun, 2004). Since the CMAQ modeling has temporal resolution of one hour, the bo undary conditions were given for every hour. However, since the GEOS-CHEM outputs had 3- hr resolution, the hourly boundary conditions were the same for three consecutive hours. An example of the resulting boundary conditions for sulfate at the surface is shown in Figure 5-6.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-17 

# Average Sulfate Boundary Conditions GEOS-CHEM for July 1, 2001

Figure 5-6. Example GEOS-CHEM projections of sulfate boundary conditions for the national CMAQ modeling grid on July 1, 2001, as indicated by the colors along the boundary of the modeling domain.

Using the boundary conditions provided by the GEOS-CHEM global simulations, VISTAS calculated the impacts of international anthropoge nic emissions on haze at U.S. Class I areas in two different ways, which are summarized in Table 5-2:

• First, as the difference between the results of two GEOS-CHEM simulations: (1) with 2002 international emissions (nat ural and anthropogenic) but only natural U.S. emissions; and (2) with only natural emissions worldwide. Such differences could be calculated with
the 4 ° x5 ° grid of the VISTAS simulations or the 1 ° x1 ° grid of the EPRI simulations. Since the chemical reactions in these GEOS-CHEM simulations do not reflect the contribution to the chemistry from anthropogeni c emissions in the U.S., the result is an estimate of what the international contributio n would be if the U.S. emissions were only from natural sources (the 2064 goal) while the emissions of the rest of the world remained at 2002 levels. (See the upper half of Table 5- 2 for portrayal of this calculation.)

• Second, as the difference betw een the results of two CMAQ 36-km grid simulations of the U.S., both using 2018 OTW Base F em issions, with boundary conditions based on GEOS-CHEM simulations: (1) with all global sources active; and (2) with U.S. anthropogenic emissions and all global natu ral emissions (Mexican and CanadianMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-18 anthropogenic emissions within the 36-km CMAQ domain were also removed). Since the GEOS-CHEM simulations are based on 2002 emissi ons, the result of this calculation is an estimate of what the inte rnational contribution would be in 2018 if international emissions remain at 2002 levels. (See the lowe r half of Table 5-2 fo r portrayal of this calculation.)

Both cases underestimate the total interna tional contribution because the GEOS-CHEM simulations assumed that all fire s outside of the US were natural, that is that there are no international fire emissions due to anthropogenic burning.

Table 5-2. Approaches for estimating international contributions using GEOS-CHEM results.
Case Natural Sources Anthropogenic Sources

GEOS-CHEM
(1) International and US International
(2) International and US --
Difference = (1) – (2) -- International CMAQ with GEOS-CHEM boundary conditions
(1) International and US International and US
(2) International and US US
Difference = (1) – (2) -- International

The CENRAP RPO applied a thir d method to estimate the impact s of international emissions. They used the PSAT (PM Source Apportionment T echnology) module of the CAMx regional air quality model to track the 2002 co ntributions of three non-domes tic source categories (Canadian emissions in the CAMx modeli ng domain, Mexican emissions in the CAMx modeling domain, and boundary conditions) at all Class I areas (ENVIRON and UCR, 2007). The boundary conditions applied at the boundaries of the CAMx 36 km scale grid were based on the outputs of the GEOS-CHEM modeling described above. The international impacts in 2002 were then calculated as the sums of the PSAT-determined c ontributions of these th ree source categories. One major difference between this approach a nd the VISTAS approaches is that the PSAT international impacts included the contributions from fire emissions. This will result in larger OC and EC international impacts compared to the VISTAS approach.

To illustrate the magnitudes of th e international contributions to two of the principal components of the visibility-impairing aerosol, Figures 5-7 an d 5-8 show the annual averages of estimates of international contributions to ammonium sulf ate and organic carbonaceous mass (OCM) at Class I areas in the VISTAS region and in adjacent stat es. Results are presented for all four of the methods described above. Two sets of GEOS-C HEM results are shown: one for 2002 with the
VISTAS 4 ° x5 ° grid and one for 2001 with the EPRI 1 ° x1 ° grid.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-19 Figure 5-7. Estimates by four methods of impacts of international emissions on annual average ammonium sulfate [(NH 4 ) 2 SO 4 ] concentrations at Class I areas.



### Transboundary Man-Made Impacts

0.00 0.05 0.10 0.15 0.20 0.25 0.30ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM Conc. (_g/m3)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 5-8. Estimates by four methods of impacts of international emissions on annual average organic carbonaceous mass (OCM) or organic carbon mass (OCM) concentrations at Class I

areas.

