Appendix D Modeling ProtocolTABLE OF CONTENTS

**I. Overview of 8-hour Ozone Modeling/Analysis Project .** 

**................................................... .....................1**  **I.A. Policy Overview of the Ozone National Ambient Air**  **Quality Standard................................... ..1**  **I.B. Designations .......................................** 

**................................................... ......................................3**  **I.C. Participating Organizations ........................** 

**................................................... ..............................3**  **I.D. Schedule ...........................................** 

**................................................... ........................................5**  **I.E. Selection of Future Year...........................** 

**................................................... ................................5**  **I.F. Organization of Air Quality Modeling Protocol......** 

**................................................... .................5** 

**II. Conceptual Description of Ozone Formation..........** 

**................................................... ............................6**  **II.A. General Geography and Topography throughout the Car**  **olinas............................................. .....6**  **II.B. North Carolina Geography and Topography............** 

**................................................... ................6**  **II.C. South Carolina Geography and Topography............** 

**................................................... ................7**  **II.D. Precursor Pollutants and Air Quality Landscape of t**  **he Carolinas ....................................... .......7**  **II.E. Meteorological Characteristics.....................** 

**................................................... ............................9**  **II.F. Regional Transport and Recirculation...............** 

**................................................... .....................15** 

**III. Ozone Episode Selection............................** 

**................................................... .......................................19**  **III.A. Overview of USEPA Guidance on Ozone................** 

**................................................... ..............19**  **III.B. Episode Selection ..................................** 

**................................................... .................................19**  **III.C. The Representativeness of the 2002 Ozone Season ....** 

**................................................... ...........21**  **III.D. Episode Classification .............................** 

**................................................... ...............................27** 

**IV. Models and Modeling Configurations.................** 

**................................................... ..............................31**  **IV.A. Recommended Models .................................** 

**................................................... ..........................31**  **IV.B. MM5 – Mesoscale Prognostic Model...................** 

**................................................... ..................31**  **IV.C. SMOKE Emissions Modeling System ....................** 

**................................................... ...............34**  **IV.D. CMAQ Modeling System...............................** 

**................................................... ........................36**  **IV.E. Model Limitations ..................................** 

**................................................... ................................37**  **IV.F. Model Input Requirements ...........................** 

**................................................... ..........................38** 

**V. Grid Specifications and Modeling Domains ...........** 

**................................................... ..........................41**  **V.A. Horizontal Modeling Domain.........................** 

**................................................... ........................41**  **V.B. Vertical Modeling Domain...........................** 

**................................................... ..........................43** 

**VI. Development of Emissions Inventories...............** 

**................................................... ..............................45**  **VI.A. Point Source Emissions .............................** 

**................................................... .............................45**  **VI.B. Stationary Area Source Emissions ...................** 

**................................................... ......................49**  **VI.C. Nonroad Mobile Source Emissions ....................** 

**................................................... ....................51**  **VI.D. On-Road Mobile Source Emissions ....................** 

**................................................... ...................52**  **VI.E. Biogenic Source Emissions ..........................** 

**................................................... ..........................54**  **VI.F. Development of Modeling Inventories................** 

**................................................... ...................55** 

**VII. Quality Assurance Plan .............................** 

**................................................... ......................................56**  **VII.A. Quality Assurance Objectives .......................** 

**................................................... .........................56**  **VII.B. Emissions Model Inputs and Outputs.................** 

**................................................... ....................56**  **VII.C. Meteorological Model Outputs.......................** 

**................................................... ........................58**  **VII.D. Air Quality Model Inputs and Outputs...............** 

**................................................... ....................59** **VIII. Model Performance Evaluation.......................** 

**................................................... ................................61** 

**VIII.A. Model Evaluation Tools (Operational Evaluation)....** 

**................................................... ....61**  **VIII.B. Model Performance Testing (Diagnostic Evaluation) ..** 

**................................................... .63**  **VIII.C. Air Quality and Ozone Column Data..................** 

**................................................... ...........65** 

**IX. Control Strategy ...................................** 

**................................................... .............................................67**  **IX.A. Control Strategy Design............................** 

**................................................... .....................67**  **IX.B. Control Strategy Evaluation........................** 

**................................................... ...................68**  **IX.C. Identification of Control Strategy Scenarios.......** 

**................................................... ...........68** 

**X. Demonstration of Attainment.....................**

**................................................... ........................................69**  **X.A. Ozone Model Attainment Test........................** 

**................................................... ...............69**  **X.B. Screening Test.....................................** 

**................................................... ...........................70**  **X.C. Corroborative Analysis.......................**

**................................................... ...........................70** 

**XI. References.....................................**

**................................................... ...................................................**  **..71**

**Appendix D-A: Summer 2002 Episode Summaries .......**

**................................................... ........................74** 

**Appendix D-B: Acronyms Used.......................**

**................................................... ......................................80** LIST OF FIGURES

**Figure D-1: 8-hour ozone design values based on 2001 through 2003 monitoring data......** 

**................. 2**  **Figure D-2: EAC and Nonattainment for the Caroli nas.............................................**  **..............2 Figure D-3: Daily Weather Maps for June 11, 2002**  **, example of an Eastern Stacked High.................10 Figure D-4: Daily Weather Maps for June 4, 2002,**  **example of a Frontal Approach............ .............11**  **Figure D-5: Daily Weather Maps for August 10, 20 02, example of a Canadian High............**  **...........12 Figure D-6: Daily Weather Maps for July 8, 2002,**  **example of a Modified Canadian High......... ........13**  **Figure D-7: Daily Weather Maps for September 4, 2002, example of a Tropical Influence............**  **....14 Figure D-8: Charlotte Wind Profiler for June 27,**  **2003........................**

**.............................. ....16**  **Figure D-9: Back Trajectory Analysis of Recircul ation...................................................**  **.......18 Figure D-10: Temperature Anomalies during 2002 O**  **3 season vs. recent 30-yr. climate avg................22**  **Figure D-11: Precipitation Anomalies during 2002 O 3**  **season vs. recent 30-yr. climate avg................22 Figure D-12: Percent Normal Precipitation of 2002**  **O 3**  **season vs. recent 30-yr. climate avg................22 Figure D-13: Soil Moisture Composite Anomaly of 20**  **02 O 3**  **season vs. recent 30-yr. climate avg........22 Figure D-14: Palmer Drought Index...................................................**  **..............................23 Figure D-15: Temperature Anomalies during May 2002**  **vs. recent 30-yr. climate avg...................... 24**  **Figure D-16: Percent Normal Precipitation during J une 2002 vs. recent 30-yr. climate avg................**  **24 Figure D-17: Temperature Anomalies during June 200**  **2 vs. recent 30-yr. climate avg...................... 25**  **Figure D-18: Percent Normal Precipitation during J une 2002 vs. recent 30-yr. climate avg................**  **25 Figure D-19: Temperature Anomalies during July 200**  **2 vs. recent 30-yr. climate avg...................... 25**  **Figure D-20: Percent Normal Precipitation during J uly 2002 vs. recent 30-yr. climate avg.................25**  **Figure D-21: Temperature Anomalies during August 2 002 vs. recent 30-yr. climate avg...................**  **26 Figure D-22: Percent Normal Precipitation during A**  **ugust 2002 vs. recent 30-yr. climate avg............. 26**  **Figure D-23: Temperature Anomalies during Septembe r 2002 vs. recent 30-yr. climate avg...............27**  **Figure D-24: Percent Normal Precipitation during S eptember 2002 vs. recent 30-yr. climate avg.........**  **27 Figure D-25: MM5 horizontal domain & CMAQ 36 km do**  **main nested in the MM5 domain..............41 Figure D-26: 36 km CMAQ continental grid & nested**  **12-km grid over the VISTAS states............... 42**  **Figure D-27: Detailed view of the 12 km grid over the VISTAS region.......................................**  **42**LIST OF TABLES

**Table D-1: Total Number of Days within 10 ppb of t he 5-year DVB for Metrolina Monitors..............20**  **Table D-2: Total Number of Days within 10 ppb of t he 3-year DVB for Metrolina Monitors..............20**  **Table D-3: Total Number of Days with Observations Greater than 84ppb for Metrolina Monitors........21**  **Table D-4: Ozone Exceedance Days During 2002.................. ..............................................29** 

**Table D-5: RPO Unified Grid Definition.................................... .......................................43**  **Table D-6: Grid Definitions for MM5 and CMAQ..................**

**.............................. ...............**  **43 Table D-7: Vertical Layer Definition for MM5 Simul**  **ations....................................................44**

**Table D-8: Statistical Metrics............................................................ ............................62**  **Table D-9: Overview of Ambient Data Monitoring Net works...................................................66**  **Table D-10: USEPA Recommendations for Defining near by Cells..............................................69** **1** MODELING PROTOCOL

I. OVERVIEW OF 8-HOUR OZONE MODELING/ANALYSIS PROJ ECT

A. Policy Overview of the Ozone National Ambient A ir Quality Standard

**On July 16, 1997, the U. S. Environmental Protectio n Agency (USEPA) revised the national**  **ambient air quality standard (NAAQS) for ground-lev el ozone. The 1-hour ozone NAAQS of 0.12 parts**  **per million (ppm) averaged over a 1-hour period (40 CFR 50.9) was modified to an 8-hour ozone**  **NAAQS of 0.08 ppm averaged over an 8-hour period (4 0 CFR 50.10). The USEPA was subsequently**  **sued over the new NAAQS, and in May 1999, the U. S. Court of Appeals for the D. C. Circuit remanded**  **the 8-hour ozone NAAQS back to the USEPA. In 2001, the USEPA proposed a response to the D.C.**  **Circuit court’s remand and reaffirmed the 8-hour oz one NAAQS, which became effective in 2003. On**  **April 30, 2004, the USEPA then made nonattainment d esignations for the 8-hour ozone NAAQS with an**  **effective date of June 15, 2004, based on air quali ty data from 2001 through 2003. The designations**  **under the 8-hour ozone NAAQS began the implementati on process. A major requirement is the**  **development of a State Implementation Plan (SIP), d ue for submittal to the USEPA three years after the**  **effective date of the nonattainment designations (J une 15, 2007).** 

**The new 8-hour ozone NAAQS is considered much more stringent than the previous NAAQS.**  **The 1-hour ozone NAAQS was remanded one year from t he effective date of the nonattainment**  **designations. An exceedance of the 8-hour ozone NA AQS occurs when a monitor measures ozone above**  **0.084 ppm on average for an eight-hour period (per the rounding convention). A violation of this**  **NAAQS occurs when the average of the annual fourth- highest daily maximum 8-hour ozone values over**  **three consecutive years is greater than or equal to 0.085 ppm. This three-year average is termed the**  **design value for the monitor. Figure D-1 indicates the counties (based on 2001 through 2003 monitorin**  **g data) that had design values that violated the 8-ho**  **ur ozone NAAQS. The USEPA designates nonattainment areas (Figure D-2) based on these mon**  **itored exceedances and adjacent areas that could be contributing to the violation. Most nonattainment**  **areas are based on Metropolitan Statistical Areas (MSA), although a few boundaries were based on smal**  **ler than a full MSA. 2** 

**Figure D-1: 8-hour ozone design values based on 20 01 through 2003 monitoring data.** 

**Figure D-2: Map indicates the USEPA’s final design ation, with the notes indicating the areas that sig**  **ned Early Action Compacts, and the area’s classificatio**  **n. 3** 

B. Designations

**Since the passage of the 1977 Clean Air Act Amendme nts (CAAA), areas of the country that**  **violated the ambient NAAQS for a particular polluta nt were formally designated as nonattainment for th**  **at pollutant. Five categories to define the degree of**  **nonattainment were created for the 1-hour ozone NAAQS (Section 181 of the 1990 CAAA). In order of**  **severity, these categories were classified as marginal, moderate, serious, severe and extreme. T**  **he highest monitor design value in a nonattainment area was used to determine its classification, with**  **the classification ultimately defining the attainm ent**  **dates.**

**With the implementation of the 8-hour ozone NAAQS, there are two subcategories of**  **designation. An area could be designated under Sec tion 172 of the 1990 CAAA (Subpart 1) as “basic”**  **and would have up to 5 years from designation to at tain the NAAQS, or the area could be designated**  **under Section 181 (Subpart 2) and classified as one of the five categories listed above with attainmen**  **t dates based on the classification. Areas with a 1-**  **hour ozone design value greater than 0.121 ppm were classified under Subpart 2, and all other areas wer**  **e classified under Subpart 1.**

**Four North Carolina and two South Carolina areas su bject to designation entered into Early**  **Action Compact programs that made provisions for st ates and local areas to achieve clean air earlier t**  **han the Clean Air Act (CAA) schedule. The four North C**  **arolina areas that have entered into an EAC are the Fayetteville area, Triad Area, Mountain Area, and U**  **nifour (Greater Hickory) Area. The two EAC areas in South Carolina are the Columbia area and the Ups**  **tate (Greenville-Spartanburg-Anderson) area. The EAC areas in both states have all attained the 8-ho**  **ur ozone NAAQS. Additionally, the Rocky Mount, Triangle and Great Smoky Mountain National Park non**  **attainment areas in North Carolina have also attained the 8-hour ozone NAAQS. Therefore, this m**  **odeling protocol will focus solely on the Charlotte**

  ** Gastonia-Rock Hill (hereinafter, Metrolina) 8-hour** **ozone nonattainment area. This area includes: Cabarrus, Gaston, Lincoln, Mecklenburg, Rowan, and**  **Union Counties in North Carolina, part of Iredell County in North Carolina, and part of York County i**  **n South Carolina.**

**The Metrolina area has been classified as moderate under Subpart 2 by the USEPA, with a**  **maximum attainment date of June 15, 2010. The Nort h Carolina Division of Air Quality (NCDAQ) and**  **the South Carolina Department of Health and Environ mental Control (SCDHEC) are committed to**  **developing a comprehensive air quality control stra tegy to attain the 8-hour ozone NAAQS in the**  **Metrolina area.**

C. Participating Organizations

**From the conceptual model of ozone formation, it is obvious that ozone is a regional problem,**  **which causes SIP modeling to be a substantial under taking that relies on the interaction of many group**  **s that are affected by overall air quality and that i**  **mpact the air shed of the affected states. It is i mperative**  **to include groups of “stakeholders” from industry, government, and the private sector represented duri**  **ng the modeling/analysis project. As each group invol**  **ved brings its own perspective, knowledge, and experience to the modeling process, the ability to**  **model and develop strategies for ozone reduction is greatly enhanced. The following organizations are**  **invited to participate in developing the ozone SIP:**

• **NCDAQ**

• **SCDHEC**

• **Carolina Environmental Programs – University of Nor th Carolina Chapel Hill** 

• **Barons Atmospheric Modeling Systems**

• **University of North Carolina at Chapel Hill**

• **North Carolina State University 4**  • **Sonoma Technology, Inc.**

• **Mecklenburg County Department of Environmental Prot ection** 

• **USEPA Region 4**

• **USEPA Office of Air Quality Planning and Standards**

• **USEPA Office of Research and Development**

• **North Carolina Department of Transportation**

• **South Carolina Department of Transportation**

• **North Carolina Department of Commerce**

• **North Carolina Department of Agriculture**

• **North Carolina State Energy Office**

• **Progress Energy**

• **Duke Energy**

• **Transcontinental Natural Gas Company**

• **Environmental Defense**

• **Sierra Club**

• **Charlotte Department of Transportation**

• **Furniture Manufacturing Representative**

• **Chemical Manufacturing Representative**

• **North Carolina Petroleum Council**

• **North Carolina Petroleum Marketers’ Association**

• **Centralina Council of Governments**

• **Catawba Council of Governments**

• **Representatives from city and county governments in the nonattainment areas** 

**Data and available expertise from participating age ncies, organizations, and universities will be**  **utilized in determining projected emissions and con trol strategies. All data and information will be**  **reviewed and evaluated by the NCDAQ and SCDHEC. Al l stakeholders are invited to contribute**  **emissions projections and control strategy informat ion.** 

**Both North Carolina and South Carolina will coordin ate with various states and other parties as**  **regional modeling is initiated to address the new o zone standards. Various other regional modeling**  **applications, such as Southeastern States Air Resou rce Managers’ seasonal ozone modeling and Visibilit**  **y Improvement and Tribal Association of the Southeast**  **(VISTAS) regional haze and fine particulate matter (PM**  **2.5 ) modeling, will also be considered. These interac**  **tions should provide a forum for discussing the latest improvements and refinements to air quality**  **modeling.**

1. Communication

**Communication between the stakeholders is an integr al part of completing the modeling/analysis**  **project. Stakeholders need the opportunity to revi ew and comment on documentation, control strategies** 

**, and modeling analysis. NCDAQ and SCDHEC will host**  **periodic technical coordination meetings on the SIP process for their respective portions of the Me**  **trolina nonattainment area. Consultation meetings on**  **control strategy development and contingency plans will be held as necessary during the process. In**  **general, as issues arise among the participants, sp ecial studies will be defined to help resolve all p**  **ertinent issues. Documentation will be developed concerning**  **these issues, including methods of resolution and any remaining uncertainties, which will be submitte**  **d as part of each state's SIP.**

**Due to the far-reaching effects of the ozone attain ment demonstration, it is important that all**  **interested parties are kept informed on the progres s of the modeling. Industries or organizations not**  **directly represented on a modeling committee can mo nitor progress through the VISTAS website.**  **NCDAQ, with involvement from SCDHEC, will also host several public meetings and focus groups with** **5**

**potentially impacted parties in order to get the mo st objective and comprehensive input in the**  **development of the final control strategies.**

2. Protocol Modification Procedures

**The model configuration, as well as the source of i nput data and evaluation process, will be**  **determined at the beginning of the process. In the event that the protocol needs to be revised to**  **incorporate new tools or methodologies, an issue pa per stating the need for modification will be**  **developed and circulated to all organizations parti cipating in the study. The issue paper will be dis**  **cussed at the next scheduled technical coordination meetin**  **g. The revised protocol would then be developed an d**  **submitted to the USEPA for their review.**

D. Schedule

**NCDAQ and SCDHEC will follow the schedule outlined by the CAA, where a SIP is due for**  **submittal by June 15, 2007. Using a 2009 modeling year, attainment will be demonstrated by at least**  **June 15, 2010 or as expeditiously as practicable.**

E. Selection of Future Year

**A key decision from both a modeling and control str ategy standpoint is the selection of the future**  **year by which attainment will be modeled. The futu re modeling year has been chosen to meet the**  **schedule previously put forth. The time line set b y the CAA requires attainment of the 8-hour ozone**  **NAAQS in a moderate area be met by June 15, 2010. Since this date is set prior to the completion of t**  **he 2010 ozone season, attainment of the NAAQS would ha**  **ve to be met by at least the end of the 2009 ozone season. NCDAQ and SCDHEC plan to use 2009 as the f**  **uture year for attainment modeling, as it would coincide with future year modeling for the PM**  **2.5 SIP and VISTAS regional haze modeling effort.** 

F. Organization of Air Quality Modeling Protocol

**The remainder of the protocol documentation is brok en down into nine additional sections as**  **follows:**

• **Section II. provides a conceptual description of oz one formation in the Carolinas.** 

• **Section III. presents details of the ozone episode selection process.** 

• **Section IV. details the models that will be used du ring this modeling project.** 

• **Section V. describes the model grid specifications.**

• **Section VI. discusses the emission inventory develo pment.** 

• **Section VII. lays out the quality assurance plan an d procedures.** 

• **Section VIII. details the tools and procedures for model performance evaluation.** 

• **Section IX. discusses how the control strategies wi ll be designed.** 

• **Section X. focuses on the model attainment test and supplemental analyses.** 

• **Section XI. lists the references.**

• **Appendices follow the final section. 6** 

II. CONCEPTUAL DESCRIPTION OF OZONE FORMATION

**To fully understand the complexity of the ozone for mation across North and South Carolina, one**  **must first examine the interactions between the bio genic and anthropogenic emissions sources within**  **North and South Carolina and throughout the surroun ding region, as well as the geographical orientatio**  **n, topography, and the meteorological patterns that de**  **velop over each of the Carolinas on a typical summertime day. All of these interacting component**  **s play a pivotal role in the day-to-day air quality response across the state. On a few select days ea**  **ch summer and often occurring together in episodes, these components work together to produce 8-hour oz**  **one concentrations in excess of the NAAQS. The frequency and duration of these high ozone episodes**  **in an individual summer season are significantly impacted by global scale meteorological patterns an**  **d climate deviations.**

A. General Geography and Topography throughout the Carolinas

**The geographic orientation and the topography of th e Carolinas help create a climate of great**  **variation and unpredictability for the centrally-lo cated Metrolina Area. Both states lie along the So**  **uth Atlantic Coast in proximity to the warm waters of t**  **he Gulf Stream. A semi-permanent subtropical ridge centered to the east, the Bermuda High, contributes**  **an abundance of moisture through much of the summer and fall seasons. Anomalies in its mean pos**  **ition greatly affect conditions needed to produce o r**  **even abate ozone production over a wide area extend ing well beyond the borders of each state.** 

**In addition, the mean positions and strengths of th e Bermuda High and Gulf Stream allow for**  **abundant atmospheric moisture influx during all sea sons of the year and allow for a variety of weather** 

**, from tropical storms to ice and snow events. Effec**  **ts of each of these features on the overall climate of the**  **Carolinas are often offset by a warm, dry west-to-n orthwest (“down-sloping”) continental flow from ove**  **r the high terrain of the Appalachians, usually follo**  **wing the passage of a frontal system or surface tro ugh**  **(or “trof”) trailing from a strong polar jet stream low sliding over the Appalachians from the Great L**  **akes into the Mid Atlantic regions. Often this flow hel**  **ps develop a weak, but broad, surface low pressure just**  **to the east of the mountains, known as the “lee-sid e trof.” Flow patterns around this feature often a**  **ffect production and/or transport of ozone. Despite the**  **fact that South Carolina contains only a tiny fract ion of**  **the mountainous terrain that is so abundant in Nort h Carolina, these “down-sloping” flow patterns affe**  **ct ozone production across portions of the state in mu**  **ch the same manner.**

B. North Carolina Geography and Topography

**North Carolina is situated in the middle of the Nor thern Hemisphere and is slightly closer to the**  **Tropics than to the Arctic region. This mid-latitu de location on the globe is characterized by mild**  **winters, extended warm-humid summers, and varied sp ring and fall seasons. This mid-latitude location**  **also allows for extended hours of daylight during t he summer season with the sun nearing its zenith at**  **noon on the summer equinox. This near vertical pos itioning of the midday sun and long duration of the**  **daylight hours are important aspects that help defi ne the overall ozone formation and air quality pote**  **ntial in North Carolina.** 

**With respect to the oceans and landmasses of the No rthern Hemisphere, North Carolina is nestled**  **along the southern portion of the Atlantic Seaboard of North America with the Atlantic Ocean to the ea**  **st and the continental United States (US) to the west.**  **From the coastal regions of North Carolina, the elevation gradually slopes upward across the Coasta**  **l Plain, the Sandhills, and the Piedmont before rapidly increasing to the highest elevation peaks o**  **f eastern North America in the Mountains. The proximity to the warm waters of the southern Atlant**  **ic and the Gulf Stream allows for abundant atmospheric moisture influx during all seasons of t**  **he year and allows for a variety of weather from tropical storms to ice and snow events. This moist**  **ure influx is often offset by a dry west to northwe st**  **down-slope flow over the high terrain of the North Carolina Mountains from the continental US. The** **7**

**geographic orientation and the topography of North Carolina ultimately creates one of the most difficu**  **lt to predict and variable climates in the western Nor**  **thern Hemisphere.**

C. South Carolina Geography and Topography

**South Carolina is situated in the middle of the Nor thern Hemisphere roughly between 33o and 34o**  **N latitude and is closer to the Tropics than to the Arctic region. This mid-latitude location on the**  **globe is characterized by mild winters, extended warm and hu**  **mid summers, and varied spring and fall seasons. This proximate location allows for extended hours o**  **f daylight during the summer season, with the solar elevation nearing zenith by noon on the summer equi**  **nox. This near vertical positioning of the midday sun and long duration of the daylight hours greatly**  **affect the development of optimal ozone formation conditions.** 

**With respect to the oceans and landmasses of the No rthern Hemisphere, South Carolina is nestled**  **along the southern portion of the Atlantic Seaboard of North America, with the Atlantic Ocean to the e**  **ast and the breadth of the continental United States to**  **the west. From the coastal regions of South Carol ina,**  **the elevation rises in a gentle upgrade traveling w estward away from the seacoast. Terrain is fairly**  **flat across the Coastal Plain (known locally as the “Low**  **country”), somewhat hilly inland through the Sand Hills and Piedmont regions, and rapidly increases t**  **o a maximum of approximately 1000 meters (m) in altitude along the Blue Ridge in the extreme northw**  **estern portion of the state bordering North Carolin a**  **and Georgia. With only a small coverage of elevate d mountainous terrain, high ozone concentrations**  **associated with valley inversions are rare in South Carolina.** 

D. Precursor Pollutants and Air Quality Landscape of the Carolinas

**Ozone forms through the reaction of oxygen with nit rogen oxides (NO** 
**x ) and volatile organic**  **compounds (VOCs). Since both NO**
**x and VOC emissions are needed for ozone formation,**  **they are often referred to as ozone precursors. To fully comprehe**  **nd the States’ ozone problem, NCDAQ and SCDHEC first started with an examination of the precursors**  **to ozone.**

**Due to the generally warm and moist climate of the Carolinas, vegetation abounds in many forms,**  **and forested lands naturally cover much of the stat e. The biogenic sector is the most abundant source**  **of VOC emissions in both North and South Carolina and**  **accounts for approximately 90% of the total VOC emissions. The overwhelming abundance of biogenic**  **VOC emissions makes the majority of North and South Carolina a NO** 
**x -limited environment for the formation of ozone. D**  **espite an industrial and urbanization trend during the past several decades**  **with increasing amounts of anthropogenic NO**
**x and**  **VOC emissions, the majority of the urbanized and po pulated regions of the Carolinas are still mostly**  **NO x**  **-limited.**

