# Appendix D
Modeling ProtocolModeling Protocol i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D North Carolina Attainment Demonstration August 21, 2009 

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# Appendix D.1

NC Division of Air Quality

Modeling ProtocolNCDAQ Modeling Protocol i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

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# Modeling Protocol

for the

Hickory and Greensboro/Winston-Salem/High Point

Annual Fine Particulate Matter

Nonattainment Areas

Prepared by:

North Carolina Department of Environment and Natural Resources Division of Air Quality

February 11, 2008NCDAQ Modeling Protocol i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### (This page intentionally left blank)NCDAQ Modeling Protocol ii The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Table of Contents

1.0 Overview of Fine Particulate Ma tter Modeling/Analysis Project ........................................... 1

1.1. Policy Overview of Fine Particulate Matter National Ambient Air Quality Standards . 1 1.2. Designations ............................................................................................................. ....... 1 1.3. Participating Organizations ............................................................................................. 2
1.4. Selection of Future Year ................................................................................................. 4
1.5. Schedule ................................................................................................................. ......... 4 1.6. Organization of Air Quality Modeling Protocol ............................................................. 4

2.0 Conceptual Description of Fine Part iculate Matter in North Carolina ................................... 5

2.1. General Description of Particulate Matter ...................................................................... 5 2.2. Composition .............................................................................................................. ...... 5 2.3. Spatial and Temporal Patterns ........................................................................................ 6

3.0 PM 2.5 

### Episode Selection ........................................................................................................ 11

3.1. Overview of the USEPA Guidance on Particulate Matter ............................................ 11

3.2. PM 2.5 

### Episode Selection ................................................................................................ 11 3.3. Episode Classification ................................................................................................... 13

4.0 Models and Modeling Configurations .................................................................................. 20

4.1. Recommended Models.................................................................................................. 20 4.2. MM5 – Mesoscale Prognostic Model ........................................................................... 20 4.3. SMOKE Emissions Modeling System .......................................................................... 24 4.4. CMAQ Modeling System ............................................................................................. 25

4.5. Model Limitations ........................................................................................................ . 27 4.6. Model Input Requirements ........................................................................................... 28

5.0 Grid Specifications and Modeling Domains ......................................................................... 31

5.1. Horizontal Modeling Domain ....................................................................................... 31 5.2. Vertical Modeling Domain ........................................................................................... 33

6.0 Development of Emi ssions Inventories ................................................................................ 35

6.1. Point Source Emissions................................................................................................. 35 6.2. Stationary Area Source Emissions ................................................................................ 40

6.3. Non-Road Mobile Source Emissions ............................................................................ 42

6.4. On-Road Mobile Source Emissions .............................................................................. 43 6.5. Biogenic Source Emissions........................................................................................... 45 6.6. Development of Modeling Inventories ......................................................................... 46

7.0 Quality Assurance Plan .................................................................................................... ..... 47

7.1. Quality Assurance Objectives ....................................................................................... 47 7.2. Emissions Model Inputs and Outputs ........................................................................... 47 7.3. Meteorological Model Outputs ..................................................................................... 50 7.4. Air Quality Model Inputs and Outputs ......................................................................... 51NCDAQ Modeling Protocol iii The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 8.0 Model Performance Evaluation ............................................................................................ 5 3

8.1. Model Evaluation Tools (Operational Evaluation) ....................................................... 53 8.2. Model Performance Testing (Diagnostic Evaluation) .................................................. 55 8.3. Air Quality and Ozone Column Data ............................................................................ 57

9.0 Control Strategy .......................................................................................................... .......... 59

9.1. Control Strategy Design ................................................................................................ 5 9 9.2. Control Strategy Evaluation .......................................................................................... 60 9.3. Identification of Control Strategy Scenarios ................................................................. 60

10.0 Demonstration of Attainment .............................................................................................. . 61

10.1. PM 2.5 

### Model Attainment Test ................................................................................... 61 10.2. Screening Test .......................................................................................................... . 64 10.3. Corroborative Analysis ............................................................................................. 64

11.0 References ............................................................................................................... .............. 66

Attachment A: Acronyms Used ................................................................................................... . 69NCDAQ Modeling Protocol 1 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 1.0 Overview of Fine Particulate Matter Modeling/Analysis Project

1.1. Policy Overview of Fine Particulate Matter National Ambient Air Quality

Standards

In July 1997, the United States Environm ental Protection Agency (USEPA) issued National Ambient Air Quality Standards ( NAAQSs) for fine particulate matter (PM 2.5 

### ). The NAAQSs include an annual standard se t at 15 micrograms per cubic meter ( μ g/m 3 

### ), based on the 3-year average of annual mean PM 2.5 

### concentrations and a 24-hour standard of 65 μ g/m 3 

### , based on the 3-year average of the 98 th 

### percentile of 24-hour concentrations. A number of events delayed the implementation of the PM 2.5 

### NAAQSs.

The new PM 2.5 

### standards were challenged by the American Trucking Association, the U.S. Chamber of Commerce, and other state an d business groups. The Transportation Equity Act for the Twenty-first Century (TEA-21) delayed the deadline to publish nonattainment designations in order to provide additional time to collect three years of air quality monitoring

data.

In February 2001, the United States Supreme Court upheld the USEP A's authority under the Clean Air Act (CAA) to set NAAQSs that prot ect the American public from harmful effects of air pollution. The United States Supreme Court also sent the case back to the D.C. Circuit Court of Appeals to resolve se veral additional issues. In Ma rch 2002, the D.C. Circuit Court rejected all remaining legal challenges to the 1997 NAAQSs for PM 2.5



### .

1.2. Designations

In April 2003, the USEPA provided guidelines to states and tribes for recommending

nonattainment area boundaries for the PM 2.5 

### NAAQSs. Consistent with the CAA, the guidance

instructed states and tribes to begin their analysis of attainment and nonattainment area boundaries based on Metropolitan Area boundaries. A Metropolitan Area was defined as a single Metropolitan Statistical Area (MSA) or a Consolidated MSA, depending on the area. The guidance instructed states to include in nonattainment areas a ny nearby counties with sources contributing to fine particle pollution in t hose metropolitan areas. In addition, the guidance

recommended that states and tribes consider using common boundaries for areas to be designated

as nonattainment for both the PM 2.5 

### and 8-hour ozone standards, which will help states and tribes facilitate future planning a nd implementation activities.

In mid-February 2004, states and tribes recommended PM 2.5 

### nonattainment area
boundaries to the USEPA. The USEPA revised these recommendations and responded to the

states and tribes in late June 2004. On D ecember 17, 2004, the USEPA designated areas for the

PM 2.5 

### NAAQSs. Two areas in North Carolina we re designated nonattainment for the annual

PM 2.5 

### NAAQS under Section 172 of the CAA as ame nded. These areas were Hickory (Catawba County) and Greensboro/Winst on-Salem/High Point (Davidson and Guilford Counties). The Greensboro/Winston-Salem/High Poin t area is referred to as the Triad area. Figure 1.2-1 shows the USEPA’s final designation of PM 2.5 

### nonattainment areas in North Carolina.NCDAQ Modeling Protocol 2 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Figure 1.2-1: Designated Annual PM 2.5 

### nonattainment areas in North Carolina.

1.3. Participating Organizations

From the conceptual model of PM 2.5 

### formation, it is clear that PM 2.5 

### is a regional problem, which results in attainment demonstrat ion State Implementation Plan (SIP) modeling to be a substantial undertaking that relies on the interact ion of many groups that are affected by overall air quality and that impact the air shed of the affected states. It is imperative to include groups of “stakeholders” from industry, govern ment, and the privat e sector during the modeling/analysis project. As each group invol ved brings its own perspective, knowledge, and experience to the modeling process, the abil ity to model and develop strategies for PM 2.5 

### reduction is greatly enhanced. The following or ganizations were invite d to participate in developing the Hickory/Triad PM 2.5 

### SIP:

• North Carolina Division of Air Quality (NCDAQ)

• Carolina Environmental Programs – Univer sity of North Carolina Chapel Hill

• Barons Atmospheric Modeling Systems

• University of North Carolina at Chapel Hill

• North Carolina State University

• Sonoma Technology, Inc.

• Mecklenburg County Department of Environmental Protection

• USEPA Region 4

• USEPA Office of Air Quality Planning and Standards

• USEPA Office of Research and Development

• North 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 DefenseNCDAQ Modeling Protocol 3 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • Sierra Club

• Charlotte Department of Transportation

• Furniture Manufacturing Representative

• Chemical Manufacturin g 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 participa ting agencies, organiza tions, and universities will be utilized in determining projected emi ssions and control strategies. All data and information will be reviewed and evaluated by th e NCDAQ. All stakeholders are invited to contribute emissions projections a nd control strategy information.

North Carolina will coordinate with various st ates and other parties as regional modeling

is initiated to address the PM 2.5 

### standards. Various other regiona l modeling applications, such as Visibility Improvement and Tribal Associat ion of the Southeast (VISTAS) regional haze modeling, will also be considered. These intera ctions should provide a forum for discussing the latest improvements and refinements to air quality modeling.

1.3.1. Communication

Communication between the stak eholders is an integral part of completing the modeling/analysis project. Stakeholders n eed the opportunity to review and comment on documentation, control strategies, and modeli ng analysis. The NCDAQ will host periodic technical coordination meetings on the SIP process. 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, special studie s will be defined to help resolve all pertinent issues. Documentation will be developed c oncerning these issues, including methods of resolution and any remaining uncertainties, which will be submitted as part of the SIP.

Due to the far-reaching effects of the PM 2.5 

### attainment demonstration, it is important that all interested parties are kept informed on the progress of the modeling. Industries or organizations not directly represented on a modeling committee can monitor progress through the VISTAS website. The NCDAQ will also host several public meetings and focus groups with potentially impacted parties in order to get the most objective and comprehensive input in the development of the final control strategies.NCDAQ Modeling Protocol 4 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 1.3.2. Protocol Modification Procedures

The model configuration, as well as the sour ce of input 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 me thodologies, an issue paper stati ng the need for modification will be developed and circulated to a ll organizations participating in the study. The issue paper will

be discussed at the next schedul ed technical coordination meeti ng. The revised protocol would then be developed and submitted to the USEPA for their review.

1.4. Selection of Future Year

A key decision from both a modeling and contro l strategy standpoint is the selection of the future year by which attainment will be modeled. The future modeling year has been chosen to meet the schedule previously put forth. The time line set by the CAA requires attainment of

the annual PM 2.5 

### NAAQS be met by April 5, 2010. Since this date is set prior to the completion of the 2010, attainment of the NAAQS would have to be met by at least the end of the 2009. The NCDAQ plans to use 2009 as the future year for attainment modeling, as it would coincide with future year modeling for the annual PM 2.5 

### and 8-hour ozone attainme nt demonstration SIPs and VISTAS regional haze modeling effort.

1.5. Schedule

The NCDAQ will follow the schedule outlin ed by the CAA, where an attainment demonstration SIP is due for submittal by April 5, 2008. Using a 2009 modeling year, attainment will be demonstrated by at least April 5, 2010 or as expeditiously as practicable.

1.6. Organization of Air Quality Modeling Protocol

The remainder of the protocol documentation is broken down into nine additional sections

as follows:

• Section 2 provides a conceptual description of PM 2.5 

### formation in North Carolina.

• Section 3 presents details of the PM 2.5 

### episode selection process.

• Section 4 details the models that will be used during this modeling project.

• Section 5 describes the m odel grid specifications.

• Section 6 discusses the emi ssion inventory development.

• Section 7 lays out the quality assurance plan and procedures.

• Section 8 details the tools and proced ures for model performance evaluation.

• Section 9 discusses how the cont rol strategies will be designed.

• Section 10 focuses on the model attainme nt test and supplemental analyses.

• Section 11 lists the references.

• Acronym Attachment follows the final section.NCDAQ Modeling Protocol 5 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 2.0 Conceptual Description of Fine Pa rticulate Matter in North Carolina

2.1. General Description of Particulate Matter

Particulate matter is generally subdivided into two categories, coarse and fine particles, based on the aerodynamic diameter (D

a 

### ) of the particle, as opposed to the actual diameter. Actual particles are irregularly shaped, making a diameter measurement problematic. To ease the process, particles are measured base on their D

a 

### , which is defined as the diameter of a spherical particle with equal gravitational settling velocity as the irregularly shaped particle, but with material density of 1gr am per cubic centimeter (g/cm 3 

### ).

The division between the fine and course categories occurs in the D

a 

### size range between 1 and 3 micrometers (μm), where concentrations are at a minimum. Particles with a D

a 

### greater than the minimum are coarse particles, while those particles less than the minimum are categorized as fine particles. Fine particle are further broken down into the accumulation mode, which includes diameters less than the minimum, but greater than 0.1 μm, and ultrafine mode, which are diameters less than 0.1 μm. The ultraf ine mode is further broken down into Aitken mode and nucleation mode. Figur e 2.1-1 illustrates the differences between the particle size

divisions.

Figure 2.1-1: This figure illustra tes the size categories or mo des for particulate matter.

Values are in units of micrometer.

The ambiguity in the cutoff between fine and coarse particles revolves around the hygroscopic properties of the accumulation mode particles. Under high relative humidity conditions, the particle can grow to sizes on the coarse end of the spectrum due to particle bound water. Under low relative humidity conditions, coarse particles can be fragmented, and the resulting particle will have a D

a 

### < 2.5 μm. The USEPA chose the cutoff of 2.5 μm for the development of a NAAQS based on the use of PM 2.5 

### in epidemiological studies, and the desire to include the accumulation-mode particles, while re cognizing that some coar se particles can occur under particular conditions.

2.2. Composition

Particulate matter can be liquid, solid, or can have a solid core surrounded by liquid. Particulate matter can include material produc ed by combustion, photochemical reactions, and can contain salt from sea spray and soil like part icles. Particles are distinguished based on theNCDAQ Modeling Protocol 6 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### method of formation. Primary particles are part icles directly emitted into the atmosphere and retain the same chemical composition as when they were released. Second ary particles are those formed through chemical reactions involving atmospheric oxygen (O 2 

### ), water vapor, hydroxyl radical (OH), nitrate (NO 3 

### ), sulfur dioxide (SO 2 

### ), nitrogen oxides (NO

x 

### ), and organic gases from natural and anthropogenic sources.

Particulate matter components can include:

• Sulfate For fine particles, sulfate is generally a s econdary particle , and is usually found in the

form of ammonium sulfate.

• Nitrate For fine particles, nitrate is generally a s econdary particle , and is usually found in the

form of ammonium nitrate.

• Ammonium

• Hydrogen ion

• Particle bound water

• Elemental carbon

• Organic compounds

o Primary organic species (from cooking and combustion)

o Secondary organic compounds

• Organic materials Generally, in the coarse particle and intermodal range, organic materials are primary particulates from organic materi al includes pollen, spores, and animal

debris.

• Crustal material Predominately found in coarse particle ra nge, crustal material includes calcium, aluminum, silicon, magnesium, and iron.

• Sea salt Sea salt is generally only found at coastal monitoring sites.

• Transitional metals

• Potassium For fine particulates, potassium is generally from wood burning or cooking.

2.3. Spatial and Temporal Patterns

2.3.1. Spatial

North Carolina currently has two nona ttainment areas for the annual PM 2.5 

### NAAQS, which are associated with the Hickory and Lexi ngton monitoring sites located in Catawba and Davidson Counties, respectivel y. The nonattainment designati ons were based on the 2001-2003 monitoring data. For that period, th e Hickory monitor had an annual PM 2.5 

### design value of

15.5 μ g/m 3 

### and the USEPA designated all of Catawb a County as nonattainment. The Lexington

monitor had an annual PM 2.5 

### design value of 15.8 μ g/m 3 

### , and all of Davidson and Guilford Counties were designated as nonattainment. These two monitoring sites are only tenths of micrograms above the current NAAQS of 15.0 μ g/m 3 

### . The rest of the state was below the NAAQS, with annual averages ranging from 9.6 to 14.9 μ g/m 3 

### . Figure 2.3.1-1 shows a map ofNCDAQ Modeling Protocol 7 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### North Carolina with the 2001-2003 annual PM 2.5 

### design values. Across the majority of the monitoring sites, including the two violating monitors , the annual average of PM 2.5 

### has been on

the decline since 1999.

Figure 2.3.1-1: 2001-2003 annual PM 2.5 

### design values.

Given that annual average PM 2.5 

### concentrations at sites across the state, one can

reasonable conclude that PM 2.5 

### is a regional issue for North Ca rolina. An examination of the daily PM 2.5 

### conditions across North Carolina again s hows similar values across the state on a consistent basis. It is very rare that a site becomes notably higher than a surrounding site, as well as experiences an exceedance of the 24-hour PM 2.5 

### NAAQS.

Both nonattainment areas are in the western portion of the state known as the Piedmont Crescent. This portion of the state has the in tersection of three major highways, and a higher

concentration of coal-fired electric generating boilers than the rest of the state.

2.3.2. Diurnal

The most distinct pattern PM 2.5 

### presents is its diurnal pattern. PM 2.5 

### levels often increase in the overnight hours and drop off during the day. This increase is, in part, due to the formation of temperature inversions in the lowest layers of the atmosphere near the surface. Inversions commonly form when the air near the surface co ols during the overnight period. Once the sun sets, the ground loses heat very quickly, which cools th e air that is in contact with the ground. Air is a very poor conductor of heat, which allows the air just above the surface to remain warm.

These inversions are referred to as nocturnal inversions.

Conditions commonly found in association w ith high-pressure systems, namely calm winds and clear skies, contribute to the forma tion of surface inversions. Calm winds prevent warmer air above the surface from mixing down to the ground, and clear skies increase the rate of radiational cooling at the earth's surface. Additionally, th e length of the overnight period greatly affects inversion formation. Winter typi cally has stronger and more frequent inversions, since the nights are longer and provide a longer period for radiational cooling to occur.NCDAQ Modeling Protocol 8 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Inversions generally weaken and disappear as the sun rises and warms the surface. However, under certain meteorological conditions, such as strong high pressure over the area, these inversions can persist fo r several days. In addition, lo cal topographical features can

enhance the formation of inversions , especially in valley locations.

Therefore, inversions create a very stable atmosphere where pollutants, such as PM 2.5 

### , become trapped near the surface. Air quality conditions under the inversion layer are greatly affected by the emissions from electric generati ng units and other industrial sources. Most electric generating units are in operation 24-hours a day, with a large portion of industrial source operating with either two shifts or around the clock. The seco nd shift production from these facilities occurs just as the nocturnal inversion is setting up. Emissions from production are spewed the atmosphere where they become tra pped in the very stable layer created by the inversion. With little mixing occurring, the pollutan ts begin to build up and concentrations rise as more pollutants are pumped into the atmo sphere with overnight production. Pollutants

continue to build until the sun warms the surface enough to ‘break ’, or mix out, the inversion.

Air quality conditions are further aggravated under the inversion by ‘rush hour’ traffic. The evening commute occurs just as the nocturn al inversion is setti ng up, contributing some

additional PM 2.5 

### components to the load from industria l sources. The remnants of the nocturnal inversion are still in place during the bulk of the morning rush hour, allowing for additional vehicle contribution during this time.

2.3.3. Weekly

Preliminary statistical studies indicate both the Hickory Federal Reference Method (FRM) monitoring site and the Lexington FRM site experience similar weekly patterns in PM 2.5 

### concentrations. Concentrations generally build from low weekend values to a peaks concentration at week’s end. Figures 2.3.3- 1 and 2.3.3-2 show the concentration of PM 2.5 

### stratified by day of the week. Th e black circle on th e graph indicates the average concentration for that particular day of the week.NCDAQ Modeling Protocol 9 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Figure 2.3.3-1: 24-hour average PM 2.5 

### concentrations at the Hickory FRM monitor by days of the week. The blac k circle represents the average concentration for the day.

Figure 2.3.3-2: 24-hour average PM 2.5 

### concentrations at the Lexington FRM monitor by days of the week. The blac k circle represents the average concentration for the day.

2.3.4. Seasonal

Summer/Fall

High PM 2.5 

### values generally correspond to high temperatures and high atmospheric moisture content (i.e., high relative humidity). Some constituents of particulate matter are hygroscopic, and will collect more water as relativ e humidity increases. This can lead to very hazy conditions, which limit visibility. Since high temperature and high relative humidity are prevalent in the southeastern United States du ring the summer and early fall, some of North Carolina’s worst particulate matter episodes occur in the months from June to September.

The 24-hour concentrations are typically between 10 and 30 μ g/m 3 

### during the summer, with spikes as high as 50 μ g/m 3 

### . The season generally has its peak PM 2.5 

### concentration in the July to August timeframe, with ammonium sulfate as the primary constituent of PM 2.5 

### during this period. Ozone is also of great concern dur ing the summer months. Generally, the same meteorological scenarios res ponsible for high ozone days also lead to high PM 2.5 

### days. Typically, those conditions are characteristic of a surface high pressure area. The approach of a tropical system or a frontal system can also lead to an increase in either pollutant.NCDAQ Modeling Protocol 10 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### High-pressure systems can act to block cold fronts from passing through the area, which would cause an exchange in air mass leading to improved air quality conditions. Regardless of their origin, high-pressure systems produce light winds and little precipitation. These conditions can persist for days allowing for an intense build up of pollutants in the area. Conditions similar to a high-pressure system can be produced by tropical cyclones. Tropical cyclones produce subsidence on their outer edges, which result s in light winds, warm temperatures and subsequently, high PM 2.5 

### concentrations. Systems that lurk just off the coast, or take a path that grazes the coast can cause subsidence in the western portion of the state.

Pre-frontal conditions can also cause increased PM 2.5 

### concentrations as the leading edge

of the front acts to collect PM 2.5 

### , driving up concentrations along its path. During an event with enough steady precipitation, PM 2.5 

### can “washout”, or remove particulates from the atmosphere, reducing concentrations. Ho wever, relief from high PM 2.5 

### usually comes after frontal passage

when a new air mass enters into the area.

Winter/Spring

PM 2.5 

### episodes during the winter and early spring are typically lower in magnitude than those episodes experience in summer and early fa ll, and are largely driven by nitrates and to a lesser extent by black carbon from combustion. P eak 24-hour averages of particle pollution are generally around 20 to 25 μ g/m 3 

### . The highest PM 2.5 

### occurs when high pressure moves overhead, creating and environment of light winds and clear skies. At night, temperatures at the surface cool rapidly and a steep nocturnal temperature inversion forms. Particle pollution accumulates under this inversion, with 1-hour values rising to 25 to 30 μ g/m 3 

### . PM 2.5 

### drops the next morning

after the sun rises and convectiv e mixing disperses the low level pollutants. The highest daily

PM 2.5 

### occurs when conditions are clear and calm th rough the nighttime hour s, then cloud cover moves overhead at or just after sunrise, reducing convective mixi ng and leaving pollution trapped at the surface.

Longer lasting episodes may occur when inve rsions are created by cold air damming (CAD). CAD events occur frequently during th e winter and early spri ng and are a phenomenon unique to the geography of the Sout heast. In a CAD event, a layer of cold air at the surface gets pushed up against the Appalachian Mountains. A low-pressure system to the south then

circulates warm moist air over th e cold air, creating an inversi on. These events are generally accompanied by cloudy skies, little precipitation, a nd light wind. The stable atmosphere created by CAD events provides an environment conducive for the continual build up of pollution near the surface with little mixing.