For ammonium sulfate there is reasonable agr eement (better than a factor of two or so everywhere except at Everglades) between all pairs of methods, while the results are more variable for OCM. The impacts estimated by the VISTAS 4 ° x5 ° GEOS-CHEM simulations tend to be smaller than those of the other methods . Not surprisingly, the biggest impacts from international emissions tend to occur at Class I areas that are near the Canadian and Mexican borders. 

### Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4ACADLYBRBRIGBOWAISLEBIBEHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN

GEOS_4x5 GEOS_1x1 PSAT CMAQ(NH4)2SO4Conc. (_g/m3)March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-20 The projected contributions from international emissions on the 20 percent worst visibility days in 2002 are compared to annual average contribu tions in Figures 5-9 and 5-10. Results obtained using both the CAMx/PSAT and CMAQ models are shown. At the VISTAS sites, CMAQ projected slightly higher internat ional contributions to sulfate on the 20 percent worst visibility days than the annual average contributions. Compared to the co rresponding annual average international contribution, CAMx/PSAT generally showed lower contribu tion to sulfate on the 20 percent worst days at the moun tain sites and equal or slightly higher contributions at the coastal sites. For organic carbonaceous mass, there were no clear tre nds between 20 percent worst and annual average days in either the CAMx/PSAT or CMAQ results.



### Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc.(_g/m3)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5-9. Estimates by the PSAT and CMAQ models of the impacts of international emissions on ammonium sulfate mass concentrations at Cla ss I areas on the 20% worst visibility days and on the annual average in 2002.


### Transboundary Man-Made Impacts

0.00 0.05 0.10 0.15 0.20 0.25 0.30BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHENOCM Conc. (_g/m3)

PSAT_20 PSAT CMAQ_20 CMAQ

Figure 5-10. Estimates by the PSAT and CMAQ model s of the impacts of international emissions on organic carbonaceous mass concentrations at Class I areas on the 20% worst visibility days and on the annual average in 2002.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-21 Figures 5-11 and 5-12 put the international impacts into the context of current measured concentrations. They compare the internationa l impact estimates with current monitored concentrations on the 20 percent worst visibility days at two VISTAS Class I areas chosen as examples, Shenandoah (SHEN) and St. Marks (S AMA). At both locations, the international impacts on concentrations on such days are sma ll, typically less than 5% of the measured concentrations.

At the same two Class I areas, the magnitudes of international contributions relative to the uniform rate of progress (URP) goal for 2018 are shown in Figures 5-13 and 5-14, where all the haze index values were calculated using the new IMPROVE algorithm. In each plot, the open triangle at 2018 indicates the es timated haze index, in decivi ews, as predicted by the CMAQ model using the Base G2 emission inventory. The open diamond indicates the haze index in 2018 if international emissions are removed from the 2018 CMAQ si mulation, so the difference between the open triangle and th e open diamond represents th e international contribution.

SHEN1 20% Worst Days

0 5 10 15 20 25 30 35 40 45

2/28 5/26 5/29 6/4 6/10 6/13 6/19 6/25 7/1 7/4 7/16 7/19 7/22 7/31 8/3 8/12 8/21 8/24 8/27 9/11 9/14 9/17 9/29 10/2Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt Figure 5-11. For the 20 percent worst visibility days in 2002 at Shenandoah, CMAQ-simulated international contributions (right bars) and domestic contribution to measured PM concentrations (measured component minus international contribution) (left bars).March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-22 SAMA1 20% Worst Days (2002)

0 5 10 15 20 25 30 35

2/4 2/13 2/16 2/25 3/30 4/5 4/8 4/17 4/26 5/2 5/5 5/8 5/11 6/4 7/4 7/7 7/16 7/19 9/17 10/1710/20 10/23 12/7Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt Figure 5-12. For the 20 percent worst visibility days in 2002 at St. Marks, CMAQ-simulated international contributions (right bars) and domestic contribution to measured PM concentrations (measured component minus international contribution) (left bars). 

### Uniform Rate of Progress Glide Path Shenandoah - 20% Worst Days

29.31 28.11

25.12

22.13

19.14

16.15

13.16
11.37 22.11
20.47

0 5 10 15 20 25 30 35

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 Method 1 Prediction w/o Intl. Trans. Figure 5-13. Illustration of the effect of removing the international emissions contribution to the CMAQ-modeled haze index on 20% worst days at Shenandoah in 2018. Open triangle = estimate with all emissions; open diamond = estimate with international emissions removed.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-23 

### Uniform Rate of Progress Glide Path Saint Marks - 20% Worst Days

26.31 25.33

22.89

20.45

18.01

15.57

13.13 11.67 22.89 21.82

0 5 10 15 20 25 30 35

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 Method 1 Prediction w/o Intl. Trans. Figure 5-14. Illustration of the effect of removing the international emissions contribution to the CMAQ-modeled haze index on 20% worst days at St. Marks in 2018. Open triangle = estimate with all emissions; open diamond = estimate with international emissions removed.

