# Appendix M .1

Technical Support Document for VISTAS Emissions

and Air Quality Modeling to Support Regional Haze State Implementation Plans

Tennessee Department of Environment and Conservation

Division of Air Pollution Control

9 th 

### Floor L&C Annex

401 Church Street Nashville, TN 37243International Corporation 

### Air Sciences

Preliminary Draft 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 Bo Wang ENVIRON International Corporation 101 Rowland Way, Suite 220 Novato, CA 94945

Gregory Stella Dennis McNalley

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

October 10, 2007

773 San Marin Drive, Suite 2115, Novato, CA 94998 415.899.0700October 2007



### i TABLE OF CONTENTS

Page

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

1.1 Background................................................................................................................. ..... 1-1 1.2 VISTAS Modeling Approach .......................................................................................... 1-2

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................................................1.24 1.3.8 Emissions Input Preparation .............................................................................. 1-26 1.3.9 Meteorological Input Preparation .......................................................................1.27

2. DEVELOPMENT OF EMISSIONS MODEL 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 Processing Characteristics.................................................................. 2-9 2.3.1 Temporal Allocation ............................................................................................ 2-10 2.3.2 Speciation........................................................................................................... 2-2 8 2.3.3 Spatial Allocation................................................................................................2.28 2.4 Development of Modeling Input Inventories/Run Scripts/ASSISGNS Files................................................................................................ 2-30

2.5 Products of the Emissions Inventory Development Process for QA............................................................................................................... 2 -30

2.6 Uncertainties and Caveats.............................................................................................. 2-33 2.6.1 Updated Inventories........................................................................................... 2-34

2.6.2 Low Level Fires ................................................................................................. 2-34 2.6.3 MMS Offshore Shipping.................................................................................... 2-34

2.6.4 Incorrect Location Parameters ............................................................................2.34

2.6.5 SMOKE/IOAPI Coordinate Transformation Error.............................................2.35October 2007



### ii 3. MODEL PERFORMANCE EVALUATION.................................................................... 3-1

3.1 Introduction............................................................................................................... ....... 3-1 3.2 CMAQ Evaluation Methodology..................................................................................... 3-1 3.2.1 Evaluation Approach ........................................................................................... 3-2

3.2.2 Particulate Matter a nd Component Species ......................................................... 3-2 3.2.3 Ambient Air Quality Data for VISTAS Model Evaluation ................................. 3-3

3.3 Model Performance Statistics and Goals ......................................................................... 3-4 3.4 Regional Model Performance Evaluation........................................................................ 3-5 3.4.1 Sulfate (SO4) Model Performance....................................................................... 3-5 3.4.2 Nitrate (NO3) Modeling Performance................................................................. 3-9 3.4.3 Organic Mass Carbon (OMC) Performance ...................................................... 3-12 3.4.4 Elemental Carbon (EC) Performance ................................................................ 3-15

3.4.5 Other PM 2.5 

### or Soil Performance ....................................................................... 3-18 3.4.6 Coarse Mass (CM) Performance........................................................................ 3-19 3.4.7 Fine Particulate (PM 2.5 

### ) Performance ................................................................ 3-24 3.5 Evaluation at VISTAS and Nearby Class I Areas ......................................................... 3-27 3.5.1 Example Spatial Distribution Model Performance............................................ 3-27 3.5.2 Sipsey (SIPS), Alabama Best and Worst 20 Percent Days Performance.............................................................................................. 3-31 3.5.3 Chassahowitzka (CHAS), Florida Best and Worst 20 Percent Days Performance................................................................................. 3-31 3.5.4 Everglades (EVER), Florid a Best and Worst 20 Percent Days Performance.............................................................................................. 3-31 3.5.5 St. Marks (SAMA), Florida Best and Worst 20 Percent Days Performance.............................................................................................. 3-38 3.5.6 Cohutta (COHU), Georgia Best and Worst 20 Percent Days Performance.............................................................................................. 3-38 3.5.7 Okefenokee (OKEF), Georgia Best and Worst 20 Percent Days Performance.............................................................................................. 3-38 3.5.8 Mammoth Cave (MACA), Kentucky Best and Worst 20 Percent Days Performance]................................................................................ 3-38