Snow and ice cover can also lead to higher concen trations of particle po llution. A snow pack

intensifies the radiational cooli ng at the surface, enhancing the nocturnal temperature inversion and allowing a greater build up of particulates. With the cold temperatures, a greater amount of combustion for heating takes place, which adds more particulates to the air. During the day, the high albedo of the snow suppresses surface h eating and convective mixing, preventing the nocturnal inversion from mixing out and keepin g particle pollution at high concentrations.NCDAQ Modeling Protocol 11 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 3.0 PM 2.5 

### Episode Selection

A crucial step to attainment demonstrati on modeling is the selection of episodes to

model. Several considerations need to be we ighed before settling on not only which days to model, but how many days for each episode. Th is section details the guidance and process by which episodes were selected for the Hickory/Triad PM 2.5 

### attainment demonstration SIP.

3.1. Overview of the USEPA Guidance on Particulate Matter

The USEPA’s draft guidance for Demonstrati ng Attainment of Air Quality Goals for

PM 2.5 

### and Regional Haze (EPA, 2001) sets out specific criteria for the selection of episodes for modeling the attainment of the PM 2.5 

### NAAQS. First, episodes should include days encompassing a variety of mete orological conditions. Episodes should also be chosen around days for which there are extens ive database air quality and me teorology measurements, including measurements speciated data, and upper air meas urements. Finally, a sufficient number of days

should be selected to ensure robust attainme nt tests at violating monitoring sites.

In addition to these primary cr iteria, the USEPA also suggest s a set of secondary criteria that may be used in the selection of episodes. This set of criteria allows st ates to give preference to previously modeled episodes. This is a very valuable consideration, as the USEPA points out, since it can save modeling res ources and effort. The USEPA also recommends selecting episodes that occur during the period correspond ing to the 5 year cu rrent design value, 2000-

2004. Additional considerations include selec ting episodes that include weekends and the selection of episodes meeting primary and second ary criteria in all other nonattainment areas, when participating in regional modeling. Usi ng these criteria laid out by the USEPA, the NCDAQ systematically examined the data availabl e to determine the best episodes for modeling.

The USEPA suggests three approaches to PM 2.5 

### modeling. The first, and preferred approach, is to use a photochemical model to mode l an entire year (or more). As an alternative to the preferred modeling, states can model a mi nimum number of days (at least 15) from each quarter. A second alternative is to classify observed air quality data into groups defined by differences in meteorological conditions, modeling at least three days from each of the identified

groups.

3.2. PM 2.5 

### Episode Selection

With the advances in computing and st orage technologies, and aided by regional modeling efforts, the NCDAQ will model an entire year for the Hickory/Triad PM 2.5 

### attainment demonstration, using a photochemical model. By modeling the whole year , several criteria are covered, including the modeling of weekends and a sufficient number of days to ensure a robust modeled attainment test. Modeling a whole year will also accomplish the goal of encompassing a myriad of meteorological conditions that may influence PM 2.5 

### concentrations.

Efforts were made to determine an appropria te year to model. One of the secondary criteria would suggest using ep isodes drawn from 2001-2003, as this period corresponds to the design value period for which nonattainment desi gnations were based. By selecting 2002, the base case year would be the same as our base line (typical) year. This would mean the 2002 emissions inventories would not have to be adjust ed to correspond to a different base case yearNCDAQ Modeling Protocol 12 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### during modeling efforts. Differen ces between the base case and ba se line (typical) inventories are explained in Section 6.

Additionally, the selection of 2002 as the ba se case year also fulfills the secondary criteria, which suggests states gi ve preferential treatment of previously modeled episodes. Through the NCDAQ’s work with the VISTAS, the 2002 calendar year is in the process of being modeled as a base case year for regional haze reduction goals.

Though the VISTAS modeling is geared towards Regional Haze on a regional scale, the modeling can easily be applied to the PM 2.5 

### . The VISTAS modeling em ploys “one atmosphere” modeling, or modeling of all atmospheric const ituents, including particulate matter and ozone. This modeling is done in parallel to capture interactions between various compounds. Since

PM 2.5 

### , along with ozone and regional haze, is be ing modeled as part of the VISTAS modeling efforts, its data can easily be ex tracted from the modeling results.

The USEPA guidance suggests that when sele cting a representative year, one should examine the annual mean concentration and the pa ttern of the quarterly mean concentrations. The mean annual concentration of the year chosen as the base case should be close to the 3-year design value at all or most of the monitoring site s. Table 3.2-1 shows the annual average for the Hickory and Triad areas, as compared to both the 2001-2003 design values and the 2002-2004 design values. The mean annual average concen tration for the 2002 calendar year is generally close to either design value, usually within ± 0.5 μ g/m 3 

### . When examining the patterns of the mean quarterly concentrations, the representa tive year should follow a similar pattern. Table 3.2-2 shows the mean quarterly concentrat ions for 2002 and the average of the quarterly mean concentrations from 1999 through 2004. The ambient data for 2002 generally follows the same trends as the quarterly averages; specif ically the third quarter has the highest mean concentration, with the lowest concentrati ons in the first or fourth quarter.

Table 3.2-1: Annual mean concentration and desi gn value for sites in nonattainment areas Monitoring

Site County Annual Mean Design Values 2002 – DV(01-03) 

### 2002 – DV(02-04) 

### 2001 2002 2003 2004 2001-2003 2002-2004 Hickory Catawba 15.98 15.36 15.04 15.00 15.5 15.1 -0.10 0.22 Lexington Davidson 16.45 15.88 15.17 15.18 15.8 15.4 0.05 0.47

Mendenhall-1 Guilford 13.72 13.32 13.97 13.2 13.7 0.55 0.05

Mendenhall-2 Guilford 13.79 13.15 14.18 12.9 13.7 0.85 0.08


### Note: The last two columns show the differe nce between the annual average for 2002 and the 2001-2003 design value (DV) and the 2002-2004 DV.

Table 3.2-2: Quarterly mean concentrations for 2002 compared to mean quarterly averages Monitoring

Site County 1Q.2002 2Q.2002 3Q.2002 4Q.2002 Q1 Q2 Q3 Q4

Hickory Catawba 13.25 14.32 21.12 12.73 14.64 15.82 20.00 13.82 Lexington Davidson 14.94 15.00 19.28 14.29 14.67 16.67 19.42 14.61

Mendenhall-1 Guilford 11.71 13.14 18.29 11.72 11.67 13.70 17.10 11.73

Mendenhall-2 Guilford 13.03 14.17 18.87 9.08 11.95 14.45 16.84 10.87 

### Note: Blue numbers indicate the minimum quart er, red numbers indicate highest quarter.NCDAQ Modeling Protocol 13 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 2002 was an active year with numerous poor air quality episodes. Across the Carolinas, instances of high PM 2.5 

### values generally coincide with high ozone values since both need similar atmospheric condition to accumulate. Both PM 2.5 

### and ozone thrive in stagnant air masses in the summer, which result from reduced wind conditions that limit vertic al mixing in the atmosphere. The limited mixing allows pollutants to collect near the surface, driving th e concentration of both pollutants up. The 2002 season was examined to verify that it was representative of the nature of

PM 2.5 

### formation in the Hickory and Triad areas to further support its use in modeling. The following section details th e results of the study.

3.3. Episode Classification

Since the NCDAQ is moving towards modeli ng an entire calendar year, a general discussion of episodes is presented in this se ction. The same cate gories of meteorological scenarios exist through out the year.

3.3.1. Definition of a PM 2.5 

### Episode

Monitoring sites across North Carolina rare ly see instances where the 24-hour NAAQS

for PM 2.5 

### is violated; making it is difficult to define PM 2.5 

### episodes. Further complicating the

issue is the fact that North Carolina ra rely has days with a 24-hour average PM 2.5 

### concentration greater then 40.5 μ g/m 3 

### , which is the lower end cutoff of the Code Orange range of the Air Quality Index (AQI). As an arbitrary met hod to classify episodes to ensure various meteorological scenarios were se lected, days with a 24-hour PM 2.5 

### concentration greater than

15 μ g/m 3 

### (the lower end of the Code Yellow range of the AQI) were flagged for closer

examination.

Table 3.3.1-1 lists the 24-hour PM 2.5 

### concentrations for the year, with each block as a separate month. Days with a 24- hour concentration greater than 15 μ g/m 3 

### , but less than 30

μ g/m 3 

### , are shade light gray. Days greater than 30 μ g/m 3 

### are shaded dark gray, and the stippling

indicates no data. The tables include the Mendenhall (MNDHL) site in Guilford County, the Lexington (LEX) site in Davidson County and the Hickory (HKY) site in Catawba County. Both the Lexington and Hickory monitors are FRM mo nitors that report every three days. The Mendenhall is a Tapered Element Oscillating Microbalance (TEOM) monitoring site and reports everyday.NCDAQ Modeling Protocol 14 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Table 3.3.1-1: 2002 PM2.5 Concentrations and Meteorology Episodes

January February March

Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et

01 10.9 01 7.7 8.6 01 10.9

02 11.5 13.6 18.3 Ls 02 9.1 02 12.4

03 03 14.2 03 7.6 7.8 7.1

04 WS 04 8.1 12.3 6.7 04 6.9

05 25.5 28.5 22.3 WS 05 4.9 05 11.3

06 23.9 Ho 06 10.8 06 15.6 22.8 24.8 Ho

07 7.2 07 6.8 10.8 10.1 07 22.3 Ho

08 8.5 10.6 6.8 08 11.1 08 17.5 Ho

09 10.9 09 12.1 09 7 12.2

10 13 10 17.6 22.3 19.4 CAD 10 4.3

11 6.8 8.5 6.3 11 7.4 11 6.5

12 12.8 12 12 12 16.2 19.3 22 F

13 8.1 13 8 8.1 10.3 13 10.3

14 17.1 15.8 H-F 14 11.2 14 12.1

15 9 15 18.2 Ho 15 21.2 23 20 Hs

16 8.5 16 12.3 13.2 16 14

17 13.9 18.8 17.6 Fsl 17 4.4 17 7.4

18 18 6.5 18 5.2 7.2

19 9.4 19 14.3 16.9 14.7 Ho 19 13.6

20 12 11.3 10.7 20 15.5 Ho 20 14.4

21 17.7 Ls 21 7.4 21 10.2 11.8 9.6

22 6.5 22 11.5 13.5 10.2 22 5.2

23 8.1 10.1 23 14.6 23 10.3

24 7.5 24 16.5 Ho 24 12.3 16.5 11 Hs

25 7 25 18.8 20.3 21.4 Ho 25 12.7

26 13.7 18.2 11.5 Ho 26 16.6 Ho 26 14.5

27 12.9 27 5.5 27 10.3 8.7 9.5

28 11.4 28 10.5 10.4 9.6 28 14

29 14.6 15.8 17.4 Hs 29 21.4 Hs

30 10.9 30 12.4 14.6 12.4

31 15.8 Ho 31 9.8

April May June

Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et

01 8.1 01 11.2 01 18.8 19.1 19.6 Hs

02 11.3 14.4 12.4 02 18.3 25.1 25 Fsl 02 20 Hs

03 11.8 03 10.2 03 22.2 CAD

04 7.5 04 8.8 04 26 26.4 29 He

05 10.7 11 11.2 05 6.3 7.7 9.2 05 20.9 He

06 9.2 06 9.1 06 19.6 F

07 11 07 18.1 07 6.3 8.9 8.7

08 12.1 12.7 13 08 18.2 24.1 20.2 CAD 08 7.2

09 8.3 09 25 H-F 09 13.8

10 11.3 10 11.2 10 23.5 22.4 27.5 Ho

11 11.2 12.7 14.3 11 11.5 15.3 11.1 F 11 23.4 Ho

12 11.1 12 16.2 He 12 19.3 Ho

13 8.7 13 9.5 13 25.8 26.9 23.1 H-F

14 8.5 6.7 14 8.7 9.1 9.2 14 14.2

15 9.8 15 10.5 15 9

16 13.5 16 16.5 Ho 16 9 10.2 10.5

17 15.9 15.4 Hs 17 16.6 16.5 18 He 17 14.4

18 Hs 18 8.8 18 20.4 Fst

19 Hs 19 9.7 19 17.7 20.2 Fst

20 15.3 Hs 20 11.2 11.9 11.6 20 14.7

21 21 12.9 21 13.6

22 11.9 22 12.2 22 8.3 8 10.5

23 7.9 8.5 7.6 23 11.1 13.2 16 Ho 23 3.9

24 15.3 Ho 24 17.3 Ho 24 6.5

25 12.4 25 25 6.5 7.8 9

26 7 7.7 7.1 26 25.3 22.6 Fst 26 11.3

27 12 27 27 12.9

28 17.3 F 28 28 9.2 10.3 7.3

29 8 5.8 29 13.8 14.5 18.3 He 29 17.9 Fst

30 12.3 30 9.1 30 25.2 Fst

31 13.1 Note: Mendenhall (MNDHL) is a TEOM monitor, with measurements every day. Lexington (LEX) and Hickory (HKY) are FRM monitors, with measurements every 3 rd day. White blocks denote PM 2.5 < 15 μg/m 3 , light gray 15- 30 μg/m 3 , dark gray > 30 μg/m 3 , and stippling denotes missing data. Meteorological Scenarios (Met) are as follows: Ho – Surface high over NC; Hs – Surface high south of NC; He – Surface high east of NC; Ls – Low pressure passing south of NC; F – Frontal approach; Fst – Front stalled near/over NC; Fsl – slow frontal approach; H-F – High pressure followed by frontal approach; CAD – Cold Air Damming; WS – Winter Storm; T – Tropical system near NCNCDAQ Modeling Protocol 15 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Table 3.3.1-1 (cont.): 2002 PM2.5 Concen trations and Meteorology Episodes

July August September

Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et

01 32.9 31.1 33.5 Ho 01 28.7 Ho 01 3.1

02 37.7 Ho 02 31.4 Ho 02 7.9 7.7 11.9

03 30.8 Ho 03 17.4 19.5 30 Fst 03 15 Ho (T)

04 29.7 26 24.6 Ho 04 10.9 04 22.9 Fsl (T)

05 28.8 Fsl 05 16.7 Ho (T) 05 19.7 20.8 24.2 Fst

06 15 Fsl 06 21 23.6 23 F (T) 06 19.5 Fst

07 20.5 21.1 28.3 Ho 07 4.9 07 18.8 Ho (T)

08 31.1 Ho 08 13.3 08 15 14.6 20.8 Ho (T)

09 34.9 Ho 09 17 19.6 22.2 Ho 09 14.1

10 23.9 25.8 27.8 Fsl 10 27 Ho 10 12.9

11 10.3 11 33.4 Ho 11 21.5 20.7 18.4 F (T)

12 8.5 12 36.9 40.7 Ho 12 8.8

13 9 9.8 16.5 Fst 13 Ho 13 15.6 Fst

14 8.2 14 8.7 14 11.9 12

15 10.9 15 13.3 12.1 12.2 15 7.3

16 34.8 33.1 33.5 Ho 16 12.7 16 7.8

17 41.8 Ho 17 12.7 17 21.2 27.6 Fsl

18 41.8 Ho 18 10.8 10.3 8.7 18 30.5 Ho

19 12.7 13.8 11.4 19 11.8 19 23.5 Ho

20 17 Fst 20 17.4 Fsl 20 14 14.9 21 He

21 28.9 Ho 21 24.9 24.9 23.5 Ho 21 7.9

22 21.9 20.5 24.7 He 22 31.1 Ho 22 11.6

23 17.4 He 23 33.2 Ho 23 16.7 17.8 21.3 F

24 23.2 Fst 24 22.6 22.2 19.7 Fsl 24 11.9

25 24.4 23.3 27.3 Fst 25 15.4 Fst 25 16.1 F

26 18.8 He 26 15.9 Fst 26 5.6 7.7 6.1

27 15.5 He 27 14.4 15 18.1 Fst 27 6.3

28 18.3 17.6 He 28 5.7 28

29 17.3 Hs 29 5.7 29 16.7 18.5 Ho

30 13.6 30 7.4 7.6 30 Ho

31 20.3 22.8 21.9 Fst 31 7.6

October November December

Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et Day M NDHL LEX HKY M et

01 17.3 Ho 01 10.2 12.6 11.9 01 12 9.2 8.7

02 17.5 22.1 17.8 Ho 02 8.9 02 16.8 H-F

03 18.5 Ho 03 11.8 03 10.8

04 24.1 Ho 04 8.8 11 8.1 04 6.6 8.5 10.1

05 17.8 19.2 14.1 F 05 9 05 11.8 WS

06 12.8 06 4.4 06 Ho

07 15.5 F 07 14.5 14.4 12.5 07 49.2 43.7 29.2 Ho

08 9.7 10.8 11.2 08 14.5 08 Ho

09 11.3 09 12.3 09 15.3 Ho

10 10.6 10 8.5 10.5 10 15.3 12.3 18.3 Ho

11 3.8 5.1 5.6 11 7.3 11 10.4

12 7.4 12 3 12

13 10.2 13 9.4 10.8 13 7.8 9.1 8.6

14 6.8 8.5 9 14 9.2 14 5

15 6.3 15 13.6 15 11.5

16 2.5 16 10.8 11.7 8.5 16 10.3 14.9 9

17 10.3 13.9 14.7 17 3.5 17 9.1

18 11.9 18 11 18 10.5

19 14.8 19 17.9 18.7 21.1 Ho 19 18.8 21.3 25 H-F

20 19.7 21.1 19.2 H-F 20 16.8 Fst 20 5.2

21 13.3 21 26.6 Ho 21 5.9

22 10.3 22 7.8 9.8 5.8 22 6.1 10.9 6.4

23 11.7 17.9 12.8 F 23 10.5 23 6.8

24 20 F 24 15.6 Ho 24 4.5

25 10.5 25 19.9 25.9 19.3 Fsl 25 4.1 14.1 4.5

26 5.9 8.2 5.8 26 22.3 Fsl 26 8.6

27 10 27 10.8 27 13.8

28 7.9 28 10.6 10.7 12.8 28 9.9 4.9 9.4

29 3.8 4.9 5 29 9.2 29 13

30 4 30 8.2 30 25.1 CAD

31 5.7 31 20.5 18.9 28.9 CAD Note: Mendenhall (MNDHL) is a TEOM monitor, with measurements every day. Lexington (LEX) and Hickory (HKY) are FRM monitors, with measurements every 3 rd day. White blocks denote PM 2.5 < 15 μg/m 3 , light gray 15- 30 μg/m 3 , dark gray > 30 μg/m 3 , and stippling denotes missing data. Meteorological Scenarios (Met) are as follows: Ho – Surface high over NC; Hs – Surface high south of NC; He – Surface high east of NC; Ls – Low pressure passing south of NC; F – Frontal approach; Fst – Front stalled near/over NC; Fsl – slow frontal approach; H-F – High pressure followed by frontal approach; CAD – Cold Air Damming; WS – Winter Storm; T – Tropical system near NCNCDAQ Modeling Protocol 16 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 3.3.2. Episode Classification of the 2002 Season

In reviewing the data in Table 3.3.1-1, it beco mes apparent the months of May through October have the days with the highest PM 2.5 

### concentrations. The peak concentrations of the year occur during the months of Ju ly and August, and remain below 40.7 μ g/m 3 

### . There is one isolated episode on December 7, 2002, which had a 24-hour PM 2.5 

### concentration of 49.2 and

43.7 μ g/m 3 

### at the Mendenhall and Lexington monitoring sites, respectively. These high values are likely in association with a winter st orm that struck North Carolina on December 4 th 

### and 5 th



### , and will be discussed further below under the winter storm section.

The column labeled “Met” contains a classifi cation of the weather pattern on that day. The meteorological scenarios are broken down into several categories. One set of categories is based on the location of surface high-pressure system s. Systems are defined as either over North Carolina, to the south of the St ate, or east of North Carolina. In addition, episodes can be defined by various frontal passage scenarios.

An additional set is defined by the type of frontal approach experienced. A typical frontal approach is capable of producing elevated PM 2.5 

### , as well as a front stalled near/over North Carolina, slow frontal appr oach, and high pressure followed by a frontal approach. Winter storms, cold air damming, low-pressure system passing to the south, and tropical systems near North Carolina are additional meteorological scenar ios that occur less fre quently, but have an impact on PM 2.5 

### concentrations in North Carolina. Tr opical influence is noted in combination

with the main surface feature on land. The follo wing sections discuss the major categories of meteorological scenarios responsible for elevated particulate matter. Table 3.3.2-1 contains a count of each the meteorological scenarios by m onth for the identified episodes. Several seasonal patterns can be ascer tained from Table 3.3.1-1.

Table 3.3.2-1: Count based on the meteorologica l scenarios associated with elevated PM 2.5



### High Pressure

Stagnation under high pressure is respons ible for, roughly 57% of the noted PM 2.5 

### episodes in 2002 (see Table 3.3.2-1). High pressure builds into the southe ast, settling nearly over North Carolina. The high lead s to clear and calm conditions across the state and most of the region. With light or calm winds and clear skie s, the nocturnal surface temperature inversion grows very strong. Particle pollution builds under the inversion, reaching a maximum in the Met scenario JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Total
High to south Hs 1 3 4 2 1 11 High to east He 3 2 5 10 High overhead Ho 3 6 3 1 3 3 11 11 7 4 3 5 60 High followed by front H-F 1 1 1 2 5 Frontal approach F 1 1 1 1 1 3 4 12

Front Stalled Fst 1 4 5 4 3 1 18

Front Slow Fsl 1 1 3 1 2 2 10
Low passing to south Ls 2 2

Winter Storm WS 2 1 3 Cold Air Damming CAD 1 1 1 2 5 Tropical system influence T 2 5 7

Total 10 7 7 6 11 13 25 19 20 9 6 10 143NCDAQ Modeling Protocol 17 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### morning hours before the sun induces surface heati ng to mix out the invers ion. Highest particle pollution may occur when clouds build in shortly af ter sunrise. The clouds diminish the daytime surface heating and reduce the amount of atmosp heric mixing, allowing the nighttime inversion layer to persist well into the daylight hours. High-pressure ep isodes may be quickly followed by a frontal approach (see section below), which can l ead to a further rise in particle pollution. If high pressure can remain overhead for 2 to 3 days, PM 2.5 

### can rise to 20 to 25 μ g/m 3 

### in the winter, and as high as 40 μ g/m 3 

### in the summer.

A milder rise in PM 2.5 

### may occur if high pressure is positioned further south, over

northern Florida or to the east of Jacksonville. A west to southwest fl ow develops over North Carolina. The flow has a long history over land, or iginating from the central Gulf coast region to the lower Mississippi River valle y. Since the high pressure is cen tered further south and/or east, winds remain light to moderate, preventing a st rong nocturnal inversion from developing. The winds also mix pollutants within a deeper laye r of the atmosphere. Under this scenario, PM 2.5 

### generally reaches a maximum between 15 to 20 μ g/m 3



### .

The winter episodes due to high pressure tend to be transient, lasting 1 to 2 days, because of the farther south position of the jet stream this time of year. Most summertime episodes tend to be of the high overhead or high just to th e east variety (see Table 3.3.2-1). This is not surprising that all the eastern high episodes occur during the summer when the Bermudas high positions itself off North Carolina’s coast for most of the summer, sometime drifting closer to

shore.

Frontal Approach

The second most common meteorological scen arios of the 2002 season involved frontal passage. Approximately 31% of the elevated epis odes of 2002 were either classified as frontal approach, front stalled near/over North Caroli na, slow frontal appro ach, or high pressure followed by a frontal approach (See Table 3.3.2-1).

North Carolina experiences numerous front al passages during the year. Mass convergence ahead of a cold front typically yiel ds a rise in particle pollution. Winds are predominantly from the south and southwest ahead of the front. The level of particle pollution with a frontal approach depends on the speed of the front. The slower moving the front, the more time pollution levels have to rise in an area. The typical fast movi ng front may cause a rise

in PM 2.5 

### for a few hours, causing a spike in 1-hour c oncentrations and a s light surge in the 24-

hour concentration.