**During extremely stagnant atmospheric conditions, t he center most portions of North Carolina’s**  **largest metropolitan cities occasionally demonstrat e some VOC limited characteristics for ozone**  **formation. This is due to the limited amount of bi ogenic VOC emissions and prevalence of mobile (on**  **road and nonroad) based NO**
**x emissions in the urban cores. With major metropol**  **itan areas far fewer and much less urbanized in South Carolina, VOC limited**  **conditions rarely occur in South Carolina.**

1. Metrolina Modeling: North Carolina Conceptual Description

**North Carolina’s most populous metropolitan regions are located in the central portions or the**  **Piedmont of the state. The three largest cities (C harlotte, Greensboro, and Raleigh) form a partial**  **crescent extending from the southwest to the northe ast. This combination of metropolitan regions is o**  **ften referred to as the Piedmont Crescent. A network of**  **interstate highways interconnects these three larg est**  **cities and further extends into adjoining states in a general southwest to northeast pattern. In a si**  **milar 8** 

**southwest to northeast orientation, a major interst ate highway also passes through the western portion**  **of the Coastal Plain and connects a string of medium-s**  **ized cities (Lumberton, Fayetteville, Smithfield, a nd**  **Rocky Mount). One additional interstate highway tr avels from the southeastern coast westward through**  **the Piedmont Crescent and then continues westward a cross the Mountains. Major state highways are**  **more random in orientation, but generally converge into the Piedmont Crescent and the major cities in**  **a spoke-like fashion. The mobile-based NO** 
**x emissions follow these highway networks with the h**  **ighest emissions occurring in or near the city centers. T**  **he industrial point sources with both anthropogenic NO**  **x and VOC emissions are also generally located in clo**  **se proximity to the cities and the major road networks. Finally, North Carolina’s largest NO** 
**x emissions sources, coal-fired electric generating**  **units, are spatially scattered around the state but are mo**  **st heavily concentrated near the state’s center.**

**By combining each of the major emission source cate gories [Biogenic VOCs, Mobile (highway**  **and nonroad) based NO**
**x , and Industrial NO** 
**x and VOCs], the highest concentrations of precursor**  **pollutants for ozone formation are focused througho ut the Piedmont Crescent described in the previous**  **paragraph. Therefore, the greatest potential for o zone formation under ideal weather conditions is al**  **so in this central portion of North Carolina. Over multi**  **ple years and a wide variety of meteorological conditions, this increased potential for ozone form**  **ation in central North Carolina is realized and demonstrated by the calculated 8-hour ozone design**  **values. The highest of these design values is inde ed**  **located very near the two largest cities of this Pi edmont Crescent, Charlotte and Raleigh. Of the 200**  **2- 2004 8-hour ozone design values, all of the design**  **values in excess of the NAAQS are located within or just downwind of the Piedmont region.** 

**High ozone conditions and 8-hour ozone design value s exceeding the NAAQS have also been**  **recorded in the higher elevations or ridge tops of the North Carolina mountains. This is a generally**  **rural region removed from the significant anthropogenic s**  **ources of precursor pollutants in North Carolina an d**  **throughout the Southeast. At these remote location s, the major contributor to the higher ozone**  **conditions is regional transport of anthropogenic p recursor pollutants and ozone. This same principle**  **applies to the small area of elevated terrain in So uth Carolina along the Appalachian escarpment. The**  **regional transport phenomenon is described in detai l in Section II.** 

2. Metrolina Modeling: South Carolina Conceptual Description

**South Carolina experiences its greatest concentrati on of urbanization in only a few widely**  **scattered places across a fairly symmetrical politi cal geography. The main areas of concern for poten**  **tial ozone violations are in the region around its capit**  **al city, Columbia, and in the “Upstate,” a broad southwesterly extension of North Carolina’s aforeme**  **ntioned Piedmont Crescent, encompassing parts of Anderson, Greenville, Spartanburg, Cherokee, and Yo**  **rk Counties (together making up the Interstate 85 corridor). It is prudent to note that the term “ur**  **banization” is quite general in its interpretation in**  **describing the Upstate region, in view of the fact that combining the population of these counties in**  **their entirety (both urban and rural) still barely exceed**  **s 1 million residents. The major concentration of industrialization and urbanization in South Carolin**  **a actually occurs in the Charleston area, which, by virtue of favorable coastal geography and meteorolo**  **gy, has never been of concern for ozone formation.**

**Like its northern neighbor, South Carolina’s landsc ape is transected by a spoke-like network of**  **Interstate highways, the majority connecting mainly rural agricultural areas and small cities with the**  **state capital, Columbia (the state capital), as its hub. Four out of the five Interstate routes also**  **extend through North Carolina, the only exception being th**  **e east-west oriented Interstate 20, though a large portion of its traffic either originates, or termin**  **ates in North Carolina (via Interstate 95 and other primary**  **routes).**

**Along with the aforementioned Interstate 85 corrido r, a second major north-south interregional**  **highway, Interstate 95, parallels the coast at vari ous distances, only coming into contact with very m**  **inor 9** 

**urbanization around Florence in the northeastern pa rt of the state (the Pee Dee region). For this stu**  **dy, the Interstate 77 corridor through northern York Co**  **unty will be of special interest, as it is the only interstate highway running directly between the cit**  **y of Charlotte and its southern suburbs across the state**  **line into South Carolina.**

**Use of these highways for interstate commerce conce rns is quite heavy, with mobile source**  **emissions patterns, much like in North Carolina, of these routes being monitored very closely. As wou**  **ld be expected, higher concentrations lie close to the**  **ir mutual convergence in and around Columbia and in the Upstate area. This is evident in that these tw**  **o areas are the only locations within the boundarie s of**  **the state to record 8-hour ozone design values in e xcess of the NAAQS for 2002-2004.** 

**In contrast to North Carolina, the major cities in South Carolina, being much smaller in scale,**  **rarely contain the majority of large stationary ant hropogenic NO** 
**x and/or VOC sources, such as the**  **boilers of coal steam electric generation plants (E GUs), within the spheres of their air sheds. These**  **sources are generally scattered about the state at random, usually near coastal harborages and inland**  **waterways. They may or may not be in close proximi ty to an Interstate or any other heavily traveled**  **major route in the US Federal / SC DOT maintained h ighway system.** 

E. Meteorological Characteristics

**Almost all high ozone episodes occurring in the Sou theastern US will have some common**  **meteorological characteristics, including warm temp eratures, lower relative humidity, little or no**  **precipitation, and relatively light winds. These c onditions are nearly universally indicative of regi**  **onal high pressure patterns causing large-scale sinking**  **(subsiding) air at various levels of the atmosphere . The**  **differences in the position, strength, and movement of these high pressure areas, along with differenc**  **es in various mid-to-upper level wind patterns, allow sta**  **ff to discern six meteorological scenarios, or “regimes,” in which high ozone episodes are likely**  **to occur in the Carolinas. These meteorological regimes are discussed in the following paragraphs.** 

1. Scenario #1: Eastern US Stacked High Scenario

**Conditions that have traditionally lead to large-sc ale exceedances of the 8-hour ozone NAAQS**  **result from the development of a broad surface high -pressure area sprawled over the eastern third of t**  **he United States, with a large mid-to-upper level high**  **-pressure area centered over the Plains and/or Grea t**  **Lakes States. The mid-to-upper level ridge serves to block the movement of fronts into the Eastern**  **United States during the ozone season. This often results in abnormally hot temperatures, little**  **precipitation, and the buildup of elevated concentr ations of ozone and its precursors over much of the**  **Midwest, Northeast, and the interior of the South A tlantic States, especially North and South Carolina** 

**. As the mid-to-upper level ridge slowly slides eastw**  **ard, it situates itself overtop of the surface high**

  ** pressure at the western extent of the mean Bermuda** **High position, creating a “stacked high” over much of the Eastern US. The resulting large-scale subsi**  **dence leads to very low vertical mixing heights prohibiting dispersion of precursor pollutants. Th**  **e stagnant air mass from the "stacked high" scenari o is**  **prime for high ozone episodes in the Eastern US. A weak surface trough can develop to the east of the**  **Mountains during this scenario producing a south-so uthwesterly flow across portions of the Carolinas.**  **The presence of the trough can enhance ozone concen trations along and just east of the trough axis**  **because of surface convergence and pooling of precu rsor pollutants in this region. The lack of signif**  **icant precursor pollutant transport can create large ozon**  **e concentration gradients across the region.**

**An example of these conditions, resulting in extrem e ozone exceedances over a wide swath of**  **North and South Carolina, is demonstrated in the Ju ne 11, 2002, Daily Weather Map, Figure D-3. The**  **presence of an upper level high-pressure area direc tly atop a surface high-pressure area in the Easter**  **n US can clearly be seen.** **10**

**Figure D-3: Daily Weather Maps for June 11, 2002, example of an**  **Eastern Stacked High.**

2. Scenario #2: Common Back-Door Frontal Approach Scenario

**The most frequently occurring meteorological scenar io is characterized by the movement of cold**  **fronts toward the Carolinas and the presence of hig h-pressure to the south or southwest of the area.**  **Cold fronts often move toward the Carolinas during the s**  **ummer months but are typically too shallow to move completely through the region. They commonly becom**  **e east west oriented and stall out as far south as southern Virginia or the northern sections of North**  **Carolina. Sometimes high pressure to the north fo rces**  **the surface boundary to retrograde into the region from the north and/or northeast. These situations**  **are known as back-door fronts. Because both vertical a**  **nd horizontal momentum is generally weak along these boundaries, and because they tend to dissolve**  **, merge with the lee-side trof, or retreat back to the**  **northeast as a warm front in time, they tend to cre ate surface wind shifts that cause pollutants to**  **recirculate over the same areas repeatedly. The ba ck-door regime is characterized by a weak upper-lev**  **el flow pattern, a down sloping westerly surface flow**  **to the south of the boundary, and maximum temperatures that range from the mid to upper 90s a**  **nd dew points approaching the upper 60’s to mid 70s**

**. Widespread exceedances of the 8-hour ozone NAAQS ma**  **y occur in all areas, save the far eastern coastal regions of the Carolinas, during these conditions.** **11**

**An example of this scenario that resulted in widesp read exceedances in both Carolinas can be**  **seen in the June 4, 2002, Daily Weather Map, Figure D-4. Note the presence of a warm front that has j**  **ust passed through North Carolina to the north.** 

**Figure D-4: Daily Weather Maps for June 4, 2002, example of a F**  **rontal Approach.**

3. Scenario #3: (Post Back-Door) Canadian High Sc enario

**A third meteorological scenario, with some similari ties to Scenario #2, that sometimes results in**  **high concentrations of ozone across the Carolinas i s characterized by a surface high-pressure area**  **building in from the north following the passage of an anomalously strong “Back-door” front. Since th**  **is type of boundary has little upper-level support, a**  **mid-to-upper level ridge over the Mid-Mississippi Valley region may be able to build east into the Ca**  **rolinas. The position of the mid-to-upper level ri dge**  **produces a northerly flow aloft throughout this sce nario. As the Canadian-borne surface high-pressure**  **builds into the Carolinas, it brings with it milder and drier air by means of a north-northeasterly br**  **eeze. These conditions can lead to scattered exceedances**  **of the 8-hour ozone NAAQS across the Carolinas. Temperatures are typically in the low to mid 80s (w**  **ith dew points in the mid 50s to low 60s) during th e**  **beginning of this type of scenario. However, high temperatures may eventually reach the upper 80’s to**  **low 90s (with dew points in the upper 60s to low 70 s) as the center of the surface high-pressure slide**  **s into the Carolinas and the wind field slackens. An exam**  **ple of these conditions from August 10, 2002, is shown in Figure D-5.** **12**

**Figure D-5: Daily Weather Maps for August 10, 2002, example of**  **a Canadian High.**

4. Scenario #4: Modified Canadian High

**The fourth meteorological scenario, initially, is v ery similar to Scenario #3 above. Canadian**  **borne surface high-pressure builds into the Carolin as delivering lower dew points and milder**  **temperatures with a light north-northeasterly wind. This cool down is short-lived, however. As the h**  **igh pressure center moves south, a light southwesterly**  **flow dominates, temperatures soar, and dew points increase. A mid-to-upper level ridge slowly sprawl**  **s eastward across the country, resulting in a very weak**  **flow aloft. An example of these conditions from Ju ly 8, 2002, is shown in Figure D-6.** **13**

**Figure D-6: Daily Weather Maps for July 8, 2002, example of a M**  **odified Canadian High**

5. Scenario #5: Continental High in a Progressive Pattern Scenario

**Meteorological scenarios other than the four alread y identified can result in high ozone**  **concentrations, but these situations are usually li mited to one day in duration. Single-day ozone epi**  **sodes can occur during a progressive meteorological patte**  **rn in parts of both Carolinas. This scenario is characterized by a surface high-pressure area that**  **moves across the continent into the Southeastern Un ited**  **States. This associated air mass ushers dry, near- cloudless weather into the Carolinas for a very sho**  **rt period (usually one or two days) as it pushes out i**  **nto the Atlantic Ocean. This often results in isol ated 8-**  **hour ozone exceedances of the NAAQS, especially in the urban areas of the Carolinas, on the day(s) tha**  **t the surface high pressure area is transitioning acr**  **oss the region.**

**This scenario is very rarely seen during the core o zone season across the Carolinas; though some**  **parts of North Carolina and extreme Upstate South C arolina may experience it in anomalous years. This**  **is due to the extreme difficulty true frontal bound aries have moving southeast across the Appalachians**  **in the summer months. Without the benefit of strong w**  **esterly (Mid-Latitude) winds aloft, these fronts normally become diffuse and die out over the rugged**  **terrain of Western North Carolina, Tennessee, and Northern Georgia. The shallow continental surface**  **highs behind them, being too weak to push the front s**  **further south, are shunted eastward into the Mid-At lantic region. As it is, the "Typical Progressive**  **Pattern" more commonly develops in the spring and f all months when the polar jet stream exerts much**  **more influence in synoptic conditions over the Sout h Atlantic States. Surface ridges are active enoug**  **h 14** 

**during normal periods in these seasons to penetrate southward over even the highest of the Appalachian**  **mountain ranges.**

6. Scenario #6: Tropical Cyclone Influence

**During the latter half of the ozone season, when th e mid-to-upper level flow is very weak along**  **the South Atlantic Coast, tropical systems working their way across the Atlantic Ocean may approach th**  **e North and South Carolina coastlines. If the storm**  **maintains enough strength and distance from the coa st,**  **its outflow aloft can greatly enhance subsidence in the mid-to-upper levels around its periphery, resu**  **lting in warm, dry air across a wide area far inland from**  **the coast. Temperatures may rise far above season al**  **norms, under nearly cloud-free skies as a result of this extreme subsidence regime.** 

**Though this occurs infrequently and rarely causes e levated ozone in inland areas on its own, the**  **influence from a tropical cyclone can modify all fi ve of the previously stated scenarios by enhancing**  **ozone production and stagnation. The resulting com bination can cause even more significant and**  **widespread ozone rises than would otherwise occur i n any of the scenarios alone. An example of these**  **conditions from September 4, 2002, is shown in Figu re D-7. It is worthy to note such a modification t**  **o the five previously identified scenarios.** 

**Figure D-7: Daily Weather Maps for September 4, 2002, example o**  **f a Tropical Influence. 15** 

F. Regional Transport and Recirculation

**Even in the 21 st** 

**century, South Carolina is still mainly a rural ag rarian state with only a few small**  **cities that are considered any part of a US Census Bureau-designated Metropolitan Statistical Area**  **(MSA). North Carolina, though possessing more urba nization than South Carolina (especially in the**  **Piedmont Crescent) is also largely rural in nature. Just outside the borders of the Carolinas, howeve**  **r, is a collection of large metropolitan regions. Many of**  **these concentrated urbanized areas are among the largest in the Continental United States. These me**  **tropolitan regions have similar emissions profiles and**  **frequencies of high ozone concentrations leading to exceedances of the 8-hour ozone NAAQS. It is**  **reasonable to conclude that precursor pollutants, a s well as formed ozone, can be transported from any**  **of these larger metropolitan regions into the Carolina**  **s with an appropriate wind flow orientation from on e or**  **more of these regions. In addition to the local pr ecursor pollutant influences already discussed in S**  **ection II., regional transport of precursor pollutants and**  **ozone is an important factor in producing ozone exceedances across the region.** 

**A closer study of observed meteorological data in t he lower portions of the atmosphere and**  **observed air quality concentration data from across the southeastern United States further supports th**  **e importance of regional transport of ozone concentra**  **tions into North and South Carolina. The NCDAQ and SCDHEC have performed a number of studies of bo**  **undary-layer characteristics over the region using NOAA-developed profiling and trajectory analysis to**  **ols, as well as a ground-based radar profiler syste m**  **located in the Charlotte area. These tools allow m eteorologists and planners on both staffs to use gr**  **idded climate data to plot detailed analyses of the winds**  **from near the surface upward to an identifiable subsidence or trapping inversion that is a common c**  **haracteristic observed during the majority of the h igh**  **ozone episodes in the Carolinas.**

**The atmospheric depth from the surface to this subs idence inversion is also the usual extent of the**  **afternoon convective mixed layer. Wind speeds thro ughout this mixed layer are fairly uniform during t**  **he midday and afternoon hours when ozone concentration**  **s are at a maximum throughout the Southeast. Many of these analyses identify several large metro**  **politan areas in close proximity to North and South Carolina as potential source regions for precursor**  **pollutants and ozone transport during the typical daytime ozone production period.** 

**Overnight, the convective mixing is greatly diminis hed because of the lack of solar radiation and**  **surface heating. The loss of the convective mixing also results in the wind profile becoming stratifi**  **ed from the surface to the subsidence inversion with t**  **he lowest winds speeds at the surface. With little or no**  **surface wind, some other atmospheric mechanism is n eeded to support regional transport during the**  **nighttime hours. It is not uncommon for a low-leve l jet to form above the surface and beneath the**  **subsidence inversion during the height of a high oz one episode in the Carolinas. The low-level jet is**  **generally oriented southwesterly in the lee of the Blue Ridge escarpment, reaching a maximum intensity**  **during the middle of the overnight period. Data fr om National Weather Service radiosonde soundings**  **and other wind profilers throughout the Southeast a nd Mid-Atlantic suggest that the spatial extent of**  **the low-level jet phenomenon can extend across multiple**  **states. Figure D-8 is a classic example of a low level jet as observed by the Charlotte wind profile**  **r during a high ozone episode on June 27, 2003. Th e**  **dark red wind barbs indicate the core of the low-le vel jet at 500 meters above the ground around 2:00**  **a.m. Eastern Daylight Time (EDT) or 6:00 a.m. Universal**  **Time (UTC). 16** 

**Figure D-8: Charlotte Wind Profiler for June 27, 2 003. The dark red barbs indicate the core of the l**  **ow level jet at 500m.** **17**

**The low-level jet phenomenon is a well-documented a nd researched contributor to regional**  **transport of precursor pollutants and ozone (Clark et al., 2003). The NCDAQ operates several ozone**  **monitoring sites at the ridge tops or peaks of seve ral mountains and also at multiple levels of a**  **television transmission tower in the center of the state. Even though elevated terrain is lacking wit**  **hin the borders of South Carolina, the SCDHEC also oper**  **ates an ozone monitoring site in the mountainous region straddling the border region wit**  **h Georgia and North Carolina in the far northwestern corner of the state. These higher alt**  **itude or elevated monitoring sites extend sufficien tly**  **above the nocturnal surface inversion, or stable la yer, into the residual layer of the atmosphere wher**  **e these low-level jets occur. Consistently during hi**  **gh ozone episodes across the region, ozone concentrations remain elevated at these sites (thro**  **ughout the duration of the overnight low-level jet) long after concentrations at lower elevation monito**  **rs nearby have abated. As the daytime convective mixing is initiated the next morning, the regionall**  **y-transported pollution aloft is mixed downward to the surface, further adding to the local influences**  **driving the total ozone response.**

**The regional transport discussion would not be comp lete without recognition of recirculated**  **pollution around the entire Southeast and Mid-Atlan tic region. As many of the meteorological scenario**  **s explained earlier progress, winds in the lower and**  **middle atmosphere can shift around in a variety of directions. In the most frequent scenarios, winds**  **transition from one direction to another in a clock wise**  **fashion during the extent of the complete synoptic cycle or scenario. This clockwise shifting of the**  **winds is a key characteristic of eastward-moving high pre**  **ssure systems in the Northern Hemisphere.**

**In a large recirculation pattern, plume(s) of precu rsor pollutants and formed ozone may leave the**  **Carolinas one day, travel across multiple states, t hen return to the very air shed from where they sta**  **rted within a matter of days. Throughout the journey of**  **air parcels in this type pattern, precursor pollut ants**  **and ozone are constantly being exchanged and added to the air parcels from each source sector and**  **metropolitan region along the way, irrespective of any geo-political boundaries. Recirculation can al**  **so occur on much smaller scales and within state bound**  **aries. In such smaller scale recirculation pattern s,**  **precursor pollutants and ozone from two neighboring metropolitan regions can exchange back and forth**  **over a series of days, especially as a strong area of high-pressure moves from west to east across the**  **region.**

**While still a form of regional transport, there are not clearly defined originating source regions or**  **destinations during a recirculation event. Figure D-9 provides a back trajectory analysis of a region**  **al scale recirculation event centered over western Nor**  **th Carolina. 18** 

**Figure D-9: Back Trajectory Analysis of Recirculat ion.** **19**

III. OZONE EPISODE SELECTION

**A crucial step to SIP modeling is the selection of episodes to model. Several considerations need**  **to be weighed before settling on not only which day s to model, but how many days for each episode need**  **to be modeled. This section details the guidance a nd process by which episodes were selected for the**  **8- hour Ozone SIP modeling package.** 

A. Overview of USEPA Guidance on Ozone

**The USEPA’s final draft guidance on 8-hour ozone mo deling (EPA, 2005) sets out specific**  **criteria for the selection of episodes to model for attainment of the 8-hour ozone NAAQS. First, epis**  **odes should include days encompassing a variety of meteo**  **rological conditions, including varying wind directions, where maximum 8-hour ozone values excee**  **d 84 parts per billion (ppb). Second, episodes should be selected that contain days where values p**  **eak sufficiently close (within ± 10 ppb) to the bas eline**  **design value (DVB). Third, episodes should be chos en around days for which there are extensive air**  **quality and meteorology measurements, including mea surements aloft, measurements of indicator species**  **and/or precursor measurements. Finally, a modeling period of a sufficient number of days should be**  **selected to ensure robust attainment tests at viola ting monitoring sites.** 

**In addition to these primary criteria, the USEPA al so suggests a set of secondary criteria that may**  **be used in the selection of episodes. These criter ia allow states to give preference to previously-mo**  **deled episodes. This is a very valuable consideration, a**  **s the USEPA points out, since it can save modeling resources and effort. The USEPA also recommends se**  **lecting episodes that occur during the period corresponding to the current design value, which fo**  **r a five-year design value would be the period from 2000 to 2004 (three-year design values would encomp**  **ass the period from 2001 to 2003). Additional considerations include selecting episodes maximizin**  **g the number of days and sites observing a violatio n,**  **include weekends and holidays, and include those me eting primary and secondary criteria in other**  **nonattainment areas, when participating in regional modeling. Using the aforementioned USEPA criteria** 

**, NCDAQ and SCDHEC systematically examined the data a**  **vailable to determine the best episodes for modeling.** 

B. Episode Selection

**With the advances in computing speed and storage-ca pacity technologies, and with the aid of**  **regional modeling efforts, NCDAQ and SCDHEC intend to move toward the modeling of the peak ozone**  **season for the 8-hour ozone SIP. By modeling the p eak season, several criteria are covered, including**  **the modeling of weekends and a sufficient number of day**  **s to ensure a robust modeled attainment test. Modeling the peak ozone season will also accomplish**  **the goal of evaluating the myriad of meteorologica l**  **conditions that influence ozone concentrations.**

**To determine an appropriate time period to model, a n examination of the 8-hour ozone maxima**  **monitored across the Metrolina area was initiated f or each ozone season from 1997 through 2004. This**  **was done in order to determine which season yields the most days at or above the 85 ppb ozone standard** 

**. The same seasons were then re-examined to first tab**  **ulate design values at each monitoring site using t he**  **recently suggested three-year design values and fiv e-year weighted design values at each monitoring si**  **te; and second, to determine the number of days each mo**  **nitoring site observed a value within 10 ppb of its design value. The five-year weighted design values**  **were derived by taking a running average of the three-year design values within the five-year windo**  **w. For example, the 2000-2004 weighted average is calculated by averaging the 1999-2001, 2000-2002, a**  **nd 2001-2003 design values at each monitoring site, whereby the fourth high from 2002 is weighted three**  **times, the fourth high from 2001 and 2003 is weighted twice, and the fourth high from 2000 and 2**  **004 is weighted once. Thus, Table D-1 displays a count of the number of days in which the center yea**  **r (e.g., 2002) of the design period (e.g., 2000-200 4)** **20**

**contains peak ozone concentrations within 10 ppb of the five-year weighted design value for all monito**  **r sites in the Metrolina area. Table D-2 presents re**  **sults for the number of days within 10 ppb of the t hree**  **year design values, again, for all monitoring sites in the Metrolina area.** 

**Table D-1: Total Number of Days within 10ppb of th e 5-year DVB for Metrolina Monitors**  **Site Name County 1995- 1999**  **1996-**

**2000 1997-** 

**2001 1998-** 

**2002 1999-** 

**2003 2000-** 

**2004 Monroe Union**  **10 34 Enochville Rowan 14 19 22 8 8 22**  **Crouse Lincoln 15 26 10 15 13 25 Rockwell Rowan 9 18 23 5 13 15**  **Arrowood Mecklenburg 5 15 18 7 9 27 County Line Mecklenburg 28 22 15 6 6 14**  **Garinger Mecklenburg 12 17 12 7 6 18 York York, SC 15 17 22 16 14 32**  **Total 98 134 122 64 79 187 Grey shading indicates incomplete data for DVB calc**  **ulation.**

**Table D-2: Total Number of Days within 10ppb of th e 3-year DVB for Metrolina Monitors**  **Site Name County 1996- 1998**  **1997-**