3.5.9 Linville Gorge (LIGO), North Carolina Best and Worst 20 Percent Days Performance................................................................................. 3-47

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 ......................................................... 4-10October 2007



### iii 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.................................................................... 4-16 4.4.3 2018 Visibility Projections for the Best 20 Percent Days.................................. 4-23

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

6. REFERENCES.................................................................................................................. ... 6-1October 2007

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### 1-1 1.0 INTRODUCTION

This is the first draft of the Technical Support Document (TSD) for the Visibility Improvements

for States and Tribal Associations in the Sout heast (VISTAS) regional em issions and air quality modeling to support the southeastern states Re gional Haze State Implementation Plans (SIPs). The VISTAS 2002 annual emissions and air qualit y modeling was performed by the contractor team of ENVIRON International Corporation (ENVIRON), Alpine Geophysics, LLC (Alpine) and the University of Califor nia at Riverside (UCR).

1.1 Background

The 1977 Clean Air Act Amendments (CAAA) adde d a new Section 169A for the protection of visibility in 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 are due December 17, 2007 and an important component of the SIP will be the 2018 Reasonable Progress Goals toward achieving natural conditions in 2064. Regional air quality models are used to project visibility to 2018 to determine whether the level of visibility improvement that is expected to be achi eved 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. The 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.

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 emissionOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 

### 1-2 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 ofOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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. The CMAQ model was update d to version 4.5 and a modified CMAQ module for Secondary Organics Aerosols (S OA) was developed and tested. Additional annual 2002 simulations were performed us ing the revised 2002 modeling inventories and revised CMAQ configurations to evaluate model performance . The model performance evaluation for the final VISTAS 2002 Actual base case CMAQ simulation

for the Base G2 emissions is discussed 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 inventor y 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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. Final 2018 emissions and air qual ity modeling for the Best and Final inventory will be provided in fall 2007 and is not availabl e to be included in this report.

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 national ambient air quality standards. The same model configuration and methods and the 2002October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-7 modeling results that are used for VISTAS are al so being used for the 2009 ASIP modeling. The ASIP Modeling Protocol dated January 31, 2 006 (Morris et al., 2006) updates the methods described in the VISTAS 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 specifically 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 and air quality Modeling Protocol (Morris, Tesche and Tonnese n, 2004) provides details on theOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-8 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);

• Asymmetric Convective Mixing (ACM) planetary boundary layer (PBL) model;October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-9 • 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 2.3 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 BAMS

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 beenOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-10 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. Details on the SMOKE emissions modeling for the VISTAS 2002 annual modeling is presented in Chapter 2 with emissions summary reports available in Appendix A.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 storageOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 Mass Carbon (OMC) concentrations, 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 volatile and stay s 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 OMC 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 = TERPOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 speci es 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 for 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 e quilibrium 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 (sesquiterpene 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 m odule in the CMAQ model were conducted as follows.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 Mass Carbon (OMC) IMPROVE and STN monitori ng sites in the VISTAS, MRPO, MANEVU and CENRAP states using the 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 OMC (i.e., OMC = 1.4 x OC). Whereas the standard CMAQ V4.4 underestimates OMC 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 fo r OMC 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 OM C across the urban STN sites is likely due to missing primary OMC emissions and uncerta inties in the STN OMC measurements. Also note that a 1.4 multiple was used to convert the measured OC to OMC, if a 1.8 factors was used for such conversion, as gi ven in the new IMPROVE equation, than the OMC underprediction bias w ould 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 OMC 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 quarterOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 EF1October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-18 Table 1-5. Results of initial evaluation of the SOAmods enhancement comparison of fractional bias performance metric for Organic Mass Carbon (OMC) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAm ods enhancement (observed OMC = 1.4 x OC).

IMPROVE OMC STN OMC 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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) 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 projectionsOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-20 Figure 1-4 . Nesting of VISTAS 36-km CMAQ grid in the MM5 36-km grid.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-21 .