Slow moving and stalled fronts are slightly less common (see Table 3.3.2-1), but can lead

to a more extended rise in PM 2.5 

### concentration, leading to elevated 24-hour concentrations. In North Carolina, a fronts tend to slow when a l ong-wave upper level trough is located over the eastern United States, between th e Appalachian Mountains and the Mi ssissippi River. As a cold front plunges southward from Canada into Alabam a and Mississippi, it then slows down as it becomes oriented parallel to the upper level fl ow. In this situati on, North Carolina remains within the dirtier air ju st to the east of the frontal bounda ry, where mass convergence leads to a buildup of particle pollution. Even tually the cold front pushes s outh and east of North Carolina, bringing cleaner air into the stat e. An example of this type of event occurred on November 25,

2002.NCDAQ Modeling Protocol 18 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Occasionally, an approaching cold front will completely stall over the state and then dissipate. As the front moves into the regi on, the upper level trough weakens, leaving no upper level dynamics to support the fr ontal boundary. The high particle pollution associated with the leading edge of the front remains in the region as a weak high-pressure area develops overhead. The stagnant conditions underneath the high pressu re cause particle pollu tion concentrations to remain elevated, and can lead to increased concen trations. An example of this type of event occurred on November 20, 2002.

Winter Storm Followed by High Pressure

In this scenario, low pressure develops along the Gulf coas t, and tracks eastnortheastward off the Carolina coast. With cold air in place, moderate to heavy snow falls to the north of the storm track, over th e Appalachian Mountains and Pied mont. In the wake of the departing storm, high pressure bu ilds over the southeast. The snowfall intensifies the radiational cooling at the surface, enhancing the overnight inversion and allowing a greater build up of particulates. During the day, the high albe do of the snow suppre sses surface heating and convective mixing, preventing the nocturnal inve rsion from mixing out and keeping particle pollution at high concentrations.

Especially severe winter storms may cause extreme rises in PM 2.5 

### . Severe winter weather may knock out power across a wide region, causing people to use alternative means, such as wood burning, to keep warm. North Carolina ex perienced a severe ice storm on December 4, 2002. Power was knocked out for several days ac ross a wide swath of the Piedmont. Wood combustion for both residential heating and debris removal caused PM 2.5 

### to top out above 40

μ g/m 3 

### . Due to the anomalous nature of severe wi nter storms, it is not appropriate to include

PM 2.5 

### concentrations for days immediately following these events.

Cold Air Damming, CAD

Longer lasting episodes may occur with the passage of a ‘backdoor’ cold front in a moderate CAD regime, which stalls in or near North Carolina. In a CAD scenario, the wind flow around a surface high pressure located over the nor theast will push colder air southward to the east of the Appalachian mountains , down into the Caro linas. Meanwhile, low pressure to the

west of North Carolina advects warm moist air over the cold air, creating a stable inversion layer

at the surface. A stra tus cloud layer usually forms as the wa rm air overrides the cold air. Particle pollution is able to build within the inversion laye r, and cloudiness and light wind prevents much significant mixing. Recirculation of pollutants often occurs at the end of CAD events, as the backdoor cold front pushes north.

Lows and Tropical Influence

The final two categories are less common (s ee Table 3.3.2-1). The presence of tropical system offshore during the late su mmer to early fall can degrade PM 2.5 

### conditions. The systems generally cause subsidence at their outer edge, which acts much like a high-pressure system. Skies are generally clear and calm winds preva il far out from the syst em’s center, allowing pollutants to build up. The subs idence caused by the presence of a tropical system combines with the meteorology present on land (usually a high-pressure system over North Carolina) to

drive PM 2.5 

### concentration to le vels between 15 and 25 μ g/m 3



### .NCDAQ Modeling Protocol 19 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### The final category involves a low-pressure system passing to the south of the State. These events produce slight elevations in PM 2.5 

### , with concentrations on the order of 18 μ g/m 3



### . With only two instances of this type of scenario , it is hard to pin point the exact cause for the slight elevation. The increase in concentration is likely due to the clear conditions setting up the night preceding the low-pressure system passage, lead ing to the formation of an inversion layer. The low-pressure system approach by morn ing shrouds the area in cloud cover, making it difficult to erode the inversion layer. The pollu tants are trapped at the surface by the inversion layer, but dissipate after the lo w-pressure system moves east.NCDAQ Modeling Protocol 20 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 4.0 Models and Modeling Configurations

The NCDAQ intends to utilize the same c onfiguration of regional meteorological, emissions processing, and photochemical air qual ity models used by the VISTAS regional haze modeling study. The underlying science behind each component of the overall modeling system

are identified and discussed brie fly in this section. Although th e configuration of each of the modeling components has been sel ected as the culmination of in tensive study by VISTAS, there remains the possibility that certain algorithms a nd parameter settings may still be updated prior to the running of the final annual 2002 base cas e simulation and subsequent model performance testing.

The NCDAQ modeling team will remain in close contact with the VISTAS, as well as other Regional Planning Organization (RPO ) regional modeling initiatives throughout the attainment demonstration modeling study, to dete rmine appropriate refinements to the model codes, input databases, and post-modeling anal ysis procedures. Notable limitations of the models, relevant to their intende d purpose in this attainment modeling analysis, will also be

evaluated in detail.

4.1. Recommended Models

Based on extensive research of available documentation of the VISTAS Regional Haze modeling analysis, it has been determined by the NCDAQ that the PM 2.5 

### attainment demonstration omodeling should utilize the following suite of models:

• MM5 : The Pennsylvania State University/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorologi cal Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale photochemical, fine particulate matter, and re gional haze regulatory modeling studies.

• SMOKE : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that ge nerates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, point , fire, and biogenic emission sources for photochemical grid models.

• CMAQ: The USEPA’s Models-3/ Community Multiscale Air Quality (CMAQ) modeling system is a “One-Atmosphere” photochemical grid model capable of addressing ozone, fine particulate matter, vi sibility, and acid de position at regional scale for periods up to one year.

4.2. MM5 – Mesoscale Prognostic Model

Over the past decade, researchers at the Pennsylvania State University (PSU) and the National Center for Atmospheric Research (NCA R) have collaborated in the refinement and
extension of the PSU Mesoscale Meteorological Model leading to the current 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 as a state-of-the-science model th rough enhancements provided by a broad user community (e.g., Chen and Dudhia, 2001; Stau ffer and Seaman, 1990, 1991; Xiu and Pleim,NCDAQ Modeling Protocol 21 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009



### 2000). The MM5 modeling system is routinely employed in forecasting projects as well as refined investigations of severe weather. Utiliz ation of MM5 within air quality applications is also a common practice. In recent years, th e MM5 modeling system ha s been successfully applied in continental scale annual simulations for the year s 1996 (Olerud et al., 2000), 2001 (McNally and Tesche, 2003), and 2002 (Johnson, 2003). Due to its ongoing scientific development worldwide, extensive historical applications, broad user community support, public availability, and establis hed performance record compar ed with other applicationsoriented prognostic models, MM5 has been select ed as the preferred meteorological model for this effort. This section provi des an overview of the MM5 and its data input requirements.

4.2.1. MM5 Overview

The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a threedimensional, limited-area, primitive equation, prognos tic model that has been used widely in regional air quality model appl ications (Seaman, 2000). The basic model has been under continuous development, improvement, testing, and has been openly peer-reviewed 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 mesoscal e studies, including cycl ogenesis, polar lows, cold-air damming, coastal fronts, severe thunder storms, tropical storms, subtropical easterly jets, mesoscale convective complexes, desert mixed layers, urban-scale modeling, air quality studies, frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and operational mesoscale forecasting.

MM5 is based on the prognostic equations fo r three-dimensional wind components (u – zonal wind component, v – meridional wind comp onent, and w – vertical wind component), temperature, water vapor mixing ratio, and the perturbation pressure. Use of a constant reference-state pressure increases the accuracy of the calculations near st eep terrain. The model uses an efficient semi-implicit temporal integra tion scheme and has a nested-grid capability that can use up to ten different domains of arbitr ary horizontal and vertical resolution. The interfaces of the nested grids can be either one-w ay 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-dimensiona lized pressure, or “sigma,” vertical coordinate similar to that used in many operation al and research models. In the non-hydrostatic MM5 (Dudhia, 1993), the sigma levels are defi ned according to the in itial hydrostaticallybalanced reference state so that the sigma le vels are also time-invariant. The gridded meteorological fields produced by MM5 are directly compatible with the input requirements of “one atmosphere” air-quality models using this coordinate (e.g., CMAQ) . MM5 fields can be easily used in other regional air quality m odels with different coordinate systems (e.g., Comprehensive Air Quality Model with Exte nsions - CAMx) by performing a vertical interpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer (PBL) para meterizations are available for air-quality applications, both of which repr esent sub-grid-scale turbulent fluxes of heat, moisture and momentum. These parameterizations employ various surface energy budget equations to estimate ground temperature based on the insola tion, atmospheric path length, water vapor, cloud cover, and longwave radiation. The surface physical properties of albedo, roughness length, moisture availability, emissivity and thermal in ertia are defined as functions of land-use forNCDAQ Modeling Protocol 22 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### numerous categories via a look- up table. One scheme uses a first-order eddy diffusivity

formulation for stable and neutra l environments and a modified first-order scheme for unstable regimes. The other scheme uses a prognostic equation for the second-or der turbulent kinetic energy while diagnosing the other key boundary layer terms.

Initial and lateral bounda ry conditions are specified from mesoscale three-dimensional analyses performed at 12-hour intervals on the outermost grid mesh selected by the user. Additional surface fields are analyzed at three-ho ur intervals. A Cressman-based technique is used to analyze standard surface and radiosonde observations, using the National Meteorological Center's (NMC) spectral analysis as a first guess . The lateral boundary data are introduced into MM5 using a relaxation technique applied in the outermost five rows and columns of the most coarse grid domain.

A major feature of the MM5 is its use of st ate-of-science methods for Four Dimensional Data Assimilation (FDDA). The theory underlying this approach and details on how it has been applied in a variety of applic ations 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 sy stem in regulatory air quality application studies have been widely reported in the litera ture (e.g., Emery et al., 1999; Tesche et al., 2000, 2003), and many studies have involved comparisons with othe r prognostic models such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application International Mesoscale Model. The MM5 enjoys a far richer applic ation history in regul atory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scale air quality application st udies since 1995, it has generally been found that the MM5 model tends to produce somewhat better photochemical model inputs than alternative

models. For these and other reasons set forth in the MM5 modeling protocol developed by the contractor performing the meteorological modeli ng, Barons Advanced Meteorological Systems, LLC (BAMS) (Olerud and Sims, 2003), MM5 wa s selected as the meteorological modeling system for this study.

4.2.2. MM5 Configuration

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

• Nested 36/12 kilometer (km) grid s, with 34 vertical layers

• Two way nesting, no feedback

• Initialization and boundary conditions from Eta analysis 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 methodNCDAQ Modeling Protocol 23 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

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

4.2.3. MM5 Evaluation

The MM5 modeling results will be evaluated using plots and statistical analyses to determine if the model performance is adequate for the air quality modeli ng exercise. Some of the plots and statistics to be generated include:

• 

### Spatial plots of model predictions with th e appropriate observations overlaid. These will provide a visual to determine how well such meteorological parameters as temperature, mixing ratios, and winds are being captured by the model.

• 

### Graphical statistical plots for surface te mperature, mixing ratio, wind speeds, wind direction, and cloud cover. These will in clude time series of modeled/observed means, bias/error, and index of agreement.

• 

### Daily accumulated precipitation plots of modeled versus observed.

• 

### Tabular statistics for temperature, winds , mixing ratio, and cloud cover for various

domains.

• 

### Comparison of satellite ve rsus modeled cloud images.

• 

### Comparison of surface analysis maps to the MM5 pressure/wind maps

• 

### Comparison of profiler observ ations with modeled winds

4.2.4. Meteorological Data

Meteorological data are being generated us ing the MM5 prognostic meteorological model by BAMS. BAMS is operating the MM5 at 5-da y increments for 2002 on the 36-km and 12-km grid with a 14-day spin up period for th e end of December 2001. The meteorological

observations to be used for statistics come primarily from University Corporation for Atmospheric Research’s (UCAR’ s) ds472.0 archive. These data are quality controlled and converted to NetCDF format, thus allowing the da ta to be visualized on the model fields via Package for Analysis and Visualization of Enviro nmental data (PAVE). Due to the unreliability in precipitation values in the UCAR dataset, precipitation statistics are calculated from the 24-hour gridded accumulations available from the Climate Prediction Center (CPC). However, these fields undergo grid transformation to ma tch our 36-km and 12-km domains from their original 0.25- degree resolution. The statistics are only calculated over cells that MM5 deems to be land since the CPC analyses are de rived primarily from rain gauges.

For aloft analyses, standard sounding obser vations from the National Center for Environmental Predictions (NCEP) ds353.4 arch ive are processed. These observations are quality controlled and used to produce model/ observation skewT sounding plots for selected sites. Additionally, the observ ations are integrated into sigma levels that match the MM5 specifications and subsequently can be statistical ly analyzed for performance at sigma levels 9, 17, and 22 (~500m, ~1600m, ~3400m, respectivel y). Qualitative pr ofiler plots showingNCDAQ Modeling Protocol 24 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### model/observed hourly winds are also created base d upon the data stored at the Forecast Systems

Lab.

4.3. SMOKE Emissions Modeling System

The SMOKE Emissions Processing System Prot otype was originally developed at the Micro-computing Center of North Carolin a (Coats, 1995; Houyoux and Vukovich, 1999). As with most “emissions models,” SMOKE is principally an emission processing system and not a

true emissions modeling system in which emissions estimates are simulated from “first principles.” This means that, with the exception of mobile and biogenic sour ces, its purpose is to provide an efficient, modern tool for converti ng emissions inventory da ta into the formatted emission files required by an air quality simulati on model. For mobile sources, SMOKE actually simulates emissions rates based on input mob ile-source activity data, emission factors and outputs from transportation tr avel-demand models.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing as applie d to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool curr ently available to the air quality modeling community. The sparse matrix approach ut ilized throughout SMOKE permits both rapid and flexible processing of emissions data. The pro cessing is rapid because SMOKE utilizes a series

of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexible because the processing step s of temporal projection, controls, chemical speciation, temporal allocation, a nd spatial allocation have been separated into independent operations wherever possible. The results from these steps are merged together at a final stage of processing.

SMOKE supports area, mobile, fi re and point source emission processing and includes biogenic emissions modeling thr ough a rewrite of the Biogenic Emission Inventory System, version 3 (BEIS3). SMOKE has been availabl e since 1996, and it has been used for emissions processing in a number of regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with the s upport of the USEPA for use with the USEPA's Models-3/CMAQ. The primary purposes of the SMOKE redesign were support of: (a) emissions processing with user-selected chemical mechanisms and (b) emissions processing for reactivity assessments.

SMOKE contains a number of ma jor features that make it an attractive component of the modeling system (Seppanen, 2003). The model suppor ts a variety of input formats from other emissions processing systems and models in cluding the Inventory Data Analyzer (IDA), Emissions Modeling System – 2003 (EMS), and th e 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 from up to 10 countries and any pollutant can be processed by the system.

Recent computational improve ments to SMOKE include:

• Enhanced disk space requirements comp ared with other emissions processing

softwareNCDAQ Modeling Protocol 25 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • Run-time memory allocation, eliminating a ny need to recompile the programs for different inventories, grids, or chemical mechanisms

• Updated Input/Output Applications Pr ogramming Interface libraries

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

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

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

• Support of plume-in-grid processing

• Integration of the BEIS3 em issions factors in SMOKE

Notable features of SMOKE from an applications standpoint include:

• Improved control strategy input formats and designs

• Control strategies can include changes in the reactivity of emitted pollutants, a useful capability, for example, when a solv ent is changed in an industrial process

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

• Integration with Models-3 f ile formats and settings

• Improved data file formats

• Support of various air quality model emissions input 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 Qualit y Assurance (QA) capabilities

• Improved temporal allocation

The Carolina Environmental Program at the Univ ersity of North Carolin a is continuing model development activities with SMOKE. The emissions modeling will employ the SMOKE version 2.0, released on September 30, 2003. The SMOKE ex ecutables, scripts and databases may be downloaded through the Community Modeling and Analysis (CMAS) center’s Model Clearinghouse.

4.4. CMAQ Modeling System

4.4.1. CMAQ Overview

For more than a decade, the USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a “One-Atmosphere” air quality modeling system capable of addressing ozone, fi ne particulate matter, visibility and acid deposition within a common plat form (Dennis et al., 1996; Byun et al., 1998a; Byun and Ching, 1999; Pleim et al., 2003). The original justif ication for the Models-3 development emergedNCDAQ Modeling Protocol 26 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### from the challenges posed by the 1990 CAA as am ended and the USEPA’s desire to develop an advanced modeling framework for “holistic” environmental modeli ng utilizing state-of-science representations of atmospheric processes in a high performance computing environment (Ching et al., 1998). The USEPA comp leted the initial stage of de velopment with Models-3 and released the CMAQ model in mi d 1999 as the initial operating science mode l under the Models3 framework (Byun et al., 1998b). The most r ecent rendition is CMAQ version 4.4, which was

released in October 2004.

CMAQ consists of a core Chemical Transpor t Model (CTM) and seve ral pre-processors including the Meteorological-C hemistry Interface Processor (MCIP), initial and boundary conditions processors (ICON and BCON), and a photolysis rates processor (JPROC). The USEPA is continuing to improve and develop new modules for the CMAQ model and typically provides a new release each year. In the past , the USEPA has also provided patches for CMAQ

as errors are discovered and corrected. More recently, the USEPA has funded the CMAS center to support the coordination, update and dist ribution of the Models-3 system.

Another reason for choosing CMAQ as the atmo spheric model is the ability to do oneatmospheric modeling. Since the NCDAQ will be using the same modeling exercise for both the

ozone and PM 2.5 

### attainment demonstrations SIPs, as well as the regional haze SIP, having a model that can handle both ozone and fine particul ate matter is essential. A number of features in CMAQ’s theoretical formulation and technical implementation make the model well-suited for annual particulate matter modeling. In CMAQ, the model approach has been adapted to dynamically represent the particul ate matter size distribution usi ng three log-normal modes (two fine and one coarse). Transfer of mass between the aerosol and gas phases is assumed to be in equilibrium and all secondary aerosols (sulfate, nitrate, secondary organic aerosols) are assumed to be in the fine modes. The thermodynamics of inorganic aerosol composition are treated using the ISORROPIA module. Aerosol composition is coupled to mass transfer between the aerosol and gas phases. For aqueous phase chemistry, the Regional Acid Deposition Model (RADM) is currently employed. This scheme includes oxidation of SO 2 

### to sulfate by ozone, hydrogen peroxide, oxygen catalyzed by metals and radicals. The impact of clouds on the particulate matter size distribution is treated empirically. For wet deposition processes, CMAQ uses the RADM/Regional Particulate Mode l approach. Particle dry de position is included as well. CMAQ contains three options for treating seconda ry organic aerosol (SOA), latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 to be a reversible semi-volatile scheme whereby VOC em issions can be converted to condensable gases that can then form SOA and then evaporat e back into condensable gases depending on atmospheric conditions.

4.4.2. CMAQ Configuration

The NCDAQ proposes to run CMAQ (version 4.4). The model would be set up and exercised on a nested 36/12-km grid domai n, employing one-way grid nesting. That is, boundary conditions for the 12-km grid simulation are extracted from the 36-km run using the CMAQ BCON processor. A total of 19 vertical la yers would be implemented, extending up to a region top of 100 mb (approximately 15 km above ground level).

The Piecewise Parabolic Method advection solver would be used along with the spatially varying (Smagorinsky) horizontal diffusion approach and K-theory for vertical diffusion. MM5NCDAQ Modeling Protocol 27 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### meteorological output based on the Pleim-Xi u Land-Surface Model (LSM) 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 B ond version 4 (CB4) gas-phase, RADM aqueousphase, and AERO3/ISORROPIA aerosol chemistr y schemes will be used. Treatment of reversible secondary organic aerosols would be simulated by the SORGAM implementation in CMAQ (version 4.4).

Testing completed with VISTAS evaluate d three photochemical mechanisms: CB4, CB4- 2002 and SAPRC99. CB4-2002 produced nearly iden tical results as CB4 but took much longer to run since it is only implemented in the sl ower SMVGEAR (Sparse Ma trix Vectorized Gear) chemistry solver, compared to CB4 that is al so implemented in the faster Euler Backward Iterative chemistry solver. Thus, CB4-2002 was dropped from consideration. Comparisons of CB4 and SAPRC99 found they produced mostly similar but different model performance. However, no one mechanism performed better th an any other mechanism across all species, sites, and periods. The testing only evaluated the mechanism’s ba se case performance, not their response to emission reductions. Given that CB4 runs twice as fast as SAPRC99, the CB4

mechanism was chosen for use.

4.4.3. 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 influen ce of the ICs. The CMAQ Boundary Conditions (BCs) for the initial simulations will be ba sed on seasonal averag es of 3-hour 2001 GEOSCHEM global simulation model output. VISTAS and other RPOs are finding a 2002 GEOSCHEM simulation that would be used to define days specific high time resolved (e.g., 3-hourly) CMAQ BCs.

4.5. Model Limitations

All mathematical models possess inhere nt limitations owing to the necessary simplifications and approximations made in fo rmulating the governing equations, implementing them for numerical solution on fast computers, and in supplying them with input 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 employed are

noted in this section.

4.5.1. MM5

Four different configurations of the MM5 LSM and PBL were evaluated. Depending on the meteorological variable (e.g., winds, temper ature, moisture) and location (e.g., mountains, coastal, east, west) different LSM_PBL conf igurations performed better. The PX_ACM LSM_PBL configuration was selected because it was consistently near the top performing configuration in the southeastern United States across variables and loca tions and was never the worst-performing configuration. However, ther e are numerous limitations in the MM5 with the LSM and PBL treatment being some of the mo st important. The MM5 PX_ACM frequently predicts very low PBL heights th at can appear as "holes" in the spatial distribution of PBL heights that do not appear physic ally realistic and may affect air quality modeling. Although the MM5 PX_ACM configuration model performance in the southeastern United States mostly metNCDAQ Modeling Protocol 28 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### performance benchmarks, the performance was much worse in the western United States. Additionally, there is a st ochastic component of real world meteorology that is not captured by MM5. For example, for some air pollution episod es stagnation is an important attribute that MM5 fails to simulate well as it tries to orga nize the flow fields. However, the MM5 model represents approximately 20 years of de velopment by various researchers.

4.5.2. SMOKE

In early testing, a number of undocumente d features of the SMOKE 1.5b version necessitated re-runs of the emissions processing software to overcome errors and/or ambiguities in source documentation and QA reporting. It is unclear whether similar conditions will be

encountered with the SMOKE 2.0 release. As a full software release, rather than a "beta" version, SMOKE 2.0 is expected to be more robust and more fully-documented than the SMOKE 1.5b release. However, with any newl y-released software system, there is the potential for errors and/or ambiguities to affect the emissions modeling schedule. Should problems arise or issues be encountered wh ich would require additional SMOKE runs or potential SMOKE modifications or alternat e modeling methods, the NCDAQ will immediately notify stakeholders and make recommendations for resolving the issues. Upon receipt of technical direction from the stakeholders, a ppropriate corrective action will be taken.

Features are continuing to be developed in th e SMOKE 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. The NCDAQ will keep abreast of SM OKE development activities to identify new features that will assist in the emissions modeling.

4.5.3. 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 formation chemistry. Testing found the CMAQ nitrate performance suspect with winter

overestimations and summer underestimations. Ot her science limitations in the current version of CMAQ include inadequate treatment of sea salt and the assumption that all secondary particulate matter is in the fine mode. Lack of any two-way grid nesting limits the ability of the model to properly resolve point source plumes or urban photochemistry. Other limitations of CMAQ include its computational requirements, su ch as the need for excessive disk space.

4.6. Model Input Requirements

Each of the modeling system components has significant database requirements. These data needs fall into two categor ies: those required for model setup and operation, and those required for model evaluation testing. The main input data base requirements for the meteorological, emissions, and air quality models are identified in the following section.