**1999 1998-** 

**2000 1999-** 

**2001 2000-** 

**2002 2001-** 

**2003 2002-** 

**2004 Monroe Union**  **21 10 10 33 8 Enochville Rowan 14 19 25 9 6 33 8**  **Crouse Lincoln 15 30 10 19 10 25 9 Rockwell Rowan 9 18 22 5 11 13 4**  **Arrowood Mecklenburg 5 14 17 10 9 29 28 County Line Mecklenburg 10 22 13 6 5 14 8**  **Garinger Mecklenburg 11 15 14 7 6 18 7 York York, SC 17 16 23 19 13 32 9**  **Total 81 134 145 85 70 197 81 Grey shading indicates incomplete data for DVB calc**  **ulation.**

**The results of these evaluations indicated that, ov erall, the 2002 ozone season contained,**  **generally, the most exceedance days for the individ ual monitoring sites, as well as the most days with**  **in 10 ppb of the calculated design values. When 2002**  **was not the highest year, it was generally either t he**  **second or third highest, for either design value co nvention. The only other years that consistently r**  **anked high for this criterion were 1998 and 1999. Furthe**  **r examination of the past eight ozone seasons revea ls**  **that 1998, 1999, and 2000 have the greatest number of days exceeding an ozone concentration of 84 ppb**  **(Table D-3). 21** 

**Table D-3: Total Number of Days with Observations Greater than 84ppb for Metrolina Monitors**  **Site Name County 1997 1998 1999 2000 2001 2002 2003 2004**  **Monroe Union 14 4 3 17 1 0**  **Enochville Rowan 11 23 30 12 15 28 7 1 Crouse Lincoln 4 19 6 15 9 21 5 0**  **Rockwell Rowan 11 31 34 7 17 22 6 1 Arrowood Mecklenburg 9 21 19 5 5 12 1 1**  **County Line Mecklenburg 22 43 33 16 10 22 4 3 Garinger Mecklenburg 18 27 18 12 9 21 4 4**  **York, SC York 2 6 7 1 0 15 0 0 Total 77 170 161 72 68 158 28 10** 

**Grey shading indicates incomplete data.**

**To narrow the period for episode selection further, other criteria were examined. One of**  **USEPA’s secondary criteria suggests consideration o f episodes drawn only from 2000 – 2004, as this**  **period corresponds to the most current design value . By selecting 2002, our base case year would be t**  **he same as our baseline (typical) year, centered in th**  **e middle of the five-year 2000 – 2004 period. This would mean the 2002 emissions inventories would not**  **have to be adjusted to correspond to a different base case year during modeling efforts. Difference**  **s between the base case and baseline (typical) inventories are explained in Section VI. suggests t**  **hat states give preferential treatment of previousl y**  **modeled episodes. Through NCDAQ’s and SCDHEC’s wor k with the Visibility Improvement, State and**  **Tribal Association of the Southeast Regional Planni ng Organization (VISTAS RPO), the 2002 calendar**  **year has already been modeled as the base case year for regional haze reduction goals.** 

**Though VISTAS modeling is geared mainly towards Reg ional Haze and PM**  **2.5 demonstrations on**  **a regional scale, the modeling can still be applied to ozone. The VISTAS modeling employs “one**  **atmosphere” modeling, or modeling of all atmospheri c constituents including particulate matter (PM) an**  **d ozone. This modeling is done in parallel to captur**  **e interactions between various compounds. Since ozone, along with PM**  **2.5 and regional haze, is being modeled as part of VIST**  **AS modeling efforts, its data can easily be extracted from the modeling results.** 

C. The Representativeness of the 2002 Ozone Season

**2002 was an active year with numerous poor air qual ity episodes. Across the Carolinas, high**  **ozone values generally coincide with high PM values since both need similar atmospheric condition to**  **accumulate. The 2002 season was examined to verify that it was representative of the nature of ozone**  **formation in North Carolina (as stated herein as in clusive of York County South Carolina) to further**  **support its use in modeling.**

1. The Overall 2002 Ozone Season

**The months of May through September 2002 were typic al of the meteorology one would expect**  **for an active ozone season, namely warmer and drier than average. Temperatures were 1 – 2 degrees**  **Fahrenheit (oF) warmer than average across the stat e and throughout the Mid-Atlantic States, which can**  **be seen in Figure D-10. Precipitation values were four to six inches below normal (Figure D-11) or 75** 

**- 90% of normal (Figure D-12) for most of the Carolin**  **as. The dry conditions were also present for much of**  **the coastal Mid-Atlantic States. The warmer and dr ier conditions led to lower soil moisture (Figure D**  **-13) throughout much of the East coast, which reduced th**  **e evaporation of moisture into the air, and thus lowered dewpoint temperatures. With less available**  **moisture in the atmosphere, cloud cover was decreased, leading to more sunlight, increased phot**  **ochemistry, and higher levels of ozone produced across the Carolinas.** **22**

**Figure D-10: Temperature Anomalies (oF) during Fi gure D-11: Precipitation Anomalies (inches)**  **the 2002 ozone season, compared to the most during the 2002 ozone season, compared to**  **recent 30-year climate average. the most recent 30- year climate average.** 

**Figure D-12: Percent of Normal Precipitation Figu re D-13: Soil Moisture Composite**  **during the 2002 ozone season, compared to the Anom aly (tenths of inches) for the 2002**  **most recent 30-year climate average. ozone season, as compared to the most**  **recent 30-year climate average.**

**With below normal precipitation and soil moisture, varying levels of drought were observed**  **throughout much of the Eastern US during the summer of 2002. Figure D-14 shows the Palmer Drought**  **Index for the May - September 2002 timeframe. The Palmer Drought Index uses 0 to denote normal, and**  **drought conditions are shown in terms of negative n umbers; for example, minus two (-2) is moderate**  **drought, minus three (-3) is severe drought, and mi nus four (-4) is extreme drought, and so on. Value**  **s in central North Carolina, including the Metrolina are**  **a were below -4, indicating extreme drought conditions.** **23**

**Figure D-14: Palmer Drought Index. Negative value s indicate long-term drier than normal conditions.** 

**2002 Meteorological data collected at the Charlotte -Douglas Airport (CLT), the official National**  **Weather Service observation station serving the Met rolina area, mirror the results of the regional ana**  **lysis. At CLT, the mean temperature was 74.8 oF compared t**  **o the average mean of 74.6 oF, or only 0.2 oF above normal. However, the observed mean maximum tempera**  **ture was 85.6 oF, versus a normal 84.5 oF. The observed mean low temperature was actually cooler t**  **han average; 64.0oF versus 64.7 oF.**

**Reduced minimum temperatures, as well as the greate r departures in daytime temperatures, were**  **a product of the dry conditions prevalent over the southeast. Less cloud cover and lower dew points b**  **oth lead to lower nighttime temperatures and increased**  **daytime temperatures. Evidence of low moisture in the air can be seen in the average relative humidit**  **y for June, July, and August, where the mean relati ve**  **humidity at CLT was approximately 5.5% below normal**

**. The number of days in excess of 90 oF recorded at C**  **LT was also above climatological norms, with a total of 50 days above 90 oF in 2002, compar**  **ed to a normal of 37 days. This is important to no te,**  **as repeated days where temperatures peak at or abov e 90 oF generally correlate well with high ozone**  **events in the Metrolina area.**

**The observed rainfall during the 2002 ozone season in the Carolinas was reflective of the drought**  **conditions seen in the overall regional analysis. Rainfall at CLT was 14.48 inches during the core se**  **ason, much below the 30-year average of 18.36 inches. Be**  **low-normal precipitation is another indication of decreased cloud cover, which in turn leads to more**  **sunlight (increased UV), enhanced photochemical transformation of precursory pollutants, and thus,**  **higher amounts of ozone.**

2. The 2002 Ozone Season Month-By-Month

**Mean temperatures for the month of May 2002 were cl ose to normal across most of the**  **Southeastern US (Figure D-15). May also proved to be relatively mild for the Metrolina area, where th**  **e mean maximum temperature for CLT was 0.7 degrees be**  **low normal. The month was considerably drier than normal for most states along the southeast coa**  **st, North and South Carolina included, with reporte d**  **precipitation of 75% to 90% of their normal values (Figure D-16). Even with local rainfall amounts**  **highly variable across the Carolinas, however, CLT actually received near normal amounts.** **24**

**Figure D-15: This map shows the Temperature Figur e D-16: This map depicts the Percent**  **Anomalies (oF) during May 2002, as compared of Nor mal Precipitation during May 2002,**  **to the most recent 30-year climate average. as com pared to the most recent 30-year**  **climate average.**

**The mean temperatures for June were approximately 2 oF to 4oF above normal from the Piedmont**  **of North Carolina back into the Midwestern US. How ever, the remainder of the Southeastern US**  **experienced temperatures rather near climatological norms (Figure D-17). During June, the Raleigh**  **Durham area experienced its highest maximum tempera tures of the summer, over 6 oF above normal.**  **However, maximum temperature departures at CLT were not as extreme, at around 1.8 oF above normal.**  **Precipitation was 50% to 75% of normal for most of the southeast and less than half that of normal in**  **south central North Carolina and parts of South Car olina (Figure D-18). Moderate to severe drought**  **persisted, with the more extreme conditions occurri ng around the Metrolina area.** **25**

**Figure D-17: This map shows the Temperature Figur e D-18: This map depicts the Percent of**  **Anomalies (oF) during June 2002, as compared Norma l Precipitation during June 2002, as**  **to the most recent 30-year climate average. compare d to the most recent 30-year climate** 
**average.**

**Mean temperatures in July 2002 were 1oF to 2oF abov e normal throughout the mid-Atlantic**  **region, with greater above-average temperature depa rtures located across the northern half of the US**  **(Figure D-19). CLT had maximum temperatures reachi ng approximately 3o F above average. Rainfall**  **continued to be below normal from eastern Georgia t hrough North Carolina (Figure D-20). Charlotte**  **rainfall was approximately one-third of normal for the month.** 

**Figure D-19: This map illustrates the Temperature Figure D-20: This map shows the Percent**  **Anomalies (oF) during July 2002, as compared to of Normal Precipitation during July 2002,**  **the most recent 30-year climate average. as compare d to the most recent 30-year**  **climate average. 26** 

**Temperatures in August were slightly above normal i n most of the eastern third of the country,**  **with some locations experiencing temperatures signi ficantly above normal (Figure D-21). The mean**  **maximum temperature at CLT followed this trend, ave raging out at 1.4 oF above normal. Precipitation**  **along the South Atlantic Coastal states, including the Carolinas was above normal due to the influence**  **of tropical systems moving across the region (Figure D**  **-22). However, long-term drought continued to persist for the remainder of the East Coast of the**  **US throughout the month.**

**Figure D-21: This map depicts the Temperature Fig ure D-22: This graphic illustrates the**  **Anomalies (oF) during August 2002, as Percent of No rmal Precipitation during**  **compared to the most recent 30-year climate August 2002, as compared to the most**  **average. recent 30-year climate average.**

**Mean temperatures in the central Carolinas were gen erally 1oF to 2oF above average throughout**  **September 2002, with most of the US east of the Roc kies significantly above climatological averages**  **(Figure D-23). Mean maximum temperatures at CLT, h owever, were actually slightly below average. As**  **in August, the influence of nearby tropical systems caused September to be wetter than normal across t**  **he area between the Mississippi River and the Appalach**  **ian Mountains (Figure D-24). The rainfall total at CLT was close to the climatological norm for the mo**  **nth even as the Piedmont region of North and South Carolina continued to hang on to drought conditions**  **due to long-term precipitation deficits. 27** 

**Figure D-23: This image shows the Temperature Fig ure D-24: This map depicts the Percent**  **Anomalies (oF) during September 2002, as of Normal Precipitation during September**  **compared to the most recent 30-year climate 2002, as compared to the most recent**  **average. 30-year climate average.**

**In summary, the 2002 ozone season was slightly warm er than normal across the Carolinas.**  **Precipitation ended up below normal, with greatest departures from May through July. The influence of**  **tropical systems brought above normal rainfall for August and September. Despite the rainfall, overal**  **l drought conditions persisted for the entire ozone s**  **eason, with the abnormally dry conditions impacting the ozone response in a number of ways. In general**  **, below normal rainfall correlates to less cloud co ver**  **and a decrease in soil moisture. Low soil moisture led to lower dew point temperatures from reduced**  **evapotranspiration, further contributing to the red uction of cloud cover over the course of the season** 

**. With more incoming solar radiation that would have**  **otherwise been absorbed by cloud cover, higher daytime maximum temperatures occurred over a broad**  **area. The combination of these factors promoted more ozone conversion in the ambient air as compare**  **d to a more climatologically-normal summer with wetter conditions.** 

D. Episode Classification

**As shown climatologically, 2002 potentially provide s an excellent year as a basis for attainment**  **modeling. Following this line of logic, 2002 shoul d provide equally adept as a basis year for the**  **evaluation of various control strategies for mainta ining the NAAQS for ozone. However, further study**  **was necessary to identify enough of a variety of ep isode periods occurring during the 2002 season to**  **justify this supposition. To perform this evaluati on, methodology was developed to correlate periods**  **of elevated ozone that occurred in the Metrolina area**  **during the 2002 ozone season with each of the six meteorological scenarios presented in Section II.**  **The following sub-sections outline the episode classification methodology and procedures, as well**  **as the resultant determinations, in further detail.**

1. Procedures for Episode Classification

**Ambient monitoring data from the Metrolina area was used to determine the days during the 2002**  **season that an exceedance of the 8-hour NAAQS for g round-level ozone occurred. These days were then**  **grouped into individual episodes and evaluated usin g the selection criteria discussed in Section I. A** **28**

**further analysis of each ozone episode, which used several sources of air quality and meteorological d**  **ata to determine the diversity and severity of the iden**  **tified episodes throughout the 2002 ozone season, w as**  **performed.**

**Ambient ozone data from 2002 for the entire eastern US, gathered from graphics provided by**  **USEPA’s AIRNOW website archive, was then examined, wherein focus was placed on 8-hour peak**  **ozone concentrations. The ability to visually insp ect the data provided easy assessment of the spatia**  **l and temporal distribution of ozone throughout the Carol**  **inas, as well as other areas of the eastern US. Th e**  **visual inspection of the ambient ozone data made it possible to easily determine whether the event was**  **regional, sub-regional, or local in nature.**

**In addition to the ambient data plots, several surf ace and upper air meteorological datasets were**  **examined to assess the atmospheric conditions contr ibuting to the build-up of ozone in each episode.**  **Local Climatological Data sheets obtained from NOAA ’s National Climatic Data Center (NCDC) were**  **used to collect diurnal (daily) temperature, precip itation, and wind speed and direction data. Daily**  **Weather Maps from NCDC were then used to determine the approximate location of surface fronts,**  **troughs, and ridges, as well as daily peak temperat ures and total daily precipitation.** 

**Prognostic model analysis charts derived from the N OAA Weather Research and Forecast**  **Model’s North American Mesoscale module (WRF/NAM, f ormerly called Eta) were used to assess**  **conditions such as wind fields, temperatures, moist ure, and the location of ridges and troughs. These**  **charts were used as an enhancement in the classific ation of high ozone episodes from the 2002 season i**  **n relating them to previously defined meteorological**  **scenarios. The combination of the spatial ozone pl ots**  **with the meteorological charts went further in illu minating instances where the potential for recircul**  **ation, a contributor to high ozone episodes, may exist.** 

2. Episode Classification of the 2002 Ozone Seaso n

**All days with ozone exceedances at any monitoring s ite in the Metrolina Area were considered in**  **the episode classification process. There were a t otal of 38 days in 2002 with an exceedance of the 8**  **-hour NAAQS at one or more monitors in the Metrolina area**  **s. These days were divided into episodes based on the distribution of measured ozone and the meteorol**  **ogical conditions that occurred throughout the peri od**  **of exceedance. In all, there were 13 different hig h ozone episodes during the 2002 ozone season.** 

**All episodes have some common characteristics. War m temperatures, little or no precipitation,**  **and relatively light winds are needed to produce oz one. Typically, these conditions are characteristi**  **c of a surface high pressure area. The differences in the**  **position, strength, and movement of the surface hi gh**  **pressure areas, along with differences in the mid-t o-upper level wind patterns, allow us to discern se**  **veral meteorological scenarios in which ozone episodes ar**  **e likely. These meteorological scenarios were previously discussed in Section II.** 

**An initial analysis of ambient data and Daily Weath er Maps was used to place each of the 13**  **ozone episodes into one of the five meteorological scenarios. A list with the number of monitors**  **exceeding the 8-hour ozone NAAQS in each of the thr ee major metropolitan areas was compiled and**  **reviewed (See Table D-4). This information was use d to exclude those episodes from each category that**  **did not have sufficient spatial or temporal distrib ution to justify further study. Nine episodes coul**  **d be considered “widespread,” with three eastern stacked**  **high episodes, three frontal approach episodes, th ree**  **Canadian high episodes, and one modified Canadian h igh episode, as well as four instances of tropical**  **influence. (Note: Some episodes contained two sce narios, such as a stacked high followed by a fronta**  **l approach.) A more detailed analysis of each of the**  **se nine episodes was made using all sources of air quality and meteorological data to select the episo**  **des that would best meet the modeling objectives. 29** 

**Table D-4: Ozone Exceedance Days During 2002**
Episode Meteorological Scenario Number of Monitors Exceeding 8 Hour NAAQS in the Metrolina Area **05/24 3**  **05/25 3**  **6 06/03**  **3 06/04**  **4 06/05**  **2 1**  **06/10 7**  **06/11 7**  **06/12 6**  **06/13 1** 

**7 07/01**  **2 07/02**  **7 07/03**  **5 07/04**  **1**

**3 07/05**  **3 07/06**  **2 2**  **07/08 8**  **07/09 4**  **5 07/16**  **5 07/17**  **8 07/18**  **1 5**  **07/29 1 (B) 2 07/31 2**  **1 08/01**  **3 08/02**  **5** → **2 6**  **08/05 1**  **08/06 2 (T)**  **1 08/09**  **6 08/10**  **7 08/11**  **7 08/12**  **6 08/13**  **3**

**4 08/21**  **2 08/22**  **3 2**  **08/23 1 6**  **09/04 2 (T) 2 09/05**  **3 09/06**  **3 (T) 1**  **09/11 2 (T) 1**

**Note: The Metrolina has a network of eight monitor s.** **30**

**Meteorological Scenarios: 1- Eastern Stacked High**  **2- Frontal Approach 3- Canadian High**  **4- Modified Canadian High 5- Progressive Continental High**  **B- High pressure in Bermuda high configuration T- Tropical system present ‘near’ North Carolina** 

**The aforementioned selected episodes provide a wide range of conditions that will provide the**  **basis for a thorough analysis of the variety of fac tors that lead to ozone exceedances in North and So**  **uth Carolina. Control strategies can be tested under c**  **onditions that range from short duration ozone peak s**  **above the 1-hour ozone NAAQS to extended periods of widespread exceedances of the 8-hour ozone**  **NAAQS. These episodes also range from exceedances that were multi-regional to exceedances confined**  **primarily to the Carolinas. For a brief summary of each episode, please see Appendix A.** 

**In summary, the wide variety of episodes in 2002 ma kes it an appropriate season to model ozone**  **for the Metrolina attainment demonstration. Additi onally, the episode characterization process identi**  **fies the period of May 15 through August 31 as containin**  **g the most significant episodes of the 2002 ozone season. NCDAQ and SCDHEC plan to conserve computat**  **ional and staff resources by modeling this slightly narrower period rather than the entire ozo**  **ne monitoring season of April to October. This wil l in**  **no way compromise the desire to capture every high ozone scenario for complete evaluation of control**  **strategies. 31** 

IV. MODELS AND MODELING CONFIGURATIONS

**NCDAQ and SCDHEC intend to utilize the same configu ration of regional meteorological,**  **emissions processing, and photochemical air quality models used by the VISTAS PM/regional haze**  **modeling study. The underlying science behind each component of the overall modeling system are**  **identified and discussed briefly in this section. Although the configuration of each of the modeling**  **components has been selected as the culmination of intensive study by VISTAS, there remains the**  **possibility that certain algorithms and parameter s ettings may still be updated prior to the running o**  **f the final annual 2002 base case simulation and subseque**  **nt model performance testing.**

**The NCDAQ and SCDHEC modeling team will remain in c lose contact with the VISTAS, as**  **well as other RPO regional modeling initiatives thr oughout the Metrolina attainment modeling study, to**  **determine appropriate refinements to the model code s, input databases, and post-modeling analysis**  **procedures. Notable limitations of the models, rel evant to their intended purpose in this attainment**  **modeling analysis, will also be evaluated in detail**

**.**

A. Recommended Models

**Based on extensive research of available documentat ion of the VISTAS Regional Haze modeling**  **analysis, it has been determined by NCDAQ and SCDHE C that the Metrolina attainment modeling should**  **utilize the following suite of models:**

• MM5 : **The Pennsylvania State University/National Center for Atmospheric Research**  **(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, prognostic**  **meteorological model routinely used for urban- and regional-scale photochemical, fine**  **particulate matter, and regional haze regulatory mo deling studies.** 

• SMOKE **: The Sparse Matrix Operator Kernel Emissions (SMOK E) modeling system is an**  **emissions modeling system that generates hourly gri dded speciated emission inputs of**  **mobile, nonroad mobile, area, point, fire, and biog enic emission sources for photochemical**  **grid models.**

• CMAQ: **USEPA’s Models-3/ Community Multiscale Air Qualit y (CMAQ) modeling system**  **is a “One-Atmosphere” photochemical grid model capa ble of addressing ozone, particulate**  **matter, visibility, and acid deposition at regional scale for periods up to one year.** 

B. MM5 – Mesoscale Prognostic Model

**Over the past decade, researchers at the Pennsylvan ia State University (PSU) and the National**  **Center for Atmospheric Research (NCAR) have collabo rated in the refinement and extension of the**  **PSU Mesoscale Meteorological Model leading to the c urrent version of the system, MM5 (version 3.6,**  **MPP). Originally developed in the 1970s at PSU and first documented by Anthes and Warner (1978),**  **the MM5 modeling system has maintained its status a s a state-of-the-science model through**  **enhancements provided by a broad user community (e. g., Chen and Dudhia, 2001; Stauffer and**  **Seaman, 1990, 1991; Xiu and Pleim, 2000). The MM5 modeling system is routinely employed in**  **forecasting projects as well as refined investigati ons of severe weather. Utilization of MM5 within a**  **ir quality applications is also a common practice. In**  **recent years, the MM5 modeling system has been successfully applied in continental scale annual si**  **mulations for the years 1996 (Olerud et al., 2000), 2001 (McNally and Tesche, 2003), and 2002 (Johnson,**  **2003). Due to its ongoing scientific development worldwide, extensive historical applica**  **tions, broad user community support, public availability, and established performance record co**  **mpared with other applications-oriented prognostic 32** 

**models, MM5 has been selected as the preferred mete orological model for this effort. This section**  **provides an overview of the MM5 and its data input requirements.** 

1. MM5 Overview

**The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a three-dimensional,**  **limited-area, primitive equation, prognostic model that has been used widely in regional air quality**  **model applications (Seaman, 2000). The basic model has been under continuous development,**  **improvement, testing, and has been openly peer-revi ewed for more than 20 years (Anthes and Warner,**  **1978; Anthes et al., 1987). It has been used world -wide by hundreds of scientists for a variety of**  **mesoscale studies, including cyclogenesis, polar lo ws, cold-air damming, coastal fronts, severe**  **thunderstorms, tropical storms, subtropical easterl y jets, mesoscale convective complexes, desert mixe**  **d layers, urban-scale modeling, air quality studies,**  **frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and operational mesos**  **cale forecasting.**

**MM5 is based on the prognostic equations for three- dimensional wind components (u – zonal**  **wind component, v – meridional wind component, and w – vertical wind component), temperature,**  **water vapor mixing ratio, and the perturbation pres sure. Use of a constant reference-state pressure**  **increases the accuracy of the calculations near ste ep terrain. The model uses an efficient semi-impli**  **cit temporal integration scheme and has a nested-grid c**  **apability that can use up to ten different domains of**  **arbitrary horizontal and vertical resolution. The interfaces of the nested grids can be either one-wa**  **y or two-way interactive. The model is also capable of**  **using a hydrostatic option, if desired, for coarse- grid**  **applications.**

**MM5 uses a terrain-following non-dimensionalized pr essure, or “sigma,” vertical coordinate**  **similar to that used in many operational and resear ch models. In the non-hydrostatic MM5 (Dudhia,**  **1993), the sigma levels are defined according to th e initial hydrostatically-balanced reference state**  **so that the sigma levels are also time-invariant. The grid**  **ded meteorological fields produced by MM5 are direc tly**  **compatible with the input requirements of “one atmo sphere” air-quality models using this coordinate**  **(e.g., CMAQ). MM5 fields can be easily used in oth er regional air quality models with different**  **coordinate systems (e.g., Comprehensive Air Quality Model with Extensions - CAMx) by performing a**  **vertical interpolation, followed by a mass-conserva tion re-adjustment.** 

**Distinct planetary boundary layer (PBL) parameteriz ations are available for air-quality**  **applications, both of which represent sub-grid-scal e turbulent fluxes of heat, moisture and momentum.**  **These parameterizations employ various surface ener gy budget equations to estimate ground temperature**  **based on the insolation, atmospheric path length, w ater vapor, cloud cover, and longwave radiation. T**  **he surface physical properties of albedo, roughness le**  **ngth, moisture availability, emissivity and thermal inertia are defined as functions of land-use for nu**  **merous categories via a look-up table. One scheme uses**  **a first-order eddy diffusivity formulation for stab le and neutral environments and a modified first-or**  **der scheme for unstable regimes. The other scheme uses**  **a prognostic equation for the second-order turbule nt**  **kinetic energy while diagnosing the other key bound ary layer terms.** 