Figure 1-5. Nesting of CMAQ 12-km grid in the CMAQ 36 km grid.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-22 Figure 1-6. Domain definition for the VISTAS higher resolution CMAQ 12 km eastern U.S. gridOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 Pres(mb) Height(m Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m )

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-24 1.3.6 2002 Calendar Year Selection

The calendar year 2002 was selected for VISTAS regional haze annual modeling 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 region al haze NAAQS. However, since th ere 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 th ree 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). Moreover, the required RRF values should be based on model results averaged over the 20% worst and 20% best visibility days

determined for each Class I 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 years 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:October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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, and 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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, 2007). Emissions for Non-VISTAS st ates 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 disc ussed previously. 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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 2.3 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 assumedOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-28 along the lateral edges of the 36 km inter-RPO continental U.S. modeling domain; and (3) air 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 mass carbon (OMC) was also variable, whereas performance for Soil and coarse mass (CM) was generally poor . More details on the model performance evaluation can be found in Chapter 3.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-29 

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

-750 -500 -250 0 250 500

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS

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 disc ussed in Chapter 2

and in the Emissions Inventor y report by MACTEC (2007). 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, 2007). 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 G2 modeling scenario:October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.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. W here 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 presumptive target, and States may establish a RPG that provides for greater, lesser, or equivalent visibility improvement as that described by the glidepath” (EPA, 2007b).October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-31 Figure 1-8 displays the VISTAS 2018 visibility projections using the EPA guidance (EPA, 2007a), the new IMPROVE equation for converting 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 region, including those in the Appa lachian Mountains as well as Sipsey and Mammoth Cave, achieve the 2018 Unif orm Rate of Progress by a fair margin. The Swanquarter and Cape Romain Class I areas on the coast of South and North Carolina, respectively, also achieve the 2018 Uniform Rate of Pr ogress. 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 and original (old) IMPRVE equation.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap_1_Intro2.doc 1-32 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 VISTS 2018 visibility proj ections with those from CENRAP, MRPO

and MANE-VU.

• Analysis of alternative visibility projections:

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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-1 2.0 DEVELOPMENT OF EMISSIONS MODEL 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 were reviewed by VISTAS stakeholders and considered complete in March of 2007.

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 Emi ssion 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.htmlOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.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 annual 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).October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.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 DayOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.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 Chapter 4).

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 modelingOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-5 schedule. Selecting representative model days fo r some or all of the source categories reduces 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 de tail 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 fire emissions were modeled for each episode day to take advantage of the available day-specific emissions and meteorology. Area sources, including non-road

mobile and dust emissions do not utilize meteorol ogical data, and are temporally allocated by monthly, daily and hourly profiles. Reviewing th ese 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 processing. 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 theOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-6 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 team 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

3 ftp://ftp.epa.gov/EmisInventory/2002finalnei/documenta tion/point/augmentation_point/2002nei_qa_augmentation_r eport0206.pdfOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-7 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 SCCmatched 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

4 http://www.epa.gov/ttn/chief /emch/invent/index.html#dustOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-8 and humidity are calculated from the hourly temper atures in each grid cel l in the state/county

groups.

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:October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-9 • PTOP – Top of the fire plume profile (meters above ground level)

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

• Lay1 – The percent of the emissions en trained in the first modeling layer

For those fires as having the necessary data elem ents to site these files with distinct time and space coordinates, these variable s were prepared and included in the modeling files used to process this emission source type.

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, the 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

5 http://www.wrapair.org/forums/fejf/documen ts/WRAP_2002_PhII_EI_Report_20050722.pdfOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-10 speciation, temporal allocation, and spatial allo cation have been separated into independent 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 altern ate 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:

6 ftp://www.airmodelingftp.com/ 7 http://www.epa.gov/ttn/chief/confere nce/ei15/session9/abraczinskas.pdfOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-11 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.