4.6.1. MM5

The databases required for setting up, exerci sing, and evaluating the MM5 model for the

2002 season consist of various fi xed and variable inputs.NCDAQ Modeling Protocol 29 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • Topography : High resolution (e.g., 30 sec to 5 min) topographic information derived from the Geophysical Data Center global data sets from the NCAR terrain databases are available for prescribing terrain elevations th roughout 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 S eaman (1990). These 3-hr synoptic-scale

initialization data include th e horizontal wind components (u and v), temperature, and relative humidity at the standard pressure levels, plus sea-leve l pressure and ground temperature. Here, ground temperature repr esents surface temperature over land and seasurface 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 temperatures are bi-linearly interpolated to each model domain and, wh ere necessary, filtered to smooth out irregularities.

• Clouds and Precipitation : While the non-hydrostatic MM5 treats cloud formation and precipitation directly through e xplicit, resolved-scale, and pa rameterized sub-grid scale processes, the model does not require preci pitation or cl oud input. The potential for precipitation and cloud formation enters th rough the thermodynamic and cloud processes formulations in the model. The only precip itation-related input required is the initial mixing ratio field that is developed from the National Weather Service (NWS) and NMC datasets previously discussed.

• Multi-Scale FDDA : The standard "multi-scale" data as similation strategy to be used on the 36-km and 12-km grids will objectively analyze three-dimensional fields produced every 3 hours from the NWS rawinsonde wind, temperature, and mixing ratio data, and similar analyses are generated every three hours from the available NWS surface data.

4.6.2. 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, day specific for actual

2002

o 5-year average CEM emissions, day specific for typical 2002

o Based on Integrated Planning Model (IPM) modeling for future yearNCDAQ Modeling Protocol 30 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • 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 specific for:

• Temporal allocation

• Spatial allocation

• Speciation

4.6.3. CMAQ

The CMAQ CTM requires the following inputs:

• Three-dimensional hourly meteorological fi elds that will be generated by the CMAQ MCIP2.3 processing of the BAMS MM5 output

• Three-dimensional hourly emi ssions generated by SMOKE

• Initial conditions and boundary conditions

• Topographic information

• Land use categories

• Photolysis rates generated by the CMAQ JPROC processorNCDAQ Modeling Protocol 31 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 5.0 Grid Specifications and Modeling Domains

This chapter summarizes the model domain definitions including the model domain, resolution, map projections and nesting sc hemes for high resolution sub-domains.

5.1. Horizontal Modeling Domain

A coarse grid continental United States domain with a 36-km horizontal grid resolution will be used as the outer grid domain for MM5 modeling. The CMAQ domain is nested within the MM5 36-km domain. Figure 5.1-1 shows th e 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 , the NCDAQ will also use a one-w ay nested high resolution grid with a 12-km grid resolution. Figure 5.1-2 sh ows the 36-km CMAQ continental grid and the high resolution, nested 12-km grid in the VISTAS states. Figure 5.1-3 shows in more detail the 12-km grid for the VISTAS region.

Figure 5.1-1: The MM5 horizontal domain is th e outer most, blue grid, with the CMAQ 36-

km domain nested in the MM5 domain.NCDAQ Modeling Protocol 32 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Figure 5.1-2: The 36-km CMAQ co ntinental grid and the high resolution, nested 12-km grid over the VISTAS states.

Figure 5.1-3: A more detailed view of the 12-km grid over the VISTAS region.NCDAQ Modeling Protocol 33 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Both MM5 and CMAQ employ the RPO unified gr id definition for the 36-km continental domain. The RPO unified grid consists of a Lambert-Conformal map pr ojection using the map projections parameters listed in Table 5.1-1.

Table 5.1-1: RPO Unified Grid Definition.

The MM5 36-km grid includes 164 cells in th e east-west 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 th e MM5 coarse grid is also nested in the Eta grid, there is a possibi lity of boundary effects near the MM5 boundary that occur as the Eta meteorological variables are being simulated by MM5 and must come into dynamic balance with MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a bu ffer of eight to nine grid cells around each boundary of the CMAQ 36-km domain. This is designed to eliminate any errors in the meteorology from boundary effects in the MM5 simulation at the interface of the MM5 and Eta grids. The buffer region used here exceeds the USEPA sugges tion of at least five grid cell buffer at each boundary.

Table 5.1-2 lists the number of rows and columns and the definition of the X and Y origin (i.e., the southwest corner) for the 36-km and 12-km grids for both MM5 and CMAQ. Note that the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are

reversed. In Table 5.1-2 “Dot” refe rs to the grid mesh defined at the vertices of the grid cells while “cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Finally, note that the grid definition for the CMAQ MCIP and CMAQ Chemical Transport Model (CCTM) are identical.

Table 5.1-2: Grid Definitions for MM5 and CMAQ.

MODEL COLUMNS DOT (CROSS) ROWS DOT (CROSS) XORIGIN YORIGIN

MM5 36km 129 (128) 165 (164) -2952000 -2304000

CMAQ 36km 149 (148) 113 (112) -2736000 -2088000

MM5 12km 190 (189) 181 (180) 7200 -1656000

CMAQ 12km 169 (168) 178 (177) 108000 -1620000



### 5.2. Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the su rface to the 100 mb. Table 5.2-1 lists the layer 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 degreesNCDAQ Modeling Protocol 34 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### definitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the computational cost of the CMAQ simula tions. The effects of layer averaging were evaluated in conjunction with the VISTAS modeli ng effort and were found to have a relatively minor effect on the model performance metric s when both the 34-layer and a 19-layer CMAQ models were compared to ambient monitoring data.

Table 5.2-1: Vertical Layer Definition for MM5 Simulations (Left Most Columns), and Approach for Reducing CMAQ Layers by Collaps ing 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 0NCDAQ Modeling Protocol 35 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 6.0 Development of Emissions Inventories

There are five different emissi on inventory source classificati ons; stationary point sources area sources, off-road mobile sour ces, on-road mobile sources, and biogenic sources. Stationary point sources are those sources that emit greater than a specified t onnage per year and the data is provided at the facility level. Stationary ar ea sources are those sources whose emissions are relatively small but due to the large number of th ese sources, the collective emissions could be significant (i.e., dry cleaners, serv ice stations, etc.) Off-road m obile sources are equipment that can move but do not use the roadways, i.e., la wn mowers, construction equipment, railroad locomotives, aircrafts, etc. On-road mobile s ources are automobiles, trucks, and motorcycles that use the roadway 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 sources, as well as for off-road mobile

sources are calculated and form atted 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 within the SMOKE system, which uses the USEPA’s MOBILE6.2 model, with modeling meteorology and various mobile inputs. Th e biogenic emissions are also estimated within the SMOKE system, using the USEPA’s BEIS model, with modeling meteorology.

In addition to the various source classificati ons, there are also various types of emission

inventories. The first is the actua l base year inventory. This inve ntory is the base year emissions that correspond to the meteorologi cal data, for this modeling effo rt is 2002. These emissions are used for evaluating the air quality model performance.

The second type of inventory is the typical base year inventor y. This inventory is similar to the actual base year, however for sources that may have significant changes from year to year a more typical emission value is used. In this modeling effort, typical emissions were developed for the electric generating units and the wildland fire emissions. The air quality modeling results using these emissions are used in calculating the relative reduction factors used in the attainment

demonstration test.

The future year base inventor y is an inventory developed fo r some future year for which

attainment of the PM 2.5 

### standard is needed. For this modeli ng project, the future year inventory will be 2009, the last complete year for which the sta ndard must be attained. It is the future year base inventory that control strate gies and sensitivities are applied to determine what controls, to which source classifications, must be made in order to attain and maintain the PM 2.5 

### standard.

In the sections that follow, the inventories used for each source classifications are

discussed.

6.1. Point Source Emissions

The point source emissions will be separated into electric generating units (EGU) and non-EGU categories. The reason fo r splitting the point source inve ntory is that the EGU sources account for the majority of the point source NO

x 

### emissions and hour specific data is available for these sources through the USEPA’s acid rain databa se. Using this more refined data will helpNCDAQ Modeling Protocol 36 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### improve the air quality modeling performance. Annual emissions will be used for the non-EGU

sources.

All point sources will be spat ially allocated in the domain based on the stationary source geographic coordinates. If a poi nt source is missing its latitude/l ongitude coordinates, the source will be placed in the center of its respective county.

6.1.1. Electric Generating Units

Actual Base Year Inventory

For EGU sources with the USEPA reporte d 2002 CEM data or with 2002 hourly emissions provided by stakeholders, actual hourly da ta will be used. For the sources where the USEPA CEM data are utilized, NO

x 

### , SO 2 

### , and heat input-based hou r-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 mainta ined, but will be distributed using hourly to annual profiles. For sources where hour-specific data wa s provided by stakeholders, this data will be
substituted for the USEPA CEM-based emissions and distributions.

To temporally allocate the remaining EGU point sources, the NO

x 

### , SO 2 

### , and heat input

data will be collected from the 2002 CEM datase ts, and used to develop unit-level temporal distributions. The hourly, day of week, and monthl y specific temporal profiles will be used in conjunction with the emissions inventory suppl ied emissions data to calculate 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 develope d for these sources to avoid anomalies in emissions due to variability in meteorology, economic and outage factors in 2002. This approach is consistent w ith the USEPA’s modeling guidance.

To develop a typical year 2002 emissions i nventory for EGU sources, for each unit the average CEM heat input for 2000 through 2004 wa s divided by the 2002 actual heat input to generate a unit specific normalizing factor. Th is normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used since the modeling current design values use monitori ng data from this same 5-year period.

If a unit was shutdown for an entire year during the 2000 through 2004 period, the average of the years the unit was operational wa s used. If a unit was shutdown in 2002, but not permanently shutdown, the emissions and h eat inputs 2001 (or 2000) were used in the normalizing calculations.

Future Base Year Inventory

As part of the VISTAS modeling, VIST AS and the Midwest Regional Planning Organization (MRPO) contracted with ICF Resour ces, L.L.C., to generate future year emission inventory for the electric generating sector of the contiguous United States using the IPM. IPMNCDAQ Modeling Protocol 37 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### is a dynamic linear optimization model that can be used to examine air pollution control policies for various pollutants throughout the contiguous United States for the entire electric power system. The dynamic nature of IPM enables the pr ojection of the behavior of the power system over a specified future period. The optimizati on logic determines the least-cost means of meeting electric generation and capacity re quirements while complying with specified constraints including air polluti on regulations, transmission bo ttlenecks, and plant-specific operational constraints. The vers atility of IPM allows users to specify which constraints to exercise and populate IPM with their own datasets.

Since the modeling is based on the USEPA’s prior analyses for which detailed public documentation is available, a summary of only th e incremental changes that were proposed by VISTAS and MRPO as part of this analysis are presented here.

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

i) The underlying database in the VISTAS anal ysis is the USEPA’s National Electric Energy Data System Database, with cha nges based upon the comments and technical

directions from VISTAS and MRPO’s stakeh olders. The changes focused on existing

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 identifi cations of selected units in the VISTAS and MRPO regions.

ii) The analysis covers the period between 2007 and 2030. To make the model size and run time tractable, IPM is run for a number of se lected years within the study horizon known as run years. Each run year represents se veral calendar years in the study horizon, and all calendar years within the study horizon are mappe d to their representative run years. Although results are only reported for the run ye ars, IPM takes into account all years in the study horizon while de veloping the projections.

iii) The Duke Power and Progress Energy contro l technology investment strategies for complying with North Carolina’s Clean Smokest acks Rule were explicitly hardwired in the analysis.

iv) The USEPA’s Clean Air Interstate Rule (CAIR) rule implemented as part of this analysis is broadly consistent with the USEPA 40 CF R Parts 51 et. al., Supplemental Proposal for

the Rule to Reduce Interstate Transport of Fine Particul ate Matter and Ozone, proposed on June 10, 2004. Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Ca rolina, Tennessee, Texas, Virginia, West Virginia, Wisconsin are the states affected by the CAIR SO 2 

### and the CAIR annual NO

x 

### policies starting 2010. Connecticut is affected by an ozone season NO

x 

### policy. The CAIR plants affected by the annual NO

x 

### policy are capped at 1.6 m illion 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.NCDAQ Modeling Protocol 38 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 6.1.2. 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-E GU category will use annual emissions, which will be temporally allocated to month, day, and hour using source catego ry code (SCC) based

allocation factors. These factor s will be based on the cross-refe rence and profile data supplied

with the SMOKE 2.0 version.

The non-EGU sources annual emissions will be the 2002 VISTAS inventory based on the 2002 Consolidated Emissions Reporting Rule (CE RR) submitted 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 y ear data is to use recently updated growth

and control data consistent w ith the USEPA’s CAIR analyses , supplement these data with available stakeholder input, and provide the results for stakeholder review to ensure credibility. To assemble growth/control data needed for th e final 2009 inventories, the VISTAS contractor will perform the following activities:

• 

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

• 

### Obtain, review, and apply the most current growth factors developed by the 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 sector-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 the 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 ozone 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 Ba se Case Emissions Projection Calculations

. The report identified and compile d a listing of the VOC and NO

x 

### control measure programs expected to be implemented after 1999, as well as an estimate of their influence on projected

emissions. Five nonattainment areas in the VISTAS region were included: Atlanta, Birmingham, metro Washington DC (including seve ral counties in Virgin ia), Louisville, and northern Kentucky.NCDAQ Modeling Protocol 39 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Emission reductions requirements from NO

x 

### Reasonably Available Control Technology (RACT) in 1-hour Ozone SIP areas were impl emented prior to 1999. These reductions should already be accounted for in the VISTAS 2002 inventory since the 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 larg e industrial boilers and turbines, and cement kilns. States in the VISTAS region affected by the NO

x 

### SIP call have developed rules for the control of NO

x 

### emissions that have been approved by the USEPA. The

VISTAS contractor has reviewed the available state rules and guidance documents to determine

the affected sources and oz one season allowances.

For the sources within Nort h Carolina, the NCDAQ has deci ded to use the 2007 emission allowances for the 2009 future year inventory. Th e allowances are given in terms of tons per ozone season (the five 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 combustion engines, which are primarily used in pipeline transmission service at compressor stations. The NC DAQ has established emissions caps for three facilities affected by the Phase II NO

x 

### SIP call rule and will apply these caps to the future year inventory.

For the other states in the VISTAS region, aff ected units were identified using the same methodology as was used by the USEPA in the pr oposed Phase 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 leve l of 82 percent for natural gas-fired internal combustion engines and 90 percent for diesel or dual fu el categories. Therefore, these control levels were applied to

the identified sources.

Maximum Achievable Control Technology Regulations

The USEPA anticipates reductions in particulate matter and SO 2 

### as a result of the

Industrial Boiler/Process Heater Maximum Achievable Contro l 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 attainme nt demonstration is utilizing one atmosphere modeling, the reductions for these pol lutants were 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 associated emission reductions were designed from Federal Register notices and discussions
with the USEPA’s Emission Standards Division sta ff. Emission reductions will be applied only for MACT standards with an initial compliance date of 2002 or greater, since effects from MACT with earlier compliance dates should al ready be accounted for in the 2002 base year inventory.NCDAQ Modeling Protocol 40 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### The future year base inve ntory does not include the NO

x 

### co-benefit effects of the Gas Turbines or stationary Reciprocating Internal Combustion Engines MACT regulations, which the USEPA estimates to be small compared to the overall inventory.

Petroleum Refinery Initiative

Three refineries in the VISTAS region ar e affected by two October 2003 Clean Air Act settlements under the USEPA Petroleum Refinery Init iative. The refineries are: (1) the Chevron refinery in Pascagoula, Mississi ppi; (2) the Ergon refi nery in Vicksburg, Mi ssissippi; and (3) the Ergon refinery in Newell, West Virginia. Alt hough these sources are not within North Carolina or South Carolina, the expected emission reducti ons will be accounted for in the 2009 modeling.

NO

x 

### RACT in 8-hour Ozone SIP

The 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 act ual controls.

Clean Air Interstate Rule

As stated in the preamble to the CAIR ru le, the rule would not require or assume

additional emission reductions from non-EGU boilers and turbines.

6.2. Stationary Area Source Emissions

Stationary area sources include sources whose emissions are relative ly small but due to the large number of these sources , the collective emissions could 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 i ndicator of collective activity, such as fuel usage, number of household or population. Thus, a variety of activity leve l data is collected, including, United States Census economic data, forestry and agri culture agency data, and other data sources. Stationary area source emi ssions are estimated on the county level.

Actual Base Year Inventory

A portion of the area source 2002 base year i nventory for North Carolina was developed by the NCDAQ and provided to th e VISTAS contractor. The remaining portion of the area source inventory was calculated by the VISTAS contractor. The sources estimated by the

contractor included emissions from animal husba ndry, wildland fires, and particulate 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 the NCDAQ were identified from a list in the USEPA guidance document EPA-450/4-91-016, Procedures for the Preparation of Emission Inventories of Carbon Monoxide and Precursors of Ozone , and from the Emission Inventory Improvement Program (EIIP) technical reports.NCDAQ Modeling Protocol 41 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### In general, emission factor estimation appro aches were used to calculate area source emissions. Emission factors may be grouped as per capita emission factors; commodity consumption-related emission factors; and leve l-of-activity based emission factors. The

emission factors were obtained from the Procedures for the Preparation of Emission Inventories 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 multiplying an emission factor by the appropriate indicator of collectiv e activity for each source categor y within the inventory area. An indicator is any parameter associated with the activity 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 ar e gathered from various sources (government reports, census, trade groups, employment da ta, direct surveys, etc.) as appropriate.

For the animal husbandry and fertilizer a pplication emissions, the Carnegie Mellon University ammonia model was used. For paved and unpaved roads particulate matter emissions, emissions developed by the USEPA as part of their 2002 National Emissions Inventory development effort was used.

Windblown dust and sea salt emissions were not included in the inventory. These source categories are insignificant sources of particulat e matter in North Carolin a and therefore, do not significantly impact the PM 2.5 

### nonattainment issues and would not affect the PM 2.5 

### attainment modeling demonstration.

For wildland fires, i.e., wildfires and pr escribed burns, monthly estimates of fire emissions, which include burn acreage and biomass loading information will be used. Depending on the completeness and quality of the data , attempts will be made to calculate spatial and temporal distributions of th e fire emissions, rather than relying on standard distribution profiles. Data will be obtained through consultation with stake holders that participate in the VISTAS Fire Special Interest Work Group. The fi re data will be spli t into two groups, small fires estimated on a county level a nd treated as an area source; and la rge fires that will be treated as a point source.

Typical Base Year Inventory

The actual base year inventory will serve as th e typical base year in ventory for all area source categories except for wildland fires. Fo r this source category, development of a typical year fire inventory provided th e capability of using a comparable dataset for both the base year and future years. Thus, fire emissions would remain the same for air quality modeling in both the base and any future years. The VISTAS Fi re Special Interest Work Group was consulted and

decided to use State level ratio s of acres over a longer term record (three or more years) developed for each fire type relative to 2002. The 2002 acreage was then scaled up or down

based on these ratios to devel op a typical year inventory.NCDAQ Modeling Protocol 42 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Future Year Base Inventory

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

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

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

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

• Controls (including control efficiency, rule e ffectiveness and rule pe netration) provided by the States or originally developed for us e in estimating projected emissions for the USEPA’s Heavy Duty Diesel rulemaking emi ssion projections and used in the CAIR projections were then used to calculate cont rolled emissions. State submitted controls had precedence over the USEPA developed controls.

• Growth factors supplied from the States or the USEPA’s CAIR emission projections were then applied to project the c ontrolled emissions to the approp riate year. In some cases, the USEPA’s Economic Growth and Analysis System Version 5 growth factors were used if no growth factor was available from e ither the States or the CAIR growth factor

files.

6.3. Non-Road Mobile Source Emissions

Non-road mobile sources are equipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad loco motives, lawn and garden equipment, etc. For the non-road mobile source inventory, the list of sources to inventory came from the USEPA’s NONROAD2005 model and the USEPA guidance document, Procedures for Emission Inventory Preparation Volume IV: Mobile Sources . For the majority of the non-road mobile sources, the emissions can be estimated using the USEP A’s NONROAD model. For the three source categories not included in the NONROAD model, i.e., aircraft engines, railroad locomotives and commercial marine, more traditional methods of estimating the emissions were used.

2002 Base Year Inventory

For the non-road mobile sources, the same inve ntory will be used for both the actual and typical base year emissions i nventories. All non-road mobile source emissions, except for aircraft engines, commercial ma rine vessels and railroad locomo tives, were estimated using the
USEPA NONROAD2005 model. Th is model predicts the emissions for non-road equipment based upon the year inputte d into the model.

For railroad locomotive emissions, em ission factors were supplied by the Procedures for Emission Inventory Preparati on Volume IV: Mobile Sources 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 ofNCDAQ Modeling Protocol 43 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Railroad Emission Inventory Methodologies (SR2004-06-02) from the Southeastern States Air Resource Managers, Inc.

Aircraft emissions at airpor ts were calculated by VISTAS contractors using landing and take off data from Federal data sources. These will be reviewed and refined as appropriate for the Charlotte, Greensboro, and Raleigh-Durham airports. Emissions are calculated using the Federal Aviation Administration’s (FAA’s) Emi ssions and Dispersion Modeling System version 4.2, when there is sufficient detail to employ it.

Commercial marine emissions are es timated by procedures described in Commercial Marine Activity for Deep Sea Ports in the United States (EPA420-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 model take s into consideration

rules that are in effect that coul d impact the emissions from these source categories. For the four largest airports in North Carolina, the FAA’s Te rminal 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 ba se 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 a nd after controls) for 1996 and 2010 will be
obtained from the USEPA’s Clean Air Inters tate Rule Technical Support Document. Straight-line interpolations between 1996 a nd 2010 will be used to create a combined growth and control factor. This is done at the State- County-SCC-Pollutant level of

detail.

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

• Apply adjustments to account for additional emission reductions do the low sulfur

non-road diesel fuels.

6.4. On-Road Mobile Source Emissions

Highway mobile sources are considered those vehicles 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 mo tor vehicles occur throughout the day while the vehicle is in motion, at idle, pa rked, and during refueling. Each of these emissions sources needs to be estimated in order to prope rly reflect the total emissions from this source category. In its simplest terms emissions from highway mobile sources are calculated by multiplying an activity level, in this case daily vehicle miles trav eled (VMT) as provided by the North Carolina Department of Transportation (NCD OT), by an emission factor.NCDAQ Modeling Protocol 44 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### The USEPA developed the MOBILE model to estimate emission 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 deve lop the on-road mobile source emissions estimates for carbon monoxide (CO), NO

x 

### , particulate matter, and VOC emissions. Key inputs

for the MOBILE model include information on the age of vehicles on the roads, the average speed of those vehicles, what types of road t hose vehicles are trave ling on, and any control programs (e.g., emissions inspection programs). I nputs are combined with gridded, day-specific temperature data to calculate the gridded, tempora lized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link locat ion and subsequently summed to the grid cell level, the off-network emissions estimates are spatially allocated based on a combination of the Federal Highway Admi nistration version 2.0 highway networks and population. For the North Carolina 36/12-km mo deling, no link-based data will be used. The MOBILE6 emissions factors are based on day- specific temperatures predicted by the meteorological model.

6.4.1. Speed Assumptions

Emissions from motor vehicles vary with th e manner in which the vehicle is operated. Vehicles traveling at 65 miles per hour (mph) emit a very different mix of pollutants than the car that is idling at a stoplight. The NCDAQ will co llect hourly speeds per functional class for this modeling effort. Information from Travel Dema nd Models will be used where available.

6.4.2. Vehicle Age Distribution

The North Carolina vehicle age distribution comes from the NCDOT annual registration data. Both statewide and area specific registratio n data is provided. The only areas with “area specific” registration data include the Charlott e/Gastonia, Raleigh/Durham and Greensboro/ Winston-Salem areas. The latest available age di stribution at the time of the modeling will be

used.