At Shenandoah, Figure 5-13 shows that the modeled 2018 haze index (22.11 dv) is already below the 2018 Uniform Rate of Progress (25.12 dv). Removing the international contribution would lower the 2018 haze index by another 1.64 dv. Although the internati onal contribution is small today, it becomes increasingly important as U.S. emissions are reduced. (For these analyses, the international emissions are assumed to remain constant from the baseline period to 2018).

At Saint Marks, Figure 5-14 shows that the m odeled 2018 haze index (22.89) is right at the 2018 Uniform Rate of Progress (22.89). Removing the international contribution would reduce the 2018 haze index by 1.07 dv, to a value that lies ju st below (indicating greater reduction than) the 2018 Uniform Rate of Progress.

Information of the type portrayed for 2018 in Fi gures 5-13 and 5-14 is summarized in Figure 5- 15 for all VISTAS Class I areas and Class I areas in adjacent states using the DotPlot format presented previously that repres ents the 2018 visibil ity projection as a percentage of achieving the 2018 Uniform Rate of Progress. Figure 5-15 shows that removing the international contribution is sufficient to raise the reductions ratios at all Class I areas in the figure above 100%. This means that when the international c ontributions are eliminated, all Class I areas exceed the Uniform Rate of Progress in 2018.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-24 CMAQ 2018g2b/2002gt2a Method 1 predictions for VISTAS+ sites

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved

CMAQ New IMPROVE Algorithm

CMAQ New IMPROVE Algorithm w/ Intl Trans.

Mountain Coastal non-VISTAS Figure 5-15. Percent of Uniform Rate of Progres s haze index reduction achieved in 2018 according to the CMAQ modeling for the Base G2 inventory. Results are given for improvements calculated using the new IMPROV E algorithm. Solid symbol indicate results when all emissions are considered; open symbols indicate results when effects of international emissions are removed from the 2018 visibility projection.

5.5 Visibility Trends

Figure 5-16 displays trends in vi sibility impairment at the VISTAS Class I areas using the period of available record of measurements (thr ough 2006) that are availa ble on the WRAP TSS (http:/vista.colostate.edu/tss/) at the associ ated IMPROVE monitor and the new IMPROVE equation. Trends are included fo r the worst 20 percent days, the best 20 percent days and all IMPROVE sampled days during a year. The EPA guidance procedures were used to construct the worst and best 20 percent days that includes a min imum data capture requirement (EPA, 2003a), whereas no such minimum data capture 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 VISTAS 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) .March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-25 The IMPROVE monitor at the SIPS Class I area has been operati ng for 13 years, however only eight years of trends are available for the worst and best 20 percent days due to the valid data capture requirements of these regional haze me trics (EPA, 2003a). Although there is a lot of year-to-year variability in the visibility due to meteorology, fires and other factors, there does appear to be a downward trend in visibility impairment at SIPS for the worst 20 percent and all IMPROVE sampled days (Figure 5-16a). There also is a reduction in visibility impairment for the worst 20 percents for before and after the year 2000.

The visibility trends at CHAS are given in Figure 5-16b. Ag ain there is lots of year-to-year variability but there also appears to be a gene ral downward trend in visi bility impairment at CHAS for the worst 20 percent and all sampled days.

The trends at EVER are confounded by poor valid da ta capture and what appears to be impacts due to biomass burning in 2003 th at enhance the OMC and EC c ontributions for the worst 20 percent days (Figure 5-16c). Also changes in IMPROVE measurement pr otocols may affect the interpretation of the visibility trends at EVER . In any event, there is no indication of any downward trend in visibility impairment at EVER.

SAMA (Figure 5-16d) and COHU (Fi gure 5-16e) only have 2-3 years of sufficient valid data to construct trends for the worst and best 20 percen t days which is insufficient to form any opinion on trends given the year-to-year variability in visibility.

There is a fairly long term record of IMPR OVE measurements at th e OKEF Class I area (16 years) with 15 years of sufficient data capture to construct the worst and best 20 percent days regional haze metric (Figure 5-16f ). For the worst 20 percent days , there appears to be a slight trend in improved visibility impairment that is driven by sulfate, although the high sulfate extinction in 2005 counters that ar gument. For the best 20 percent days, there is so much yearto-year variability that no trend can be distinguished. There is also a slight trend towards improvements in visibility for the all sampled days metric.