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. As part of this anal ysis, specific day-of-week (Monday, Tuesday, Wednesday, etc.) and diurnal profiles were de veloped for each month and State to better represent operating conditions at units within each State. The da y of week and diurnal profiles were developed from averages of CEM-based em issions and heat input activity occurring on that day-of-week or during that hour-of-day. These prof iles are intended to be applied to units were

CEM matches cannot 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 determine 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

8 http://www.epa.gov/ttn/chief/confe rence/ei14/session11/stella.pdf 

### 9 http://cfpub.epa.gov/gdm/index.cfm ?fuseaction=prep ackaged.selectOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-12 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.

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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-13 Table 2-2. CEM data file format.



### 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. 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 t he 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
x Rate (lb/mmBtu) NOx emissions rate in pounds per million British thermal units (lbs/mmBtu).

NO
x Rate Measurement Flag Indicates whether the value for NO
x rate was measured or derived due to missing data.

NO
x Mass (lbs) NO
x released for the hour in pounds.

NO
x Mass Measurement Flag Indicates whether the value for NO
x mass 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-14 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 individual 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 specific 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 are 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 operating 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-15 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 datehr datehrratio 

### 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. As 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-16 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 1.0000October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-17 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 1.0000October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-18 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 notOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-19 in operation in late January th rough the middle of April in 2002. Given the unique operation of 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-20 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/c onference/ei14/session11/stella.pdfOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-21 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 WedOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-22 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 5359.43October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-23 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 sumdatehr sumdatehrratio 

### 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 12635319.86October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-24 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 1.0000October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-25 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,

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 1.0000October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-26 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 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 developed 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 datasets, 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-27 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-28 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 usuallyOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-29 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 for the entire county or other area as an aggregate 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 data, for that county, for the surrogate assigned. For example, a county with class 1 locomotive emissions may not have data for the class 1 railroad surrogate. In this cas e we have selected to incorporate, within the assigned surrogate, a differe nt source of data (a different surrogate) for th at particular county. We incorporate secondary surrogates even if th ere is no emission source that requires it for that particular county. We denote this proces s as “gap-filling.” All surrogates resulting from the gap-filling process have ratios for all counties.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-30 For each surrogate, we assign a secondary or tertia ry surrogate where needed for gap filling. For the class 1 railroad surrogate example menti oned above, we chose total railroads as the secondary surrogate. The secondary or tertiary surroga te chosen would be the same across all counties and apply to all SCCs that use the particular primary surrogate. We pull in and substitute the secondary surrogate for counties where the primary surrogate is missing. Tertiary surrogates will then be assigned to those c ounties that 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. Ch ecks 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 exactly to 1.00 due to rounding. However, SMOKE will normalize surrogates greater than 1.00.

5. Create SMOKE-formatted spatial surrogate files. The resulting data from the previous steps is then reconfigured into SMOKE-ready format a nd used in the spatial allocation process.

2.4 Development of Modeling Input Inve ntories/Run Scripts/ASSISGNS 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.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-31 • 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 confirm 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.October 2007F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc2-32Table 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.txtOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-33 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 Appendices of this document.

2.6 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 question is felt to have minimal impact on regional haze or visibility

results domain wide.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-34 2.6.1 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 modeling 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.

2.6.2 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.

2.6.3 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.

2.6.4 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),

12 http://www.gomr.mms.gov/homepg/regulate/envi ron/airquality/gulfwid e_emission_inventory/20 00GulfwideEmissionInventory.htmlOctober 2007

F:\VISTAS Phase II\TSD\draft#2\Chap2_Emissions2.doc 

### 2-35 those that were incorrectly sited had emission values of very low (less than 80 tons per year) of any single pollutant.

2.6.5 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=511October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-1 3.0 MODEL PERFORMANCE EVALUATION

3.1 Introduction

The discussion of model performan ce evaluation in this Chapter 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 SOAm ods enhancement. This evaluation focuses on monitoring sites within the VISTAS states and VISTAS 12 km grid. 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), 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 evaluation primarily focuses on the operational model evaluation of the air quality model’s performance with respec t to individual component s 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 (i.e., visibility). The FRM networks only collects total PM 2.5 mass and these monitors are almost exclusively located in urban areas so are not as relevant for judging how well the model is predicting the components of light extinction at the more ru ral Class I areas. 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 analyz ing the ability of the model to reproduce gaseous PM precursor (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.