6.4.3. Vehicle Mix Assumptions

The North Carolina statewide vehicle mix will be developed by the NCDAQ using the latest available, at the time of the modeling, Highway Performance Maintenance System count

data. The raw data is converted into MOBILE 6.2 format following the method outlined in the August 2004 guidance document EPA420-R-04-013, Technical Guidance on the Use of MOBILE6.2 for Emissions Inventory Preparation. For the Hickory and Triad nonattainment areas, local vehicle count data wi ll be used to generate the vehi cle mix for all road types except

for urban and rural interstates. Lo cal data is not available for the interstates; therefore, the Statewide mix data will be used.

Version 2 of the SMOKE model uses the MO BILE5 eight vehicle classification format for the vehicle mix. Therefore, the current ve hicle mix format used by the NCDAQ had to be

converted from the sixteen MOBILE6 vehicle cla ssification format to co rrelate to the MOBILE5 eight vehicle classification system. This wa s done using the guidance provided by the USEPA.NCDAQ Modeling Protocol 45 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 6.4.4. Temperature Assumptions

MOBILE6 in the SMOKE emissions model uses the gridded (modele d) meteorology data to calculate temperature. Spa tial and temporal temperature aver aging will be implemented to minimize the SMOKE (mobile) run times.

6.4.5. Vehicle Inspection and Maintenance Program Assumptions

In the early 1990’s, North Carolina adopted emissions inspection requirements for

vehicles in nine urban countie s. This program tests emissi ons at idle for 1975 and newer gasoline powered light and heavy duty vehicles. The program is a basic, decentralized tailpipe test for Hydrocarbons and CO only.

In 2002, North Carolina implemented a new vehicle emissions inspection program referred to as onboard diagnostics (OBDII). This program covers all light-duty gasoline powered vehicles that are model year 1996 and newer. Th e program was implemented in the original nine tailpipe test counties and expa nded to a total of forty-eight counties by January 1, 2006. In addition, the idle test will be phased-out in 2006 in the original nine counties. In order to accurately reflect these OBDII te sts, two separate programs must be incorporated into the 2002 input files. The implementation dates of each pr ogram are also included in the input files.

6.4.6. RVP Assumptions

Reid Vapor Pressure (RVP) reflects a gasoli ne’s volatility. Nort h Carolina has adopted the Phase II RVP of 7.8 psi dur ing June-September as a cont rol measure for the following counties: Davidson, Durham, Forsyth, Gaston, Guilford, Mecklenburg, Wake, Granville, and Davie. Lower RVP leads to lower VOC emissions from gasoline handling and lowers vapor losses from motor vehicles. The remaining areas have a RVP of 9.0 psi 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

6.4.7. VMT Assumptions

Mobile source emissions are calculated by multiplying emission factors by daily VMT. In this modeling exercise, the NCDAQ will use VMT from Travel Demand Models where

available. For all other areas the VMT da ta will be provided by the NCDOT.

6.5. Biogenic Source EmissionsNCDAQ Modeling Protocol 46 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### A revised version of a commonly used bi ogenic emissions model, the Biogenic Emissions Inventory System, has recently been developed and tested by the USEPA over two separate modeling domains/episodes. This versi on of the model (BEIS-3, v0.9) contains several changes over BEIS-2, in cluding the following:

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

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

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

A series of sensitivity modeling simulations has been completed and concluded that the more recent BEIS-3 methodology will impact base cas e model ozone predictions in most parts of the United States. The preliminary tests have also shown that the newer biogenic emissions do

not appear to have a large effect on: 1) the cont rol signal response, 2) relative reduction factors resulting from a projected emissions change, or 3) overall regional model performance in the
eastern United States.

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 nitric oxide (NO) emissions shall be prepared without the input of specific soil moisture and precip itation data and 2) methanol emissions will not be modeled explicitly. Otherwise, the modeling should be identical to a BEIS-3 (v1.0) application.

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

6.6. Development of Modeling Inventories

The SMOKE emissions model will be used to create the air quality model ready files. The chemical speciation method used is the CB4 mechanism. The gridding surrogates are based

off the 2000 census data and are th e most up to date available. The temporal profiles used to disaggregate the annual emissions to the appropria te month, day and hour ar e the latest available profiles provided with the SMOKE model with th e exception of the EGU profiles, which will be developed based on CEM data.

For each model-ready emissions inventory, separa te air quality model-ready files will be created for the EGU point sources, non-EGU point sources, area sources, dust, low-level fires, elevated fires, non-road mobile sources, on-ro ad mobile sources, and biogenic emissions.NCDAQ Modeling Protocol 47 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 7.0 Quality Assurance Plan

This section discusses the QA procedures that will be used in the SIP modeling. The QA procedures listed here describe the combin ed efforts to be employed by VISTAS and the NCDAQ. The VISTAS contra ctors will perform QA on modeli ng inputs and outputs for the modeling region as a whole. The NCDAQ w ill perform QA on their respective emission inventories, as well as look at near state data for reasonablen ess. Additionally, the NCDAQ will review the modeling output s for reasonableness.

7.1. Quality Assurance Objectives

In December 2002, the USEPA published exte nsive guidance 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 scientifica lly sound, robust, and defensible. The new USEPA guidance suggests that a QAPP should include the following elements:

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

• Peer reviewed theory and equations

• A carefully designed life-cycle developm ent process that minimizes errors

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

• Input data and parameters that are ac curate and appropriate for the problem

• Output data that can be used to help inform decision-making

• Documentation of any changes from th e original quality assurance plan

Moreover, the USEPA guidance specifies that different levels of QAPP may be required depending on the intended application of th e model, with a modeling study designed for regulatory purposes requir ing the highest level of quality assurance.

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

choices are consistent with the project objectives.

The guidance document also addresses model development, coding and selection of models, and model performance requirements. VISTAS/NCDAQ modeling are using an existing USEPA sponsored model hence our QAPP will focu s primarily on documenting data sources and QA of data processing performed by the model t eam. The QA objectives for specific aspects of the project are discussed below, a nd these will be incorporated into a QAPP that conforms to the USEPA guidance document for modeling studies.

7.2. Emissions Model Inputs and Outputs

Emissions QA and Quality Control (QC) are th e most critical steps in performing air quality modeling studies. Emissions processi ng can be time consuming and involves complex manipulation of many different types of large da tasets. If errors are made and rigorous QANCDAQ Modeling Protocol 48 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### measures are not in place, these errors may re main undetected, resulting in delays and wasted

time and resources.

7.2.1. VISTAS QA Effort

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

• Emissions inventory data obtained fr om the VISTAS emissions inventory

contractors

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

Specifically, the Emissions Gatekeeper will review the content and format of the provided emission inventories, ensuring an appr opriate appraisal of the emissions data and

estimates for the VISTAS States. Other tasks wi ll include any additiona l translation from mass emissions files into the emissions modeling input file structure necessary for modeling. The VISTAS Study Team will supplement these activ ities with QA checks on the intermediate and model output files using internal and public domain visualiz ation and diagnostic packages.

This multistep emissions QA/QC approach in cludes the initial emissions QA/QC by the Emissions Gatekeeper described above, as we ll 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 proce ss, 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.

7.2.2. Emissions Modeling QA/QC

Modeling QA involves performing data quality checks, assuring simulation accuracy, and recognizing and identifying problems as they happ en; it is the process of looking for glaring faults in the model input and output data (I/O) and determinin g whether the input data are producing the desired results. Scru tiny of the I/O using standard st atistical analyses can reveal problems in the data and/or the model setup. Us ing a standard approach for analyzing emissions model I/O establishes reference points to use when scrutinizing the data. Seeking these indicators of correct model performance allows QA personnel to determine the accuracy of the

simulations and whether faults in the da ta or model configuration exist.

QA documentation will include records of model configuration, details about data files, simulation records, and final report generation. After finishing each QA step, the modeler will

record the result and his/he r initials on a QA checklist.

Data formats will be confirmed using the SMOKE manual to check text files and using PAVE to check binary netCDF files, such as the meteorology inputs. Sanity checks look for glaring errors in the file contents and ensure that the data make sense in the context of how they

will be used and relative to similar or reference datasets.NCDAQ Modeling Protocol 49 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Lead modelers will oversee the entire mode ling process, perform the majority of the SMOKE modeling, and receive and archive input and output data. Secondary modelers will perform some of the SMOKE modeling, orga nize the SMOKE QA reports into emissions summaries for data QA and reporting, and will generate custom QA summaries and reports for troubleshooting any problems encountered during the modeling process.

Outside reviewers will be solicited from outside the emissions modeling team on a volunteer basis to conduct periodi c reviews/audits of the data and modeling process. Outside reviewers will consist of peers, co-workers no t working directly on the inventory in question, state inventory contac ts and stakeholders.

7.2.3. SMOKE Log Files

Each of the programs that make up SMOKE pr oduces a log at run time. Stored in a single directory for each unique simulation, the logs contain information about the configuration of SMOKE, the names and locations of the input and output files used in the simulation, and any warnings, notes, or errors (co llectively called "flags ") that occurred during model execution. Generated as text files, the logs are named acco rding to the program that created them and the emissions source modeled by the simulation, and th e names include identifiers that distinguish the simulation from all others. The logs are usua lly the first source of information consulted in determining whether a simulation completed as expected or for troubleshooting suspected problems.

7.2.4. SMOKE QA Reports

Two types of QA reports are generated by SM OKE. One set is created by the program Smkmerge and the other by the program Smkreport . While both programs allow users to configure the conten t of the reports, Smkreport is a more powerful reporting program that was designed specifically as a QA tool. C ontrolled by configuration files, Smkreport can create text reports at every step in the em issions generation process. In addition to creating reports from

information drawn from the intermediate SMOKE data matrices (e.g., the temporal matrix), Smkreport can summarize the amount of emissions assi gned to different temporal, spatial, and chemical profiles; normalize emissions by popul ation; and report the amount of emissions

allocated to each vertical layer per model-hour. Smkreport also allows the ta rgeted reporting of emissions at specific sources, plants, grid cells, or subdomains.

The program Smkmerge creates either state- or county-level reports at each of the major steps in the emissions generation process (spa tial allocation, 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 repor t biogenic emissions totals,

whereas Smkreport can create reports only for an thropogenic emissions sources.

7.2.5. Visualization Tools

Visualization is an important part of the QA/ QC procedure. Viewi ng bar charts and pie charts of the data verifies that more populous urban counties have greater emissions then the rural counties. Additionally, the P AVE visualization tool is used to graphically view the data to make sure that the data appears reason able both spatially and temporally.NCDAQ Modeling Protocol 50 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Visualization tools will be used to assist in the QA process for the emissions data both before and after being processed through the SMOKE emissions model. The air quality data will also employ visualization tools to view the mode ling results to ensure that the modeling results

look reasonable.

7.2.6. Document Tracking

In order to keep track of the details of modeling, certa in 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 properties 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.

7.3. Meteorological Model Outputs

As part of the VISTAS QA effort, a "Meteoro logical Gatekeeper" will be tasked with providing an independent review and quality assurance of th e meteorological modeling and related datasets developed by the VISTAS meteorological mode ling contractor (BAMS) and used subsequently by the emissions and air qu ality modeling teams. This Gatekeeper QA review ensures that any potential problems with the datasets (should they exist) are identified and corrected in a timely manner. In the cas e of meteorology, the Gatekeeper’s independent QA analysis of the MM5 meteorolog ical datasets serves to prov ide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model perfor mance and sensitivity analyses.

In addition to having personal responsibility for the quality and chain of custody of the meteorological datasets supplied by other VISTAS contractors, the Meteor ological Gatekeeper will be responsible for ensuring and maintaining th e integrity of the data files uploaded to the project website. This website, hosted by UCR (Uni versity California – Riverside), serves as the repository of data for the ENVIRON/UCR/Al pine modeling centers and for the VISTAS Technical Analysis Workgroup participants. In performing the Gatekeep er quality assurance activity, one of the first steps is to conduct an independent op erational evaluation on the MM5 model results at 36-km and 12-km grid scale. This evaluation covers surface and aloft wind direction, temperature, mixing ratio, precipitation, a nd PBL depths on a c ontinental scale (36- km) and subregional scale (12-km) basis.

The Gatekeeper will also perform supplemental , ad hoc analysis of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to ex ercise MCIP version 2.3 to read the MM5 outputs from BAMS and produce binary input files fo r the CCTM to provide the complete set of parameters necessary in the emission s processing and air quality modeling.

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

• Upon receiving the MM5 and MCIP 2.3 output files from BAMS, the NCDAQ will verify the integrity of the file transfer (i.e., no missing and/or corrupted files).NCDAQ Modeling Protocol 51 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • Since the CMAQ modeling domain is a s ubset of the MM5 domain, the NCDAQ will verify that the modeling domain and vertical 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 MM 5 files with MCIP v2.3 using the predetermined MCIP options. The MCIP f iles will then be compared with those provided by BAMS to verify that identic al results from the MCIP processing

were obtained.

• Horizontal and vertical plots of temperat ure, pressure, precipitation, modeled flow patterns, PBL heights, etc. will be cr eated to assess whether the MCIP output

fields are reasonable.

• The VISTAS 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the Western Regional Air Partnership 2002 MM5 simu lation as an independent QA and

evaluation of the database.

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

7.4. Air Quality Model Inputs and Outputs

Key aspects of QA for the CMAQ input a nd output data include the following:

• Verification that correct c onfiguration and science options are used in compiling and running each model of the CMAQ mode ling system, where these include the MCIP, JPROC, ICON, BCON and the CCTM.

• Verification that correct input datasets are used when running each model.

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

• Processing of ambient monitoring data for use in the model performance
evaluation.

• Evaluation of the CCTM results against concurrent observations.

• Backup and archiving of cri tical model input data.

The most critical element in the QA plan for CMAQ simulations is the QA/QC of the meteorological and emissions input files. The major QA issue specifically associated with the air quality model simulations is ve rification that the correct scien ce options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model, a system of naming conventions was empl oyed which uses environment variables in the compile and run scripts that gua rantee that correct inputs and science options are used. A redundant naming system is also used so that th e name of key science options or inputs is included in the name of CMAQ executable program , in the name of the CMAQ output files, and in the name of the directory in which the file s are located. This is accomplished by using the

environment variables in the scri pts to specify the names and locations of key input files. For example, if a model simulation is performed using the CB4 mechanism, all compile and run scripts contain the variable defi nition “$MECH = CB4,” and this va riable is hard coded into the script for the executable name, the output file name, and the output directory name. ThisNCDAQ Modeling Protocol 52 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### procedure produces long file/directory names but it effectively prevents mistakes or makes mistakes readily apparent if they do occur.

A second key QA procedure is to never “recycle” run scripts (i.e., the or iginal runs scripts and directory structure that were used in perf orming a model simulation). For example, if a simulation is performed with the SAPRC mechanis m, instead of editing the original scripts to specify “$MECH = SAPRC,” a parall el directory structure with a new set of scripts to perform the SAPRC simulations will be created. This prov ides a permanent archive of the scripts that were used in performing model simulations. In addition, output from the model simulation will be directed to a log file that pr ovides a record of i nput file names, warni ng messages, etc., that

will be archived.

Post-processing QA of the CMAQ output files si milar to that described for the emissions processing will be performed. Animated graphics interchange format (GIF) files using PAVE will be generated to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated GIFs will be prepared as difference plots for the sensitivity case minus the base case. Often, erro rs in the emissions inpu ts can be discovered by viewing the animated GIFs. Finally, 24-hour average plots for each day of the CMAQ simulations will be produced. This provides a summary that can be useful for more quickly comparing various model simulations.NCDAQ Modeling Protocol 53 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 8.0 Model Performance Evaluation

The USEPA’s April 2007 guidance document Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of the Air Quality Goals for Ozone, PM 2.5 

### and Regional
Haze suggests that model performance be evaluated based on two components:

• How well the model is able to repl icate observed concentrations of PM 2.5 

### components, ozone and/or precursors (surface and aloft), and

• How accurately the model characterizes the sensitivity of changes in ozone and/or

PM 2.5 

### to changes in emissions.

Each component suggests a different type of evaluation procedure, with the first being “operational evaluation,” and the second being “dia gnostic evaluation.” Sinc e the attainment test is a relative test, it is not as necessary to exactly duplicate ozone concentrations. As a result, there is now more emphasis placed on the diagnostic model evaluation.

This section outlines the method used to eval uate model performance. Working with the knowledge that many states involved with the VI STAS 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 atmosphe ric constituents, including ozone. The NCDAQ intends to build off the modeling efforts with VISTAS; therefore, the model performance

evaluation will be an extension of VISTAS efforts.

8.1. Model Evaluation Tools (Operational Evaluation)

8.1.1. Statistical Performance Metrics

In compliance with the aforementioned USEPA guidance (EPA, 2007), VISTAS will compile a suite of metrics for use in evaluati ng model performance. The standard set of statistical performance measures s uggested by the USEPA for evaluating PM 2.5 

### models includes: normalized bias, normalized gross (unsigned) erro r, fractional bias, fractional gross error, and

fractional bias in standard deviations. Several ot her measures will be included in the final report to fulfill the requirements in the 8-hour ozone guidance (addition of average peak prediction accuracy), and to better accommodate other mode ling groups with their comparison of modeling efforts. A list of metrics for calculation on a routine basis using the UCR analysis package is

listed in Table 8.1-1. The metrics calculated in conjunction with VISTAS will include the examination of various atmospheric constitu ents, including the major components of PM 2.5



### .

Typically, the statistical metrics are calcul ated at each monitoring site across the full computational domain for all simulation days. During the VISTAS CMAQ evaluation, the gasphase and aerosol statistical measures shown in Table 8.1-1 will be computed for the full 36-km and 12-km domains, as well as for the individual RPOs and on other subdomains as appropriate. Temporally, the statistical measures will be com puted for the appropriate averaging times: 1 hr for ozone, and gas-phase precursors su ch as NO, nitrogen dioxide (NO 2 

### ), CO, SO 2 

### , 8-hour for ozone, and 24-hour for sulfate, nitrate, PM 2.5 

### , and other aerosol species. These results will then be averaged over annual, mont hly, and seasonal periods for di splay, further analysis, and reporting. Should it become necessary as part of model performance dia gnosis, the statistics willNCDAQ Modeling Protocol 54 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### be aggregated in other ways, e.g., (a) day vs. ni ght, (b) weekday vs. weekend, (c) precipitation vs. non-precipitation days, (d) month of the year, and (e) the 20% haziest/cleanest days, in order to help elucidate model performance problems. For the purposes of the Hickory/Triad PM 2.5 

### SIP, only the statistics for PM 2.5 

### and its component species will be reported. The statistics for the pollutants and precursors will be reviewed internally for reasonableness.

Table 8.1-1: Stat istical 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 %NCDAQ Modeling Protocol 55 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Table 8.1-1: Statistica l Metrics (Continued)

Statistical

Measure Shorthand

Notation Mathematical Expression Notes

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)

8.1.2. Graphical Representations

The core operational air quality model ev aluation will utilize numerous graphical displays to facilitate quantitative and qualita tive comparisons between CMAQ predictions and measurements. Together with the statistica l metrics listed in Table 8.1-1, the graphical procedures are intended to help : (a) identify obviously flawed model simulations, (b) guide the implementation of any performance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ simulations. These graphical t ools are intended to depict the model’s ability to predict the observed gaseous species, su ch as ozone, and fine particulate species concentrations. The core graphi cal 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 c oncentration maps (i.e., tile plots)



### • Concentration scatter plots stratifi ed by station, by time, and by network



### • Soccer and bugle plots



### • Histogram plots of the statistical metr ics, stratified 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 pollutant concentrations

These graphical displays will be generated, where appropriate, for the full annual cycle as well as for monthly and seasonal periods.

8.2. Model Performance Testing (Diagnostic Evaluation)

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance expectations. Based on experience, initial simulations that “look very good” usually do so as the result of compensating errors. The norm is to engage in a logical, documented process of model performance improve ment wherein a variety of diagnostic probing tools and sensitivity testing methods are used to id entify, analyze, and then attempt to remove theNCDAQ Modeling Protocol 56 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### causes of inadequate model performance. This is invariably the most technically-challenging and time consuming phase of a modeling study. The annual CMAQ model base case simulations are expected to present some performance cha llenges that may necessitate focused diagnostic and sensitivity testing in order fo r them to be resolved. It is hoped that these diagnostic and/or sensitivity tests can be adequa tely carried out within the re sources and schedule. Where practical, diagnostic or sensitivit y analyses, if needed, could be performed on selected episodes within the annual cycle, there by avoiding the time-consuming task of running CMAQ for the full 2002 period. Below, the types of diagnostic and sensitivity te sting methods that might be employed in diagnosing inadequate model perf ormance and devising appropriate methods for improving the model res ponse are identified.

8.2.1. Traditional Sensitivity Testing

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



### • Level I : Model algorithm evaluatio n and configuration testing



### • Level II : Model performance testing, uncerta inty 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 operational evaluation, while Level III covers diagnostic evaluation.

The Level I sensitivity tests with CMAQ have already been completed in the initial VISTAS configuration and diagnostic analyses. However, give n that open community nature of CMAQ and the frequent science upd ates to the model and supporting databases, it is possible that some additional configuration sensitivity testing will be necessary.

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

tasks:



### • To reveal internal inconsistencies in the model



### • To provide a basis for comp ensatory 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 refine ment and data collection programs

The merits of performing Level II se nsitivity testing will depend upon whether performance problems are encountered. In a ddition, the number of tests possible, should performance difficulties arise, will be limited by the available schedule and resources. From past experience with CMAQ and other models, it is po ssible to identify examples of sensitivity runs that could be useful in model performance improvement exercises with the annual 2002 CMAQ

simulation. These include:NCDAQ Modeling Protocol 57 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### • 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 Pe ntoxide/Nitric acid (HNO 3 

### ) chemistry



### • Modified ammonia and HNO 3 

### deposition velocities

Note that in a few cases [e.g., verti cal grid structure, ammonium (NH 4 

### ) emissions estimates], some sensitivity experimentation has already been carried out by VISTAS. To the extent that this information can help guide the future diagno stics analyses, this earlier work will be used.

Level III sensitivity analyses have two main purpo ses. First, they facilitate the emissions

control scenario identificati on and evaluation processes. Currently, four complimentary sensitivity “tools” can be used in regional photochemical models depending upon the platform being used. These methods include: (a) traditiona l or “brute force” testing, (b) Decoupled Direct Method, (c) Ozone Source Apportionment T echnology and Particulate Matter Source Apportionment Technology, and (d) Process Analys is. Each method has its strong points and they will be employed where needed. The second purpose of Level III sensitivity analyses is to help quantify the estimated reliability of the ai r quality model in simulating the atmosphere’s response to significant emissions changes.

Examples of Level III monthly or annual se nsitivity runs for Phase II might include:



### • Ozone, sulfate, nitrate, ammonium a nd other aerosol sensitivities to SO 2 

### emissions



### • Ozone, sulfate, nitrate, ammonium and othe r aerosol sensitivities to elevated point

source NO

x 

### emissions



### • Ozone, sulfate, nitrate, ammonium and ot her aerosol sensitivities to ground level

NO

x 

### emissions



### • Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia

The need to perform sensitivity experimentati on (Levels I, II, or III) will depend on the outcome of operational performance evaluations. If such a need arises, the ability to actually carry out selected sensitivity and/or diagnos tic experiments will hinge on the availability resources and sufficient time to carry out the analys es. Clearly, selection of the specific analysis method will depend upon the nature of the technical question(s) being addressed at the time.

8.3. Air Quality and Ozone Column Data

Data from ambient monitoring networks for bot h gas and aerosol species are used in the model performance evaluation. Table 8.3-1 summarizes ambient monitoring networks used to collect data for Air Quality model performance evaluation. Data have been compiled for all networks listed except the Photochemical Assessment Monitoring Stations (PAMS) and particulate matter Super-sites.NCDAQ Modeling Protocol 58 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Additional data used in the air quality modeling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data provides oz one column data, is available for 24-hour average, and is obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . The TOMS data is used in the CMAQ radiation model to calculate photolysis rates.

Table 8.3-1: Overview of Ambien t Data Monitoring Networks.