There appears to be a slight downwind tren d at MACA (Figure 5-16g), although the high extinction in 2005 confounds the analysis. Visibil ity at LIGO for the four years of sufficient valid data (2001-2004) is fairly flat for the wo rst and all sampled days metrics (Figure 5-16h).

SHRO has sufficient valid data capture for th e older four years of 1995-1998 and four more recent years of 2003-2006 to develop best/worst h aze metrics (Figure 5-16i). 2004 has the least impaired worst 20 percent days of record at SHRO which suggests a trend toward improvements in visibility. However, 2004 was also a very wet year with cleaner than normal pollution conditions and removing it from the SHRO trend pl ots would make the more recent visibility within the range of year-to-y ear variability in the past.

SWAN has only three years with sufficient data cap ture so no discussions on trends can be made (Figure 5-16j).

ROMA has a fairly rich trend history dating back to 1995 with the cleanest three years occurring recently in 2001-2003 (Figure 5-16k). Although 2004- 2006 is dirtier, so the occurrence of the recent cleaner years may not suggest a downward trend in visibility impairment at ROMA.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-26 GRSM has one of the longest periods of reco rd with the IMPROVE monitor operating for 19 years and collecting sufficient valid data to pro duce 17 years of best and worst 20 percent days regional haze metrics (Figure 5-16l). Again there is lots of year-to-year variability in the visibility impairment, but it is encouraging that over the 6 most recent years light extinction for the worst 20 percent days is approximately 210 Mm -1 or less for 5 of them a level achieved only once in the previous ten years. A trend toward imp rovements in visibility appears to occur in the worst 20 percent and all sampled days metrics.

There is a lot of year-to-year variability in visib ility at JARI, so no trend can be inferred (Figure 5-16m).

Like GRSM, SHEN has a 19 year history of IMPROVE measurem ents that include sufficient data capture to produce 16 years of best and wo rst 20 percent days regional haze metrics (Figure 5-16n). There is a clear downward trend in visibility impairment for the worst 20 percent days with the values for the first five y ears of the 1990s ranging from 240 to 285 Mm -1 , whereas the values for the first five years of the 2000s ranging from 160 to 210 Mm -1 . There also appears to be a distinct trend in improved visibility in the al l sampled days metric and, although confounded by a lot of year-to-year variabili ty. Even the best 20 percent days metric exhibits a downward trend.

The DOSO Class I area also ha s a long record of IMPROVE m easurements (Figure 5-16o). There is a slight trend toward visibility improvements in the worst 20 percent and all sampled days metrics. Although this is driven in part by the high visibility years of 1994-1995 which if removed suggests a more flat tre nd in visibility.

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-27 Figure 5-16a. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Sipsey (SIPS), Alabama 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-28 Figure 5-16b. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at Chassahowitzka (CHAS), Florida 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-29 Figure 5-16c. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Everglades (EVER), Florida 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-30 Figure 5-16d. Time series of observed IMPROVE reconstructed light extinction (New IMPROVE) at St. Marks (SAMA), Florida 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-31 Figure 5-16e. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Cohutta (COHU), Georgia for the average of the Worst 20 Percent days (top), Best 20 Percent days (middle) days and all IMPROVE samp ling days during the period of record

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-32 Figure 5-16f. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Okefenokee (OKEF), Georgia 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

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-33 Figure 5-16g. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Mammoth Cave (MACA), Kentucky 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-34 Figure 5-16h. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Linville Gorge (LIGO), North Carolina 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-35 Figure 5-16i. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Shinning Rock (SHRO), North Carolina 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-36 Figure 5-16j. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Swanquarter (SWAN), North Carolina 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-37 Figure 5-16k. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Cape Romain (ROMA), South Carolina 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-38 Figure 5-16l. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Great Smokey Mountains (GRSM), Tennessee-North Carolina for the average of the Worst 20 Percent days (top), Best 20 Percent days (mi ddle) days and all IMPROVE sampling days during the period of record (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-39 Figure 5-16m. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at James River Face (JARI), Virginia 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-40 Figure 5-16n. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Shenandoah (SHEN), Virginia 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-41 Figure 5-16o. Time series of observed IMPROVE rec onstructed light extinction (New IMPROVE) at Dolly Sods (DOSO), West Virginia 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 (bottom)

.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-42 5.6 Geographic Areas of Influence of Visi bility Impairment for Class I Areas