**Initial and lateral boundary conditions are specifi ed from mesoscale three-dimensional analyses**  **performed at 12-hour intervals on the outermost gri d mesh selected by the user. Additional surface fi**  **elds are analyzed at three-hour intervals. A Cressman-b**  **ased technique is used to analyze standard surface and**  **radiosonde observations, using the National Meteoro logical Center's (NMC) spectral analysis as a first**  **guess. The lateral boundary data are introduced in to MM5 using a relaxation technique applied in the**  **outermost five rows and columns of the most coarse grid domain.** 

**A major feature of the MM5 is its use of state-of-s cience methods for Four Dimensional Data**  **Assimilation (FDDA). The theory underlying this ap proach and details on how it has been applied in a** **33**

**variety of applications throughout the country are described in depth elsewhere (Stauffer and Seaman,**  **1990, 1991; Seaman et al., 1992, 1997). Results of detailed performance evaluations of the MM5**  **modeling system in regulatory air quality applicati on studies have been widely reported in the literat**  **ure (e.g., Emery et al., 1999; Tesche et al., 2000, 200**  **3), and many studies have involved comparisons with other prognostic models such as the Regional Atmosp**  **heric Modeling System (RAMS) and the Systems Application International Mesoscale Model. The MM5**  **enjoys a far richer application history in regulatory modeling studies compared with RAMS or o**  **ther models. Furthermore, in evaluations of these models in over 60 recent regional scale air quality**  **application studies since 1995, it has generally b een**  **found that the MM5 model tends to produce somewhat better photochemical model inputs than alternative**  **models. For these and other reasons set forth in t he MM5 modeling protocol developed by the contracto**  **r performing the meteorological modeling, Barons Adva**  **nced Meteorological Systems, LLC (BAMS) (Olerud and Sims, 2003), MM5 was selected as the me**  **teorological modeling system for this study.**

2. MM5 Configuration

**Based on the extensive sensitivity testing carried out by Olerud and Sims (2003), the MM5**  **(version 3.6, MMP) configuration to be used by BAMS modelers will consist of the following:** 

• **Nested 36/12 kilometer (km) grids, with 34 vertical layers** 

• **Two way nesting, no feedback**

• **Initialization and boundary conditions from Eta ana lysis fields** 

• **Pleim-Xiu (PX) soil model**

• **Asymmetric Convective Mixing (ACM) PBL model**

• **Kain-Fritsch 2 cumulus parameterization**

• **Mixed phase (Reisner 1) cloud microphysics**

• **Rapid Radiative Transfer Model radiation**

• **Snow effect turned on**

• **ETA model sea surface temperature**

• **24-category United States Geological Survey (USGS) vegetation datasets** 

• **Thermal roughness by the Garratt method**

• **Standard FDDA analysis nudging on 36 km and 12 km g rid nests** 

3. MM5 Evaluation

**The MM5 modeling results will be evaluated using pl ots and statistical analyses to determine if**  **the model performance is adequate for the air quali ty modeling exercise. Some of the plots and statis**  **tics to be generated include:** 

• **Spatial plots of model predictions with the appropr iate observations overlaid. These will**  **provide a visual to determine how well such meteoro logical parameters as temperature,**  **mixing ratios, and winds are being captured by the model.** 

• **Graphical statistical plots for surface temperature , mixing ratio, wind speeds, wind direction,**  **and cloud cover. These will include time series of modeled/observed means, bias/error, and**  **index of agreement.**

• **Daily accumulated precipitation plots of modeled ve rsus observed.** 

• **Tabular statistics for temperature, winds, mixing r atio, and cloud cover for various domains.** 

• **Comparison of satellite versus modeled cloud images**

**. 34**  • **Comparison of surface analysis maps to the MM5 pres sure/wind maps** 

• **Comparison of profiler observations with modeled wi nds** 

4. Meteorological Data

**Meteorological data are being generated using the M M5 prognostic meteorological model by**  **BAMS. BAMS is operating the MM5 at 5-day increment s for 2002 on the 36 km and 12 km grid with a**  **14-day spin up period for the end of December 2001. The meteorological observations to be used for**  **statistics come primarily from University Corporati on for Atmospheric Research’s (UCAR’s) ds472.0**  **archive. These data are quality controlled and con verted to NetCDF format, thus allowing the data to**  **be visualized on the model fields via Package for Anal**  **ysis and Visualization of Environmental data (PAVE)**

**. Due to the unreliability in precipitation values in**  **the UCAR dataset, precipitation statistics are cal culated**  **from the 24-h gridded accumulations available from the Climate Prediction Center (CPC). However,**  **these fields undergo grid transformation to match o ur 36-km and 12-km domains from their original 0.25**  **- degree resolution. The statistics are only calcula**  **ted over cells that MM5 deems to be land since the CPC**  **analyses are derived primarily from rain gauges.**

**For aloft analyses, standard sounding observations from the National Center for Environmental**  **Predictions (NCEP) ds353.4 archive are processed. These observations are quality controlled and used**  **to produce model/observation skewT sounding plots for**  **selected sites. Additionally, the observations are integrated into sigma levels that match the MM5 spe**  **cifications and subsequently can be statistically analyzed for performance at sigma levels 9, 17, and**  **22 (~500m, ~1600m, ~3400m, respectively). Qualitative profiler plots showing model/observed h**  **ourly winds are also created based upon the data stored at the Forecast Systems Lab.** 

C. SMOKE Emissions Modeling System

**The SMOKE Emissions Processing System Prototype was originally developed at the Micro**  **computing Center of North Carolina (Coats, 1995; Ho uyoux and Vukovich, 1999). As with most**  **“emissions models,” SMOKE is principally an** emission processing system **and not a true** emissions modeling system **in which emissions estimates are simulated from “f irst principles.” This means that,**  **with the exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool**  **for converting emissions inventory data into the format**  **ted emission files required by an air quality simul ation**  **model. For mobile sources, SMOKE actually simulate s emissions rates based on input mobile-source**  **activity data, emission factors and outputs from tr ansportation travel-demand models.** 

**SMOKE was originally designed to allow emissions da ta processing methods to utilize emergent**  **high-performance-computing as applied to sparse-mat rix algorithms. Indeed, SMOKE is the fastest**  **emissions processing tool currently available to th e air quality modeling community. The sparse matri**  **x approach utilized throughout SMOKE permits both rap**  **id and flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series**  **of matrix calculations instead of less efficient algorithms used in previous systems. The processin**  **g is flexible because the processing steps of tempo ral**  **projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separ**  **ated into independent operations wherever possible. The**  **results from these steps are merged together at a final**  **stage of processing.**

**SMOKE supports area, mobile, fire and point source emission processing and includes biogenic**  **emissions modeling through a rewrite of the Biogeni c Emission Inventory System, version 3 (BEIS3).**  **SMOKE has been available since 1996, and it has bee n used for emissions processing in a number of**  **regional air quality modeling applications. In 199 8 and 1999, SMOKE was redesigned and improved**  **with the support of the USEPA for use with USEPA's Models-3/CMAQ. The primary purposes of the** **35**

**SMOKE redesign were support of: (a) emissions proc essing with user-selected chemical mechanisms and**  **(b) emissions processing for reactivity assessments**

**. SMOKE contains a number of major features that make**  **it an attractive component of the modeling system (Seppanen, 2003). The model suppor**  **ts a variety of input formats from other emissions processing systems and models including the Invento**  **ry Data Analyzer (IDA), Emissions Modeling System – 2003 (EMS), and the Emissions Preprocessor**  **System 2.x (EPS). It supports both gridded and county total land use scheme for biogenic emissions**  **modeling. Although it is not necessary for our purposes, SMOKE can accommodate emissions files fro**  **m up to 10 countries and any pollutant can be processed by the system.** 

**Recent computational improvements to SMOKE include:**

• **Enhanced disk space requirements compared with othe r emissions processing software** 

• **Run-time memory allocation, eliminating any need to recompile the programs for**  **different inventories, grids, or chemical mechanism s** 

• **Updated Input/Output Applications Programming Inter face (I/O API) libraries** 

**A number of science features have been incorporated into the latest version of SMOKE (version**  **2.0), including:**

• **Any chemical mechanism can be used to partition pol lutants to model species, as long as**  **the appropriate input data are supplied**

• **Integration with the MOBILE6.2 on-road mobile sourc e emissions model including link**  **based processing**

• **Support of plume-in-grid processing**

• **Integration of the BEIS3 emissions factors in SMOKE**

**Notable features of SMOKE from an applications stan dpoint include:** 

• **Improved control strategy input formats and designs**

• **Control strategies can include changes in the react ivity of emitted pollutants, a useful**  **capability, for example, when a solvent is changed in an industrial process** 

• **No third party software is required to run SMOKE, a lthough some input file preparation**  **may require other software**

• **Integration with Models-3 file formats and settings**

• **Improved data file formats**

• **Support of various air quality model emissions inpu t formats (e.g., CMAQ, MAQSIP,**  **UAMIV, UAM-V, REMSAD and CAMx)**

• **Enhanced quality assurance pre- and post-processing**

• **Fully integrated with Models-3, which will provide the SMOKE Tool for SMOKE input**  **file preparation**

• **Enhanced treatment of growth and control factors**

• **Improved emissions reporting and Quality Assurance (QA) capabilities** 

• **Improved temporal allocation**

**The Carolina Environmental Program at the Universit y of North Carolina is continuing model**  **development activities with SMOKE. The emissions m odeling will employ the SMOKE version 2.0,**  **released on September 30, 2003. The SMOKE executab les, scripts and databases may be downloaded**  **through the Community Modeling and Analysis (CMAS) center’s Model Clearinghouse.** **36**

D. CMAQ Modeling System

1. CMAQ Overview

**For more than a decade, USEPA has been developing t he Models-3 CMAQ modeling system**  **with the overarching aim of producing a “One-Atmosp here” air quality modeling system capable of**  **addressing ozone, fine particulate matter, visibili ty and acid deposition within a common platform**  **(Dennis et al., 1996; Byun et al., 1998a; Byun and Ching, 1999; Pleim et al., 2003). The original**  **justification for the Models-3 development emerged from the challenges posed by the 1990 CAAA and**  **USEPA’s desire to develop an advanced modeling fram ework for “holistic” environmental modeling**  **utilizing state-of-science representations of atmos pheric processes in a high performance computing**  **environment (Ching et al., 1998). USEPA completed the initial stage of development with Models-3 and**  **released the CMAQ model in mid 1999 as the initial operating science model under the Models-3**  **framework (Byun et al., 1998b). The most recent re ndition is CMAQ version 4.4, which was released in**  **October 2004.**

**CMAQ consists of a core Chemical Transport Model (C TM) and several pre-processors**  **including the Meteorological-Chemistry Interface Pr ocessor (MCIP), initial and boundary conditions**  **processors (ICON and BCON), and a photolysis rates processor (JPROC). USEPA is continuing to**  **improve and develop new modules for the CMAQ model and typically provides a new release each year.**  **In the past, USEPA has also provided patches for CM AQ as errors are discovered and corrected. More**  **recently, USEPA has funded the CMAS center to suppo rt the coordination, update and distribution of the**  **Models-3 system.**

**Another reason for choosing CMAQ as the atmospheric model is the ability to do one**  **atmospheric modeling. Since NCDAQ will be using th e same modeling exercise for both the ozone and**  **PM 2.5**  **attainment demonstrations SIPs, as well as the reg ional haze SIP, having a model that can handle**  **both ozone and particulate matter is essential. A number of features in CMAQ’s theoretical formulatio**  **n and technical implementation make the model well-su**  **ited for annual PM modeling. In CMAQ, the model approach has been adapted to dynamically represent**  **the PM size distribution using three log-normal modes (2 fine and 1 coarse). Transfer of mass betw**  **een the aerosol and gas phases is assumed to be in equilibrium and all secondary aerosols (sulfate, ni**  **trate, secondary organic aerosols) are assumed to b e in**  **the fine modes. The thermodynamics of inorganic ae rosol composition are treated using the ISORROPIA**  **module. Aerosol composition is coupled to mass tra nsfer between the aerosol and gas phases. For**  **aqueous phase chemistry, the Regional Acid Depositi on Model (RADM) is currently employed. This**  **scheme includes oxidation of sulfur dioxide (SO 2**  **) to sulfate by ozone, hydrogen peroxide, oxygen catalyzed by metals and radicals. The impact of cl**  **ouds on the PM size distribution is treated empiric ally.**  **For wet deposition processes, CMAQ uses the RADM/Re gional Particulate Model approach. Particle dry**  **deposition is included as well. CMAQ contains thre e options for treating secondary organic aerosol**  **(SOA), latest being the Secondary Organic Aerosol M odel (SORGAM) that was updated in August 2003**  **to be a reversible semi-volatile scheme whereby VOC s can be converted to condensable gases that can**  **then form SOA and then evaporate back into condensa ble gases depending on atmospheric conditions.** 

2. CMAQ Configuration

**NCDAQ and SCDHEC propose to run CMAQ (version 4.4). The model would be set up and**  **exercised on a nested 36/12 km grid domain, employi ng one-way grid nesting. That is, boundary**  **conditions for the 12 km grid simulation are extrac ted from the 36 km run using the CMAQ BCON**  **processor. A total of 19 vertical layers would be implemented, extending up to a region top of 100 mb**  **(approximately 15 km above ground level). 37** 

**The Piecewise Parabolic Method advection solver wou ld be used along with the spatially varying**  **(Smagorinsky) horizontal diffusion approach and K-t heory for vertical diffusion. MM5 meteorological**  **output based on the Pleim-Xiu Land-Surface Model (L SM) and the ACM PBL scheme will be used, and**  **the recently updated CMAQ MCIP2.3 would process the MM5 data using the "pass through" option. The**  **Carbon Bond version 4 (CB4) gas-phase, RADM aqueous -phase, and AERO3/ISORROPIA aerosol**  **chemistry schemes will be used. Treatment of rever sible secondary organic aerosols would be simulated**  **by the SORGAM implementation in CMAQ (version 4.4).**

**Testing completed with VISTAS evaluated three photo chemical mechanisms: CB4, CB4-2002**  **and SAPRC99. CB4-2002 produced nearly identical re sults as CB4 but took much longer to run since it**  **is only implemented in the slower SMVGEAR chemistry solver, compared to CB4 that is also**  **implemented in the faster Euler Backward Iterative chemistry solver. Thus, CB4-2002 was dropped from**  **consideration. Comparisons of CB4 and SAPRC99 foun d they produced mostly similar but different**  **model performance. However, no one mechanism perfo rmed better than another was across all species,**  **sites, and periods. The testing only evaluated the mechanism’s base case performance, not their respo**  **nse to emission reductions. Given that CB4 runs twice**  **as fast as SAPRC99, the CB4 mechanism was chosen for use.** 

3. Initial and Boundary Condition Data

**The CMAQ default Initial Concentrations (ICs) will be used along with a ~15 day spin up period**  **to eliminate any significant influence of the ICs. The CMAQ Boundary Conditions (BCs) for the initial**  **simulations will be based on seasonal averages of 3 -hour 2001 GEOS-CHEM global simulation model**  **output. VISTAS and other Regional Planning Organiz ations (RPOs) are finding a 2002 GEOS-CHEM**  **simulation that would be used to define days specif ic high time resolved (e.g., 3-hourly) CMAQ BCs.** 

E. Model Limitations

**All mathematical models possess inherent limitation s owing to the necessary simplifications and**  **approximations made in formulating the governing eq uations, implementing them for numerical solution**  **on fast computers, and in supplying them with input datasets and parameters that are themselves**  **approximations of the full state of the atmosphere and emissions processes. The more important**  **limitations of the various modeling systems to be e mployed are noted in this section.** 

1. MM5

**Four different configurations of the MM5 LSM and PB L were evaluated. Depending on the**  **meteorological variable (e.g., winds, temperature, moisture) and location (e.g., mountains, coastal, e**  **ast, west) different LSM_PBL configurations performed be**  **tter. The PX_ACM LSM_PBL configuration was selected because it was consistently near the top p**  **erforming configuration in the southeastern US acro ss**  **variables and locations and was never the worst-per forming configuration. However, there are numerous**  **limitations in the MM5 with the LSM and PBL treatme nt being some of the most important. The MM5**  **PX_ACM frequently predicts very low PBL heights tha t can appear as "holes" in the spatial distribution**  **of PBL heights that do not appear physically realis tic and may affect air quality modeling. Although**  **the MM5 PX_ACM configuration model performance in the s**  **outheastern US mostly met performance benchmarks, the performance was much worse in the w**  **estern US. In addition, there is a stochastic component of real world meteorology that is not cap**  **tured by MM5. For example, for some ozone episodes stagnation is an important attribute that**  **MM5 fails to simulate well as it tries to organize the**  **flow fields. However, the MM5 model represents app roximately 20 years of development by various**  **researchers. 38** 

2. SMOKE

**In early testing, a number of undocumented features of the SMOKE 1.5b version necessitated**  **re-runs of the emissions processing software to ove rcome errors and/or ambiguities in source**  **documentation and QA reporting. It is unclear whet her similar conditions will be encountered with the**  **SMOKE 2.0 release. As a full software release, rat her than a "beta" version, SMOKE 2.0 is expected to**  **be more robust and more fully-documented than the S MOKE 1.5b release. However, with any newly**  **released software system, there is the potential fo r errors and/or ambiguities to affect the emissions**  **modeling schedule. Should problems arise or issues be encountered which would require additional**  **SMOKE runs or potential SMOKE modifications or alte rnate modeling methods, NCDAQ and**  **SCDHEC will immediately notify stakeholders and mak e recommendations for resolving the issues.**  **Upon receipt of technical direction from the stakeh olders, appropriate corrective action will be taken** 

**. Features are continuing to be developed in the SMOK**  **E emissions model. As it is not as mature as some other emission models (e.g., EMS, EPS, etc.**  **), SMOKE does not include as many features. NCDAQ and SCDHEC will keep abreast of SMOKE develop**  **ment activities to identify new features that will assist in the emissions modeling.** 

3. CMAQ

**Like all air quality models, a major limitation of CMAQ is the input for emissions,**  **meteorological, and IC/BC data. Key science limita tions in the model itself include the nitrate forma**  **tion chemistry. Testing found the CMAQ nitrate performa**  **nce suspect with winter overestimations and summer underestimations. Other science limitations**  **in the current version of CMAQ include inadequate treatment of sea salt and the assumption that all s**  **econdary PM is in the fine mode. Lack of any two-w ay**  **grid nesting limits the ability of the model to pro perly resolve point source plumes or urban**  **photochemistry. Other limitations of CMAQ include its computational requirements, such as the need fo**  **r excessive disk space.** 

F. Model Input Requirements

**Each of the modeling system components has signific ant database requirements. These data**  **needs fall into two categories: those required for model setup and operation, and those required for**  **model evaluation testing. The main input data base requi**  **rements for the meteorological, emissions, and air quality models are identified in the following sect**  **ion.**

1. MM5

**The databases required for setting up, exercising, and evaluating the MM5 model for the 2002**  **season consist of various fixed and variable inputs**

**.**

• **Topography : High resolution (e.g., 30 sec to 5 min) topograp**  **hic information derived from the Geophysical Data Center global datasets from the NC**  **AR terrain databases are available for prescribing terrain elevations throughout the 36 km**  **and 12 km grid domain.**

• **Vegetation Type and Land Use : Vegetation type and land use information on the**  **36 km grid may be developed using the PSU/NCAR 10 min. (~18.5 km)**  **databases while for the 12 km grids, the USGS data are available.** 

• **Atmospheric Data : Initial and boundary conditions to the MM5 may be**  **developed from operationally analyzed fields derived from the NCEP**  **ETA (40 km resolution) following the procedures outlined by Stauffer and Seaman (1990).**  **These 3-hr synoptic-scale initialization data 39** 

**include the horizontal wind components (u and v), t emperature, and relative humidity at the**  **standard pressure levels, plus sea-level pressure a nd ground temperature. Here, ground**  **temperature represents surface temperature over lan d and sea-surface temperature over water.** 

• **Water Temperature : Water temperatures required on both 36 km and 12**  **km grids can be derived from the ETA skin temperature variable. These temp**  **eratures are bi-linearly interpolated to each model domain and, where necessary, filtered to smoo**  **th out irregularities.**

• **Clouds and Precipitation : While the non-hydrostatic MM5 treats cloud format**  **ion and precipitation directly through explicit, resolved-s**  **cale, and parameterized sub-grid scale processes, the model does not require precipitation or cloud i**  **nput. The potential for precipitation and cloud formation enters through the thermodynamic and clou**  **d processes formulations in the model. The only precipitation-related input required is the in**  **itial mixing ratio field that is developed from the National Weather Service (NWS) and NMC datasets pre**  **viously discussed.**

• **Multi-Scale FDDA : The standard "multi-scale" data assimilation stra**  **tegy to be used on the 36 km and 12 km grids will objectively analyze three-dime**  **nsional fields produced every 3 hours from the NWS rawinsonde wind, temperature, and mixing ra**  **tio data, and similar analyses are generated every three hours from the available NWS**  **surface data.**

2. SMOKE

**The databases required to set up and operate SMOKE are as follows:** 

• **Area source emissions in IDA format**

• **Off-road mobile source emissions in IDA format**

• **Stationary point source emissions in IDA format**

• **Utility emissions**

o **Continuous Emissions Monitoring (CEM) emissions, da y specific for actual 2002** 

o **5-year average CEM emissions, day specific for typi cal 2002** 

o **Based on Integrated Planning Model (IPM) modeling f or future year** 

• **Wildfire emissions**

o **Day specific for actual 2002**

o **Multi-year average for typical year 2002 and future year** 

• **On-road motor vehicle activity data**

• **MOBILE6.2 input parameters**

**Also required for annual modeling are data files sp ecific for:** 

• **Temporal allocation**

• **Spatial allocation**

• **Speciation**

3. CMAQ

**The CMAQ CTM requires the following inputs:**

****
• **Three-dimensional hourly meteorological fields that will be generated by the CMAQ** 

**MCIP2.3 processing of the BAMS MM5 output**

• **Three-dimensional hourly emissions generated by SMO KE** 

• **Initial conditions and boundary conditions (IC/BC) 40** 

• **Topographic information**

• **Land use categories**

• **Photolysis rates generated by the CMAQ JPROC proces sor** **41**

V. GRID SPECIFICATIONS AND MODELING DOMAINS

**This chapter summarizes the model domain definition s including the model domain, resolution,**  **map projections and nesting schemes for high resolu tion sub-domains.** 

A. Horizontal Modeling Domain

**A coarse grid continental US domain with a 36 km ho rizontal grid resolution will be used as the**  **outer grid domain for MM5 modeling. The CMAQ domai n is nested within the MM5 36 km domain.**  **Figure D-25 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36 km**  **domain nested in the MM5 domain. To achieve finer spatial resolution in the VISTAS states, NCDAQ**  **and SCDHEC will also use a one-way nested high reso lution grid with a 12 km grid resolution. Figure D** 

**** **26 shows the 36 km CMAQ continental grid and the hi gh resolution, nested 12-km grid in the VISTAS**  **states. Figure D-27 shows in more detail the 12 km grid for the VISTAS region.** 

**Figure D-25: The MM5 horizontal domain is the oute r most, blue grid, with the CMAQ 36 km domain**  **nested in the MM5 domain. 42** 

**Figure D-26: The 36 km CMAQ continental grid and t he high resolution, nested 12-km grid over the**  **VISTAS states.**

**Figure D-27: A more detailed view of the 12 km gri d over the VISTAS region.** **43**

**Both MM5 and CMAQ employ the RPO unified grid defin ition for the 36 km continental**  **domain. The RPO unified grid consists of a Lambert -Conformal map projection using the map**  **projections parameters listed in Table D-5.**

**Table D-5: RPO Unified Grid Definition.**

**The MM5 36 km grid includes 164 cells in the east-w est dimension by 128 cells in the north**  **south dimension. The CMAQ 36 km grid includes 148 cells in the east-west dimension and 112 cells in**  **the north-south dimension. Since the MM5 coarse gr id is also nested in the Eta grid, there is a possi**  **bility of boundary effects near the MM5 boundary that occu**  **r 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 g**  **rid cells around each boundary of the CMAQ 36 km domain. This is designed to eliminate any errors i**  **n the meteorology from boundary effects in the MM5 simulation at the interface of the MM5 and Eta grid**  **s. The buffer region used here exceeds the USEPA suggestion of at least 5 grid cell buffer at each b**  **oundary.**

**Table D-6 lists the number of rows and columns and the definition of the X and Y origin (i.e., the**  **southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that the CMAQ grid is**  **rotated 90 degrees relative to the MM5 grid, so row s and columns are reversed. In Table 5-2 “Dot” ref**  **ers to the grid mesh defined at the vertices of the gri**  **d cells while “cross” refers to the grid mesh defin ed by**  **the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Fina**  **lly, note that the grid definition for the CMAQ MCIP and**  **CMAQ Chemical Transport Model (CCTM) are identical.** 

**Table D-6: Grid Definitions for MM5 and CMAQ.**

MODEL COLUMNS DOT

(CROSS) ROWS DOT

(CROSS) XORIGIN YORIGIN

**MM5 36km 129 (128) 165 (164) -2952000 -2304000 CMAQ 36km 149 (148) 113 (112) -2736000 -2088000**  **MM5 12km 190 (189) 181 (180) 7200 -1656000 CMAQ 12km 169 (168) 178 (177) 108000 -1620000** 

B. Vertical Modeling Domain

**The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 modeling.**  **The MM5 model employed a terrain following coordina te system defined by pressure, using 34 layers**  **that extend from the surface to the 100 mb. Table D-7 lists the layer definitions for both MM5 and fo**  **r CMAQ. A layer averaging scheme is adopted for CMAQ**  **to reduce the computational cost of the CMAQ simulations. The effects of layer averaging were e**  **valuated in conjunction with the VISTAS modeling effort and were found to have a relatively minor ef**  **fect on the model performance metrics when both the 34-layer and a 19-layer CMAQ models were compared t**  **o ambient monitoring data.**

PARAMETER VALUE **projection Lambert-conformal**
**1 st**  **true latitude (alpha) 33 degrees**
**2 nd**  **true latitude (beta) 45 degrees x center - 97 degrees**  **y center 40 degrees 44** 

**Table D-7: Vertical Layer Definition For MM5 Simul ations (Left Most Columns), And Approach For**  **Reducing CMAQ Layers By Collapsing Multiple MM5 Lay ers (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 1.000 1000 0 0**45**