• 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-2 3.2.1 Evaluation Approach

The CMAQ model performance evaluation effort fo cused on the first two components, namely:

• Operational Evaluation : Tests the ability of the model to estimate PM concentrations (both fine and coarse) and the components at PM 10 and PM 2.5 including the quantities used to characterize visibility, that is, sulfate, 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/.

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:October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-3 • Sulfate (SO4)

• Nitrate (NO3)

• Ammonium (NH4)

• Organic Matter Carbon (OMC)

• Elemental Carbon (EC)

• Coarse particulate matter or Coarse Mass (i.e., PM10 - PM2.5 or CM).

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

• 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-grade databases. The focus of the regional evaluation of the CMAQ model for use in simulating visibility in the sout heastern is on the PM components that can cause visibility impairment. The prim ary monitoring networks available to evaluate this component of the CMAQ are: (a) Interagency Monitoring 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 Speci ation Trends Network (STN) of PM 2.5 species; and (f) National Acid Deposition Network (NADP). Thes e PM monitoring networks may also provide ozone and other gas phase precursor s and product species, and visi bility measurements at some sites. There are also gas-phase criteria polluta nt measures from the EPA’s Air Quality System (AQS) that are available for use. However, thes e sites are urban-oriented and not as useful for the regional evaluation of the CMAQ modeli ng system. The study team has performed a detailed evaluation of the CMAQ 2002 base case si mulation using the AQS that is available on the modeling website: http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtml . Table 3-1 and Figure 3- 1 summarizes the species collected and locatio ns of the monitoring sites for the IMPROVE, STN, CASTNet, NADP and SEARCH monitori ng networks use in the VISTAS model

evaluation.

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; HourlyOctober 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-4 

### 250 500 750 1000 1250 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.

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 based on the recommendations

outlined in section 18.4.1 of the USEPA’s Guidan ce 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) based on previous m odel performance for ozone and fine particles. EPA modeling guidance for fine pa rticulate matter at the time noted that PM models might not be able to achieve the same level of perfor mance as ozone models. VISTAS’ evaluation considered several statistical perf ormance measures and displays.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-5 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 specie s 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).

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 perf ormance statistics were calculated across the VISTAS 12 km grid versus just the VISTAS states for used in the monthly performance evaluation in the scatter plots and statistics gi ven in Figures B-2 through B-5 in Appendix B. The underprediction 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 severe usually not going lower than -20% and never going lower than -25%. 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% (Figure 3-2). The exception is the SEARCH Hourly network with error values ranging from 50% to 70%.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-6 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 concentrations become lo w and are not 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 model performance goal for both bias and error. The one exception is for fractional error across the SEARCH Hourly network that is above the performance goal, but still achieves the performance criteria (Figure 3-3, bottom).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-7 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-8 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-9 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 regional 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 underprediction 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 fractional error and NO3 (Figure 3-5, bottom), but the goal is always achieved across the CASTNet and usually achieved across the IMPROVE monitoring networks.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-10 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-11 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-12 3.4.3 Organic Mass Carbon (OMC) Performance

The monthly fractional bias and error plot for the 12 km CMAQ base case simulation in Figure 3-6 displays fairly good performance across the IMPROVE monitors with bias usually within ±15% and errors between 40% and 50%. However, at the urban STN sites the model exhibits a large OMC underprediction bias of -35% to -60%. Part of the underprediction bias may be due to measurement uncertainties (e .g., blank corrections) and part may be due to the model over diluting the urban OMC emissions through the coar se 36/12 km grid. The underprediction of urban OMC is a common problem in PM modeling and likely also points to uncertainties in the OMC and SOA precursor emission inventories.