VISTAS has determined that SO2 emission reduc tions from EGU and non- EGU point sources in the VISTAS and nearby states would be the mo st effective sources to control to improve visibility at the Class I areas in the VISTAS region and vicinity. One of the next steps was to identify the specific geographic areas that most likely influence visibility in each Class I area, and then to identify the major SO2 point sour ces located in those geographic areas. An SO2 Area of Influence was defined for each Class I ar ea to represent the geographic area containing sources that would likely have th e greatest impact on visibility at that Class I area. All SO2 point sources within these Areas of Influe nce were identified and ranked by their 2018 emissions. The following sections contain a broad overview of the steps in the Area of Influence analyses and the final results. See Appendix H for a more detailed discussion of how these analyses were developed. The Area of Influe nce analyses were condu cted by Joe Adlhoch and Cassie Archuleta of Air Resources Specialists, Gerry Mansell of ENVIRON, Greg Stella of Alpine Geophysics and Pat Brew er of VISTAS and the reader is referred to additional documentation for more details at the analys es (Adlhoch et al., 2007; Mansell, 2007).

5.6.1 Back Trajectory Analyses

The first step in the Area of Influence analyses was to generate meteorological back trajectories for IMPROVE monitoring sites in VISTAS re gion and neighboring Class I areas for the 2000- 2004 worst 20 percent days baseline period. Back tr ajectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air masses that arrive at a receptor at a given time. The method essentia lly follows a parcel of air backward in hourly steps for a specified length of tim e. Trajectories were started at 100 meters and 500 meters above the surface and run backward from the site for 72-hours. Figure 5-17 is an example of a back trajectory analysis for Great Smoky Mountains National Park for the worst 20 percent days in 2002.

Figure 5-17. Example back trajectories for worst 20 percent visibility days in 2002 for Great Smoky Mountains National Park.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-43 Figure 5-18. Example residence time plot for 20 percent worst visibility days in 2000-2004 for Great Smoky Mountains National Park based on trajectories with 100m start height.

5.6.2 Residence Time Plots

The next step was to plot residence time for each Class I area using five years of back trajectories for the worst 20 percent visibility days in 2000-2004. Residence time is the frequency that winds pass over a specific geographic area on the path to a Class I area. Separate residence time plots were generated using back trajectories with 10 0m and 500m start heights. As illustrated in Figure 5-18, winds influencing Gr eat Smoky Mountains on the wors t 20 percent days come from all directions and there is no single predominan t wind direction influencing the 20 percent worst visibility days. In contrast, at Swanquarter, th e residence time plot indi cates a clear north-south gradient of influence along the eastern co ast of the United States (Figure 5-19). GRSM1 2000-04 20% Worst Monitored Days 100m Residence Times (%)

< 0.01

0.01 - 0.02

0.02 - 0.04

0.04 - 0.08

0.08 - 0.16

0.16 - 0.32

> 0.32March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-44 Figure 5-19. Example residence time plot for 20 perc ent worst visibility days in 2000-2004 for Swanquarter based on trajectories with 100m start height.

5.6.3 SO2 Areas of Influence

The next step was to develop su lfate extinction weighted reside nce time plots to define the geographic area with highest probability of infl uencing the receptor on the worst 20 percent days in 2000-2004 that were dominated by sulfate. Each back traj ectory was weighted by sulfate extinction for that day. This allows the focus to be on the worst 20 percent days that are influenced by sulfate and places less importan ce on days influenced by, for example, organic carbon from fires. Sulfate-weighted back traj ectories for the 20 per cent worst days were combined for the 5 years of data. The resulti ng sulfate extinction-weight ed residence time plots were used to define the geogra phic Area of Influence for sources of SO2 emissions. In Figure 5- 20 the area representing 10 percen t or greater residence time is outlined in red and the area representing 5 percent or greate r residence time is outlined in gr ay. The VISTAS states focused their analyses on the Area of Influence define d by 5 percent or greater sulfate extinctionweighted residence time.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-45 Figure 5-20. Example SO2 Area of Influence plot for sulfate extinction weighted residence time for 20 percent worst visibility days in 2000-2004 for Great Smoky Mountains National Park based on trajectories with 100m start height.

5.6.4 Emissions Sources within SO2 Areas of Influence

Residence time plots were then combined with geographically-gridded em ission data based for the 2002 baseline and 2018 emissi ons inventories. Plots were generated for the Areas of Influence defined by trajectories with 100m and 500 m start heights. As a way of incorporating the effects of transport, deposition, and chemical transformation of point source emissions along the path of the trajectories, these data were weighted by 1/d, where d was calculated as the distance, in kilometers, between the center of the grid cell in which a source is located and the center of the grid cell in which the IMPROVE m onitor is located. The distance-weighted point source SO2 emissions are then combined with th e gridded extinction-wei ghted back-trajectory residence times at a spatia l resolution of 36 km.