VI. DEVELOPMENT OF EMISSIONS INVENTORIES

**There are five different emission inventory source classifications; stationary point sources area**  **sources, off-road mobile sources, on-road mobile so urces, and biogenic sources. Stationary point sour**  **ces are those sources that emit greater than a specifie**  **d tonnage per year and the data is provided at the facility**  **level. Stationary area sources are those sources w hose emissions are relatively small but due to the**  **large number of these sources, the collective emissions c**  **ould be significant (i.e., dry cleaners, service st ations,**  **etc.) Off-road mobile sources are equipment that c an move but do not use the roadways, i.e., lawn**  **mowers, construction equipment, railroad locomotive s, aircrafts, etc. On-road mobile sources are**  **automobiles, trucks, and motorcycles that use the r oadway system. Biogenic sources are emissions from**  **natural sources, such as trees, crops, grasses and natural decay of plants.** 

**Emission estimates for stationary point and area so urces, as well as for off-road mobile sources**  **are calculated and formatted for processing through the SMOKE emissions processing system, which**  **formats the data into air quality model ready files . On-road mobile source emissions are estimated wi**  **thin the SMOKE system, which uses the USEPA’s MOBILE6.2**  **model, with modeling meteorology and various mobile inputs. The biogenic emissions are**  **also estimated within the SMOKE system, using the USEPA’s BEIS model, with modeling meteorology.** 

**In addition to the various source classifications, there are also various types of emission**  **inventories. The first is the actual base year inv entory. This inventory is the base year emissions**  **that correspond to the meteorological data, for this mod**  **eling effort is 2002. These emissions are used for evaluating the air quality model performance.** 

**The second type of inventory is the typical base ye ar inventory. This inventory is similar to the**  **actual base year, however for sources that may have significant changes from year to year a more typic**  **al emission value is used. In this modeling effort, t**  **ypical emissions were developed for the electric generating units and the wildland fire emissions.**  **The air quality modeling results using these emissi ons**  **are used in calculating the relative reduction fact ors used in the attainment demonstration test.** 

**The future year base inventory is an inventory deve loped for some future year for which**  **attainment of the ozone standard is needed. For th is modeling project, the future year inventory will**  **be 2009, the last complete year for which the standard**  **must be attained. It is the future year base inve ntory**  **that control strategies and sensitivities are appli ed to determine what controls, to which source**  **classifications, must be made in order to attain an d maintain the ozone standard.** 

**In the sections that follow, the inventories used f or each source classifications are discussed.** 

A. Point Source Emissions

**The point source emissions will be separated into e lectric generating units (EGU) and non-EGU**  **categories. The reason for splitting the point sou rce inventory is that the EGU sources account for t**  **he majority of the point source NO** 
**x emissions and hour specific data is available for**  **these sources through the USEPA’s acid rain database. Using this more re**  **fined data will help improve the air quality modeli ng**  **performance. Annual emissions will be used for the non-EGU sources.** 

**All point sources will be spatially allocated in th e domain based on the stationary source**  **geographic coordinates. If a point source is missi ng its latitude/longitude coordinates, the source w**  **ill be placed in the center of its respective county.** **46**

1. Electric Generating Units

**Actual Base Year Inventory**

**For EGU sources with USEPA reported 2002 CEM data o r with 2002 hourly emissions provided**  **by stakeholders, actual hourly data will be used. For the sources where USEPA CEM data are utilized,**  **NO x**  **, SO 2**  **, and heat input-based hour-specific profiles were developed and applied to NO** 
**x , SO**  **2 , and all**  **other emissions, respectively. The annual emission values that have been provided will be maintained,**  **but will be distributed using hourly to annual prof iles. For sources where hour-specific data was pro**  **vided by stakeholders, this data will be substituted for**  **USEPA CEM-based emissions and distributions.**

**To temporally allocate the remaining EGU point sour ces, the NO** 
**x , SO**  **2 , and heat input data will**  **be collected from the 2002 CEM datasets, and used t o develop unit-level temporal distributions. The**  **hourly, day of week, and monthly specific temporal profiles will be used in conjunction with the**  **emissions inventory supplied emissions data to calc ulate hourly EGU emissions by unit.** 

**Typical Base Year Inventory**

**Since the NO**
**x**

**emissions from EGU sources are a significant part of the emissions inventory, a**  **typical base year emissions inventory was developed for these sources to avoid anomalies in emissions**  **due to variability in meteorology, economic and out age factors in 2002. This approach is consistent w**  **ith the USEPA’s modeling guidance.** 

**To develop a typical year 2002 emissions inventory for EGU sources, for each unit the average**  **CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit**  **specific normalizing factor. This normalizing fact or was then multiplied by the 2002 actual emissions** 

**. The heat inputs for the period 2000 through 2004 we**  **re used since the modeling current design values us e**  **monitoring data from this same 5-year period.**

**If a unit was shutdown for an entire year during th e 2000 through 2004 period, the average of the**  **years the unit was operational was used. If a unit was shutdown in 2002, but not permanently shutdown** 

**, the emissions and heat inputs 2001 (or 2000) were u**  **sed in the normalizing calculations.**

**Future Base Year Inventory**

**As part of the VISTAS modeling, VISTAS and the Midw est Regional Planning Organization**  **(MRPO) contracted with ICF Resources, L.L.C., to ge nerate future year emission inventory for the**  **electric generating sector of the contiguous US usi ng the IPM. IPM is a dynamic linear optimization**  **model that can be used to examine air pollution con trol policies for various pollutants throughout the**  **contiguous US for the entire electric power system. The dynamic nature of IPM enables the projection**  **of the behavior of the power system over a specified f**  **uture period. The optimization logic determines th e**  **least-cost means of meeting electric generation and capacity requirements while complying with specifi**  **ed constraints including air pollution regulations, tr**  **ansmission bottlenecks, and plant-specific operatio nal**  **constraints. The versatility of IPM allows users t o specify which constraints to exercise and populat**  **e IPM with their own datasets.** 

**Since the modeling is based on the USEPA’s prior an alyses for which detailed public**  **documentation is available, a summary of only the i ncremental changes that were proposed by VISTAS**  **and MRPO as part of this analysis are presented her e.** **47**

**The VISTAS analysis is based on the USEPA modeling applications using IPM (V.2.1.6). As per**  **the analytical needs of VISTAS and MRPO, the follow ing changes were made to the underlying**  **assumptions in the USEPA Base Case (V2.1.6):**

**i) The underlying database in the VISTAS analysis is U**  **SEPA’s National Electric Energy Data System Database, with changes based upon the commen**  **ts and technical directions from VISTAS and MRPO’s stakeholders. The changes focused on ex**  **isting installations of NO**
**x , SO**  **2 and**  **particulate matter controls, NO**
**x emission rates, SO**  **2 emission limits, capacity of existing units,**  **heat rate and unit identifications of selected unit s in the VISTAS and MRPO regions.** 

**ii) The analysis covers the period between 2007 and 203**  **0. To make the model size and run time tractable, IPM is run for a number of selected year**  **s within the study horizon known as run years. Each run year represents several calendar years in**  **the study horizon, and all calendar years within the study horizon are mapped to their representativ**  **e run years. Although results are only reported for the run years, IPM takes into account all years**  **in the study horizon while developing the projections.** 

**iii) The Duke Power and Progress Energy control technolo**  **gy investment strategies for complying with North Carolina’s Clean Smokestacks Rule were e**  **xplicitly hardwired in the analysis.**

**iv) The USEPA’s Clean Air Interstate Rule (CAIR) rule i**  **mplemented as part of this analysis is broadly consistent with the USEPA 40 CFR Parts 51 e**  **t. al., Supplemental Proposal for the Rule to Reduce Interstate Transport of Fine Particulate**  **Matter and Ozone, proposed on June 10, 2004. Alabama, Arkansas, Delaware, District of Columbia,**  **Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusett**  **s, Michigan, Minnesota, Mississippi, Missouri, New Jersey, New York, North Carolina, Ohi**  **o, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, Wisconsi**  **n are the states affected by the CAIR SO 2**  **and the CAIR annual NO**
**x policies starting 2010. Connecticut is affected b**  **y an ozone season NO**  **x policy. The CAIR plants affected by the annual NO** 
**x policy are capped at 1.6 million tons**  **starting 2010 and 1.33 million tons starting 2015. The power plants affected by the CAIR SO**  **2 policy have to surrender two Title IV SO**  **2 allowances for every ton of SO**  **2 emitted starting 2010**  **and three Title IV SO 2**  **allowances for every ton of SO 2**  **emitted starting 2015.**

2. Non-Electric Generating Units

**2002 Base Year Inventory**

**For the non-EGU sources, the same inventory will be used for both the actual and typical base**  **year emissions inventories. The non-EGU category w ill use annual emissions, which will be temporally**  **allocated to month, day, and hour using source cate gory code (SCC) based allocation factors. These**  **factors will be based on the cross-reference and pr ofile data supplied with the SMOKE 2.0 version.** 

**The non-EGU sources annual emissions will be the 20 02 VISTAS inventory based on the 2002**  **Consolidated Emissions Reporting Rule (CERR) submit ted data for all states in the modeling domain**  **unless a state or RPO provides updated data.**

**Future Year Base Inventory**

**The general approach for assembling future year dat a is to use recently updated growth and**  **control data consistent with USEPA’s CAIR analyses, supplement these data with available stakeholder**  **input, and provide the results for stakeholder revi ew to ensure credibility. To assemble growth/contr**  **ol data needed for the final 2009 inventories, the VIS**  **TAS contractor will perform the following activitie s:** **48**

• **Use the final 2002 VISTAS inventory as the starting point for the future base year inventory.** 

• **Obtain, review, and apply the most current growth f actors developed by USEPA, based on**  **forecasts from an updated Regional Economic Models, Inc. model (version 5.5) and the latest**  Annual Energy Outlook **published by the Department of Energy.**

• **Obtain, review, and apply any State-specific or sec tor-specific growth factors submitted by**  **stakeholders.**

• **Obtain information regarding sources that have shut down after 2002 and work with the states to**  **determine if these sources should be removed from t he future year inventory.** 

• **Obtain, review, and apply control assumptions that are expected to be in place by 2009.** 

**Controls Applied to the Non-EGU Inventory**

1-hour Ozone SIP

**Information about control programs for the 1-hour o zone nonattainment areas came from the**  **report by E.H. Pechan and Associates entitled** VOC and NO
**x** Control Measures Adopted by States and Nonattainment Areas for 1999 NEI Base Case Emission s Projection Calculations **. The report identified and compiled a listing of the VOC and NO** 
**x control measure programs expected to be implemente**  **d after 1999, as well as an estimate of their influence on**  **projected emissions. Five nonattainment areas in t he**  **VISTAS region were included: Atlanta, Birmingham, metro Washington DC (including several counties**  **in Virginia), Louisville, and northern Kentucky.**

**Emission reductions requirements from NO**
**x Reasonably Available Control Technology (RACT)**  **in 1-hour Ozone SIP areas were implemented prior to 1999. These reductions should already be**  **accounted for in the VISTAS 2002 inventory since th e 2002 inventory was based on 2002 actual**  **emissions submitted by the States.**

NO x SIP Call

**For non-EGU sources, Phase I of the NO**
**x SIP call applies to large industrial boilers and t**  **urbines, and cement kilns. States in the VISTAS region affe**  **cted by the NO**
**x SIP call have developed rules for the**  **control of NO**
**x emissions that have been approved by the USEPA. T**  **he VISTAS contractor has reviewed the available state rules and guidance documents to**  **determine the affected sources and ozone season allowances.** 

**For the sources within North Carolina, NCDAQ has de cided to use the 2007 emission allowances**  **for the 2009 future year inventory. The allowances are given in terms of tons per ozone season (the f**  **ive month period from May to September). To calculate**  **annual emissions, the capped allowances were multiplied by a factor of 12/5.** 

**The Phase II rule applies to large internal combust ion engines, which are primarily used in**  **pipeline transmission service at compressor station s. NCDAQ has established emissions caps for three**  **facilities affected by the Phase II NO**
**x SIP call rule and will apply these caps to the fut**  **ure year inventory.**

**For the other states in the VISTAS region, affected units were identified using the same**  **methodology as was used by USEPA in the proposed Ph ase II rule (i.e., a large internal combustion**  **engine is one that emitted, on average, more than 1 ton per day during 2002). The final rule reflects**  **a control level of 82 percent for natural gas-fired i**  **nternal combustion engines and 90 percent for diese l or**  **dual fuel categories. Therefore, these control lev els were applied to the identified sources.** **49**

Maximum Achievable Control Technology Regulations

**The USEPA anticipates reductions in PM and SO 2**  **as a result of the Industrial Boiler/Process Heater maximum achievable control technology (MACT)**  **standard. The methods used to account for these reductions are the same as those used for the**  **Interstate Air Quality Transport Rule. Since the attainment demonstration is utilizing one atmospher**  **e modeling, the reductions for these pollutants wer e**  **accounted for.**

**MACT requirements were also applied, as documented in the USEPA report entitled**  Control Packet Development and Data Sources, **dated July 14, 2004. The point source MACTs and as sociated**  **emission reductions were designed from Federal Regi ster notices and discussions with USEPA’s**  **Emission Standards Division staff. Emission reduct ions will be applied only for MACT standards with**  **an initial compliance date of 2002 or greater, sinc e effects from MACT with earlier compliance dates**  **should already be accounted for in the 2002 base ye ar inventory.** 

**The future year base inventory does not include the NO**  **x co-benefit effects of the Gas Turbines or**  **stationary Reciprocating Internal Combustion Engine s MACT regulations, which USEPA estimates to be**  **small compared to the overall inventory.**

Petroleum Refinery Initiative

**Three refineries in the VISTAS region are affected by two October 2003 Clean Air Act**  **settlements under the USEPA Petroleum Refinery Init iative. The refineries are: (1) the Chevron refine**  **ry in Pascagoula, Mississippi; (2) the Ergon refinery**  **in Vicksburg, Mississippi; and (3) the Ergon refine ry in**  **Newell, West Virginia. Although these sources are not within North Carolina or South Carolina, the**  **expected emission reductions will be accounted for in the 2009 modeling.** 

NO x RACT in 8-hour Ozone SIP

**NCDAQ will make every effort to include NO**
**x RACT controls for 8-hour ozone nonattainment**  **areas in the VISTAS region. However, since cost is a factor of consideration in a RACT determination,**  **it may not be known at the time of the final modeling**  **which sources will be subject to actual controls.**

**SCDHEC will make every effort to include NO**
**x RACT controls for 8-hour ozone nonattainment**  **areas in the VISTAS region. Specifically, the plan is to submit expected 2009 and 2018 emissions for**  **Bowater RACT accounting for RACT controls (if appli cable).** 

Clean Air Interstate Rule

**As stated in the preamble to the CAIR rule, the rul e would not require or assume additional**  **emission reductions from non-EGU boilers and turbin es.** 

B. Stationary Area Source Emissions

**Stationary area sources include sources whose emiss ions are relatively small but due to the large**  **number of these sources, the collective emissions c ould be significant (i.e., combustion of fuels for**  **heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission**  **factor by some known indicator of collective activity, suc**  **h as fuel usage, number of household or population. Thus, a variety of activity level data is collected**  **, including, US Census economic data, forestry and agriculture agency data, and other data sources. S**  **tationary area source emissions are estimated on th e**  **county level. 50** 

**Actual Base Year Inventory**

**A portion of the area source 2002 base year invento ry for North Carolina was developed by**  **NCDAQ and provided to the VISTAS contractor. The r emaining portion of the area source inventory**  **was calculated by the VISTAS contractor. The sourc es estimated by the contractor included emissions**  **from animal husbandry, wildland fires, and particul ate matter from paved and unpaved roads. For the**  **other states within the modeling domain, the state supplied data or the CERR data was used.** 

**Area source categories estimated by NCDAQ were iden tified from a list in the USEPA guidance**  **document EPA-450/4-91-016,** Procedures for the Preparation of Emission Inventor ies of Carbon Monoxide and Precursors of Ozone **, and from the Emission Inventory Improvement Progr am (EIIP)**  **technical reports.**

**In general, emission factor estimation approaches w ere used to calculate area source emissions.**  **Emission factors may be grouped as per capita emiss ion factors; commodity consumption-related**  **emission factors; and level-of-activity based emiss ion factors. The emission factors were obtained fr**  **om the**  Procedures for the Preparation of Emission Inventor ies of Carbon Monoxide and Precursors of Ozone **, from the EIIP technical reports, or the USEPA's** AP-42, Compilation of Air Pollutant Emission Factors, Fifth Edition

**. The emissions from area sources were estimated by m**  **ultiplying an emission factor by the appropriate indicator of collective activity for ea**  **ch source category within the inventory area. An indicator is any parameter associated with the acti**  **vity level of a source that can be correlated with the air**  **pollutant emissions from that source, such as fuel usage, number of households, or population. The**  **values of these indicators are gathered from variou s sources (government reports, census, trade groups** 

**, employment data, direct surveys, etc.) as appropria**  **te.**

**For the animal husbandry and fertilizer application emissions, the Carnegie Mellon University**  **ammonia model was used. For paved and unpaved road s particulate matter emissions, emissions**  **developed by the USEPA as part of their 2002 Nation al Emissions Inventory development effort was**  **used.**

**Wind blown dust and sea salt emissions were not inc luded in the inventory. These source**  **categories are sources of particulate matter and th erefore, do not effect the 8-hour ozone attainment**  **modeling demonstration.**

**For wildland fires, i.e., wildfires and prescribed burns, monthly estimates of fire emissions, which**  **include burn acreage and biomass loading informatio n will be used. Depending on the completeness and**  **quality of the data, attempts will be made to calcu late spatial and temporal distributions of the fire**  **emissions, rather than relying on standard distribu tion profiles. Data will be obtained through**  **consultation with stakeholders that participate in the VISTAS Fire Special Interest Work Group. The f**  **ire data will be split into two groups, small fires est**  **imated on a county level and treated as an area sou rce;**  **and large fires that will be treated as a point sou rce.** 

**Typical Base Year Inventory**

**The actual base year inventory will serve as the ty pical base year inventory for all area source**  **categories except for wildland fires. For this sou rce category, development of a typical year fire**  **inventory provided the capability of using a compar able dataset for both the base year and future year**  **s. Thus, fire emissions would remain the same for air**  **quality modeling in both the base and any future years. The VISTAS Fire Special Interest Work Group**  **was consulted and decided to use State level ratio s** **51**

**of acres over a longer term record (three or more y ears) developed for each fire type relative to 2002**  **. The 2002 acreage was then scaled up or down based on th**  **ese ratios to develop a typical year inventory.**

**Future Year Base Inventory**

**The VISTAS contractor generated the future base yea r emissions inventory used in the attainment**  **demonstration modeling. The general approach used to calculate the future base year emissions for**  **stationary area sources was as follows:**

• **Use the final 2002 VISTAS base year inventory as th e starting point for the future base year**  **inventory.**

• **Obtain any State specific growth factors and/or fut ure controls from the States to use in**  **developing the projections.**

• **Back calculate uncontrolled emissions for the 2002 base year inventory based on existing controls**  **reported for the 2002 base year inventory.**

• **Controls (including control efficiency, rule effect iveness and rule penetration) provided by the**  **States or originally developed for use in estimatin g projected emissions for the USEPA’s Heavy**  **Duty Diesel rulemaking emission projections and use d in the CAIR projections were then used to**  **calculate controlled emissions. State submitted co ntrols had precedence over the USEPA**  **developed controls.**

• **Growth factors supplied from the States or the USEP A’s CAIR emission projections were then**  **applied to project the controlled emissions to the appropriate year. In some cases, the USEPA’s**  **Economic Growth and Analysis System Version 5 growt h factors were used if no growth factor**  **was available from either the States or the CAIR gr owth factor files.** 

C. Nonroad Mobile Source Emissions

**Nonroad mobile sources are equipment that can move but do not use the roadways, such as**  **construction equipment, aircraft, railroad locomoti ves, lawn and garden equipment, etc. For the nonro**  **ad mobile source inventory, the list of sources to inv**  **entory came from the USEPA’s NONROAD2005 model and the USEPA guidance document,**  Procedures for Emission Inventory Preparation Volum e IV: Mobile Sources **. For the majority of the nonroad mobile sources, the emissions can be estimated using the**  **USEPA’s NONROAD model. For the three source catego ries not included in the NONROAD model,**  **(aircraft engines, railroad locomotives, and commer cial marine vessels), more traditional methods of**  **estimating the emissions were used.**

**2002 Base Year Inventory**

**For the non-road mobile sources, the same inventory will be used for both the actual and typical**  **base year emissions inventories. All non-road mobi le source emissions, except for aircraft engines,**  **commercial marine vessels and railroad locomotives, were estimated using the USEPA NONROAD2005**  **model. This model predicts the emissions for non-r oad equipment based upon the year inputted into the**  **model.**

**For railroad locomotive emissions, emission factors were supplied by the**  Procedures for Emission Inventory Preparation Volume IV: Mobile So urces **document, which were then multiplied by a variety of different activity levels (i.e., gallons**  **of fuel per county for railroad locomotive engines ).**  **Refinements could be made using information from** Development of Railroad Emission Inventory Methodologies (SR2004-06-02) **from the Southeastern States Air Resource Managers , Inc.** **52**

**Aircraft emissions at airports were calculated by V ISTAS contractors using landing and take off**  **data from Federal data sources. These will be revi ewed and refined as appropriate for the Charlotte,**  **Greensboro, and Raleigh-Durham airports. Emissions are calculated using the Federal Aviation**  **Administration’s (FAA’s) Emissions and Dispersion M odeling System version 4.2, when there is**  **sufficient detail to employ it.**

**Commercial marine emissions are estimated by proced ures described in**  Commercial Marine Activity for Deep Sea Ports in the United States (E PA420-R-99-020)

**.**
**Future Base Year Inventory**

**For the source categories estimated using the USEPA NONROAD model, the model was used to**  **create a future base year inventory. The NONROAD m odel takes into consideration rules that are in**  **effect that could impact the emissions from these s ource categories. For the four largest airports in**  **North Carolina, the FAA’s Terminal Area Forecast will be**  **used to project growth in aircraft emissions.**

**For the commercial marine, railroad locomotives and the remaining airport emissions, the**  **VISTAS contractor will project the future base year emissions using the following guidelines:** 

• **Use the final 2002 VISTAS inventory as the starting point for the future base year inventory.** 

• **Detailed inventory data (both before and after cont rols) for 1996 and 2010 will be obtained**  **from the USEPA’s Clean Air Interstate Rule Technica l Support Document. Straight-line**  **interpolations between 1996 and 2010 will be used t o create a combined growth and control**  **factor. This is done at the State-County-SCC-Pollu tant level of detail.** 

• **Obtain, review and apply any State-specific growth factors submitted.** 

• **Apply adjustments to account for additional emissio n reductions do the low sulfur non-road**  **diesel fuels.**

D. On-Road Mobile Source Emissions

**Highway mobile sources are considered those vehicle s that travel on the roadways and comprise**  **over 30 percent of the NO**
**x emissions in North Carolina, and 42 percent of the**  **NO x**  **emissions in South Carolina. Emissions from motor vehicles occur thro**  **ughout the day while the vehicle is in motion, at i dle,**  **parked, and during refueling. Each of these emissi ons sources needs to be estimated in order to prope**  **rly reflect the total emissions from this source catego**  **ry. In its simplest terms emissions from highway mobile sources are calculated by multiplying an act**  **ivity level, in this case daily vehicle miles trave led**  **(VMT) as provided by the North Carolina Department of Transportation (NCDOT), by an emission**  **factor.**

**The USEPA developed the MOBILE model to estimate em ission factors based on information on**  **the way vehicles are driven in a particular area. The newest version of the MOBILE model, MOBILE6.2,**  **will be used to develop the on-road mobile source e missions estimates for carbon monoxide (CO), NO** 
**x ,**  **PM, and VOC emissions. Key inputs for the MOBILE m odel include information on the age of vehicles**  **on the roads, the average speed of those vehicles, what types of road those vehicles are traveling on,**  **and any control programs (e.g., emissions inspection pr**  **ograms). Inputs are combined with gridded, day specific temperature data to calculate the gridded,**  **temporalized emission estimates. Of note, whereas the**  **on-network emissions estimates are spatially alloca ted based on link location and subsequently summed**  **to the grid cell level, the off-network emissions e stimates are spatially allocated based on a combina**  **tion of the Federal Highway Administration version 2.0 h**  **ighway networks and population. For the North Carolina 36/12 km modeling, no link-based data will**  **be used. The MOBILE6 emissions factors are based 53** 

**on day-specific temperatures predicted by the meteo rological model. For the South Carolina 36/12 km**  **modeling, no link-based data will be used.**

1. Speed Assumptions

**Emissions from motor vehicles vary with the manner in which the vehicle is operated. Vehicles**  **traveling at 65 miles per hour (mph) emit a very di fferent mix of pollutants than the car that is idli**  **ng at a stoplight. NCDAQ will collect hourly speeds per fu**  **nctional class for this modeling effort. Informati on**  **from Travel Demand Models will be used where availa ble. SCDHEC will use MOBILE 6.2 default speed**  **assumptions for this modeling effort.**

2. Vehicle Age Distribution

**The North Carolina vehicle age distribution comes f rom NCDOT annual registration data. Both**  **statewide and area specific registration data is pr ovided. The only areas with “area specific” regist**  **ration data include the Charlotte/Gastonia, Raleigh/Durham**  **and Greensboro/ Winston-Salem areas. The latest available age distribution at the time of the model**  **ing will be used. SCDHEC will use MOBILE 6.2 default vehicle age distribution for this modeling**  **effort.**

3. Vehicle Mix Assumptions

**The North Carolina statewide vehicle mix will be de veloped by NCDAQ using the latest**  **available, at the time of the modeling, Highway Per formance Maintenance System count data. The raw**  **data is converted into MOBILE6.2 format following t he method outlined in the August 2004 guidance**  **document** EPA420-R-04-013, Technical Guidance on the Use of MO BILE6.2 for Emissions Inventory Preparation. **For the Metrolina nonattainment area, area-specif ic vehicle count data will be used to**  **generate the vehicle mix for all road types except for urban and rural interstates. Local data is not**  **available for the interstates; therefore, the State -wide mix data will be used.** 