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/IMP ROVE/improve_data.htm

Clean Air Status and

Trends
Network ( CASTNET ) Speciated PM 2.5 , Ozone (see species mappings) Approximat ely 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 , 

### O 3 , 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.ht

mNCDAQ Modeling Protocol 59 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 9.0 Control Strategy

It is important to remember that photochemi cal models are tools; they do not make decisions. The results from photochemical models ar e one of several pieces of information that decision-makers must consider when adopting cont rol strategies. To ensure that the modeling analyses provide information that meets the needs of the decision makers, it is imperative that the air quality modelers and decision makers agree upon the type and amount of information that is needed to meet the study objectives. This se ction outlines the process behind developing and evaluating emission control strate gies to be employed for the PM 2.5 

### attainment demonstration.

9.1. Control Strategy Design

9.1.1. Emission Sensitivity Test

To begin the process of control strategy desi gn a series of simula tions using across-theboard reductions of direct PM 2.5 

### emissions and PM 2.5 

### precursors can be run. The purpose of

these simulations is to evaluate the relative effectiveness of various pollutant reductions to help tailor effective control strategy measures.

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, the NCDAQ can find out if changes, within the known accuracy of the emission estimates, can lead to different decision s for control strategies. Once the future year inventories are assembled, the sources with the highest uncertainty can be identified. These sources could include biogenic emissions, motor vehicle exhaust, and gasoline evaporation. VOC speciation profiles can also be included in these sensitivity tests.

9.1.2. Isopleth Construction

From the emissions sensitivity tests, is opleths relating unifo rm reductions of PM 2.5 

### precursor emissions to PM 2.5 

### formation can be constructed. These isopleths can give some insight into emission reduction goals , but are not designed to evaluate specific control strategies. They do not simulate real controls that change temporal and spat ial distributions as well as the organic mix of species. With these limitations in mind, the isopleths can help design the control measures that may reduce levels close to ambient st andards. If resources are available, a series of simulations covering a range of actual control measures will be run. These simulations can be used to design appropriate and de fensible control strategies. In addition, isopleths of population exposure can be prepared and used to assess propos ed control measures in an integrated manner.

9.1.3. Ranking Control Strategies

Control strategies should be implemented in an ordered fashion that reduces both PM 2.5 

### concentrations and population exposu re. Emission controls that a ffect multiple pollutants should be sorted separately from single pollutant controls. Estimates of control levels that are expected in future years should be made. An attemp t to reduce population exposure to a minimum each year while reducing PM 2.5 

### , can be made by looking at all potentially availa ble controls.NCDAQ Modeling Protocol 60 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 9.2. Control Strategy Evaluation

Selection of candidate control strategies will take into consideration the results of the combination of analyses described in the previous sections. Once candidate control strategies are identified, the strategies may be simulated. If needed, an an alysis will be performed to investigate the predicted impact of each stra tegy on air quality and popul ation exposure. The results of these analyses will be summarized both in tabular and graphical form to allow systematic comparison and cont rast of all strategies.

To assist decision makers in fully unde rstanding the impact of proposed control strategies, the following products may be prepared as a part of the contro l strategy evaluation:

1. Total PM 2.5 

### Spatial Plots

2. Difference Plots

3. Population Exposure Tables and Histograms

4. Change in predicted future design values

Each of these products will compare future year base simulations with one or more control simulations. An attempt should be made to minimize population exposure as controls are

introduced. To assist in this effort, population exposure and PM 2.5 

### statistics can be organized by future year and control strategy. Upon completion of this evaluation, a final control strategy will

be selected for detailed evaluation.

9.3. Identification of Control Strategy Scenarios

A designated subcommittee will select the cont rol strategy scenarios to be modeled for demonstrating attainment. The control strategy selection process will follow the current the USEPA guidance, and will incorporate our present understanding of PM 2.5 

### 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 legisl ation, federal engine st andards, federal fuel standards, etc.). If at tainment of the annual PM 2.5 

### NAAQS is not shown, additional alternative control strategies identified by the preceding step s, will be modeled until attainment is reached.

A “frozen” future year dataset will be avai lable for use in testing alternative control strategies. This will consist of a set of model input and output files for each episode. Anyone with access to the model (e.g., power companies and universities) can use th ese files as long as they do not change future base case emission inventories, meteorology, growth factors, or

mandated controls. Alternative controls can be modeled in addition to controls strategies modeled by the states.NCDAQ Modeling Protocol 61 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 10.0 Demonstration of Attainment

This section summarizes the procedures that will be used to demonstrate attainment of

the annual PM 2.5 

### NAAQS. An attainment demonstration co nsists of (a) anal yses which estimate

whether selected emissions reductions will result in ambient concentrations that meet the NAAQS, and (b) an identified set of measures that will resu lt in the required emissions reductions. Determining necessary emission re ductions may be done by relying exclusively on results obtained with air quality models. These include the outcomes of the modeled attainment test plus a screening test to estimate whether a proposed emission reduction suffices to meet the NAAQS.

10.1. PM 2.5 

### Model Attainment Test

The PM 2.5 

### model attainment test is similar to the ozone model attainment test, in that both

test use the model estimate in a relative sense using relative res ponse factors (RRFs). A RRF is

calculated for each constituent of PM 2.5 

### and is then used to calculate the projected PM 2.5 

### concentration for the future modeling y ear. Since the attainment test for PM 2.5 

### utilizes both total

PM 2.5 

### and the individual component species, the te st is referred to the Speciated Model Attainment Test (SMAT). In its entiret y, SMAT consists of four basic steps.

First, the observed quarterly mean PM 2.5 

### and quarterly mean composition for each monitor is calculated. This is achieved by multiplying the monitored quarterly mean

concentration of PM 2.5 

### from FRM monitors by the mon itored fractional composition of PM 2.5 

### species for each quarter (e .g., (20% sulfate) x (15.0 μ g/m 3 

### PM2.5 mass) = 3.0 μ g/m 3 

### sulfate mass).

The monitored quarterly mean concentration of PM 2.5 

### from FRM monitors are the 5 year baseline design values (DVB) that are the result of averaging the 3 current design values (DVC) that straddle the modeling base ye ar. The fractional composition of PM 2.5 

### species is derived from STN monitoring site data th at has been processed by the “sul fate, adjusted nitrate, derived water, inferred carbonaceous material balance approach”, or SANDWICH method, so STN and FRM masses are equivalent. The mean co mposition derived from the SANWICH method includes the percent of PM 2.5 

### that can be attributed to SO 4 

### , NO 3 

### , OC, EC, other primary inorganic particulates (o r crustal materials), NH 4 

### , and particle bound water (PBW).

The second step is to use model results to derive component specific RRF for each monitor for each quarter.

(RRF) ij 

### = ([C j, projected 

### ]/[C j, current 

### ]) i 

### Equation 10.1-1

Where:

C

j, current 

### is the quarterly mean concentration predic ted at or near the monitoring site with emissions characteristic of the period used to calculate the baseline design value for annual

PM 2.5

.



### C

j, projected 

### is the future year quarterly mean concentra tion predicted at or n ear the monitoring site.NCDAQ Modeling Protocol 62 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### For the third step, the compone nt specific RRFs are applied to the observed air quality concentrations to projected quart erly species estimate. For each quarter, the current quarterly mean component concentration (step 1) are mu ltiplied by the component-specific RRF obtained in step 2. This leads to an estimated future quarterly mean concentration for each component.

The fourth step sums the quarterly compone nts to get a quarterly mean PM2.5 value. These quarterly mean values are then averaged to produce a future year annual average PM2.5 estimate, or future design value (DVF), for each FRM monitoring site. This final value is then compared to the NAAQS (15.0 μ g/m 3 

### ) to determine if attainment is reached. An example calculation for site “X” is presented in Example 10.1 to further demonstrate the procedure for the

PM 2.5 

### nonattainment test.

Example 10.1:

Step 1: Site X has the following observed quarterly mean PM 2.5 

### mass for the four quarters in the 2000-2004 period:

Table 10.1 –1: Observed Quarterly PM2.5 Concentrations for Site X

Quarter 2000 2001 2002 2003 2004

1 16.14 15.30 13.25 12.94 13.10

2 16.58 16.61 14.32 16.08 14.92

3 18.90 18.83 21.12 19.34 19.60

4 18.89 13.16 12.73 11.81 12.39



### This data yields the following 3-year DVCs, and 5-year DVB:

Table 10.1-2: 3year DVC and 5 year DVB for Site X

Quarter 2000-2002

3-YR DVC 2001-2003

3-YR DVC 2002-2004

3-YR DVC 2000-

2004 5-

YR DVB

1 14.90 13.83 13.10 13.94

2 15.84 15.67 15.11 15.54

3 19.62 19.76 20.02 19.80

4 14.93 12.57 12.31 13.27



### Based on a collocated STN site, the quarte rly speciation profile for site X is:

Table 10.1-3: Quarterly Component Fraction at Site X

Quarter Fraction

Crustal Fraction

EC Fraction

OC Fraction

SO4 Fraction

NO3 Fraction

NH4 Fraction

H2O

1 2.43% 5.96% 46.88% 24.94% 4.79% 8.37% 6.63%

2 4.13% 3.69% 36.73% 34.88% 0.07% 10.33% 10.16%

3 2.86% 2.52% 33.08% 39.29% 0.05% 11.30% 10.90%

4 2.04% 4.82% 48.63% 24.12% 4.17% 8.83% 7.38%



### Multiplying the 5-year quarterly DVB by the quart erly speciation profile yields the following quarterly mean composition:NCDAQ Modeling Protocol 63 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Table 10.1-4: Quarterly Mean Composition for Site X

Quarter FRM

Mass Blank

Mass NonBlank

Mass Crustal EC OC SO4 NO3 NH4 PBW

1 13.94 0.50 13.44 0.33 0.80 6.30 3.35 0.64 1.12 0.89

2 15.54 0.50 15.04 0.62 0.56 5.52 5.25 0.01 1.55 1.53

3 19.80 0.50 19.30 0.55 0.49 6.38 7.58 0.01 2.18 2.10

4 13.27 0.50 12.77 0.26 0.62 6.21 3.08 0.53 1.13 0.94



### Step 2: Modeling produced the following RRFs for Crustal, EC, OC, SO 4 

### , and NO 3 

### component
of PM2.5 near site X:

Table 10.1-5: RRFs for Site X

Quarter Crustal EC OC SO4 NO3

1 0.9987 0.7752 0.8814 0.8856 0.9417

2 1.1504 0.7894 0.9516 0.7614 0.7045

3 1.2178 0.8145 0.9629 0.6326 0.6050

4 1.0373 0.7436 0.8966 0.8096 0.8672



### Step 3: The quarterly mean RRFs from table 10.1-5 are multiplied by th e weighted quarterly average species concentrations from table 10.1-4 to derive future ye ar concentrations. The future year ammonium concentrations are calculated from the sulfate, nitrate, and (current year) degree of neutralization (DON) values. Assuming that the DON is unchanged from the current year, the

ammonium is calculated usi ng the following formula:

NH4 future 

### = DON * SO4 future 

### + 0.29*NO3 future 

### Equation 10.1-2

In the example above, assuming the base year DON is:

Table 10.1-6: DON for Each Quarter for Site X

Quarter DON

1 0.280

2 0.296

3 0.287

4 0.316



### then the Quarter 1 Ammonium Future 

### = 0.280 * 3.35 + 0.29 * 0.64 = 1.007 μ g/m3, etc.

The NH4 future 

### , SO4 future, 

### and NO3 future 

### concentrations can then be us ed to develop a polynomial equation to predict future year particle bound water concentration. This step yields estimated future quarterly component concentrations of:

Table 10.1-7: Future Quarterly Compon ent Concentrations for Site X

Quarter Crustal EC OC SO4 NO3 NH4 PBW

1 0.326 0.621 5.553 2.969 0.606 1.007 0.792

2 0.714 0.438 5.256 3.994 0.008 1.183 1.161

3 0.673 0.397 6.148 4.797 0.006 1.379 1.335

4 0.271 0.458 5.567 2.493 0.462 0.921 0.764NCDAQ Modeling Protocol 64 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Step 4: These quarterly compone nts are then added to get a quarterly mean PM2.5 value.

Table 10.1-8: Future Quarterly Component Concentrations and Quarterly Mean PM2.5 Concentrations (SUM) for Site X

Quarter Crustal EC OC SO4 NO3 NH4 PBW SUM

1 0.326 0.621 5.553 2.969 0.606 1.007 0.792 12.083

2 0.714 0.438 5.256 3.994 0.008 1.183 1.161 13.593

3 0.673 0.397 6.148 4.797 0.006 1.379 1.335 16.399

4 0.271 0.458 5.567 2.493 0.462 0.921 0.764 14.172



### These quarterly sums are then average to produce a future year annual average PM2.5 estimate, or DVF, of 13.08 μ g/m 3 

### for monitoring site X. This DVF at site X is less than 15

μ g/m 3 

### ; therefore, the site passes the attainment test.

10.2. Screening Test

Per the USEPA Guidance, the states will perf orm an analysis of unmonitored areas to

determine if attainment of the annual PM 2.5 

### standard is expected in these areas. The USEPA is working on developing the Model Attainment Test Software (MATS) to perform the unmonitored area analysis, or screening test. This tool will allow for spatial interpolation of baseline monitoring data, which will provide modeling current design values for an entire area and not just 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 th at area predicted not to meet the PM 2.5 

### NAAQS. The NCDAQ will implement this tool, should it be ava ilable in time to contribute to a timely SIP submittal. Should a peer reviewed MATS releas e be delayed, and is unavailable at the time of SIP submittal NCDAQ will examine the modeling data and current monitoring network to determine if any additional violations are sugge sted and is the logical course of action that

should be taken.

10.3. Corroborative Analysis

After the completion of the attainment test, the USEPA PM 2.5 

### modeling guidance suggests additional measures should be taken to fu rther support or refutes the attainment test results. This corroboratory evidence is referred to as supplemental analysis when used to further support an attainment demonstration. A weight of evidence determination can be used to conclude that attainment is likely, especially wh en the predicted future design values are between

14.5 and 15.5 μ g/m 3 

### . Analysis can include a wide variet y of tests and analyses, including the application and results of air qua lity models, observed air quality trends and estimated emissions trends, and the outcome of observational models.

Should the area, clearly demons trate attainment (DVF < 14.5 μ g/m 3 

### ), then basic supplemental analysis will be performed to furt her support the test’s findings. If either the attainment or screening te sts are greater than 15.5 μ g/m 3 

### , it is doubtful that the more qualitative arguments made in a weight of evidence dete rmination can be sufficiently convincing to conclude that the NAAQS for PM 2.5 

### will be attained.NCDAQ Modeling Protocol 65 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### For DVFs between 14.5 and 15.5 μ g/m 3 

### , a weight of evidence determination will be preformed to supplement the conclusion that the ar ea is expected to attain the NAAQS. The end product of a weight of evidence determinati on is a document which describes analyses performed, data used, key assumptions and out comes of each analysis, and why the State believes that the evidence, viewed as a whole, su pports a conclusion that the area will attain the annual NAAQS for PM 2.5



### .NCDAQ Modeling Protocol 66 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### 11.0 References

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Seaman, N. L., et al., 1997. "The Use of the San Joaquin Valley Meteorological Model in Preparation of a Field Program in the Sout h Coast Air Basin and Surrounding Regions of

Southern California: Volume II -- Numerical Modeling Studies for the Development and Application of a Guidance T echnique to Support of the Design of the 1997 Southern California Ozone Study Field Program,” prep ared for Alpine Geophysics, LLC and the California Air Resources Board, prepar ed by the Department of Meteorology, Pennsylvania State Universi ty, 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: E xperiments with Synoptic Data.” Mon. Wea. Rev., 118, 1250-1277.

Stauffer, D.R., N.L. Seaman and F.S. Binkow ski, 1991. “Use of Four-Dimensional Data

Assimilation in a Limited-Area Mesoscale Model. Part II: Effects of Data Assimilation Within the Planetary Boundary Layer.” Mon. Wea. Rev., 119, 734-754.

Tesche, T. W., et al., 2000. “O zone Modeling Protocol for the Peninsular Florida Ozone Study (Version 1.0),” prepared for the Florida Department of Environmental Protection, prepared by Alpine Geophysic s, LLC, Ft. Wright, KY.

Tesche, T. W., et al., 2003. “Peninsular Florid a Ozone Study Final Report,” prepared for the Florida Department of Environmental Protec tion, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Xiu, A., and J.E. Pleim, 2000: Development of a land surface model. Part I: Application in a mesoscale meteorology model. Jour nal of Applied Meteorology, 40, 192-209.NCDAQ Modeling Protocol 69 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### Attachment A: Acronyms Used

μ g/m 3 

### Microgram per meter cubed

μ m Micrometer ACM Asymmetric Convective Mixing AERO3/ISORROPIA Aerosol Chemistry Scheme for CMAQ AIRS Aerometric Information Retrieval System AQI Air Quality Index AQS Air Quality System BAMS Barons Advanced Meteorological, LLC BCON Boundary Condition Processor BCs Boundary Conditions BEIS3 Biogenic Emission I nventory System, version 3 BELD3 Biogenic Emissions Landuse Database

CAA Clean Air Act CAD Cold Air Damming

CAIR Clean Air Interstate Rule CAMx Comprehensive Air Quality Model with Extensions

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 CMAQ Community Multiscale Air Quality CMAS Community Modeling and Analysis
CO Carbon monoxide

CPC Climate Prediction Center CTM Chemical Transport Model

D
a 

### Aerodynamic Diameter DON Degree of Neutralization DV Design Value DVC Current Design Value DVB Baseline Design Value DVF Future Design Value EGU Electric Generating Unit EIIP Emissions Inventory Improvement Program EMS Emissions Modeling System EPS Emissions Preprocessing System

ETA NCEP meteorological model named for the vertical coordinate system used in the model.

FAA Federal Aviation Administration

FDDA Four Dimensional Data Assimilation

FRM Federal Reference Method GIF Graphics Interchange Format HKY Hickory

HNO 3 

### Nitric Acid I/O input/outputNCDAQ Modeling Protocol 70 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### ICON Initial Condition Processor

ICs Initial Conditions IDA Inventory Data Analyzer IMPROVE Integrated Monitoring of Protected Visual Environments IPM Integrated Planning Model ISORROPIA Inorganic Aerosol Th ermodynamics/Partitioning: Model that calculates the compos ition and phase state of an

ammonia-sulfate-nitrate-chlo ride-sodium-water inorganic aerosol in thermodynamic equilibrium with gas phase

precursors. JPROC Photolysis Rate Processor

km kilometer LEX Lexington air quality monitoring site

LSM Land Surface Model

m meter MACT Maximum Achievable Control Technology
MATS USEPA’s Model Attainment Test Software mb millibar, Measure of atmospheric pressure MCIP Meteorological-Chemi stry Interface Processor MCIP2.3 Meteorological-Chemistry Interface Processor (ver. 2.3) MM5 Mesoscale Meteorological Mode l, 5 refers to the version

number MNDHL Mendenhall air quality monitoring site
MOBILE6.2 USEPA vehicle emission fact or model, which is a software tool for predicting gram per mile emissions of hydrocarbons, carbon monoxide, oxides of nitrogen, carbon dioxide, particulate 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 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 3 

### Nitrate

NO

x 

### Oxides of NitrogenNCDAQ Modeling Protocol 71 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### NO

x 

### SIP Call Finding of Significant Contribution and Rulemaking for Certain States in the Ozone Transport Assessment Group Region 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 2 

### Atmospheric Oxygen

O 3 

### Ozone OH Hydroxyl radical OBDII Onboard diagnostics

PBW Particle Bound Water PAMS Photochemical Assessment Monitoring Stations PAVE Package for Analysis and Visualization of Environmental

data PBL Planetary Boundary Layer

PM Particulate Matter

PM 2.5 

### Particulate Matter with a diameter less than 2.5 μm PSU Pennsylvania State University PSU/NCAR Pennsylvania State Univ ersity/National Center 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 Ava ilable Control Technology RADM Regional Acid Deposition Model RAMS Regional Atmospheric Modeling System REMSAD Regional Modeling System for Aerosols and Deposition RPO Regional Planning Organization RRF Relative Response Factor RVP Reid Vapor Pressure

SANDWICH S ulfate, A djusted N itrate, D erived W ater, I nferred

C arbonaceous material balance approac h SAPRC99 Photochemical Mechanism in CMAQ

SCC Source Classification Code

SEARCH Southeastern Aerosol Research and Characterization SIP State Implementation Plan SMAT Speciated Model Attainment Test SMOKE Sparse Matrix Operator Kernel Emissions

SO 2 

### Sulfur Dioxide SOA Secondary Organic Aerosol SORGAM Secondary Organic Aerosol Model STN Speciation Trends Network TEOM Tapered Element Oscillating MicrobalanceNCDAQ Modeling Protocol 72 The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 North Carolina Attainment Demonstration August 21, 2009 

### TOMS Total Ozone Mapping Spectrometer u, v, w Three Dimensional Wind Components in spherical coordinates : u = E/W; v = N/S; w = vertical
UAMIV Urban Airshed Model - Version 4
UAM-V Urban Airshed Model - Version 5 UCAR University Corporation for Atmospheric Research UCR University of Califor nia at Riverside and Davis USEPA United States Environmental Protection Agency USGS United States Geological Service VISTAS Visibility Improvement Stat e and Tribal Association of the

Southeast

VMT Vehicle Miles Traveled

# Appendix D.2

Association for Southeastern Integrated Planning

Modeling ProtocolASIP Modeling Protocol i The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.2 North Carolina Attainment Demonstration August 21, 2009 

### (This page intentionally left blank)January 2006



### Draft Report



### Modeling Protocol

For

Association for Southeastern Integrated Planning (ASIP)

Emissions and Air Quality Modeling to Address

8-Hour Ozone and PM 2.5 

### Nonattainment in the

Southeastern United States

Prepared by:

Ralph E. Morris Bonyoung Koo Abby Hoats

Steven Lau

ENVIRON International Corporation 101 Rowland Way Novato, California 94945

Gregory Stella Dennis McNally Cyndi Loomis

T.W. Tesche

Alpine Geophysics LLC 7341 Poppy Way Arvada, CO 80007

January 31, 2006

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700January 2006

i TABLE OF CONTENTS

Page

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

1.1 SESARM Organization........................................................................................ 1-1 1.2 ASIP Project Background.................................................................................... 1-2 1.3 Purpose of the ASIP Modeling Protocol.............................................................. 1-3

1.4 Problem Definition............................................................................................... 1-3 1.5 Background.......................................................................................................... 1-6 1.5.1 VISTAS Two-Phased Approach...................................................................... 1-6 1.6 ASIP Modeling Approach.................................................................................... 1-9 1.6.1 Data Gatekeepers ............................................................................................. 1-9 1.6.2 Emissions QA/QC.......................................................................................... 1-10 1.6.3 Meteorology QA/QC ..................................................................................... 1-11 1.6.4 Air Quality Modeling QA/QC ....................................................................... 1-12 1.6.5 Overview of Data Flow and Quality Assurance Process ............................... 1-12 1.7 Project Management .......................................................................................... 1-17 1.7.1 Project Organization ...................................................................................... 1-17

1.8 ASIP Project Manager and Co-Principal Investigator....................................... 1-19 1.8.1 ENVIRON and Alpine Geophysics Co-Principal Investigators .................... 1-19 1.8.2 ASIP Contracting Officer .............................................................................. 1-20

1.8.3 ASIP Technical Contact................................................................................. 1-20

1.8.4 ASIP States .................................................................................................... 1-21

1.9 Communications Plan ........................................................................................ 1-21

2.0 MODEL SELECTION .................................................................................................. 2-1

2.1 Recommended Models......................................................................................... 2-1 2.2 MM5 Mesoscale Prognostic Model..................................................................... 2-1

2.2.1 MM5 Overview................................................................................................ 2-2 2.2.2 MM5 Configuration for ASIP and VISTAS Phase II Modeling ..................... 2-3 2.3 SMOKE Emissions Modeling System................................................................. 2-4