The final step was to combine the residence times and gridded emissions data in plots and data sets. The distance weighted (1/d) gridded poin t source SO2 emissions were multiplied by the total extinction-weighted back-tra jectory residence times on a grid cell by grid cell basis. These results were then normalized by the domain-wide total and displayed as a percentage. The analysis was done using both the 2002 a nd 2018 emissions inventories.

Figure 5-21 illustrates the 2002 and 2018 distance weighted gridded emissions multiplied by sulfate extinction weighted residence time fo r Great Smoky Mountains National Park. These maps help visualize where the emissions redu ctions will be occurring between 2002 and 2018. SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% Orange line perimeter indicate Area of Influence with Residence Time > 5%. SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% Orange line perimeter indicate Area of Influence with Residence Time > 5%.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-46 The change in SO2 emissions between 2002 a nd 2018 can be seen by comparing emissions source strengths in the two plots. Note the emissions from each sour ce are normalized by the total emissions in the domain. Sources that reduce SO2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018, map the grid cells w ith these sources will show a lighter color gradient than on the 2002 map. For example, SO2 reductions from EGU in western and central North Carolina under the Nort h Carolina Clean Smokestacks Act can be seen by comparing the 2002 and 2018 maps. Because the total emissions in the domain are smaller in 2018, a source that does not change emissions be tween 2002 and 2018 may actually appear to increase in importance in 2018 compared to 2002.

Figure 5-21. Great Smoky Mountains 2002 (left) and 2018 (right) SO2 distance weighted emissions times SO4 extinction-weighted residence time plots.

5.6.5 Specific Source Types in the Areas of Influence

The next step in the analysis was to review the emissions inventories to determine the source categories, as well as specific sources, found to have the greatest impact on visibility in VISTAS and nearby Class I areas. Lists of SO2 point sour ces within the Areas of Influence for each Class I areas were developed using the VISTAS 2002 ba se year and 2018 future year emissions. For this purpose, the Area of Influence was defined as the counties with maximum sulfate extinction weighted residence time greater th an five. For SO2 sources within each Area of Influence, the following attributes were defined for each individual unit:

• State, county, and source (plant), and industry iden tification codes

• SO2 emissions for 2002 and 2018

• 2018 control efficiency

• Distance to Class I areas (defined by dist ance to the monitor at the Class I area) 

### 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46% 

### 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46% 

### 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46%March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-47 • Emissions divided by distance (Q/d), a metr ic that accounts for the dispersion of emissions over distance

• Maximum sulfate extinction weighted residence time (RTmax)

Our review was conducted in a top down fashion st arting with an analysis of the major source categories in each SO2 Area of Influence to de termine which major categories had the highest residual contribution to the area in 2018. It was also important to identify reductions that are projected to occur between 2002 and 2018 within each category or at specific units. This allowed VISTAS States to determine if certain source categories or units that had yet to be controlled under the future ye ar base case had the potential for reduction. Once the highest source types were identified, subcategories with in those source types were reviewed. The contributions from major source categories to th e 2018 inventory for the SO2 Areas of Influence for the Great Smokey Mountains Cla ss I area is listed in Table 5- 3. In this table, the source categories are broken out by the USEPA’s Tier 1 report categories and are defined below:

• Fuel Comb Elec Utility Emissions from all fuel co mbustions at utility boilers

• Fuel Comb Industrial Emissions from all fuel combustions at industrial boilers

• Fuel Comb Other Emissions from all fuel combustions from commercial/institutional and residential sources (i.e., fireplaces, nat ural gas stoves, oil heaters, etc.)

• Chemical & Allied Product Mfg Emissions from chemical manufacturing processes

• Metal Processing Emissions from metal processing operations

• Petroleum & Related Industries Emissions from petroleum refineries & related industries

• Other Industrial Processes All other industrial pr ocessing not previously mentioned

• Solvent Utilization Emissions from solvent utilization su ch as degreasing operations, surface coating operations, etc.

• Storage & Transport Emissions from storage and transport of petroleum, organic and inorganic products

• Waste Disposal & Recycling Emissions from open burning, incineration, landfills, publicly owned treatment works, treatment storage and/ or disposal facilities, wastewater treatment facilities

• Highway Vehicles Emissions from on-road mobile sources

• Off-highway Emissions from off-road mobile sources

• Miscellaneous (Ag, Fires) Emissions from agricultural operations, wildland fires, and other emissions sources not previously mentionedMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-48 Table 5-3. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Great Smoky Mountains.