The Bugle Plots of monthly frac tional bias and gross error (Figure 3-7) c oncurs that the OMC performance across the IMPROVE monitoring sites is fairly good achieving the model performance goals. But the OMC 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. The OMC performance across the SEARCH network is mixed usually achieving the model performance goal and always achieving the model performance criteria.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-13 Figure 3-6. Monthly OMC fractional bias (top) and fractional gross error (bottom) statistical measures for IMPROVE, STN and SEARCH monitoring sites in the VISTAS region.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-14 Figure 3-7 . Bugle Plots of monthly fractional bias (top) and gross error (bottom) for OMC in the VISTAS region and comparison to model performance goals (blue) and criteria (yellow).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-15 3.4.4 Elemental Carbon (EC) Performance

The monthly bias and error performance statistics for EC across the VIST AS region are shown in Figure 3-8. Bias and error are lower in the firs t quarter of the year, and then the IMPROVE and SEARCH networks begin to exhib it an underprediction bias that peaks in the summer and gets lower again with the onset of winter. Surpri singly, given the large OMC underprediction, the STN network exhibits very low EC bias duri ng the summer to fall which suggests that the anthropogenic EC emissions inventory may be adequately 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-16 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-17 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-18 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 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-19 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-20 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-21 3.4.6 Coarse Mass (CM) Performance

Coarse Mass (CM) measurements are defined as the difference between the PM 10 mass and

PM 2.5 mass measurements. Since IMPR OVE network only speciates the PM 2.5 fraction of PM, then any coarse SO4 or NO3 would be in the CM measurement. In the model, on the other hand, CM is due solely to primary PM emissions that have been speciated to the coarse fractions (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 (by ~75% on average in the VISTAS region) to account for the fact that these emissions are deposited loca lly and are not transported out of the grid cell where they are emitted. However, such local 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.

Figure 3-12 displays the monthly fraction bias and gross error values for CM across the IMPROVE monitors in the VISTAS region. The underprediction 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-22 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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-23 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-24 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 is exhibiting good performance for PM 2.5 concentrations. For

the first three and last three 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%) and the errors are also larger.

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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-25 Figure 3-14. Monthly PM 2.5 fractional bias (top) and fractional gross error (bottom) statistical measures in the VISTAS region.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-26 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).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-27 3.5 Evaluation at VISTAS a nd Nearby Class I Areas

Projections of light extinction for 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% most impaired days, or “worst days”, and the 20% least impair ed days, or “best days”, for use in 2018 visibility projecti ons. Thus, the model performan ce of the visibility impairment pollutants for the best and worst 20 percent days at the Class I areas is very important for the VISTAS modeling. Table C-1 in Appendix C lists of the best 20 percent and worst 20 percent days for all of Class I areas within and nearby of the VISTAS region. Appendix C provides details on the CMAQ 2002 Actual Base G2 base case model performance evaluation at each

Class I area within and near th e VISTAS region including model performance statistics, scatter plots, time series plots and other summary performance eval uation measures. Appendix D displays the spatial dist ribution of the predicted and observed PM species concentrations for each day from 2002 that is a best and worst 20 percent day for a VISTAS or near by Class I areas (i.e., days in Table C-1 in Appendix C). Below in S ection 3.5.1 we provide an example of the spatial distribution evaluation plots for one day (July 10, 2002) that ar e reproduced from Appendix D, the reader is referred to Appendix D for the spat ial 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 a nd worst 20 percent days separately for each

Class I area in and near the VISTAS region. This summary model performance uses Bugle Plots. Additional summaries of model performan ce for each Class I area and the best and worst 20 percent days are provided in Appendix C that includes summary tables of a complete set of model performance statistics, scatter plots of pr edicted and observed PM species concentrations and time series of predicted and observed concentrations across all monitoring days in 2002.

3.5.1 Example Spatial Distribution Model Performance

Table 3-2 and Figure 3-16 reproduce the spa tial model performance pots 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 percent days in Appendi x D we first display a ta ble like the one shown in Table 3-2 for July 10, 2002 that shows which Class I areas this day is a worst 20 percent or best 20 percent day for. In the case of July 10, 2002, it is a worst 20 percent day for 13 Class I areas and a best 20 percent day for 5 Class I area that tend to be in the southern (e.g., Florida) or coastal (e.g., ROMA) areas on the VISTAS region..