2.3.1 SMOKE Overview........................................................................................... 2-4 2.3.2 SMOKE Configuration for ASIP Modeling .................................................... 2-5 2.4 CMAQ Modeling System .......................................................................................... 2-6 2.4.1 CMAQ Overview............................................................................................. 2-6 2.4.3 CMAQ Version 4.5.......................................................................................... 2-7

2.4.4 SOAmods Enhancement .................................................................................. 2-9 2.4.5 CMAQ Configuration for ASIP Modeling .................................................... 2-12

2.5 Model Limitations.................................................................................................... 2-12

2.5.1 MM5 .............................................................................................................. 2-13

2.5.2 SMOKE.......................................................................................................... 2-13 2.5.3 CMAQ............................................................................................................ 2-14 2.6 Model Input Requirements ................................................................................ 2-14

2.6.1 MM5 .............................................................................................................. 2-14January 2006

ii 2.6.2 SMOKE........................................................................................................... 2-15 2.6.3CMAQ.............................................................................................................. 2-16

3.0 EPISODE SELECTION................................................................................................ 3-1

3.1 Overview of EPA Guidance................................................................................. 3-1 3.2 Episode Selection Methodology .......................................................................... 3-1

3.3 Selection of CY 2002 For ASIP and VISTAS..................................................... 3-2

4.0 MODELING DOMAINS AND DATA AVAILABILITY.......................................... 4-1 4.1 Horizontal Modeling Domain.............................................................................. 4-1 4.2 Vertical Modeling Domain .................................................................................. 4-2 4.3 Data Availability.................................................................................................. 4-7

4.3.1 Emissions Data................................................................................................. 4-7 4.3.2 Air Quality ....................................................................................................... 4-7

4.3.3 Ozone Column Data....................................................................................... 4-10 4.3.4 Meteorological Data....................................................................................... 4-10 4.3.5 Initial and Boundary Conditions Data ........................................................... 4-10

5.0 MODEL INPUT PREPARATION PROCEDURES .................................................. 5-1 5.1 Meteorological Inputs to Emissions and Air Quality Models ............................. 5-1 5.1.1 MCIP Reformatting Methodology................................................................... 5-2 5.1.2 Products of the Meteorological Input Development Process........................... 5-3 5.2 Development of Emissions Model Inputs and Resultant Inventories.................. 5-3 5.2.1 Emissions Modeling Methodology.................................................................. 5-4 5.2.2 Set-up of SMOKE Over the VISTAS Domain................................................ 5-5 5.2.3 Development of Point Source Emissions......................................................... 5-8 5.2.4 Development of Area and Non-Road Source Emissions................................. 5-9 5.2.5 Development of On-Road Mobile Source Emissions...................................... 5-9 5.2.6 Development of Biogenic Source Emissions................................................. 5-10 5.2.7 Wildfires, Prescribed Burns, Wind Blown Dust and

Sea Salt Source Emissions............................................................................. 5-11 5.2.8 Speciation and Reformatting of Emissions.................................................... 5-12 5.2.9 Development of Modeling Inventories .......................................................... 5-12 5.3 Model Configuration and Modeling Approach.................................................. 5-14 5.3.1 CMAQ Science Configuration....................................................................... 5-14 5.3.2 Spin-Up Initialization..................................................................................... 5-16 5.3.3 Boundary Conditions ..................................................................................... 5-16 5.3.4 Photolysis Rates............................................................................................. 5-16

6.0 QUALITY ASSURANCE PLAN.................................................................................. 6-1 6.1 Quality Assurance Objectives.............................................................................. 6-1 6.2 Emissions Model Inputs and Outputs .................................................................. 6-1 6.2.1 Emissions Modeling QA/QC ........................................................................... 6-2 6.2.2 QA of the Model-Ready Emissions Impacts ................................................... 6-3 6.3 Meteorological Model Outputs............................................................................ 6-3 6.4 Air Quality Model Inputs and Outputs ................................................................ 6-5

7.0January 2006

iii

MODEL PERFORMANCE EVALUATION.......................................................................... 7-1

7.1 Overview.............................................................................................................. 7-1

7.2 Context for the Model Performance Evaluation .................................................. 7-3 7.3 Multi-Layered Model Testing Process................................................................. 7-4 7.4 Development of Consistent Evaluation Data Sets ............................................... 7-5

7.4.1 Surface Measurements..................................................................................... 7-5

7.4.2 Aloft Measurements......................................................................................... 7-6

7.5 Model Evaluation Tools....................................................................................... 7-7

7.5.1 Statistical Performance Metrics ....................................................................... 7-7 7.5.2 Graphical Representations ............................................................................... 7-9 7.5.3 MAPS/Flying Data Grabber Routines ........................................................... 7-10 7.5.4 Use of Multiple Evaluation Packages ............................................................ 7-11 7.5.5 Probing Tools and Allied Methods................................................................ 7-11

7.6 Model Evaluation Procedures............................................................................ 7-14 7.6.1 Assessment of Ground-Level Gas-Phase and Aerosol Species ..................... 7-14 7.6.2 Assessment of Aloft Gas-Phase and Aerosol Species ................................... 7-16

7.7 Performance Goals and Benchmarks................................................................. 7-16 7-8. Diagnostic and Sensitivity Testing .................................................................... 7-19 7.8.1 Traditional Sensitivity Testing....................................................................... 7-19 7.8.2 Diagnostic Tests............................................................................................. 7-21 7.9 Corroborative and Weight of Evidence Modeling Analyses ............................. 7-22

7.9.1 Corroborative Models .................................................................................... 7-22 7.9.2 Weight of Evidence Analyses........................................................................ 7-23 7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes........................................................................................ 7-24

8.0 8.0 OZONE AND PM2.5 ATTAINMENT DEMONSTRATION ............................. 8-1

8.1 8-Hour Ozone Attainment Demonstration........................................................... 8-1

8.1.1 Modeled 8-Hour Ozone Attainment Demonstration Test................................ 8-1 8.1.2 Additional Analysis to Support the Attainment Demonstration...................... 8-3

8.2 PM2.5 Attainment Demonstration....................................................................... 8-3 8.2.1 Speciation of FRM PM 2.5 Mass Measurements............................................... 8-4 8.2.2 Speciated Modeled Attainment Test (SMAT) ................................................. 8-4 8.2.3 Additional Supporting Analysis....................................................................... 8-6

9.0 DATA MANAGEMENT............................................................................................... 9-1

9.1 Project Website .................................................................................................... 9-2

9.2 Data Transfer ....................................................................................................... 9-2 9.3 Data Backup and Archiving................................................................................. 9-2

9.3.1 ENVIRON........................................................................................................ 9-2 9.3.2 Alpine Geophysics........................................................................................... 9-3January 2006

iv

10.0 DOCUMENTATION................................................................................................... 10-1

10.1 Planned Documentation..................................................................................... 10-1 10.2 Procedures for Updating Modeling Protocol and QA Plan ............................... 10-1

11.0 ASIP STATEMENT OF WORK AND SCHEDULE .............................................................. 11-1

11.1 Task 1: Project Management ............................................................................. 11-1 11.2 Task 2: 2002/2009 Emissions Modeling ........................................................... 11-2 11.3 Task 3: 2002/2009 36/12 km Base F CMAQ Modeling................................... 11-3 11.4 Task 4: Data Analysis and Data Management.................................................. 11-3

REFERENCES.......................................................................................................................... R-1

TABLES

Table 1-1. Model performance goals to help interpret modeling results ............................ 1-14 Table 1-2. ASIP Emissions and Air Quality Modeling project participants

and contacts........................................................................................................ 1-18 Table 1-3. VISTAS listservs that will be used to distribute information by the ASIP Emissions and Air Quality Modeling Team and other ASIP participants. .............................................................................................. 1-22

Table 2-1. Parameters use in enhanced SOA module (see Figure 2-1).............................. 2-16 Table 2-2. Comparison of fractional bias performance metric for Organic Carbon (OC) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement .......................................... 2-16 Table 2-3. MM5 Meteorological Model Configuration for ASIP....................................... 2-17 Table 2-4. SMOKE Emissions Model Configuration for ASIP.......................................... 2-18 Table 2-5. CMAQ Air Quality Model Configuration for ASIP.......................................... 2-19

Table 4-1. RPO Unified Grid Definition............................................................................... 4-1 Table 4-2. Grid Definitions For MM5 and CMAQ............................................................... 4-2 Table 4-3. Vertical Layer Definition For MM5 Simulations (Left Most Columns), And Approach For Reducing CMAQ Layers By Collapsing Multiple MM5 Layers (Right Columns)............................................................................. 4-6 Table 4-4. Overview of Ambient Data Monitoring Networks. ............................................. 4-9

Table 5-1. MCIP V3.0 Configuration used In the ASIP Modeling....................................... 5-3 Table 5-2. SMOKE Modeled Holidays................................................................................. 5-5 Table 5-3. ASIP Model Configuration for the CMAQ Chemical Transport Model. .......... 5-15

Table 7-1. Ground-Level Ambient Data Monitoring Networks and

Stations Available in VISTAS 12 Km Domain for CY-2002............................ 7-25 Table 7-2. Aircraft Sampling Programs Performed in the VISTAS 12 Km

Domain for CY-2002. ........................................................................................ 7-26January 2006

v

Table 7-3. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIRON, 2003b,d for details). ....................................................................... 7-26 Table 11-1. ASIP Task 1 Project Management task deliverables and due dates. ................. 11-2 Table 11-2. ASIP Task 2 2002/2009 Emissions Modeling task deliverables

and due dates...................................................................................................... 11-2 Table 11-3. ASIP Task 3 2002/2009 36/12/ km Base F CMAQ Modeling. ......................... 11-3 Table 11-4. ASIP Task 4 2002 CMAQ Modeling task deliverables and due dates. ............. 11-3

FIGURES

Figure 1-1. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004. ......................................................................................... 1-4 Figure 1-2. PM 2.5 nonattainment counties designed by EPA in December 2004................... 1-5 Figure 1-3a. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling......................................................................................... 1-15 Figure 1-3b. Concluded. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling. ............................................................... 1-16 Figure 1-4. ASIP Emissions and Air Quality Modeling Project Organizational Chart.......................................................................................... 1-17

Figure 2-1. Schematic describing the addition of new SOAmods processes (bold italic) within the existing CMAQ SOA module (regular font) to treat polymerization and SOA from sesquiterpenes and isoprene (see Table 2-1 for parameters). .......................................................................... 2-21

Figure 4-1. Nesting of 36-km CMAQ Grid in the MM5 36-km Grid. ................................... 4-3 Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid. ............................................. 4-4 Figure 4-3. Domain Definition for High Resolution 12-km Grid. ......................................... 4-5 Figure 5-1. EPA 36-km National CMAQ Domain................................................................. 5-6January 2006

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1-1 1.0 INTRODUCTION

This report constitutes the first draft of the Air Quality Modeling Protocol for the Association for Southeastern Integrated Planning (ASIP) to address the regional component of emissions and air quality modeling of 8-hour ozone and PM 2.5 nonattainment in the Southeastern United States. The ASIP emissions and air quality modeling activities are being carried out by the contractor team of ENVIRON International Corporation and Alpine Geophysics, LLC. Southeastern States may use the regional emissions and air quality modeling from ASIP and also may conduct more refined ozone and PM 2.5 modeling of their own nonattainment areas. Previously, the ASIP team has prepared a Quality Assurance Project Plan (QAPP) that details the extensive quality assurance (QA) and quality control (QC) activities being performed as part of ASIP (Morris and Stella, 2005).

1.1 SESARM Organization

Southeastern States Air Resource Managers, Inc., commonly known as SESARM, is a Georgia corporation organized and operated under the provisions of the Official Code of Georgia Annotated, Title 14, Chapter 3, also known as the Georgia Nonprofit Corporation Code. SESARM qualifies as a charitable, tax-exempt, nonprofit corporation as provided in the United States Code, Title 26, Subtitle A, Chapter 1, Subchapter F, Part 1, Section 501(c)(3). SESARM does not engage in for-profit activities nor does it use federal funds to influence legislation.SESARM was incorporated February 24, 1997. Its tax-exempt status was most recently reconfirmed in correspondence from the United States Internal Revenue Service dated December 12, 2001.

SESARM directly represents the eight southeastern state air pollution control agencies located within Region 4 of the United States Environmental Protection Agency (EPA). The member states are Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee. The Board of Directors of SESARM consists of the air pollution control agency director from each state air pollution control agency.

SESARM’s Articles of Incorporation describe its purposes as being to enhance communication and thus promote more effective air pollution management in the Southeast, improve the effectiveness of its members in meeting national and regional air pollution goals, conduct and facilitate research and training necessary to meet its purposes, evaluate air quality issues, recommend actions to resolve air quality problems, and develop steps to accomplish air quality improvements.

When EPA promulgated regional haze regulations on July 1, 1999, it established requirements that states and tribes submit implementation plans to demonstrate reasonable progress towards the ultimate visibility goals of the rule. The first demonstration of reasonable progress is to be made for the year 2018 in state implementation plans due December 2007. EPA also offered an optional approach that groups of states might collaborate in regional analyses of the haze problem. This option showed promise in allowing more cost-effective analyses and the southeastern states opted to follow this option. Southeastern States Air Resource Managers Inc.January 2006

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1-2 (SESARM) accepted responsibility for regional planning organization work tasks on behalf it its member agencies pursuant to this option.

The member agencies of SESARM determined that it was appropriate to expand the collaborative effort for the regional haze program beyond their member boundaries and invited Virginia and West Virginia to join the group. A memorandum of agreement was arranged among the agencies and executed on August 22, 2001. The effort was named the Visibility Improvement State and Tribal Association of the Southeast (VISTAS). Since that time, the agencies have worked together to organize their efforts, develop work task lists and schedules, procure professional services, and support administrative operations. Bylaws were developed and agreed upon by the participating members. An organizational structure was created including an oversight committee, an operations committee, and various work groups. This early planning has served the project well and much progress has been made.

1.2 ASIP Project Background

On December 17, 2004, EPA made fine particle (PM 2.5 ) nonattainment determinations for at least one area in seven of the states participating in the VISTAS regional haze project. They are Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, and West Virginia. In addition, South Carolina has one three-county area that was designated as unclassifiable in the same action. EPA’s Clean Air Interstate Rule (CAIR) modeling indicated that certain nonattainment areas may still be in nonattainment after full implementation of CAIR. These areas include Jefferson County, Alabama and Clayton and Fulton Counties in Georgia.

The PM 2.5 compliance date is April 2010 unless a state demonstrates that more time is necessary in which case up to five additional years may be granted. The nonattainment designations triggered the requirement for development of state implementation plans (SIPs) that will be due in April 2008. The draft guidance from EPA indicates that a significant requirement

of PM 2.5 SIPs will be attainment demonstrations using, at least in part, modeling analyses to define effective emissions control strategies and confirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling year for the PM 2.5 attainment demonstration and also is an interim analysis year for the VISTAS regional haze demonstration.

In April of 2004, EPA determined areas that were not meeting the 8-hour ozone standard. States having one or more 8-hour ozone nonattainment areas in the Southeast are Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. EPA will require attainment of the 8-hour ozone standard in basic nonattainment areas by June 15, 2009 and in moderate nonattainment areas by June 15, 2010. This will require states with basic 8-hour ozone nonattainment areas to model 2008 as the SIP modeling demonstration year while moderate nonattainment areas will require 2009 as the modeling year. Given that North Carolina and Virginia have two year SIP approval processes, there is an immediate need to complete an analysis of ozone attainment using air quality modeling.

The states participating in the VISTAS project (the SESARM EPA Region 4 states plus Virginia and West Virginia from Region 3) have concluded that a collaborative process will be the most efficient approach for the collective states to develop information upon which to baseJanuary 2006

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Protocol \ Draft#2 \ Sec1.doc 1-3 the PM 2.5 and 8-hour ozone attainment demonstrations. The local air regulatory agencies for Jefferson County, AL, Jefferson County, KY, Mecklenburg County, NC, Forsythe County, NC, Knox County, TN, and Shelby County, TN have also become signatory parties to this collaborative effort. SESARM will coordinate among participating agencies and oversee the performance of the inventory and modeling tasks in parallel with the VISTAS regional haze project tasks.

The name of this collaborative effort is the Association for Southeastern Integrated Planning (ASIP). SESARM was awarded a grant from EPA on February 8, 2005 to conduct what was originally called the fine particle SIP development support project but is now known as

ASIP.

1.3 Purpose of the ASIP Modeling Protocol

The ASIP Modeling Protocol sets forth the procedures, data sources and modeling approach to be used in performing the ASIP 8-hour ozone and PM 2.5 modeling for the Southeastern United States that will be the basis of the regional modeling component for the

Southeastern States 8-hour ozone and PM 2.5 State Implementation Plans (SIPs) due June 2007 and April 2008 respectively. The procedures will be reviewed by States, Federal Agencies, Stakeholders and others so that a full and complete understanding of the modeling approach will be understood by all. States, Agencies and Stakeholders and invited to comment on the procedures outlined in the Modeling Protocol and will warranted the procedures will be refined

to address comments.

1.4 Problem Definition

Figures 1-1 and 1-2 display the 8-hour ozone and PM 2.5 nonattainment areas designed by EPA in April and December 2004, respectively. Of the 10 States in the VISTAS region, 8 include counties that have been designed as nonattainment of the 8-hour ozone standard (Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia). Similarly, seven of the VISTAS states have counties that are designated as

nonattainment for PM 2.5 (Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, and West Virginia). Of the 10 VISTAS states only Florida and Mississippi do not have any 8-hour

ozone and PM 2.5 nonattainment areas, South Carolina includes 8-hour ozone nonattainment counties (near Charlotte) but no PM 2.5 nonattainment areas.

The states need to submit the 8-hour ozone State Implementation Plans (SIPs) to EPA by June 2007; the PM 2.5 SIPs are due by April 2008. Some of the states involved in the ASIP ozone/PM modeling have two-year legislative review processes. Thus, the definition of the SIP control plans is needed in early 2006. Consequently, the ASIP regional ozone and PM modeling has an aggressive schedule.January 2006

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1-4 Figure 1-1. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004.January 2006

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Protocol \ Draft#2 \ Sec1.doc 1-6 1.5 Background

The Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality Modeling Team is operating regional scale, three-dimensional air quality models for ozone and fine particulate matter (PM 2.5 ) that simulate the emissions, chemical transformations, and transport of gaseous and particulate matter (PM) species in the eastern United States. A key element of this work includes the integration of emissions inventories and models with regional transport models. The general services provided by the ASIP Emissions and Air Quality Modeling Team include, but are not limited to:

• Emissions processing and modeling;

• Air quality modeling simulations;

• Analysis, display, and reporting of modeling results; and

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

The ASIP regional emissions and air quality modeling is leveraging the modeling databases developed by VISTAS to provide the technical basis for the regional haze SIPs due in December 2007. Regional haze is caused by primary and secondary fine particles and is simulated using “one-atmosphere” regional photochemical grid models. Such models also simulate regional

ozone and fine PM so can also be used to address the ASIP 8-hour ozone and PM 2.5 issues. VISTAS initiated their regional haze modeling in 2003 using a two-phase approach.

1.5.1 VISTAS Two-Phased Approach

The VISTAS Emissions and Air Quality Modeling activities are being performed in two Phases. Phase I, which occurred primarily during the 2003 calendar year, consisted of emissions and regional haze modeling for three episodes to identify the optimal model configuration(s) for simulating regional haze, ozone and fine PM in the southeastern US. Phase II, initiated in 2004, consists of operating the emissions and air quality models for the 2002 calendar year to develop the regional haze modeling databases needed to address the requirements of the Section 308 RHR SIPs and TIPs. The ASIP regional ozone and PM 2.5 modeling is building off the VISTAS Phase II 2002 36/12 km annual modeling activities.

1.5.1.1 VISTAS Phase I

The objective of VISTAS Phase I was to determine the optimal modeling configuration for use in the subsequent Phase II visibility assessment. Accordingly, Phase I entailed a comprehensive literature review of recent relevant visibility studies using various photochemical/aerosol modeling platforms in order to assess and identify model configurations, data bases, and model testing methodologies that were appropriate for use in conducting the VISTAS Phase I emissions and PM modeling assessment. Key elements of Phase I included:

 Review all relevant air quality model simulations that have been completed related to regional haze and PM 2.5 modeling and document the relevant sensitivity analyses, model configuration testing, and performance evaluations that have been performed (ENVIRON, 2003b);January 2006

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Protocol \ Draft#2 \ Sec1.doc 1-7  Review the current science in regional emissions modeling (e.g., EPS, EMS and SMOKE) and PM air quality modeling (e.g., CMAQ, CMAQ-MADRID, CMAQ-AIM, REMSAD, UAM-V/PM, CAMx4 and PMCAMx) to determine the most appropriate model(s) for use by VISTAS (ENVIRON, 2003b);

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

 Develop and implement a plan or Modeling Protocol for testing and evaluating alternative science configurations of the recommended Phase I model(s) and document the results (ENVIRON, 2003a); and

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

VISTAS formed three standing workgroups to plan and direct the project. These included: (a) the Technical Analysis (emissions and modeling) Workgroup; (b) the Data (monitoring) Workgroup; and (c) the Planning Workgroup. Under Phase I, the VISTAS Technical Analysis Workgroup (TAWG) managed the comprehensive model configuration testing program aimed, as noted above, at evaluating the capabilities of current state-of-science regional emissions, prognostic meteorological and PM/visibility models. The resultant modeling system (models and databases) identified and tested in Phase I were intended to be applied in Phase II following the procedures set forth in the Phase II Modeling Protocol (ENVIRON, 2004a).

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

1.5.1.2 VISTAS Phase II

The VISTAS Phase II modeling is performing annual PM/regional haze simulations for the 2002 calendar year. Detailed performance testing has been completed. The modeling system has been demonstrated using several inventory versions for the base year (2002) and future years (2009 and 2018). These results are posted to the VISTAS modeling website managed by University of California at Riverside ( http://pah.cert.ucr.edu/vistas/vistas2/ ). Beginning in winter 2005-2006, the modeling system will be exercised with a variety of emissions control scenarios enabling VISTAS to assess the effects of future year emission control strategies on visibility and other air quality issues. The modeling system will also allow VISTAS to trackJanuary 2006

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Protocol \ Draft#2 \ Sec1.doc 1-8 reasonable progress toward regional haze goals. More specifically, the VISTAS Phase II program will focus on the use of the CMAQ modeling system for calendar year 2002 over the same 36/12 km horizontal grid system used in Phase I. A potentially large number of annual (and episodic) model simulations has been or will be performed; the list below reflects current plans:

 2002 Annual Run

. The initial annual model simulations and performance evaluations using the 2002 inventory for VISTAS and non-VISTAS states, Canada and Mexico. Multiple iterations of the 2002 annual simulation have been required to confirm the appropriateness of the model science configuration(s) recommended by the Phase I work, to evaluate updates to the model and model inputs (especially emissions inventory versions) and to refine model performance.

 2002 Annual Run with “Typical Year” EGU/Fire Inventory. An annual 2002 simulation representing the 2000-2004 baseline period for EGU and fire emissions and using 2002 revised inventory for all other source sectors. The primary objective of this inventory is to provide the base line modeled air quality condition against which future year modeling runs will be compared to develop relative reduction factors for each pollutant species.

 2018 Future Year Annual Runs. 2018 future year emission inventory simulations using the 2002 calendar year meteorological conditions involving a base case inventory of typical EGU and fire emissions. Initially a 2018 On-the-Books (OTB) base case scenario, which consisted of all promulgated regional controls measures as of the beginning of 2005, and a 2018 On-the-Way (OTW) scenario that consisted of OTB plus regional SOx and NOx controls expected to be part of the Clean Air Interstate Rule (CAIR) and Clean Air Mercury Rule (CAMR) were modeled. After the final CAIR was released in June 2005, the 2018 OTB scenario was dropped. The Integrated Planning Model (IPM) was used to project future EGU emissions; all other inventories were forecasted to 2018 using growth and control factors documented by MACTEC (2005).