**Version 2 of the SMOKE model uses the MOBILE5 eight vehicle classification format for the**  **vehicle mix. Therefore, the current vehicle mix fo rmat used by NCDAQ had to be converted from the**  **sixteen MOBILE6 vehicle classification format to co rrelate to the MOBILE5 eight vehicle classification**  **system. This was done using the guidance provided by the USEPA.** 

**SCDHEC will use the MOBILE 6.2 default vehicle mix assumptions for this modeling effort.** 

4. Temperature Assumptions

**MOBILE6 in the SMOKE emissions model uses the gridd ed (modeled) meteorology data to**  **calculate temperature. Spatial and temporal temper ature averaging will be implemented to minimize the**  **SMOKE (mobile) run times.**

5. Vehicle Inspection and Maintenance Program Ass umptions

**In the early 1990’s, North Carolina adopted emissio ns inspection requirements for vehicles in**  **nine urban counties. This program tests emissions at idle for 1975 and newer gasoline powered light a**  **nd heavy duty vehicles. The program is a basic, decen**  **tralized tailpipe test for Hydrocarbons and CO only**

**. In 2002, North Carolina implemented a new vehicle e**  **missions inspection program referred to as onboard diagnostics (OBDII). This program covers a**  **ll light-duty gasoline powered vehicles that are model year 1996 and newer. The program was impleme**  **nted in the original nine tailpipe test counties an d**  **expanded to a total of forty-eight counties by Janu ary 1, 2006. In addition, the idle test will be ph**  **ased-out 54** 

**in 2006 in the original nine counties. In order to accurately reflect these OBDII tests, two separate**  **programs must be incorporated into the 2002 input f iles. The implementation dates of each program are**  **also included in the input files.**

**South Carolina does not have any inspection and mai ntenance programs.** 

6. RVP Assumptions

**Reid Vapor Pressure (RVP) reflects a gasoline’s vol atility. North Carolina has adopted the Phase**  **II RVP of 7.8 psi during June-September as a contro l measure for the following counties: Davidson,**  **Durham, Forsyth, Gaston, Guilford, Mecklenburg, Wak e, Granville, and Davie. Lower RVP leads to**  **lower volatile organic compound emissions from gaso line handling and lowers vapor losses from motor**  **vehicles. The remaining areas have a RVP of 9.0 ps i during June-September. For remaining months,**  **RVPs are as follows:**

• **October RVP = 13.5 psi statewide**

• **November RVP = 13.5 psi statewide**

• **December RVP = 15 psi statewide**

• **January RVP = 15 psi statewide**

• **February RVP = 13.5 psi statewide**

• **March RVP = 13.5 psi statewide**

• **April RVP = 13.5 psi statewide**

• **May RVP = 9.0 psi statewide**

**South Carolina has an RVP of 9.0 psi for all counti es during May-September, as indicated in**  **USEPA’s Guide on Federal and State Summer RVP Stand ards for Conventional Gasoline Only**  **(EPA420-B-05-012 November 2005). For remaining mon ths, RVPs follow the ASTM D4814**  **Standard Specification for Automotive Spark-Ignitio n Engine Fuel. They are as follows:** 

• **October RVP = 13.5 psi statewide**

• **November RVP = 13.5 psi statewide**

• **December RVP = 13.5 psi statewide**

• **January RVP = 13.5 psi statewide**

• **February RVP = 13.5 psi statewide**

• **March RVP = 13.5 psi statewide**

• **April RVP = 13.5 psi statewide**

7. VMT Assumptions

**Mobile source emissions are calculated by multiplyi ng emission factors by daily VMT. In this**  **modeling exercise, NCDAQ will use VMT from Travel D emand Models where available. For all other**  **areas the VMT data will be provided by the NCDOT. SCDHEC will use VMT provided by the South**  **Carolina Department of Transportation (SCDOT).**

E. Biogenic Source Emissions

**A revised version of a commonly used biogenic emiss ions model, the Biogenic**  **Emissions Inventory System, has recently been devel oped and tested by USEPA over two separate**  **modeling domains/episodes. This version of the mod el (BEIS-3, v0.9) contains several changes over**  **BEIS-2, including the following: 55** 

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

• **Emission factors – many updates including some rece nt North American Research Strategy**  **for Tropospheric Ozone (NARSTO) modifications,**

• **Environmental algorithm -- includes a sunlit/shaded leaf solar radiation model.** 

**A series of sensitivity modeling simulations has be en completed and concluded that the more**  **recent BEIS-3 methodology will impact base case mod el ozone predictions in most parts of the US. The**  **preliminary tests have also shown that the newer bi ogenic emissions do not**  **appear to have a large effect on: 1) the control signal response, 2) relative red**  **uction factors resulting from a projected emissions change, or 3) overall regional model performance in**  **the eastern US.**

**For this particular application of BEIS-3, version 0.9 as currently incorporated in the SMOKE**  **processor will be used. This means that: 1) soil n itric oxide (NO) emissions shall be prepared withou**  **t the input of specific soil moisture and precipitation d**  **ata and 2) methanol emissions will not be modeled explicitly. Otherwise, the modeling should be iden**  **tical to a BEIS-3 (v1.0) application.**

**The BELD-3 landuse data on a Lambert conformal grid at 1-km resolution have already been**  **developed, are available, and will be used to estim ate biogenic emissions in this study. The BEIS mod**  **el also requires as input hourly, gridded temperature**  **and solar radiation data to estimate biogenic emiss ions,**  **and these data will be derived from the MM5 predict ions.** 

F. Development of Modeling Inventories

**The SMOKE emissions model will be used to create th e air quality model ready files. The**  **chemical speciation method used is the CB4 mechanis m. The gridding surrogates are based off the 2000**  **census data and are the most up to date available. The temporal profiles used to disaggregate the ann**  **ual emissions to the appropriate month, day and hour ar**  **e the latest available profiles provided with the SMOKE model with the exception of the EGU profiles,**  **which will be developed based on CEM data.**

**For each model-ready emissions inventory, separate air quality model-ready files will be created**  **for the EGU point sources, non-EGU point sources, a rea sources, dust, low-level fires, elevated fires,**  **non road mobile sources, on-road mobile sources, and bi**  **ogenic emissions. 56** 

VII. QUALITY ASSURANCE PLAN

**This section discusses the Quality Assurance (QA) p rocedures that will be used in the SIP**  **modeling. The QA procedures listed here describe t he combined efforts to be employed by VISTAS,**  **NCDAQ, and SCDHEC. The VISTAS contractors will per form QA on modeling inputs and outputs for**  **the modeling region as a whole. NCDAQ and SCDHEC w ill perform QA on their respective emission**  **inventories, as well as look at near state data for reasonableness. Additionally, the State agencies**  **will review the modeling outputs for reasonableness.** 

A. Quality Assurance Objectives

**In December 2002, the USEPA published extensive gui dance on developing a Quality Assurance**  **Project Plan (QAPP) for modeling studies (EPA, 2002 ). The objective of a QAPP is to ensure that a**  **modeling study is scientifically sound, robust, and defensible. The new USEPA guidance suggests that**  **a QAPP should include the following elements:** 

• **A systematic planning process including identificat ion of assessments and related**  **performance criteria**

• **Peer reviewed theory and equations**

• **A carefully designed life-cycle development process that minimizes errors** 

• **Clear documentation of assumptions, theory, and par ameterization that is detailed enough**  **so others can fully understand the model output**

• **Input data and parameters that are accurate and app ropriate for the problem** 

• **Output data that can be used to help inform decisio n-making** 

• **Documentation of any changes from the original qual ity assurance plan** 

**Moreover, the USEPA guidance specifies that differe nt levels of QAPP may be required**  **depending on the intended application of the model, with a modeling study designed for regulatory**  **purposes requiring the highest level of quality ass urance.** 

**The QAPP also provides a valuable resource for proj ect management. It can be used to document**  **data sources and assumptions used in the modeling s tudy, and it can be used to guide project personnel**  **through the data processing and model application p rocess to ensure that choices are consistent with t**  **he project objectives.** 

**The guidance document also addresses model developm ent, coding and selection of models, and**  **model performance requirements. VISTAS and NCDAQ/S CDHEC modeling are using an existing**  **USEPA sponsored model hence our QAPP will focus pri marily on documenting data sources and QA of**  **data processing performed by the model team. QA ob jectives for specific aspects of the project are**  **discussed below, and these will be incorporated int o a QAPP that conforms to the USEPA guidance**  **document for modeling studies.**

B. Emissions Model Inputs and Outputs

**Emissions QA and Quality Control (QC) are the most critical steps in performing air quality**  **modeling studies. Emissions processing can be time consuming and involves complex manipulation of**  **many different types of large datasets. If errors are made and rigorous QA measures are not in place,**  **these errors may remain undetected, resulting in de lays and wasted time and resources.** **57**

1. VISTAS QA Effort

**As part of the VISTAS QA effort, an "Emissions Gate keeper" function will be implemented.**  **The role of this Gatekeeper is to perform quality a ssurance activities on the following emissions**  **inventory data:**

• **Emissions inventory data obtained from the VISTAS e missions inventory contractors** 

• **The emission inventory to be used for modeling outs ide of the states in the VISTAS**  **region.**

**Specifically, the Emissions Gatekeeper will review the content and format of the provided**  **emission inventories, ensuring an appropriate appra isal of the emissions data and estimates for the**  **VISTAS States. Other tasks will include any additi onal translation from mass emissions files into the**  **emissions modeling input file structure necessary f or modeling. The VISTAS Study Team will**  **supplement these activities with QA checks on the i ntermediate and model output files using internal a**  **nd public domain visualization and diagnostic packages** 

**. This multistep emissions QA/QC approach includes th**  **e initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA**  **/QC by the Emissions Modeler during the processing of emissions, and then additional QA/QC**  **by the air quality modeler of the processed model ready emission files. This multistep process, with**  **three separate groups involved in the QA/QC of the emissions, is much more likely to catch any errors**  **prior to the air quality model simulations.**

2. Emissions Modeling QA/QC

**Modeling QA involves performing data quality checks , assuring simulation accuracy, and**  **recognizing and identifying problems as they happen ; it is the process of looking for glaring faults i**  **n the model input and output data (I/O) and determining w**  **hether the input data are producing the desired results. Scrutiny of the I/O using standard statis**  **tical analyses can reveal problems in the data and/ or the**  **model setup. Using a standard approach for analyzi ng emissions model I/O establishes reference points**  **to use when scrutinizing the data. Seeking these i ndicators of correct model performance allows QA**  **personnel to determine the accuracy of the simulati ons and whether faults in the data or model**  **configuration exist.**

**QA documentation will include records of model conf iguration, details about data files,**  **simulation records, and final report generation. A fter finishing each QA step, the modeler will recor**  **d the result and his/her initials on a QA checklist.** 

**Data formats will be confirmed using the SMOKE manu al to check text files and using PAVE to**  **check binary netCDF files, such as the meteorology inputs. Sanity checks look for glaring errors in t**  **he file contents and ensure that the data make sense i**  **n the context of how they will be used and relative to**  **similar or reference datasets.**

**Lead modelers will oversee the entire modeling proc ess, perform the majority of the SMOKE**  **modeling, and receive and archive input and output data. Secondary modelers will perform some of the**  **SMOKE modeling, organize the SMOKE QA reports into emissions summaries for data QA and**  **reporting, and will generate custom QA summaries an d reports for troubleshooting any problems**  **encountered during the modeling process.**

**Outside reviewers will be solicited from outside th e emissions modeling team on a volunteer**  **basis to conduct periodic reviews/audits of the dat a and modeling process. Outside reviewers will con**  **sist 58** 

**of peers, co-workers not working directly on the in ventory in question, state inventory contacts and**  **stakeholders.**

a. SMOKE Log Files

**Each of the programs that make up SMOKE produces a log at run time. Stored in a single**  **directory for each unique simulation, the logs cont ain information about the configuration of SMOKE, t**  **he names and locations of the input and output files u**  **sed in the simulation, and any warnings, notes, or errors (collectively called "flags") that occurred**  **during model execution. Generated as text files, t he logs**  **are named according to the program that created the m and the emissions source modeled by the**  **simulation, and the names include identifiers that distinguish the simulation from all others. The lo**  **gs are usually the first source of information consulted i**  **n determining whether a simulation completed as expected or for troubleshooting suspected problems.** 

b. SMOKE QA Reports

**Two types of QA reports are generated by SMOKE. On e set is created by the program**  Smkmerge **and the other by the program** Smkreport **. While both programs allow users to configure the content of the**  **reports,** Smkreport

**is a more powerful reporting program that was desi gned specifically as a QA tool.**  **Controlled by configuration files,** Smkreport **can create text reports at every step in the emiss ions**  **generation process. In addition to creating report s from information drawn from the intermediate**  **SMOKE data matrices (e.g., the temporal matrix),** Smkreport **can summarize the amount of emissions assigned to different temporal, spatial, and chemic**  **al profiles; normalize emissions by population; and report the amount of emissions allocated to each ve**  **rtical layer per model-hour.** Smkreport **also allows the targeted reporting of emissions at specific sources**  **, plants, grid cells, or subdomains.**

**The program** Smkmerge **creates either state- or county-level reports at e ach of the major steps in**  **the emissions generation process (spatial allocatio n, temporal allocation, chemical allocation**  **or speciation, and merging). Although**  Smkmerge **cannot create as many different report types as** Smkreport

**,** Smkmerge **does have the ability to report biogenic emissions totals, whereas**  Smkreport **can create reports only for anthropogenic emissions sources.** 

4. Visualization Tools

**Visualization is an important part of the QA/QC pro cedure. Viewing bar charts and pie charts of**  **the data verifies that more populous urban counties have greater emissions then the rural counties.**  **Additionally, the PAVE visualization tool is used t o graphically view the data to make sure that the d**  **ata appears reasonable both spatially and temporally.** 

**Visualization tools will be used to assist in the Q A process for the emissions data both before and**  **after being processed through the SMOKE emissions m odel. The air quality data will also employ**  **visualization tools to view the modeling results to ensure that the modeling results look reasonable.** 

5. Document Tracking

**In order to keep track of the details of modeling, certain notes and files will be maintained. Notes**  **will be kept of files produced on desktop computers as to origin and purpose. These notes may be**  **maintained in a logbook or by using the file proper ties summary tag available for files in the Windows**  **operating system. Files in the workstation will be similarly tracked. It may be useful to maintain a**  **log within directories for this purpose.** 

C. Meteorological Model Outputs**59**

**As part of the VISTAS QA effort, a "Meteorological Gatekeeper" will be tasked with providing**  **an independent review and quality assurance of the meteorological modeling and related datasets**  **developed by the VISTAS meteorological modeling con tractor (BAMS) and used subsequently by the**  **emissions and air quality modeling teams. This Gat ekeeper QA review ensures that any potential**  **problems with the datasets (should they exist) are identified and corrected in a timely manner. In th**  **e case of meteorology, the Gatekeeper’s independent Q**  **A analysis of the MM5 meteorological datasets serves to provide direct assistance to the emission**  **s and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ**  **model performance and sensitivity analyses.**

**In addition to having personal responsibility for t he quality and chain of custody of the**  **meteorological datasets supplied by other VISTAS co ntractors, the Meteorological Gatekeeper will be**  **responsible for ensuring and maintaining the integr ity of the data files uploaded to the project websi**  **te. This website, hosted by UCR (University California**  **– Riverside), serves as the repository of data for the**  **ENVIRON/UCR/Alpine modeling centers and for the VIS TAS Technical Analysis Workgroup**  **participants. In performing the Gatekeeper quality assurance activity, one of the first steps is to c**  **onduct an independent operational evaluation on the MM5 mo**  **del results at 36 km and 12 km grid scale. This evaluation covers surface and aloft wind direction,**  **temperature, mixing ratio, precipitation, and PBL depths on a continental scale (36 km) and subregion**  **al scale (12 km) basis.**

**The Gatekeeper will also perform supplemental, ad h oc analysis of pertinent MM5 fields (e.g.,**  **PBL depths) where that might be useful to the emiss ions and air quality modeling teams. Another task**  **of the Gatekeeper will be to exercise MCIP version 2.3**  **to read the MM5 outputs from BAMS and produce binary input files for the CCTM to provide the comp**  **lete set of parameters necessary in the emissions processing and air quality modeling.** 

**In summary, the quality assurance plan for the mete orological data will include the**  **following elements:**

• **Upon receiving the MM5 and MCIP 2.3 output files fr om BAMS, NCDAQ and**  **SCDHEC will verify the integrity of the file transf er (i.e., no missing and/or corrupted**  **files).**

• **Since the CMAQ modeling domain is a subset of the M M5 domain, NCDAQ and**  **SCDHEC will verify that the modeling domain and ver tical layer structures in the MCIP**  **files are identical to the CMAQ modeling domain.**

• **Several days of the MM5 output will be selected and the meteorological modeling team**  **will reprocess the MM5 files with MCIP v2.3 using t he predetermined MCIP options.**  **The MCIP files will then be compared with those pro vided by BAMS to verify that**  **identical results from the MCIP processing were obt ained.** 

• **Horizontal and vertical plots of temperature, press ure, precipitation, modeled flow**  **patterns, PBL heights, etc. will be created to asse ss whether the MCIP output fields are**  **reasonable.**

• **The VISTAS 2002 MM5 simulation will be evaluated us ing the same surface**  **observations, subdomains and procedures as used to evaluate the Western Regional Air**  **Partnership 2002 MM5 simulation as an independent Q A and evaluation of the**  **database.**

• **Plots constructed by the VISTAS Gatekeeper will be made available on the VISTAS**  **website for viewing and download (http://pah.cert.u cr.edu/vistas/vistas2/index.shtml**  **).**

D. Air Quality Model Inputs and Outputs

**Key aspects of QA for the CMAQ input and output dat a include the following:** **60** • **Verification that correct configuration and science options are used in compiling and**  **running each model of the CMAQ modeling system, whe re these include the MCIP,**  **JPROC, ICON, BCON and the CCTM.**

• **Verification that correct input datasets are used w hen running each model.** 

• **Evaluation of CCTM results to verify that model out put is reasonable and consistent with**  **general expectations.**

• **Processing of ambient monitoring data for use in th e model performance evaluation.** 

• **Evaluation of the CCTM results against concurrent o bservations.** 

• **Backup and archiving of critical model input data.**

**The most critical element in the QA plan for CMAQ s imulations is the QA/QC of the**  **meteorological and emissions input files. The majo r QA issue specifically associated with the air qua**  **lity model simulations is verification that the correct**  **science options were specified in the model itself and**  **that the correct input files were used when running the model. For the CMAQ model, a system of naming**  **conventions was employed which uses environment var iables in the compile and run scripts that**  **guarantee that correct inputs and science options a re used. A redundant naming system is also used so**  **that the name of key science options or inputs is i ncluded in the name of CMAQ executable program, in**  **the name of the CMAQ output files, and in the name of the directory in which the files are located. T**  **his is accomplished by using the environment variables**  **in the scripts to specify the names and locations o f**  **key input files. For example, if a model simulatio n is performed using the CB4 mechanism, all compile**  **and run scripts contain the variable definition “$M ECH = CB4,” and this variable is hard coded into th**  **e script for the executable name, the output file nam**  **e, and the output directory name. This procedure produces long file/directory names but it effective**  **ly prevents mistakes or makes mistakes readily appa rent**  **if they do occur.**

**A second key QA procedure is to never “recycle” run scripts (i.e., the original runs scripts and**  **directory structure that were used in performing a model simulation). For example, if a simulation is**  **performed with the SAPRC mechanism, instead of edit ing the original scripts to specify “$MECH =**  **SAPRC,” a parallel directory structure with a new s et of scripts to perform the SAPRC simulations will**  **be created. This provides a permanent archive of t he scripts that were used in performing model**  **simulations. In addition, output from the model si mulation will be directed to a log file that provid**  **es a record of input file names, warning messages, etc.,**  **that will be archived.**

**Post-processing QA of the CMAQ output files similar to that described for the emissions**  **processing will be performed. Animated graphics in terchange format (GIF) files using PAVE will be**  **generated to search for unexpected patterns in the CMAQ output files. In the case of model sensitivit**  **y studies, the animated GIFs will be prepared as diff**  **erence plots for the sensitivity case minus the bas e**  **case. Often, errors in the emissions inputs can be discovered by viewing the animated GIFs. Finally,**  **24- hour average plots for each day of the CMAQ simulat**  **ions will be produced. This provides a summary that can be useful for more quickly comparing vario**  **us model simulations. 61** 

VIII. MODEL PERFORMANCE EVALUATION

**USEPA guidance (EPA, 2005, pg. 85; EPA, 2001, pg 22 7) suggests that model performance be**  **evaluated based on two components:**

• **How well the model is able to replicate observed co ncentrations of ozone and precursors,**  **and**

• **How accurately the model characterizes the sensitiv ity of changes in component**  **concentrations to changes in emissions.**

**Each component suggests a different type of evaluat ion procedure, with the first being “operational**  **evaluation,” and the second being “diagnostic evalu ation.” Since the attainment test is a relative te**  **st, it is not as necessary to exactly duplicate ozone concent**  **rations. As a result, there is now more emphasis placed on the diagnostic model evaluation.** 

**This section outlines the method used to evaluate m odel performance. Working with the**  **knowledge that many states involved with the VISTAS regional haze work would want to apply some of**  **the work to their individual SIPs for 8 hour ozone and PM**  **2.5 , plans were put in place to perform**  **exhaustive analysis of all atmospheric constituents , including ozone. NCDAQ and SCDHEC intend to**  **build off the modeling efforts with VISTAS; therefo re, the model performance evaluation will be an**  **extension of VISTAS efforts.**

A. Model Evaluation Tools (Operational Evaluation )

1. Statistical Performance Metrics

**In compliance with USEPA’s September 2006 guidance document**  Guidance on the Use of Models and Other Analyses for Demonstrating Attainm ent of the Air Quality Goals for Ozone, PM 2.5 and Regional Haze **, VISTAS will compile a suite of metrics for use in evaluating model performance. The**  **standard set of statistical performance measures su ggested by USEPA for evaluating ozone models**  **includes: normalized bias, normalized gross (unsign ed) error, fractional bias, fractional gross error,**  **and fractional bias in standard deviations. Several ot**  **her measures will be included in the final report t o fulfill**  **the requirements in the 8-hour ozone guidance (addi tion of average peak prediction accuracy), and to**  **better accommodate other modeling groups with their comparison of modeling efforts. A list of metrics**  **for calculation on a routine basis using the UCR an alysis package is listed in Table 8-1. The metrics**  **calculated in conjunction with VISTAS will include the examination of various atmospheric constituents** 

**, including ozone.** 

**Typically, the statistical metrics are calculated a t each monitoring site across the full**  **computational domain for all simulation days. Duri ng the VISTAS CMAQ evaluation, the gas-phase and**  **aerosol statistical measures shown in Table D-8 wil l be computed for the full 36 km and 12 km domains,**  **as well as for the individual RPOs and on other sub domains as appropriate. Temporally, the statistica**  **l measures will be computed for the appropriate avera**  **ging times: 1 hr for ozone, and gas-phase precursor s**  **such as NO, nitrogen dioxide (NO 2**  **), CO, SO 2**  **, 8-hour for ozone, and 24-hour for sulfate, nitrat e, PM**  **2.5 ,**  **and other aerosol species. These results will then be averaged over annual, monthly, and seasonal per**  **iods for display, further analysis, and reporting. Shou**  **ld it become necessary as part of model performance diagnosis, the statistics will be aggregated in oth**  **er ways, e.g., (a) day vs. night, (b) weekday vs. w eekend,**  **(c) precipitation vs. non-precipitation days, (d) m onth of the year, and (e) the 20% haziest/cleanest**  **days, in order to help elucidate model performance proble**  **ms. For the purposes of the 8-hour ozone SIP for t he**  **Metrolina nonattainment area, only the statistics f or 1-hour and 8-hour ozone will be reported. The**  **statistics for the pollutants and precursors will b e reviewed internally for reasonableness.** **62**

**Table D-8: Statistical Metrics Statistical**  **Measure Shorthand** 

**Notation Mathematical Expression Notes** 

**Accuracy of Paired Peak**  **A p** 

peak peak 

### O O P

−

**Coefficient of Determination**  **r 2**  

# ( ) 

# ( )

∑ ∑ ∑

= = =



### − − 

    

− −

N

i N

i i i N

i i i



### O O P P O

O P P

**1 1 2** 

**2 2** 

**1** 

### )

**( )**  **( P**  **i = prediction at time and**  **location i; O**  **i = observation at time and**  **location i;** 

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

### O **= arithmetic average of O i**  **, i = 1, 2, . . ., N;** 

**Normalized Mean Error**  **NME**



# ∑ ∑

= = 

### −

N

i i N

i i i



### O O P

**1 1**  **Reported as %**

**Root Mean Square Error**  **RMSE** 

## ( ) **2 1** 

**1 2**  

### 1 

    

− 

# ∑

= N

i i i 

### O P N **Reported as %**

**Fractional Gross Error**  **F E**  

# ∑

= 

### + − N

i i i i i 

### O P O P
N **1** 

### 2 **Reported as %**

**Mean Absolute Gross Error**  **MAGE** 

# ∑

= 

### − N

i i i 

### O P N **1** 

### 1

**Mean Normalized Gross Error**  **MNGE** 

# ∑

= 

### − N

i i i i 

### O O P

N **1** 

### 1 **Reported as %**

**Mean Biased MB** 

## ( ) 

# ∑

= 

### − N

i i i 

### O P N **1** 

### 1 **Reported as concentration**

**Mean Normalized Bias**  **MNB** 

## ( ) 

# ∑

= 

### − N

i i i i 

### O O P
N **1** 

### 1 **Reported as %**

**Mean Fractionalized**  **Bias (Fractional Bias)**  **MFB** 

# ∑

= 

### 

      