Table 5-3 indicate that for the Great Smoky Mountains Class I area , EGUs and industrial boilers are the two major sources categories contribut ing to 2018 SO2 emissions in the Areas of Influence, even after implementation of th e CSA and CAIR. Together these two source categories contribute 84 percent of the 2018 SO2 emissions for the Area of Influence of the Great Smokey Mountains Class I area. Other fuel combustion and other industrial processes comprise another 8 percent of the 2018 SO2 emissions.

These types of tables can also be used to eval uate the major source categories contributing to emissions of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and off road vehicles are major sources of NOx emissions, in addition to electric utilities and industrial boilers. The source category “miscellaneous” (which includes agricultural sources and fires) is the major contributor to NH3 and primary PM.

The contributions to SO2 emissions in 2018 from the three highest source categories, electric utilities, industrial boilers, and other fuel combustion have been further broken out into subcategories. Table 5-4 indicates subcateg ories for the Areas of Influence for the Great Smokey Mountains Class I areas. W ithin electric u tilities, all the SO2 emi ssions are attributable to coal fired power plants. Within industrial boilers, most emissions are attributable to coal fired boilers with lesser contri butions from oil and gas boilers. Co mmercial and institutional coal and oil boilers have smaller contributions. 86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier

86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC TierMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-49 Table 5-4. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Great Smoky Mountains Class I area.

5.6.6 Evaluating the Four Statutory Factors fo r Specific SO2 Emissions Sources in Each Area of Influence

The next step in the Area of Influence analys es was to identify emission reductions that have already occurred within each source category and at specific units. Unit level tables of emission comparisons from 2002 to 2018 were developed, a llowing VISTAS States to review existing emission reductions. These tables assigned futu re year control technology from IPM forecasting and State modification for EGU and from control e fficiency tables for non-EGU point sources.

Once emission control profiles for specific units were defined, the next step is to determine what, if any, additional control measures would feasibly be available, and to assign costs to those control measures. For EGUs, the 2018 IPM file used by VISTAS was obtained and matched to the 2018 base case inventory of EGU sources. This step was conducte d to ensure that incremental controls assigned to these sour ce types did not duplic ate existing base case assumptions.

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS emission inventories, for the non-EGUs. The core of Ai rControlNET is a relational database system in which control technologies are linked to sources within the USEPA emissions inventories. The system contains a database of control measure a pplicability, efficiency, and cost information for reducing emissions. The control measure data file in AirControlNET includes not only the technology's control efficiency, and calculated emission reductions for that source, but also estimates the costs (annual and capital) for application of the control measure. S

1% Fuel Comb. Other-Residential Other 0% Fuel Comb. Other-Residential Wood 0% Fuel Comb. Other-Misc. (Except Residential) 0% Fuel Comb. Other-Comm/Institutional Gas 3% Fuel Comb. Other-Comm/Institutional Oil 2% Fuel Comb. Other-Comm/Institutional Coal 0% Fuel Comb. Industrial-Internal Combustion 1% Fuel Comb. Industrial-Other 2% Fuel Comb. Industrial-Gas 5% Fuel Comb. Industrial-Oil 22% Fuel Comb. Industrial-Coal 0% Fuel Comb. Elec. Util.-Internal Combustion 0% Fuel Comb. Elec. Util.-Other 0% Fuel Comb. Elec. Util.-Gas 0% Fuel Comb. Elec. Util.-Oil 54% Fuel Comb. Elec. Util.-Coal GRSM Tier S

1% Fuel Comb. Other-Residential Other 0% Fuel Comb. Other-Residential Wood 0% Fuel Comb. Other-Misc. (Except Residential) 0% Fuel Comb. Other-Comm/Institutional Gas 3% Fuel Comb. Other-Comm/Institutional Oil 2% Fuel Comb. Other-Comm/Institutional Coal 0% Fuel Comb. Industrial-Internal Combustion 1% Fuel Comb. Industrial-Other 2% Fuel Comb. Industrial-Gas 5% Fuel Comb. Industrial-Oil 22% Fuel Comb. Industrial-Coal 0% Fuel Comb. Elec. Util.-Internal Combustion 0% Fuel Comb. Elec. Util.-Other 0% Fuel Comb. Elec. Util.-Gas 0% Fuel Comb. Elec. Util.-Oil 54% Fuel Comb. Elec. Util.-Coal GRSM TierMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-50 Using the modified inventories identified a bove, VISTAS ran every available SO2 control strategy in AirControlNET against the EGU and non-EGU point source inventories to develop a master list of incremental control strategies for each unit in the VI STAS 36 km domain.