Table 3-2. Class I areas with worst or best 20 percent days from 2002 occurring on July 10,

2002.

Date J ulian

Da y 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 (Figure 3-16c) concentrations. In this case, the model isOctober 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-28 capturing the very high sulfate concentrations across the Appal achian Mountains and interior part of the VISTAS region, as well as the much lower SO4 cond itions in southern Georgia, Florida and the coastal regions of South Carolin a and North Carolina (Figure 3-16a). The low NO3 conditions are reproduced well by the model and even the spatial distribution of OMC is reproduced well (Figure 3-16a). Th e observed low EC levels are re plicated well by the model, but the spatial distribution of th e observed Soil and CM is not reproduced well with the model underpredicting the CM in the eas tern and overpredicting the Soil in the western portions of the VISTAS region (Figure 3-16b). Also shown in Figure 3-16b are compares of modeled and measured Sea Salt which the model grossly underes timates. Finally, Figure 3-16c shows that the model does an excellent job in reproducing the spatial distribution of the observed total PM 2.5 concentrations on this day, which is not surprising since it is dominated by SO4.

Figure 3-16a. Modeled Predicted And Observed Daily Average Sulfate (SO4) Component Concentrations (top left), Daily Average Nitrate (NO3) Component Concentrations (top right), Daily Average Ammonium (NH4) Component Concentrations, And Daily Average Organic Carbon (OC) Component Concentrations Spatial Plots For July 10, 2002.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-29 Figure 3-16b. Modeled Predicted And Observed Daily Average Elemental Carbon (EC) Component Concentrations (top left), Daily Average Coarse Particle Mass (CM) Component Concentrations (top right), Daily Average Sea Salt Component Concentrations, And Daily Average Soil Component Concentrations Spatial Plots For July 10, 2002.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-30 Figure 3-16c. Modeled Predicted And Observed Daily Average Total Fine Particulate Matter (PM 2.5 ) Concentration Spatial Plots For July 10, 2002.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-31 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 OMC is the best performance 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 achieve the performance goal but NO3, SO4 and SO4 do not ev en achieve the performance criteria, with SO4 falling barely out of the criteria range.

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 Figure 3-18. For the best 20 percent days EC, Soil and SO4 achieve the model performance goals and OC achieves the model performance criteria. However, NO3 and CM fall outside of the performance criteria. On the worst 20 percent days EC and CM achieve the performance goal and OC is right at the goal. SO4 achieves the pe rformance criteria but not the performance goal and the Soil over prediction bi as 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 for the best 20 percent days at EVER, SO4 is right at the criteria and CM and NO3 fall out of the criteria range (Figure 3-20). 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 falling out of the performan ce criteria range with SO4 just fall out of the range for bias, but within the criteria range for error.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-32 Figure 3-17a. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, Alabama and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-33 Figure 3-17b. Bugle Plots of Fractional Bias and Fractional Gross Error for Sipsey, Alabama and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-34 Figure 3-18a. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-35 Figure 3-18b. Bugle Plots of Fractional Bias and Fractional Gross Error for CHAS and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-36 Figure 3-19a. Bugle Plots of Fractional Bias and Fractional Gross Error for EVER and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-37 Figure 3-19b. Bugle Plots of Fractional Bias a nd Fractional Gross Error for EVER and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-38 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 pr oblem follows by NO3 and the remainder of the species performing fairly well. NO3 has worse performance statistics th an 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 OC right at the performance criteria.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-39 Figure 3-20a. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-40 Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SAMA and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-41 Figure 3-21a. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-42 Figure 3-21b. Bugle Plots of Fractional Bias and Fractional Gross Error for COHU and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-43 Figure 3-22a. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-44 Figure 3-22b. Bugle Plots of Fractional Bias and Fractional Gross Error for OKEF and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-45 Figure 3-23a. Bugle Plots of Fractional Bias and Fractional Gross Error for MACA and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-46 Figure 3-23b. Bugle Plots of Fractional Bias an d Fractional Gross Error for MACA and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-47 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 achieves the performa nce criteria and CM exceeding all the goa ls and 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 are similar results only SO4 just 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 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 between the perf ormance 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 fa lls out of the performance criteria range.