+ − N

i i i i i 

### O P O P
N **1** 

### 2 **Reported as %**

**Normalized Mean Bias**  **NMB** 

## ( )


# ∑ ∑

= = 

### −

N

i i N

i i i



### O O P

**1 1**  **Reported as %**

**Bias Factor BF** 

# ∑

= 

###         N

i i i 

### O P
N **1** 

### 1 **Reported as BF:1 or 1:BF or in fractional notation (BF/1 or**  **1/BF) 63** 

2. Graphical Representations

**The core operational air quality model evaluation w ill utilize numerous graphical displays to**  **facilitate quantitative and qualitative comparisons between CMAQ predictions and measurements.**  **Together with the statistical metrics listed in Tab le 8-1, the graphical procedures are intended to he**  **lp: (a) identify obviously flawed model simulations, (b) gu**  **ide the implementation of any performance improvements in the 2002 model input files in a log**  **ical, defensible manner, and (c) to help elucidate the**  **similarities and differences between the alternativ e CMAQ simulations. These graphical tools are**  **intended to depict the model’s ability to predict t he observed gaseous species, such as ozone, and fin**  **e particulate species concentrations. The core graph**  **ical displays to be considered for use in model performance evaluation include the following:** 

• **Spatial mean concentration time series plots**

• **Time series plots at monitoring locations**

• **Ground-level gas-phase and particulate concentratio n maps (i.e., tile plots)** 

• **Concentration scatter plots stratified by station, by time, and by network** 

• **Bias and error stratified by concentration**

• **Bias and error stratified by time**

• **Histogram plots of the statistical metrics, stratif ied by day, by pollutant, by subregion**  **(e.g., 12 km vs. 36 km, by RPO), and by monitoring network** 

• **Quantile - Quantile (Q-Q) plots**

• **Animations of predicted hourly ozone concentration**

**These graphical displays will be generated, where a ppropriate, for the full annual cycle as well as fo**  **r monthly and seasonal periods.** 

B. Model Performance Testing (Diagnostic Evaluati on)

**Rarely does a modeling team find that the first sim ulation satisfactorily meets all (or even most)**  **model performance expectations. Based on experienc e, initial simulations that “look very good” usuall**  **y do so as the result of compensating errors. The no**  **rm is to engage in a logical, documented process of model performance improvement wherein a variety of**  **diagnostic probing tools and sensitivity testing methods are used to identify, analyze, and then att**  **empt to remove the causes of inadequate model performance. This is invariably the most technical**  **ly-challenging and time consuming phase of a modeling study. The annual CMAQ model base case si**  **mulations are expected to present some performance challenges that may necessitate focused**  **diagnostic and sensitivity testing in order for th em**  **to be resolved. It is hoped that these diagnostic and/or sensitivity tests can be adequately carried**  **out within the resources and schedule. Where practical**  **, diagnostic or sensitivity analyses, if needed, co uld be**  **performed on selected episodes within the annual cy cle, thereby avoiding the time-consuming task of**  **running CMAQ for the full 2002 period. Below, the types of diagnostic and sensitivity testing methods**  **that might be employed in diagnosing inadequate mod el performance and devising appropriate methods**  **for improving the model response are identified.**

1. Traditional Sensitivity Testing

**Model sensitivity experiments are useful in three d istinct phases, or “levels,” of an air quality**  **modeling study and all will be used as appropriate. These levels are:** 

• Level I **: Model algorithm evaluation and configuration tes ting** **64** • Level II **: Model performance testing, uncertainty analysis and compensatory error**  **diagnosis**

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

**The Level I and Level II cover the aspect of operat ional evaluation, while Level III covers diagnostic**  **evaluation.**

**The Level I sensitivity tests with CMAQ have alread y been completed in the initial VISTAS**  **configuration and diagnostic analyses. However, gi ven that open community nature of CMAQ and the**  **frequent science updates to the model and supportin g data bases, it is possible that some additional**  **configuration sensitivity testing will be necessary**

**. Potential Level II sensitivity analyses might be he**  **lpful in accomplishing the following tasks:**

• **To reveal internal inconsistencies in the model**

• **To provide a basis for compensatory error analysis**

• **To reveal the parameters (or inputs) that dominate (or do not dominate) the model’s**  **operation**

• **To reveal propagation of errors through the model**

• **To provide guidance for model refinement and data c ollection programs** 

**The merits of performing Level II sensitivity testi ng will depend upon whether performance**  **problems are encountered. In addition, the number of tests possible, should performance difficulties**  **arise, will be limited by the available schedule and resou**  **rces. From past experience with CMAQ and other models, it is possible to identify examples of sens**  **itivity runs that could be useful in model performa nce**  **improvement exercises with the annual 2002 CMAQ sim ulation. These include:** 

• **Modified biogenic emissions estimates**

• **Modified on-road motor vehicle emissions**

• **Modified air quality model vertical grid structure**

• **Modified boundary conditions**

• **Modified fire emissions**

• **Modified EGU emissions**

• **Modified ammonia emission estimates**

• **Modified aerosol/Nitric Pentoxide/Nitric acid (HNO 3**  **) chemistry**

• **Modified ammonia and HNO3 deposition velocities**

**Note that in a few cases [e.g., vertical grid struc ture, ammonium (NH**  **4 ) emissions estimates], some**  **sensitivity experimentation has already been carrie d out by VISTAS. To the extent that this informati**  **on can help guide the future diagnostics analyses, thi**  **s earlier work will be used.**

**Level III sensitivity analyses have two main purpos es. First, they facilitate the emissions control**  **scenario identification and evaluation processes. Currently, four complimentary sensitivity “tools” c**  **an be used in regional photochemical models depending upo**  **n the platform being used. These methods include: (a) traditional or “brute force” testing, (b) Decou**  **pled Direct Method, (c) Ozone Source Apportionment Technology and PM Source Apportionment Technology,**  **and (d) Process Analysis. Each method has its strong points and they will be employed where neede**  **d. The second purpose of Level III sensitivity analyses is to help quantify the estimated reliabil**  **ity of the air quality model in simulating the atmosphere’s response to significant emissions chan**  **ges.**

**Examples of Level III monthly or annual sensitivity runs for Phase II might include:** **65** • **Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO**  **2 emissions** 

• **Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point source**  **NO x**  **emissions**

• **Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level NO** 
**x emissions** 

• **Sulfate, nitrate, ammonium and other aerosol sensit ivities to ammonia** 

**The need to perform sensitivity experimentation (Le vels I, II, or III) will depend on the outcome**  **of operational performance evaluations. If such a need arises, the ability to actually carry out sele**  **cted sensitivity and/or diagnostic experiments will hing**  **e on the availability resources and sufficient time to**  **carry out the analyses. Clearly, selection of the specific analysis method will depend upon the natur**  **e of the technical question(s) being addressed at the ti**  **me.**

C. Air Quality and Ozone Column Data

**Data from ambient monitoring networks for both gas and aerosol species are used in the model**  **performance evaluation. Table D-9 summarizes ambie nt monitoring networks used to collect data for Air**  **Quality model performance evaluation. Data have be en compiled for all networks listed except the**  **Photochemical Assessment Monitoring Stations (PAMS) and PM Super-sites.** 

**Additional data used in the air quality modeling in clude the Total Ozone Mapping Spectrometer**  **(TOMS). TOMS data provides ozone column data, is a vailable for 24-hour average, and is obtained from**  **http://toms.gsfc.nasa.gov/eptoms/ep.html . The TOMS data is used in the CMAQ radiation mode**  **l to calculate photolysis rates.** **66**

**Table D-9: Overview of Ambient Data Monitoring Net works.**  Monitoring Network Chemical Species

Measured Sampling Period Data Availability/Source **The Interagency Monitoring of**  **Protected Visual Environments**  **(** IMPROVE **) Speciated PM**  **2.5 and PM**  **10**

**(see species mappings) 1 in 3 days;**  **24 hr**
**average http://vista.cira.colostate.edu/improve/Data/I** 

**MPROVE/improve_data.htm**

**Clean Air Status and Trends**  **Network (**  CASTNET **) Speciated PM**  **2.5 , Ozone** 

**(see species mappings) Approxima**  **tely 1- week** 
**average http://www.epa.gov/castnet/data.html** 

**National Atmospheric**  **Deposition Program (**  NADP **) Wet deposition (hydrogen** 

**(acidity as pH), sulfate, nitrate, ammonium,**  **chloride, and base cations (such as calcium,**  **magnesium, potassium and sodium)), Mercury**  **1-week**
**average http://nadp.sws.uiuc.edu/** 

**Air Quality System (**  AQS **) Aka Aerometric**  **Information Retrieval System**  **(** AIRS **) CO, NO**  **2 , O3, SO**  **2 , PM**  **2.5 ,** 

**PM 10**  **, Pb Typically**  **hourly**
**average http://www.epa.gov/air/data/** 

**Speciation Trends Network (**  STN **) Speciated PM**  **24-hour**
**average http://www.epa.gov/ttn/amtic/amticpm.html** 

**Southeastern Aerosol Research**  **and Characterization**  **(** SEARCH **) 24-hr PM**  **2.5 (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)**  **Hourly or 24hour** 
**average, depending** 

**on parameter.**  **Electric Power Research Institute (EPRI),**

**Southern Company, and other companies. http://www.atmospheric-research.com** 

**USEPA Particulate Matter Supersites**  **Speciated PM 2.5**  **http://www.epa.gov/ttn/amtic/supersites.html**

**Photochemical Assessment**  **Monitoring Stations (**  PAMS **) Varies for each of 4 station** 

**types. http://www.epa.gov/ttn/amtic/pamsmain.html** 

**National Park Service Gaseous**  **Pollutant Monitoring**  **Network Acid deposition (Dry; SO**  **4 ,** 

**NO 3**  **, HNO 3**  **, NH 4**  **, SO 2**  **), O**  **3 , meteorological data**  **Hourly http://www2.nature.nps.gov/ard/gas/netdata1.**  **htm 67** 

IX. CONTROL STRATEGY

**It is important to remember that photochemical mode ls are tools; they do not make decisions.**  **The results from photochemical models are one of se veral pieces of information that decision-makers**  **must consider when adopting control strategies. To ensure that the modeling analyses provide**  **information that meets the needs of the decision ma kers, it is imperative that the air quality modeler**  **s and decision makers agree upon the type and amount of i**  **nformation that is needed to meet the study objectives. This section outlines the process behi**  **nd developing and evaluating emission control strat egies**  **to be employed for the 8-hour ozone SIP.**

A. Control Strategy Design

1. Emission Sensitivity Test

**To begin the process of control strategy design a s eries of simulations using across-the-board**  **reductions of VOC, NO**
**x , or VOC and NO** 
**x combined can be run. The purpose of these simulat**  **ions is to evaluate the relative effectiveness of VOC and N**  **O x**  **controls.**

**Errors in emission estimates can lead to errors in control decisions. Important sources in future**  **year inventories can be simulated at the lower and upper bounds of their estimated accuracy. In doing**  **this, NCDAQ and SCDHEC can find out if changes, wit hin the known accuracy of the emission**  **estimates, can lead to different decisions for cont rol strategies. Once the future year inventories a**  **re assembled, the sources with the highest uncertainty**  **can be identified. These sources could include biogenic emissions, motor vehicle exhaust, and gaso**  **line evaporation. VOC speciation profiles can also be included in these sensitivity tests.** 

2. Isopleth Construction

**From the emissions sensitivity tests, isopleths rel ating uniform reductions of VOC and NO** 
**x emissions to peak ozone can be constructed. These**  **isopleths can give some insight into emission reduction goals, but are not designed to evaluate s**  **pecific control strategies. They do not simulate r eal**  **controls that change temporal and spatial distribut ions as well as the organic mix of species. With t**  **hese limitations in mind, the isopleths can help design**  **the control measures that may reduce levels close t o**  **ambient standards. If resources are available, a s eries of simulations covering a range of actual con**  **trol measures will be run. From these simulations, peak**  **ozone isopleths can be generated for the entire ar ea**  **as well as for individual sites. These isopleths c an be used to design appropriate and defensible con**  **trol strategies. In addition, isopleths of population e**  **xposure can be prepared and used to assess proposed control measures in an integrated manner.** 

3. Ranking Control Strategies

**Control strategies should be implemented in an orde red fashion that reduces both peak ozone**  **concentrations and population exposure. Emission c ontrols that affect both VOC and NO** 
**x should be**  **sorted separately from VOC or NO**
**x -only controls. Estimates of control levels that a**  **re expected in future years should be made. An attempt to reduce populat**  **ion exposure to a minimum each year while reducing peak ozone, can be made by looking at all potential**  **ly available controls and at subsets which focus on VOC only, NO** 
**x**

**only, and combinations of VOC and NO**
**x control.** **68**

B. Control Strategy Evaluation

**Selection of candidate control strategies will take into consideration the results of the**  **combination of analyses described in the previous s ections. Once candidate control strategies are**  **identified, the strategies may be simulated. If ne eded, an analysis will be performed to investigate**  **the predicted impact of each strategy on air quality an**  **d population exposure. The results of these analys es**  **will be summarized both in tabular and graphical fo rm to allow systematic comparison and contrast of a**  **ll strategies.** 

**To assist decision makers in fully understanding th e impact of proposed control strategies, the**  **following products may be prepared as a part of the control strategy evaluation:** 

**1. Peak Ozone Tables**  **2. Peak Ozone Spatial Plots**  **3. Difference Plots**  **4. Population Exposure Tables and Histograms**  **5. Change in predicted future design values** 

**Each of these products will compare future year bas e simulations with one or more control**  **simulations. An attempt should be made to minimize population exposure as controls are introduced. T**  **o assist in this effort, population exposure and peak**  **ozone statistics can be organized by future year a nd**  **control strategy. Upon completion of this evaluati on, a final control strategy will be selected for d**  **etailed evaluation.** 

C. Identification of Control Strategy Scenarios

**A designated subcommittee will select the control s trategy scenarios to be modeled for**  **demonstrating attainment. The control strategy sel ection process will follow the current USEPA**  **guidance, and will incorporate our present understa nding of ozone formation on an urban and regional**  **scale.**

**Mandated controls will be modeled first (inspection and maintenance programs, NO** 
**x SIP Call,**  **North Carolina’s Clean Smokestacks Act legislation, federal engine standards, federal fuel standards,**  **etc.). If attainment of the ozone NAAQS is not sho wn, additional alternative control strategies ident**  **ified by the preceding steps, will be modeled until attai**  **nment is reached.**

**A “frozen” future year dataset will be available fo r use in testing alternative control strategies.**  **This will consist of a set of model input and outpu t files for each episode. Anyone with access to th**  **e model (e.g., power companies and universities) can**  **use these files as long as they do not change futur e**  **base case emission inventories, meteorology, growth factors, or mandated controls. Alternative contro**  **ls can be modeled in addition to controls strategies m**  **odeled by the states. 69** 

X. DEMONSTRATION OF ATTAINMENT

**This section summarizes the procedures that will be used to demonstrate attainment of the 8-hour**  **ozone NAAQS. An attainment demonstration consists of (a) analyses which estimate whether selected**  **emissions reductions will result in ambient concent rations that meet the NAAQS, and (b) an identified**  **set of measures that will result in the required emissi**  **ons reductions. Determining necessary emission reductions may be done by relying exclusively on re**  **sults obtained with air quality models. These incl ude**  **the outcomes of the modeled attainment test plus a screening test to estimate whether a proposed emiss**  **ion reduction suffices to meet the NAAQS.** 

A. Ozone Model Attainment Test

**The modeled attainment test is the practice of usin g an air quality model to simulate current and**  **future air quality. For the 8-hour ozone NAAQS, th e model estimates are used in a “relative” rather t**  **han “absolute” sense. That is, the ratios of the model**  **’s future to current predictions at each ozone moni tor are**  **used to determine attainment. These site-specific ratios are called relative reduction factors. Futu**  **re ozone design values are estimated at existing monit**  **ors by multiplying the monitor-specific modeling current design value by the modeled relative reduct**  **ion factor “near” each monitor. If the resulting predicted site-specific future design values are gr**  **eater than 87 ppb, the modeled attainment test is f ailed.**  **If the predicted future design values are between 8 2 and 87 ppb, then additional weight of evidence is**  **needed to corroborate a positive attainment demonst ration. If the predicted future design values are**  **below 82 ppb, the area is determined to clearly att ain the standard. Equation 10.1 describes the mode**  **led attainment test, applied near monitoring site “x.”** 

**(DVF) = (RRF) (DVB)** Equation 10.1

**(DVB) = the modeling baseline design value monitore d at site "x," in units of ppb. Currently the**  **USEPA recommends calculating the DVB by averaging t he three design value periods to include**  **the baseline inventory year. In other words, the D VB is based on a 5-year weighted average**  **centered on the baseline inventory year.**

**(RRF) = the ratio of the future 8-hr daily maximum concentration predicted near a monitor**  **(averaged over each day of the episode) to the curr ent 8-hr daily maximum concentration**  **predicted near the monitor (averaged over each day of the episode).** 

**(DVF) = the estimated future design value for the t ime attainment is required, ppb.** 

**It is important to consider an array of cells “near ” a monitor rather that focusing on the individual**  **cell containing the monitor. This diminishes the l ikelihood of quirks in the test’s results resulting**  **from geometry of the superimposed grid system. Table D-**  **10 provides the USEPA recommendations for defining “nearby” cells for grid systems having cel**  **ls of various sizes.**

**Table D-10: USEPA Recommendations for Defining nea rby Cells.**  **Size of Cell, Size of the Array of Nearby Cells, (km) (unit less)**  **<5 7 x 7**
**>5-8 5 x 5 >8-15 3 x 3**  **>15 1 x 1 70** 

B. Screening Test

**Per the USEPA Guidance, the states will perform an analysis of unmonitored areas to determine**  **if attainment of the ozone standard is expected in these areas. The unmonitored area analysis, or**  **screening test, will be achieved through the use of the USEPA’s Model Attainment Test Software**  **(MATS). This tool will allow for spatial interpola tion of baseline monitoring data, which will provid**  **e modeling current DVBs for an entire area and not ju**  **st at monitoring sites. This field is then paired with**  **the modeling results in MATS to produce DVFs for an entire geographic area. This final gradient**  **adjusted spatial field can then be examined for any unmonitored areas that area predicted not to meet**  **the ozone NAAQS. Any violation suggested by this analy**  **sis then will then be carefully evaluated.**

C. Corroborative Analysis

**After the completion of the attainment test, the US EPA 8-hour Ozone Guidance suggests**  **additional measures should be taken to further supp ort or refute the attainment test results. This**  **corroboratory evidence is referred to as supplement al analysis when used to further support an attainm**  **ent demonstration. A weight of evidence determination**  **can be used to conclude that attainment is likely, especially when the predicted future design values**  **are between 82 and 87 ppb. Analysis can include a wide variety of tests and analyses, including the a**  **pplication and results of air quality models, obser ved air**  **quality trends and estimated emissions trends, and the outcome of observational models.** 

**Should the area, clearly demonstrate attainment (DV F < 82 ppb), then basic supplemental**  **analysis will be performed to further support the t est’s findings. If either the attainment or screen**  **ing tests are greater than 87 ppb, it is doubtful that the mo**  **re qualitative arguments made in a weight of eviden ce**  **determination can be sufficiently convincing to con clude that the NAAQS for ozone will be attained.** 

**For DVFs between 82 to 87 ppb, a weight of evidence determination will be preformed to**  **supplement the conclusion that the area is expected to attain the NAAQS. The end product of a weight**  **of evidence determination is a document which describe**  **s analyses performed, data used, key assumptions and outcomes of each analysis, and why the State be**  **lieves that the evidence, viewed as a whole, suppor ts**  **a conclusion that the area will attain the NAAQS fo r ozone.** **71**

XI. REFERENCES

**Anthes, R.A. and T.T. Warner. 1978. “The Developme nt of Mesoscale Models Suitable for Air Pollution**  **and Other Mesometeorological Studies,”** Mon. Wea. Rev. **, Vol. 106, pp, 1045-1078.**

**Anthes, R.A., Hsie, E.-Y. and Kuo, Y.-H., 1987. De scription of the Penn State/NCAR Mesoscale Model**  **Version 4 (MM4). NCAR/TN-282+STR, National Center for Atmospheric Research.** 

**Byun, D.W., and J.K.S. Ching, 1999. “Science Algor ithms of the EPA Models-3 Community Multiscale**  **Air Quality (CMAQ) Modeling System,” EPA/600/R-99/0 30.** 

**Byun, D.W., J. Young, G. Gipson, J. Godowitch, F. B inkowski, S. Roselle, B. Benjey, J. Pleim, J.K.S.**  **Ching, J. Novak, C. Coats, T. Odman, A. Hanna, K. A lapaty, R. Mathur, J. McHenry, U.**  **Shankar, S. Fine, A. Xiu and C. Jang (1998a). Desc ription of the Model-3 Community**  **Multiscale Air Quality (CMAQ) model. Proceedings o f the American Meteorological Society**  **78 th**  **Annual Meeting, Phoenix, AZ, Jan 11-16, 1998: 264 -268.** 

**Byun, D.W., J. Young, G. Gipson, J. Schere, J. Godo witch, J. Pleim, F. Binkowski, S. Roselle, B. Benje**  **y, J. Ching, J. Novak, and S. LeDuc. “An urban air qu**  **ality simulation with the Community Multi Scale Air Quality (CMAQ) modeling system.” (1998b).**  **Preprint, Second Urban Environment Symposium, 13th Conference on Biometeorology and Ae**  **robiology, November 2-6, 1998, Albuquerque, NM. American Meteorological Society,**  **Boston, 36-39.**

**Clark, Richard D., R. Walker Jr., D. O'Donnell, E. Hohman, and D. Unruh, 2003. Low level jets over th**  **e mid-Atlantic region during NE-OPS 1999–2002. Paper**  **No. 6.7 presented at the Fifth Conference on Atmospheric Chemistry: Gases, Aerosols, and Clou**  **ds (American Meteorological Society Annual Meeting), Long Beach, CA, February 9-13.** 

**Chen, F., and J. Dudhia, 2001: Coupling an advance d land-surface/hydrology model with the Penn**  **State/NCAR MM5 modeling system. Part I: Model impl ementation and sensitivity. Mon. Wea.**  **Rev., 129, 569-585.**

**Ching, J.K.S., D.W. Byun, J. Young, F. Binkowsk., J . Pleim, S. Roselle, J. Godowitch, W. Benjey., and**  **G. Gipson, 1998: Science features in Models-3 Comm unity multiscale air quality system.**  **Proceedings of the American Meteorological Society 78th Annual Meeting, Phoenix, AZ, Jan.**  **11-16, 1998: 269-273.**

**Coats, C. J., A.H. Hanna, D. Hwang, and D.W. Byun, 1995: Model engineering concepts for air quality**  **models in an integrated environmental modeling syst em. Transactions of Air and Waste**  **Management Association Specialty Conference on Regi onal Photochemical Measurement and**  **Modeling Studies, Nov. 8-12, San Diego, CA. 1993, 213-223.** 

**Coats, C.J, 1995: The EDSS/Models-3 I/O applicatio ns programming interface.**  http://envpro.ncsc.org/products/ioapi/index.html **MCNC Env. Programs, RTP NC. 72** 

**Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Galluppi, C.J. Coats, and M.A. Vouk, 1996: The next**  **generation of integrated air quality modeling: EPA 's Models-3. Atmospheric Environment, 30,**  **No. 12, 1925-1938.**

**Dudhia, J. 1993. "A Non-hydrostatic Version of the Penn State/NCAR Mesoscale Model: Validation**  **Tests and Simulation of an Atlantic Cyclone and Col d Front,”**  Mon. Wea. Rev. **, Vol. 121. pp. 1493-1513.** 

**Emery, C. et al., 1999. “Ozone Modeling for the Ka nsas City Nonattainment Area: Final Protocol,”**  **prepared for the Kansas Department of Health and En vironment, prepared by ENVIRON**  **International Corporation and Alpine Geophysics.**

**ENVIRON, Alpine Geophysics, UC Riverside and UC Dav is. 2003a. “VISTAS Emissions and Air**  **Quality Modeling – Phase I Task 4a/b Report: Revie w of Model Sensitivity Simulations and**  **Recommendations of Initial CMAQ Model Configuration s and Sensitivity Tests.” Revised Draft**  **July 25.**

**EPA, 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional**  **Haze,” Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC.** 

**EPA, 2002. “Guidance for Quality Assurance Project Plans (G-5),” U.S. Environmental Protection**  **Agency, Office of Environmental Information, Washin gton, DC 20460.** 

**EPA, 2005. “Guidance on the Use of Models and Othe r Analyses in Attainment Demonstrations for the**  **8-hr Ozone NAAQS.” Draft Final (February 2005), U. S. Environmental Protection Agency,**  **Office of Air Quality Planning and Standards, Resea rch Triangle Park, N.C.** 

**Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994. "A Description of the Fifth Generation Penn**  **State/NCAR Mesoscale Model (MM5).” NCAR Tech. Note , NCAR TN-398-STR, 138 pp.** 

**Houyoux M. R., and J. M. Vukovich, 1999. “Updates to the Sparse Matrix Operator Kernel Emissions**  **(SMOKE) Modeling System and Integration with Models -3,” The Emissions Inventory: Regional**  **Strategies for the Future, Air and Waste Management Association, 26-28 October, Raleigh, NC.** 

**Johnson, M., Meteorological Modeling Protocol: Iowa Department of Natural Resources (IDNR) 2002**  **Annual MM5 Application, 2003.**

**McNally, D. E., and T W. Tesche, 2003. “Annual Appl ication of MM5 to the Continental United States,”**  **prepared for the U. S. Environmental Protection Age ncy, Office of Air Quality Planning and**  **Standards, prepared by Alpine Geophysics, LLC, Arva da, CO.** 

**Olerud, Donald, K. Alapaty, and N. Wheeler, 2000: M eteorological Modeling of 1996 for the United**  **States with MM5. Prepared for Normal Possiel, Clie nt Representative, U.S. EPA/OAQPS. EPA**  **Task Order Number CAA689805.**

**Olerud, D and A. Sims. 2003. “MM5 Sensitivity in Su pport of VISTAS (Visibility Improvement – State**  **and Tribal Association): Task 2e Deliverables.” Ba ron Advanced Meteorological Systems, LLC,** **73**