For the sources within the Area of Influences for each VISTAS and nearby Class I area, the master list of incremental control measures was sorted to determine the costs of incremental control measures. These data were combined in a master spreadsh eet with the distance from the emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual emissions and distance (Q/d ) or squared distance (Q/d 2 ), and the normalized 2018 SO2 point source emissions times distance-weighted residence time (RTMax) values for the county in which the emission release point was located. Each VISTAS state evaluated these control measures and costs as part of th eir review of the statutory factor s for reasonable further progress.

The regional haze rule requires th at states consider the followi ng factors and demonstrate how these factors were taken into considerati on in selecting the reasonable progress goal:

• the costs of compliance

• the time necessary for compliance

• the energy and non-air quality environm ental impacts of compliance, and

• the remaining useful life of any potentially affected sources.

5.6.6.1 Cost of Compliance

As defined in Section 5.6.5, coal-fir ed electric utilities and coal-fired industrial boilers were the largest source categories contributing to SO2 in 2018 in the Areas of Influence defined for the Great Smoky Mountains Class I area. Indus trial boilers using o il and commercial and institutional boilers using coal or oil had small cont ributions to SO2 in 2018.

Sulfur dioxide emissions from utility, industria l, commercial, or inst itutional boilers can be controlled either by switching the fuel source to a lower sulfur fuel content or by installing postcombustion controls. Costs vary by fuel source and boiler type and re quire source specific analyses for accuracy.

Bituminous coal is commonly burned in boilers in the eastern US. Switching to another bituminous coal with lower su lfur content or blending b ituminous coals can reduce SO2 emissions with least impact to boiler performance. While sub-b ituminous coal has a lower sulfur content than bituminous coal, it al so has a lower heat rate and so more coal has to be burned to generate the same energy output. For boilers that are designed for bituminous coal, only a small fraction of the fuel can be switched to low sulf ur sub-bituminous coal without affecting boiler operations. Costs of boiler modi fications to accommodate low sulfur sub-bituminous coal may make switching to sub-bituminous coal impractical . Contract initiation and termination costs, differential fuel prices and heat rates, transpor tation costs, and modifica tion to boiler operations, fuel handling and waste handling systems will have to be considered specific to each source.

Flue-gas desulfurization is the common post-combus tion control for coal-fir ed boilers. Flue gas is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either a wet scrubber, dry scrubber, or spray dryer. A cal cium sulfate by-product is produced that may be further processed to produce gypsum as a commercial byproduct. Costs of flue-gasMarch 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-51 desulfurization include initial construction costs a nd ongoing operational and maintenance costs for the absorber tower, sorbent handling, and wa ste product handling facilities. Costs per ton vary with boiler size, type, and f acility siting considerations.

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential fuel prices and heat rate, boiler modifications, fuel handling costs, and maintenance costs. Conceptually, post-combustion controls can be used for oil boilers, but th ere is little precedence for such installations.

5.6.6.2 Time Necessary for Compliance

For fuel switching, the time n ecessary to terminate existing fu el contracts and initiate new contracts needs to be considere d. Generally two to three years may be required. Installation of post-combustion controls will require 3 or more years depending on market availability of labor and materials and utility system-wide priorities. Time necessary for compliance will need to be refined for specific sources.

5.6.6.3 Energy and Non-Air Environmental Impacts

Switching to lower sulfur fuel or installing post-combus tion controls may reduce boiler heat rate and energy output. Scrubbers and spray dryers will require additional safeguards for fuel handling and waste handling systems to avoid add itional non-air environmental impacts such as increased effluents in waste water discharges and storm water runoff. These factors will need to be considered specific to individual sources. Carbon dioxide is emitted as a by-product of flue gas desulfurization, therefore im pacts of increased carbon emissions will need to be considered, particularly if carbon emissions are limited in the future under climate change mitigation strategies.

5.6.6.4 Remaining Useful Life

The useful remaining life is specific to the unit for which controls are considered.March 2009

F:\VISTAS Phase II\TSD\draft#4\Chapter_5_AddAnal4.doc 5-52 5.7 Area of Influence Analyses Results for VISTAS and Nearby Class I Areas

VISTAS grouped the Area of Influence (AOI) resu lts by Class I area for discussion of the four factors to be determined in defining reasonable pr ogress. Figure 5-22 displays the Class I area groupings used by VISTAS for the AOI and four factors analysis that can be found at:

http://www.vistas-sesarm.or g/documents/FinalDocs.asp

Figure 5-22. Class I area groupings for VISTAS AOI displays and four factor analysis. 

### .

. . .

Hercules Glade, MO



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. 

## VISTAS Class IMarch 2009

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