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 fallout outside of th e criteria range, with SO4 just at the criteria and other species achieving the performance goals.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-48

Figure 3-24a. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-49 Figure 3-24b. Bugle Plots of Fractional Bias and Fractional Gross Error for LIGO and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-50 Figure 3-25a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-51 Figure 3-25b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHRO and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-52 Figure 3-26a. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-53 Figure 3-26b. Bugle Plots of Fractional Bias and Fractional Gross Error for SWAN and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-54 Figure 3-27a. Bugle Plots of Fractional Bias and Fractional Gross Error for ROMA and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-55 Figure 3-27b. Bugle Plots of Fractional Bias an d Fractional Gross Error for ROMA and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-56 Figure 3-28a. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-57 Figure 3-28b. Bugle Plots of Fractional Bias and Fractional Gross Error for GRSM and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-58 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 OMC for the worst 20 percent days that just achieve the 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 and 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 with most species achieving the performance goal and almost all species achieving the performance criteri a, with the exception of CM for the worst 20 percent days (Figure 3-21).

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 performance criteria due to an underprediction bias (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.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-59 Figure 3-29a. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-60 Figure 3-29b. Bugle Plots of Fractional Bias and Fractional Gross Error for JARI and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-61 Figure 3-30a. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-62 Figure 3-20b. Bugle Plots of Fractional Bias and Fractional Gross Error for SHEN and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-63 Figure 3-31a. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-64 Figure 3-31b. Bugle Plots of Fractional Bias and Fractional Gross Error for DOSO and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-65 Figure 3-32a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-66 Figure 3-32b. Bugle Plots of Fractional Bias and Fractional Gross Error for BRET and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-67 Figure 3-33a. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-68 Figure 3-33b. Bugle Plots of Fractional Bias and Fractional Gross Error for CACR and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-69 Figure 3-34a. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-70 Figure 3-34b. Bugle Plots of Fractional Bias and Fractional Gross Error for UPBU and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-71 3.5.19 Mingo (MING) and Hercules, Missour i 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, but performance for the remainder of the species achieves the performance goal (Figure 3-36). Th e overprediction of Soil is also a problem for the worst 20 percent days at these two sites.

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

With the exception of CM and NO3 error for the worst days, all OM species achieve the performance criteria for the best and worst 30 pe rcent days at BRIG. The SO4 performance is also a concern as it falls between the pe rformance goal and criteria (Figure 3-37).October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-72 Figure 3-35a. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-73 Figure 3-35b. Bugle Plots of Fractional Bias and Fractional Gross Error for MING and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-74 Figure 3-36a. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-75 Figure 3-36b. Bugle Plots of Fractional Bias and Fractional Gross Error for HEGL and the Worst 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-76 Figure 3-37a. Bugle Plots of Fractional Bias and Fractional Gross Error for BRIG and the Best 20 Percent Days.October 2007

G:\VISTAS Phase II\TSD\draft#2\Chap_3_MPE2.doc 3-77 Figure 3-37b. Bugle Plots of Fractional Bias and Fractional Gross Error for BGIG and the Worst 20 Percent Days.October 2007

F:\VISTAS Phase II\TSD\draft#2\Chapter_4_VisProj2.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 Uniform Rate of Progress. As noted in Chapter 1, the Regional Haze Rule (RHR) requires states with Class I areas to develop State Implementation Plans (SIPs) that include reasonable progress goa ls for improving visibility in

each Class I area and emission reduction measures to meet those goals. Fo r the initial SIPs due in December 2007, states are required to adopt re asonable progress goals for improving visibility

from observed Baseline Conditions . The 2000-2004 five-year period is used to define Baseline Conditions and the first future pr ogress 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.

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).