**Research Triangle Park, NC. (available at: http://w ww.baronams.com/projects/VISTAS/#reports).**  **December 4.**

**Pleim, J., Gipson, G., Roselle, S., Young, J., 2003 , New features of the 2003 release of the CMAQ mode**  **l. 2nd Annual CMAS Models-3 Conference, Research Trian**  **gle Park, NC.**

**Seaman, N.L. 2000. “Meteorological Modeling for Ai r Quality Assessments,”**  Atmos. Environ **., Vol. 34, No. 12-14, 2231-2260.** 

**Seaman, N.L., D.R. Stauffer, and T.W. Tesche. 1992 . "The SARMAP Meteorological Model: A Four**  **Dimensional Data Assimilation Technique Used to Sim ulate Mesobeta-Scale Meteorology**  **During a High-Ozone Episode in California,” Interna tional Specialty Conference on Tropospheric**  **Ozone Nonattainment and Design Value Issues, U.S. E PA/AWMA, 27-30 October, Boston, MA.** 

**Seaman, N. L., et al., 1997. "The Use of the San J oaquin Valley Meteorological Model in Preparation o**  **f a Field Program in the South Coast Air Basin and Su**  **rrounding Regions of Southern California: Volume II -- Numerical Modeling Studies for the Dev**  **elopment and Application of a Guidance Technique to Support of the Design of the 1997 Sout**  **hern California Ozone Study Field Program,” prepared for Alpine Geophysics, LLC and t**  **he California Air Resources Board, prepared by the Department of Meteorology, Pennsylv**  **ania State University, University Park, PA.**

**Seppanen, C., 2003. “Status of SMOKE,” 2003 Models -3 Users’ Workshop October 27-29, Research**  **Triangle Park, NC.**

**Stauffer, D.R. and N.L. Seaman, 1990. “Use of Four -Dimensional Data Assimilation in a Limited-Area**  **Mesoscale Model. Part I: Experiments with Synoptic Data.” Mon. Wea. Rev., 118, 1250-1277.** 

**Stauffer, D.R., N.L. Seaman and F.S. Binkowski, 199 1. “Use of Four-Dimensional Data Assimilation in**  **a Limited-Area Mesoscale Model. Part II: Effects o f Data Assimilation Within the Planetary**  **Boundary Layer.” Mon. Wea. Rev., 119, 734-754.**

**Tesche, T. W., et al., 2000. “Ozone Modeling Proto col for the Peninsular Florida Ozone Study (Version**  **1.0),” prepared for the Florida Department of Envir onmental Protection, prepared by Alpine**  **Geophysics, LLC, Ft. Wright, KY.**

**Tesche, T. W., et al., 2003. “Peninsular Florida Oz one Study Final Report,” prepared for the Florida**  **Department of Environmental Protection, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.** 

**Xiu, A., and J.E. Pleim, 2000: Development of a lan d surface model. Part I: Application in a mesoscal**  **e meteorology model. Journal of Applied Meteorology,**  **40, 192-209. 74**  APPENDIX D-A.: SUMMER 2002 EPISODE SUMMARIES

Ozone Monitors in North Carolina Piedmont Area

**Below is a map that labels all of the ozone monito rs in the North Carolina Piedmont area,**  **including York County, South Carolina. Since the m ajority of the ozone exceedances occur in the three**  **major urban areas, these are the monitors of greate st concern. The three urban areas include the**  **Metrolina area (Charlotte/Gastonia/Rock Hill nonatt ainment area), the Triangle area**  **(Raleigh/Durham/Chapel Hill nonattainment area) and the Triad area (Greensboro/Winston-Salem/High**  **Point nonattainment area).**

May Episode

**The first ozone episode of the year occurred on May 24**  **th and 25**  **th . 8-hour ozone exceedances**  **were largely concentrated around North Carolina, wi th some scattered exceedances up the east coast to**  **New Jersey. An area of Canadian high pressure move d southward into the eastern United States, bringin**  **g cooler than normal temperatures from May 20**  **th through the 22**  **nd . By May 23**  **rd , the surface high pressure**  **was positioned over the southeast, and remained in that location through May 25**  **th . At upper levels, a**  **ridge axis became collocated over the surface high on May 24**  **th through the 25**  **th , which helped to increase**  **stagnation over the southeast. On the 24 th**  **conditions were ideal for ozone formation. High t emperatures**  **shot upward into the middle to upper 80s on the 24 th**  **, the air mass was dry with dewpoints in the 50s, a nd**  **winds were calm or very light. Every monitor in th e Triangle exceeded 0.085 ppm, with the maximum of**  **0.095 ppm at the St. Augustine monitor in downtown Raleigh. Winds were light southerly in the**  **Metrolina area, leading to 8-hour ozone exceedances in the northern metropolitan area, with a maximum** **75**

**of 0.098 ppm at the Rockwell monitor. A similar pa ttern occurred in the Triad, with the highest 8-hou**  **r ozone values at the Cherry Grove monitor at 0.091 p**  **pm.**

**The high-pressure area began to move toward the coa st on May 25**  **th , while a backdoor cold front**  **pushed into the northernmost areas of the state. T he winds turned more southerly across much of North**  **Carolina. The higher winds, combined with higher l evels of moisture, led to less ozone formation.**  **Additionally, another area of high pressure pushed south into New England and forced a backdoor cold**  **front into the northernmost areas of the state. Th ere were no 8-hour ozone exceedances in the Triad,**  **while in the Triangle ozone exceedances were confin ed to downtown and areas to the south. The highest**  **and most widespread 8-hour ozone exceedances were i n the Metrolina area, where some recirculation**  **occurred.**

June Episodes

**Two major ozone episodes occurred in June, the firs t was from June 3**  **rd through the 5**  **th**

**and the second was from June 10**  **th through the 14**  **th . The first episode was a regional event, characte**  **rized by moderately high 1-hour and 8-hour ozone averages ac**  **ross the southeast US. The onset of the first even t**  **was a bit unusual. An area of high pressure moved into New England on June 2**  **nd . The position of the**  **high caused some cold air damming against the Appal achian Mountains, and forced a weak cold front**  **southeastward through North Carolina, bringing low humidity air (dewpoints near 55**  ° **F) into the state. The high then transitioned offshore on June 4**  **th , switching the flow to the southwest, which caused**  **dewpoints to rise toward 70** ° **F. The highest ozone values during this period wer e generally on June 4**  **th**

**, with ozone concentrations of 0.106 ppm in Metrolina**  **, as some of the air that was transported to the southwest was redirected toward the northeast due t**  **o the wind shift. PM 2.5**  **peaked above 30 μg/m 3**  **in the Metrolina area on June 4**  **th , and was above 20 μg/m**  **3 in the rest of the state. High pressure remained**  **offshore on June 6 th**  **, while a cold front was moving through the Midwest . Highest 8-hour ozone was**  **confined to the western Piedmont, where the Metroli na area ozone was 0.097 ppm and the Triad was**  **0.096 ppm, while the Triangle ozone was lower at 0. 087 ppm. PM**  **2.5 was in the range of 16 to 23 μg/m**  **3**

**. High temperatures throughout the episode were in th**  **e 90s. The first episode came to an end on June 6 th** 

**, as the cold front pushed through North Carolina and**  **off the east coast.**

**High pressure built into the Great Lakes region fro m Canada behind the cold front, causing ozone**  **values to rise above 0.085 ppm on June 9 th**  **in that region, while ozone in North Carolina bega n to rebound**  **as the winds began to slacken. The high pressure c ontinued southward and positioned itself over the**  **Southeast on June 10 th**  **, triggering stagnation underneath the area of high pressure and causing high ozone**  **up and down the eastern seaboard. Ozone values in North Carolina were highest in the northern half of**  **the state, with ozone at 0.111 ppm in the Triad, an d 0.107 ppm in the Triangle. The peak of the episo**  **de for the Triad and Metrolina areas occurred on June**  **11 th**  **as the high pressure was beginning to transition offshore. Ozone peaked at 0.118 ppm in Metrolina,**  **at 0.115 in the Triad, and 0.105 ppm in the Triangl e.** 

**By the June 12 th** 

**and June 13 th**  **, the area of high pressure was in a classic “Bermu da High”**  **position. With the high in this position, a southw est flow of around 5-10 mph developed, which helped**  **provide some mixing. In this regime, the highest o zone in North Carolina was in the Metrolina area,**  **where 8-hour ozone reached 0.103 ppm on June 12 th**  **and 0.106 on June 13 th**  **. Ozone levels above 0.085 ppm were restricted to the Triangle on June 14**  **th as a cold front moved through, bringing an end to**  **the second episode of June. High temperatures during t**  **he episode ranged from 88-100** ° **F, and dewpoints ranged from the 58-66**  ° **F.**

**The episode of June 18 th**  **was caused by an area of high pressure that moved relatively quickly**  **eastward through the Mid-Atlantic States and off th e east coast. A weak frontal boundary was located**  **along the North Carolina/South Carolina border. Th is boundary drifted northward though the day and**  **dissipated. Temperatures were a relatively mild 84 -87**  ° **F, with dewpoints near 60** ° **F. Of the three major 76** 

**metro areas within North Carolina, only the Triad h ad monitors barely reach the 8-hour ozone standard**  **of 0.085 ppm, with all other monitors in North Carolin**  **a below the standard. In succeeding days, the high pressure moved off the mid-Atlantic and transported**  **cleaner maritime air into the state.**

July Episodes

**July had three major air quality episodes, and a le sser episode at the beginning of the month. The**  **month began with a few scattered exceedances, all b elow 0.090 ppm, during the period from July 1**  **st through the 4**  **th . The synoptic trigger for this episode was a stac**  **ked high pressure centered over Tennessee and Kentucky. Ozone above 0.085 ppm appeared first**  **over the northeastern US, and then translated southward. High temperatures in the southeast were**  **in the lower to middle 90s, and dewpoints were in the upper 60s to lower 70s. In North Carolina, a g**  **eneral light north/northwest flow caused the format ion**  **of a lee trough. Some recirculation was evident as the lee trough shifted east to west.** 

**The first major episode began on July 5 th**  **as Bermuda high-pressure system that expanded westward, while at middle and upper levels, a high-**  **pressure ridge developed over the Ohio Valley, whil e**  **a weak upper trough was positioned along the east c oast. The two upper features produced a weak north**  **to northwesterly airflow. An upper level shortwave dropped into the northeast US on July 5**  **th , forcing a**  **cold front southward through the mid-Atlantic. Hig h temperatures peaked near 100**  ° **F during this time. The front stalled over the Carolinas late on the 5**  **th and lingered through the 7**  **th , triggering some scattered**  **showers and thunderstorms as well as some cloud cov er, causing a slight decline in ozone levels.** 

**The frontal boundary weakened and the Bermuda high pressure reasserted its influence over the**  **eastern seaboard by late afternoon of July 8 th**  **. At the same time, the mid and upper level ridge was**  **located over the western two-thirds of the United S tates, leaving the Carolinas in a light northwester**  **ly upper flow. High temperatures pushed into the 90s,**  **with dewpoints in the upper 50s to mid 60s. Ozone levels peaked on July 8**  **th**

**and July 9 th**  **, with values above 0.105 ppm in Metrolina, and bet ween 0.090 and**  **0.101 ppm in the Triad. A cold front brought an en d to the second major episode of the month on July**  **10 th**  **, where only the Triangle and the Triad were above 0.085 ppm.** 

**The third episode of July was shorter in duration, but more intense. In the middle and upper**  **atmosphere, a low-pressure system cut off from the main steering flow and remained qausi-stationary**  **over Kansas and Missouri from July 15 th**  **through July 18 th**  **. To the east of the upper level low, an upper level ridge axis setup from Alabama northward to Ke**  **ntucky. The position of the ridge caused a northwesterly downslope flow through the Carolinas.**  **The downsloping caused a considerable amount of warming and drying (see GSO sounding from 00 UTC 16**  **July and 00 UTC 17 July below). High temperatures were in the mid 90s, with dewpoints ra**  **nging from 65-72** ° **F. Ozone values rose from mostly less than 0.065 ppm on July 15**  **th to values between 0.095 and 0.106 ppm on July 16**  **th , with highest values**  **in the Triangle. 77** 

**The northwest flow continued on July 17 th**  **and July 18 th**  **, but decreased slightly, allowing for more stagnation and ozone formation. The northwest flow**  **caused the formation of a lee trough on July 17 th**  **and July 18**  **th , helping to focus pollutants. Fayetteville experi**  **enced their worst ozone day of the year, rising to 0.108 ppm on July 17**  **th , while ozone in Metrolina was 0.116 ppm and the Tr**  **iangle was 0.107 ppm. Ozone levels in most areas subsided a bit on July 1**  **8 th**  **, into the 0.090-0.100 ppm range, likely due to som e**  **high cirrus that overspread the area. The Metrolin a area was an exception, where 8-hour ozone levels**  **rose to 0.119 ppm, the highest values of the ozone season. During this episode, PM**  **2.5 values at many**  **locations reached their highest values of the summe r, with values approaching and in some locations**  **exceeding 50 μg/m 3** 

**. 78** 

**July 29 th**  **was very minor episode as a Bermuda high set up of fshore. Only the Metrolina area had**  **exceedances. High temperatures were in the upper 9 0s, with dewpoints running quite high in the low to**  **mid 70s. The July 31 st** 

**episode was caused by a cold front that dropped so uthward, to just north of North**  **Carolina, stalled and weakened. High temperatures were in the middle 90s with dewpoints near 70**  ° **F. Only the Pittsboro monitor in the Triangle area exc**  **eeded the 8-hour ozone standard.**

August Episodes

**On August 1 st** 

**through 2 nd**  **, a moderate episode occurred as a weak continental high pressure**  **moved into the Central Appalachian Mountains, just north of the North Carolina border. High**  **temperatures were in the mid 90s with dewpoints 65- 70**  ° **F. Ozone levels peaked August 2 nd**  **, ranging from 0.090 to 0.096 ppm in the three major metro areas.**  **The episode ended with the passage of a cold front on**  **August 3 rd** 

**. Isolated exceedances occurred on August 5**  **th and 6**  **th in the Triad and Metrolina areas with the**  **approach of a cold front. A tropical storm lurking offshore may have triggered some subsidence to**  **enhance a capping inversion. High temperatures dur ing this second moderate episode were in the low to**  **mid 90s, and dewpoints were 65-70** ° **F.**

**Ozone levels subsided across the eastern US as a se ries cold front pushed south from Canada and**  **cleaned out the atmosphere. High pressure moved so uth behind the front and settled over the Ohio Vall**  **ey from Canada by August 7**  **th . Ozone levels first began to build in eastern Ten**  **nessee, northern Georgia, and western North Carolina on August 8**  **th . By August 9**  **th , the area of high pressure had sunk south and was**  **centered over the Carolinas, causing stagnation ove r the state and leading to 8-hour ozone above 0.090**  **ppm from the Triad to the Metrolina and Hickory are as. High temperatures were in the upper 80s, with**  **dewpoints near 55** ° **F. From August 10 th**  **through the 13 th**  **, the high pressure transitioned offshore to a typical Bermuda high configuration, while a ridge r**  **emained over the Carolinas at upper levels. High temperatures rose through the period, peaking in th**  **e upper 90s. Dewpoints gradually rose from the mid 50s into the low 60s. Ozone levels continued to ri**  **se until the peak of the episode on August 12 th**  **, when 8- hour ozone rose to 0.117 ppm in Metrolina, 0.115 pp**  **m in the Triad, and 0.112 in the Triangle. 8-hour ozone levels remained above 0.100 ppm at many locat**  **ions on August 13 th**  **. A cold front brought an end to the episode on August 14**  **th . This episode also contained the highest levels o**  **f ozone on a weekend (on August 10**  **th and 11**  **th ). PM**  **2.5 did not get as high as in other episodes, rising t**  **o around 25-30 μg/m 3**  **on August 11**  **th**

**through the 13 th** 

**. The final air quality episode in August occurred fr**  **om August 21 st**  **through the 23 rd**  **. This episode was largely confined to North Carolina, with some e**  **levated ozone in southeast Virginia and northern South Carolina. A weak trough pushed a cold front**  **south through the state late on August 20 th**  **. As the trough departed, a Canadian high pressure built dow**  **n the eastern seaboard on August 21 st**  **. The high pressure became vertically stacked ridge built over**  **southeast and remained overhead on August 22 nd**  **and especially on August 23**  **rd . High temperatures averaged 95-100**  ° **F with dewpoints 68-73** ° **F. Ozone levels were moderately high on August 21**  **st and 22**  **nd , peaking at 0.091 ppm in the Hickory and Metrolina**  **areas on August 21**  **st**

**and on the 22 nd** 

**reached 0.091 and 0.090 ppm in Metrolina and the T riad, respectively. On**  **August 23 rd**  **, a downslope northwest flow developed, pushing a l ee trough eastward to the vicinity of the**  **Triangle. Precursors pooling at the lee trough hel ped to push ozone at every monitor around the Trian**  **gle above the 8-hour ozone standard. The West Johnston**  **monitor was the highest, with a 1-hour ozone exceedance of 0.127 ppm, and an 8-hour ozone exceed**  **ance of 0.112 ppm. Many 1-hour and 8-hour ozone exceedances also occurred around Metrolina, w**  **ith the Monroe monitor, southeast of Charlotte, having a 1-hour ozone exceedance of 0.125 ppm, and**  **an 8-hour ozone exceedance of 0.109 ppm. The Triad also had widespread 8-hour ozone exceedances.**  **The episode drew to a close on August 24 th**  **as a frontal boundary neared the state and triggered sca**  **ttered convection. PM 2.5**  **was elevated from August 21 st** **79**

**to the 24 th**  **, ranging from 25 to 35 μg/m 3**  **in most areas. The Triad experienced the highest PM**  **2.5 , topping**  **out at 39.6 μg/m 3**  **on August 23 rd**  **and 42.7 μg/m 3**  **on August 24 th** 

**.**
September

**From September 4 th** 

**through September 11 th** 

**a widespread episode occurred over the Midwest and Northeast, with North Carolina largely on the perip**  **hery of the polluted air mass. The first exceedanc es in**  **North Carolina occurred on September 4 th**  **. During this time a cold front and associated pre frontal trough**  **were approaching North Carolina, while a tropical s torm was making landfall in northern Florida. The**  **highest ozone was located between the front and the prefrontal trough, and the subsidence to the north**  **of the tropical storm may have aided in capping the at**  **mosphere. Temperatures were in the low 90s, and dewpoints were in the mid 60s, as ozone near Metrol**  **ina reached 0.093 ppm and ozone in the Triad peaked at 0.089 ppm.** 

**Ozone peaked near the same levels on September 5 th**  **and 6 th**  **as an area of high pressure built southward into the northern Mid-Atlantic States. H**  **igh temperatures were in the mid 80s, and dewpoints were near 60**  ° **F. The weakening tropical storm may have also cont ributed to a strong capping inversion**  **during this event. September 7 th**  **to the 9 th**  **saw the high-pressure shift north, with poor air q uality shifting**  **north with it. The final ozone exceedance of 2002 occurred on September 11**  **th with an approaching cold**  **front and a departing tropical system north and eas t of Cape Hatteras. Temperatures held to near 90**  ° **F in North Carolina, and dewpoints were in the upper 50s** 

**. 80**  APPENDIX D-B: ACRONYMS USED

**ACM Asymmetric Convective Mixing AERO3/ISORROPIA Aerosol Chemistry Scheme for CMAQ**  **AIRS Aerometric Information Retrieval System AQS Air Quality System**  **BAMS Barons Advanced Meteorological, LLC BCON Boundary Condition Processor**  **BCs Boundary Conditions BEIS3 Biogenic Emission Inventory System, version**  **3 BELD3 Biogenic Emissions Landuse Database**  **CAA Clean Air Act CAAA Clean Air Act Amendments**  **CAIR Clean Air Interstate Rule CAMx Comprehensive Air Quality Model with Extensio**  **ns CASNET Clean Air Status and Trends Network**  **CB4 Carbon Bond Version 4 CB4-2002 Carbon Bond Version 4 - 2002 update**  **CCTM CMAQ Chemical Transport Model CEM Continuous Emissions Monitoring**  **CERR Consolidated Emissions Reporting Rule CLT Charlotte-Douglas International Airport**  **CMAQ Community Multiscale Air Quality CMAS Community Modeling and Analysis**  **CO Carbon monoxide CPC Climate Prediction Center**  **CTM Chemical Transport Model DVB Baseline Design Value**  **DVF Future Design Value EAC Early Action Compact**  **EGU Electric Generating Unit EIIP Emissions Inventory Improvement Program**  **EMS Emissions Modeling System EPA Environmental Protection Agency, see also USEP**  **A EPS Emissions Preprocessing System**  **ETA NCEP meteorological model nam ed for the vertical coordinate**  **system used in the model. FAA Federal Aviation Administration**  **FAY Fayetteville FDDA Four Dimensional Data Assimilation**  **FRM Federal Reference Method GIFs Graphics Interchange Format**  **GSO Greensboro HKY Hickory**  **HNO 3**  **Nitric Acid I/O input/output**  **I/O API Input/Output Applications Programming Inte rface**  **IC/BC Initial Condition/Boundary Condition ICON Initial Condition Processor**  **ICs Initial Conditions IDA Inventory Data Analyzer**  **IMPROVE Integrated Monitoring of Protected Visual Environments** **81**

**IPM Integrated Planning Model ISORROPIA Inorganic Aerosol Thermodynamics/Partitio**  **ning: Model that calculates the composition and phase state of an am**  **monia sulfate-nitrate-chloride-sodium-water inorganic aer**  **osol in thermodynamic equilibrium with gas phase precursors** 

**. JPROC Photolysis Rate Processor**  **km kilometer LSM Land Surface Model**  **MACT Maximum Achievable Control Technology MATS USEPA’s Model Attainment Test Software**  **mb millibar, Measure of atmospheric pressure MCIP Meteorological-Chemistry Interface Processor**  **MCIP2.3 Meteorological-Chemistry Interface Process or (ver. 2.3)**  **MM5 Mesoscale Meteorological Model, 5 refers to the version number**  **MM5 PX_ACM MM5 use of the Pleim-Xiu Soil Model and Asymmetric**  **Convective Mixing PBL model MOBILE6.2 USEPA vehicle emission factor model, whi**  **ch is a software tool for predicting gram per mile emissions of hydrocarb**  **ons, carbon monoxide, oxides of nitrogen, carbon dioxide, parti**  **culate matter, and toxics from cars, trucks, and motorcycles under**  **various conditions.**  **mph miles per hour MPP Massively Parallel Processors**  **MRPO Midwest Regional Planning Organization MSA Metropolitan Statistical Area**  **NAAQS National Ambient Air Quality Standard NADP National Atmospheric Deposition Program**  **NARSTO North American Research Strategy for Tropos pheric Ozone**  **NCAR National Center for Atmospheric Research NCDAQ North Carolina Division of Air Quality**  **NCDOT North Carolina Department of Transportation NCEP National Center for Environmental Predictions**  **NEI National Emissions Inventory NH**  **4 Ammonium**  **NMC National Meteorological Center NO Nitric Oxide**  **NO 2**  **Nitrogen Dioxide NO**  **x Oxides of Nitrogen** **82**

**NO x**  **SIP Call Finding of Significant Contribution and R ulemaking for Certain**  **States in the Ozone Transport Assessment Group Regi on for**  **Purposes of Reducing Regional Transport of Ozone. Rule**  **designed to mitigate significant transport of NO**
**x , one of the**  **precursors of ozone. NO**  **y Total Available Nitrogen**  **NWS National Weather Service O**  **3 Ozone**  **OBDII Onboard diagnostics PAMS Photochemical Assessment Monitoring Stations**  **PAVE Package for Analysis and Visualization of Envi ronmental data**  **PBL Planetary Boundary Layer PM Particulate Matter**  **PM 2.5**  **Particulate Matter with a diameter less than 2.5 μm**  **ppb parts per billion ppm parts per million**  **PSU Pennsylvania State University PSU/NCAR Pennsylvania State University/National Cen**  **ter for Atmospheric Research**  **PX Pleim-Xiu PX_ACM LSM_PBL MM5 configuration of Pleim-Xiu Land**  **Surface Model, Asymmetric Convective Mixing PBL model**  **QA Quality Assurance QAPP Quality Assurance Project Plan**  **QC Quality Control Q-Q plots Quantile-Quantile plots**  **RACT Reasonably Available Control Technology RADM Regional Acid Deposition Model**  **RAMS Regional Atmospheric Modeling System RDU Raleigh-Durham International Airport**  **REMSAD Regional Modeling System for Aerosols and D eposition**  **RPO Regional Planning Organization RRF Relative Reduction Factor**  **RVP Reid Vapor Pressure SAPRC99 Photochemical Mechanism in CMAQ**  **SCBAQ South Carolina Bureau of Air Quality SCC Source Classification Code**  **SCDHEC South Carolina Department of Health and Envi ronmental**  **Control SEARCH Southeastern Aerosol Research and Character**  **ization SIP State Implementation Plan**  **SMOKE Sparse Matrix Operator Kernel Emissions SMVGEAR Sparse Matrix Vectorized Gear solver**  **SO 2**  **Sulfur Dioxide SOA Secondary Organic Aerosol**  **SORGAM Secondary Organic Aerosol Model STN Speciation Trends Network**  **T Temperature TOMS Total Ozone Mapping Spectrometer**  **u, v, w Three Dimensional Wind Components in spheri cal coordinates :**  **u = E/W; v = N/S; w = vertical UAMIV Urban Airshed Model - Version 4** **83**

**UAM-V Urban Airshed Model - Version 5 UCAR University Corporation for Atmospheric Resear**  **ch UCR University of California at Riverside and Davi**  **s US United States**  **USEPA United States Environmental Protection Agenc y**  **USGS United States Geological Service UTC Universal Time or Greenwich Mean Time**  **VISTAS Visibility Improvement State and Tribal Asso ciation of the**  **Southeast VMT Vehicle Miles Traveled**  **VOCs Volatile Organic Compounds WRF Weather Research Forecast**  **Z Zulu Time, or UTC (Universal Time), or Greenwich Mean Time**