 2009 Intermediate Future Year Annual Runs. Simulations for the 2009 future-year were performed to provide estimates of visibility improvements at Class I areas for an intermediate future year.

 Future Year Emission Control Strategies. Prescription of the future year emissions control strategies to be performed in 2006 will be defined after the foregoing simulations and analyses have been completed. Currently, a 2018 CAIR plus BART control strategy is being developed.

Closely integrated with the annual meteorological, emissions and air quality modeling will be ongoing project management, technical review, and quality assurance activities performed under the guidance of the VISTAS Contracting Officer and the TAWG. The modeling team members will participate with VISTAS management in regular monthly conference calls, as well as ad hoc topical conference calls as needed, and will attend periodic meetings with the TAWG members throughout Phase II.January 2006

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Protocol \ Draft#2 \ Sec1.doc 1-9 Complementing the data acquisition, modeling input development activities, and project management activities, four other Phase II activities will be performed, consistent with the VISTAS Quality Assurance Project Plan (QAPP) (ENVIRON, Alpine and UCR, 2004).

1.6 ASIP Modeling Approach

The ASIP regional ozone and PM modeling builds off of the VISTAS Phase II 2002 annual modeling and uses many of the same QA/QC procedures.

1.6.1 Data Gatekeepers

The ASIP and VISTAS emissions and air quality modeling team receive emissions, meteorological and air quality data from other contractors or other sources. As a first line of QA, we have defined a Gatekeeper function to assure the data have been received correctly, the quality of the data has been evaluated, and that the data received have been documented. The same Gatekeeper QA approach will be used in the ASIP ozone and PM 2.5 modeling. Separate air quality, meteorological and emissions Gatekeepers have been identified whose roles are defined below. In addition, a Data Management Gatekeeper has been defined who will post data, reports and results to the project website and archive all key data generated in the project.

 Air Quality Data Gatekeeper . As necessary, obtain air quality data as appropriate for model input development and model performance evaluation and assure that the quality of all air quality data obtained is consistent with the approved QAPP. Provide documentation of evaluation and generate IC/BC inputs for CMAQ for all modeling

runs.

 Meteorological Gatekeeper . As necessary, obtain meteorological data, as MM5 or MCIP files, as appropriate for annual 2002 modeling runs and other episode periods and perform data quality checks as approved in the QAPP, together with appropriate documentation of model performance evaluation activities.

 Emissions Gatekeeper. Obtain emissions inventory data necessary to support annual 2002 and future year modeling and recommend sources of emissions data to be used for Canada and Mexico. Assure quality of all emissions data received is consistent with the approved QAPP, and develop all emissions modeling files to support modeling runs for 2002 and future years. Develop the chemical speciation files and temporal and spatial allocation files necessary to convert annual inventories into hourly and daily emissions modeling files, as appropriate. Develop all emissions modeling files for non-VISTAS states to support modeling runs for future year base case and emissions strategies.

 Data Management Gatekeeper : Maintain the ASIP results and other documents as requested by the ASIP group to support all 8-hour ozone and PM 2.5 modeling tasks. This includes, for example, the storage of model inputs and outputs for annual runs and the transfer (via USB/firewire portable disk or alternative media) of electronic files to ASIP states, EPA, other contractors, and stakeholders.January 2006

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1-11 1.6.2 Emissions QA/QC

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical steps in performing air quality modeling studies. Because emissions processing is tedious, time consuming and involves complex manipulation of many different types of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected. In ASIP we will continue with the multistep emissions QA/QC approach applied in the VISTAS Phase I and II modeling. This includes the initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the Air Quality Modeler of the processed model ready emission files. This multistep process with three separate groups involved in the QA/QC of the emissions is intended to detect and correct errors prior to the air quality model simulations.

Emissions QA/QC performed as part of the emissions modeling includes:

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

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

SMOKE Emissions Summaries : We will use QA functions built into the SMOKE processing system to provide summaries of processed emissions as daily totals according to species, source category and county and state boundaries. These summaries will then be compared with summary data prepared for the pre-processed emissions, e.g., state and county totals for emissions from the augmented emissions data.

Once the CMAQ-ready emission inputs have been prepared, we will perform additional emissions QA/QC as follows:

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

Vertical Profile : For point sources the emissions total for each layer will be summed and plotted to show the vertical distribution of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The objective of this step is to identify possible errors in vertical distribution of emissions.January 2006

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1-12 Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared that display the diurnal variation in total hourly emissions. The objective of this step is to identify errors in temporal profiles.

Long Term Temporal Summary : The total domain emissions for each day will be accumulated and displayed as time series plots that show the daily total emissions across the domain as a function of time. The objective of this step is to identify particular days for which emissions appear to be inconsistent with other days for no reason (e.g., not a weekend) and compare against the general trend.

Control Strategy Spatial Displays : Spatial summary plots of the daily total emissions differences between a control strategy and base case emissions scenarios will be generated. These plots can be used to immediately identify a problem in a control strategy. For example, if a state’s SO 2 control strategy is being analyzed and there are changes in emissions for other pollutants or for SO 2 outside of the state under study problems in emissions processing can be identified prior to the air quality model

simulation.

1.6.3 Meteorology QA/QC

The meteorological modeling contractor (BAMS) had primary responsibility in the QA/QC of the MM5 meteorological fields. ASIP will rely on the QA/QC conducted by the VISTAS emissions and air quality modeling team as part of Phase II modeling to assure that the data has transferred correctly, to obtain an assessment of the quality of the data and to assist in the interpretation of the air quality modeling results.

The VISTAS Phase II Meteorological Gatekeeper performed the following activities that serve to QA/QC the meteorological fields used in the ASIP modeling:

 Analyzed the MM5 data to assure it had been transferred correctly.

 Evaluated the MM5 using METSTAT and the surface meteorological network.

 Evaluated upper-air MM5 meteorological estimates by comparing them to upper-air observations and satellite images.

 Compared the VISTAS 2002 MM5 simulation with the one generated by WRAP.

 Generated the CMAQ-ready meteorological inputs using the MCIP2.2 processor.

The CMAQ meteorological input files were updated to MCIP version 3.0 in late 2005.January 2006

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Protocol \ Draft#2 \ Sec1.doc 1-13 1.6.4 Air Quality Modeling QA/QC

 Key aspects of QA/QC for the ASIP CMAQ modeling input and output data include the following:

 Verification that correct configuration and science options are used in compiling and running each model in the CMAQ modeling system, where these include the MCIP, JPROC, ICON, BCOM and the CCTM.

 Verification that correct input data sets are used when running each model.

 Evaluation of CMAQ results to verify that model output is reasonable and consistent with general expectations.

 Backup and archiving of critical model input data.

The most critical element for ASIP CMAQ simulations is the QA/QC of the emissions input files, which is discussed above. The major QA issue specifically associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files were used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the names of key science options or inputs are included in the name of the CMAQ executable program, in the name of the CMAQ output files, and in the name of the directory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files.

A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the original runs scripts and directory structure that were used in performing a model simulation.

We will also perform a post-processing QA of the CMAQ output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case minus the base case. Often, errors in the emissions inputs can be discovered by viewing the animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This provides a summary that can be useful for quickly comparing various model

simulations.

1.6.5 Overview of Data Flow and Quality Assurance Process

Figure 1-3 displays an overview of the data flow and quality assurance process in the ASIP Emissions and Air Quality Modeling study. The ASIP Modeling Team receives different types of data from various contractors and other sources that have performed their own Quality Assurance (QA) and Quality Control (QC). Whenever data are received by the Modeling Team,January 2006

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1-14 it is first subjected to a QA check by a Gatekeeper who assesses the accuracy and quality of the data and prepares a summary presentation on the QA check. Figure 1-3a lists the Gatekeepers in the Modeling Team for emissions, boundary conditions, meteorological, ozone column (TOMS) and air quality data. If the Gatekeeper identifies any problems with the data, the provider of the data is contacted and asked to correct the data. Once the Gatekeeper has conducted a QA check of the data it is passed on to the modeler who performs their QA of the data. The data are then used in the modeling and resultant output (e.g., model-ready emissions or meteorological files) are then subjected to another round of QA to assure the integrity of the data is retained.

Once the model-ready inputs have been developed and subjected to QA/QC, the CMAQ model is applied using Base Case emissions and the modeling results subjected to a model performance evaluation. The model performance evaluation (MPE) represents an extensive QA effort and is the most time consuming component of the study. EPA has developed draft guidance for evaluating regional PM and haze models that includes performance goals (EPA, 2001). In addition, the Modeling team has adapted EPA MPE approaches and goals for 1-hour (EPA, 1991) and 8-hour (EPA, 1999; 2005b) ozone modeling. The MPE/QA process is being performed under VISTAS since VISTAS and ASIP share the same modeling platform database and approach. The MPE/QA approach is using as many different tools and analysis as possible in order to fully understand the accuracy and reliability of the model simulation. As seen in Figure 1-3b, the MPE process in VISTAS/ASIP is a multistep process using several different techniques:

UCR Analysis Tools : The University of California at Riverside (UCR) Analysis Tools were used extensively in VISTAS and are run on a Linux platform separately for each network. Graphics are automatically generated using gnuplot and the software generates the following:

• Tabular statistical measures;

• Time Series Plots; and

• Scatter Plots by allsite_allday, allday_onesite and allsite_oneday.

MAPS Analysis Tools : Alpine Geophysics (Alpine) has a MAPS Analysis Tool that also runs under Linux and is based on Fortran and NCAR Graphics. It was originally developed for evaluating ozone models and has been extended to treat PM species as well. In addition to calculating similar statistics, scatter plots and time series plots as the UCR Analysis Tools, it also can generate spatially averaged time series plots of concentrations, bias and error, performs analysis of peak concentrations and includes a Flying Data Grabber (FDB) for comparing modeling results with aircraft data.

ENVIRON Analysis Tools : ENVIRON has developed specialized evaluation tools to analyze ozone and PM model performance for urban-scale modeling and comparison against EPA model performance goals.

GA DNR Analysis Plots : Dr. James Boylan of the Georgia Department of Natural Resources has extended the concept in EPA’s draft PM fine particulate and regional haze modeling guidance that model performance for species that make up a major contribution to visibility impairment be subjected to more stringent goals than species that are minorJanuary 2006

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Protocol \ Draft#2 \ Sec1.doc 1-15 contributors by developing concentration-dependent performance goals and “Bugle Plots” to display them. The evaluation of the VISTAS/ASIP 2002 CMAQ Base Case simulation used each of the analysis tools listed above demonstrating their descriptive and complimentary nature.

The issue of model performance goals for PM species is an area of ongoing research and debate. For ozone modeling, EPA has established performance goals for 1-hour ozone normalized mean bias and gross error of ≤± 15% and ≤ 35%, respectively (EPA, 1991). EPA’s draft fine particulate modeling guidance notes that performance goals for ozone should be viewed as upper bounds of model performance, which PM models may not be able to always achieve and we should demand better model performance for PM components that make up a larger fraction of the PM mass than those that are minor contributors (EPA, 2001). Measuring PM species is not as precise as ozone monitoring. In fact, the differences in measurement techniques for some species likely exceed the more stringent performance goals, such as those for ozone. For example, recent comparisons of the PM species measurements using the IMPROVE and STN measurement technologies found differences of approximately ± 20% (SO 4 )

to ± 50% (EC) (Solomon et al., 2004).

In the VISTAS/ASIP 2002 CMAQ Base Case modeling, we have adopted three levels of model performance goals for bias and gross error as listed in Table 1-1 that are used to help evaluate model performance. Note that we are not suggesting that these performance goals be generally adopted or that they are the most appropriate goals to use. Rather, we are just using them to frame and put the PM model performance into context and to facilitate model performance intercomparison across episodes, species, models and sensitivity tests.

As noted in EPA’s draft PM modeling guidance, less abundant PM species should have less stringent performance goals. Accordingly, we are also using performance goals that are a continuous function of average observed concentrations proposed by Dr. James Boylan at the Georgia Department of Natural Resources that have the following features:

• Asymptotically approaching proposed performance goals or criteria when the mean of the observed concentrations are greater than 2.5 ug/m 3

.

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

Dr. Boylan uses bias/error goals and criteria of ± 30%/50% and ± 60%/75% and plots bias and error as a function of average observed concentrations. As the mean observed concentration approaches zero the bias performance goal and criteria flare out to ± 200% creating a horn shape, hence the name “Bugle Plots”.

Table 1-1. Model performance goals to help interpret modeling results.
Fractional
Bias Fractional

Error Comment

≤ ±15% ≤ 35% Ozone model performance goal for which PM model performance would be considered good.

≤ ±30% ≤ 50% A level of model performance that we would hope each PM species could meetJanuary 2006

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Protocol \ Draft#2 \ Sec1.doc 1-16 ≤ ±60% ≤ 75% At or above this level of performance indicates fundamental problems with the modeling system.December 2005C:\Temp\ASIP\Sec1.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec1.doc1-17Figure 1-3a.Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.December 2005C:\Temp\ASIP\Sec1.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec1.doc1-18Figure 1-3b.Concluded.Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.December 2005

C:\Temp\ASIP\Sec1.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec1.doc 1-19 1.7 Project Management

1.7.1 Project Organization

The ASIP ozone and PM modeling project is conducted by ENVIRON International Corporation (ENVIRON) and Alpine Geophysics, LLC (Alpine), with input from SESARM and the ASIP States. Organizational commitment is an essential element for developing and implementing a successful research project. Ralph Morris of ENVIRON would be the ASIP Emissions and Air Quality Modeling Project Manager (PM). The ASIP Modeling Team has two Co-Principal Investigators that coordinate activities at each of the modeling centers, Ralph Morris of ENVIRON and Gregory Stella of Alpine. The PM and two Co-PIs are kept apprised of all project activities, from identifying the need to develop sound experimental and project designs to delivering reports. Commitments to research and project activities, such as those described in this QAPP are made only after the activities are thoroughly reviewed and approved by the PM and Co-PIs and SESARM and the ASIP States. Figure 1-4 presents the organizational chart that shows the lines of responsibility and information flow for activities under this project. Table 1-2 lists the project responsibilities for participants in the ASIP Emissions and Air Quality Modeling study, with more details on their roles provided next.

Southeastern States Air Resources Managers, Inc. (Contracting Agency)

ASIP
States Coordinating Group

Chairperson:

Sheila Holman, North

Carolina ASIP

Contracting Officer:

Patricia Brewer, ASIP ASIP

Technical Contact:
James Boylan, Georgia

Subcontractor
Alpine Geophysics
Co-Principal Investigator
Gregory Stella

Staff Prime Contractor

ENVIRON
Project Manager and CoPrincipal Investigator
Ralph Morris

Staff

Figure 1-4. ASIP Emissions and Air Quality Modeling Project Organizational Chart.December 2005

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Person & Role Affiliation/Address Contact Information

Patricia Brewer (Contracting Officer) ASIP Technical Coordinator

2090 US Highway 70 Swannanoa, NC 28778 (828) 296-4500 (Fax) (828) 299-7043 pat.brewer@ncmail.net James Boylan (Technical Contact for Emissions & AQ Modeling) Georgia DNR

Air Protection Branch 4244 International Pkwy, Ste 120 Atlanta, GA 30354-3906 (404) 362-4851 (Fax) (404) 363-7100 James_Boylan@mail.dnr.state.ga.us

Michael Abraczinskas (Technical Contact for MM5 Modeling) North Carolina DENR
1641 Mail Service Center
Ra l eigh, NC 27699-1641 (91 9) 715-3743 Michael.Abraczinskas@ncmail.net

Ralph Morris (Project Manager and Co-Principal Investigator) ENVIRON 101 Rowland Way Novato, CA 94945 (415) 899-0708 (Fax) (415) 899-0707 rmorris@environcorp.com

Gregory Stella (Co-Principal Investigator) Alpine Geophysics, LLC

387 Pollard Mine Road Burnsville, NC 28714 (828) 675-9045 (Fax) (828) 675-5801 gms@alpinegeophysics.com Key ENVIRON Participants Bonyoung Koo ENVIRON 101 Rowland Way Novato, CA 94945 (415) 899-0727 bkoo@environcorp.com

Abby Hoats ENVIRON (415) 899-0735 ahoats@environcorp.com
Steven Lau ENVIRON (415) 899-0739 slau@environcorp.com Key Alpine Geophysics Participants Dennis McNally Alpine Geophysics, LLC

7341 Poppy Way Arvada, CO 80007 (303) 421-2211 (Fax) (303) 421-9553 dem@alpinegeophysics.com Cyndi Loomis Alpine Geophysics, LLC

7341 Poppy Way Arvada, CO 80007 (303) 421-2211 (Fax) (303) 421-9553 cfl@alpinegeophysics.com T. W. Tesche Alpine Geophysics, LLC
3479 Reeves Drive Ft. Wright, KY 41017 (859) 341-7502 twt@iac.netDecember 2005

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Mr. Ralph Morris of ENVIRON is the Project Manager (PM) and Co-Principal Investigator (Co-PI) for the Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality Modeling Team. He provides overall direction to the project and establishes a policy relationship with the sponsor, ensuring that all issues of importance to the ASIP group are addressed. The PM is responsible for the overall conduct of the project, experimental design, reporting of the results, and interacting with the client, consultants, and project staff. The specific responsibilities of the PM include, but are not necessarily limited to, the following:

• Directs and coordinates the activities of the project team and computer facilities to conduct the test program

• Ensures that this QAPP and the Modeling Protocol are followed during the course of the project

• Guides the overall approach for performing modeling evaluations

• Keeps current on project status and delivers progress reports

• Conducts initial modeling or analysis of experiments to determine if inconsistencies or unexpected results suggest possible experimental or measurement problems

• Evaluates overall data quality, characterization results, and overall system performance with regard to meeting project objectives

• Reviews and delivers modeling and assessment reports

• Interacts with external scientific reviewers, collaborators and other external groups in their area of expertise in the development of study priorities, reporting of results, and obtaining external input

• Oversees the project team in responding to any issues raised in assessment reports and initiates corrective actions as necessary

• Serve as ENVIRON’s primary point of contact for contract issues

• Establishes a project budget and monitors the effort to ensure that budget is not exceeded

• Establishes a Subcontract with Alpine Geophysics, LLC to perform the work, and adhere

to the terms and conditions of that contract

• Assists in the performance of the modeling program in accordance with its contract and

the Work Plan

• Provides information to assist the ASIP group in achieving its goals as stated in its Work Plan and Strategic Plan

• Develops individual test protocols and reports as directed

• Analyzes modeling data and provides assessment reports

• Supports the Principal Investigator and ASIP in responding to any issues raised in
assessment reports

1.8.1 ENVIRON and Alpine Geophysics Co-Principal Investigators

The two Co-Principal Investigators of Ralph Morris and Gregory Stella perform the following

functions:

• Direct and coordinate the day-to-day project activities of the project team and computer facilities to conduct the test programDecember 2005

C:\Temp\ASIP\Sec1.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec1.doc 1-22 • Ensure that this QAPP and Modeling Protocol are followed during the course of the project

• Manage the activities in each of the two modeling centers

• Direct personnel working on this project

• Guide the approach for performing modeling evaluations following the direction of the Project Manager

• Keep current on project status and deliver information to Project Manager for progress
reports

• Conduct initial modeling or analysis of experiments to determine if inconsistencies or unexpected results suggest possible experimental or measurement problems

• Evaluate overall data quality, characterization results, and overall system performance with regard to meeting project objectives

• Review and deliver data and sections for integration into modeling and assessment
reports

• With the Project Manager, interact with external scientific reviewers, collaborators and other external groups in their area of expertise in the development of study priorities, reporting of results, and obtaining external input

• Oversee the project team in each modeling center responding to any issues raised in assessment reports and initiate corrective actions as necessary with the Project Manager

• Monitor the effort to ensure that budget is not exceeded

• Assist in the performance of the modeling program in accordance with its contract and

the Work Plan

• Develop individual test protocols and report as directed

• Analyze modeling data and provide assessment reports

1.8.2 ASIP Contracting Officer

The ASIP Contracting Officer (Patricia Brewer) serves as the primary contact between the Emissions and Air Quality Modeling Team and ASIP and performs the following functions:

• Provides day-to-day oversight of the ASIP Emissions and Air Quality Modeling Team

activities

• Works with the Project Manager, Co-Principal Investigators, SESARM, ASIP States, collaborators, Stakeholders, etc. to define scope of work and to assure that the interests and concerns of all of the ASIP participants are appropriately represented as project priorities are developed or modified due to external input

• Assists in organizing and conducting meetings, conference calls, and workshops where this and related projects are discussed

• Reviews work products

1.8.3 ASIP Technical Contact

The ASIP Technical Contact (James Boylan) for the Emissions and Air Quality Modeling Team works with the ASIP Contacting Officer in the day-to-day oversight and management of the modeling analysis:December 2005

C:\Temp\ASIP\Sec1.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec1.doc 1-23 • Provides day-to-day oversight of ASIP Emissions and Air Quality Modeling Team

activities

• Works with the ASIP Contracting Officer, Project Manager, and Co-Principal Investigators to assure that the study is being carried out in a technically correct fashion following the QAPP and Modeling Protocol

• Prepares and gives presentations to VISTAS groups on the activities of the Modeling

team.

• Reviews work products.

1.8.4 ASIP States

The primary purpose of the ASIP Emissions and Air Quality Modeling work is to

de ve l op the regional modeling component for 8-hour ozone and PM 2.5 State Implementation Plans (SIPs) being developed by several southeastern U.S. states that are due June 2007 and April 2008, respectively. Most of the ten VISTAS states also participate in ASIP. Alabama, Georgia, North Carolina, Kentucky, Tennessee, Virginia, and West Virginia have PM 2.5 nonattainment areas and Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia have 8-hour ozone non-attainment areas. The ASIP states will

oversee the regional emissions inventory development and ozone and fine particulate m odeling that will be required for the State Implementation Plans (SIP's). Emissions Inventory efforts include the development of 2002 base case emissions inventories and future year forecasts to be utilized in the ASIP modeling efforts. Modeling efforts will include identification, evaluation, and application of air quality modeling tools to quantify the effects of emission management options upon air quality in 8-hour ozone and PM 2

. 5 nonattainment areas in the southeastern

Unit ed States. Specific activities of the ASIP States include:

• O ve r see the activities of the ASIP Emissions and Air Quality Modeling Team through the Contracting Officer, conference calls, and periodic in-person meetings and workshops

• Provides the Contracting Officer, technical Contact, Project Manager and Co-Principal Investigators input on the research plans and their ability to meet the needs of the various stakeholders relevant to the overall objectives of the project

• Provides input as needed to assure that the project has effective and appropriate peer

review

• Makes the Project Manager and Co-Principal Investigators aware of other projects that may be of relevance to this project

• Reviews the Modeling Protocol and QAPP and conducts critical project reviews

1.9 Communications Plan

The ASIP Emissions and Air Quality Modeling Team members, other ASIP Contractors and ASIP representatives are linked by e-mail correspondence, and also use this as a means to communicate and exchange data, either as e-mail attachments, website or by network-accessible files. A considerable amount of information is exchanged by e-mail within this project. The ASIP Modeling Team will use the same four listservs as used by VISTAS to distribute information to different groups as indicated in Table 1-3.December 2005

C:\Temp\ASIP\Sec1.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec1.doc 1-24 Table 1-3. VISTAS listservs that will be used to distribute information by the ASIP Emissions and Air Quality Modeling Team and other ASIP participants.

Listserv Purpose Vistas-all@cert.ucr.edu Contacts all participants including Modeling Team, VISTAS
TAWG and Stakeholders Vistas-modeling@cert.ucr.edu Contacts Modeling team and VISTAS TAWG Modeling
Contacts
Vistasemissions@cert.ucr.edu Contacts emissions staff in the Modeling team and emissions people in the VISTAS TAWG Vistas-met@cert.ucr.edu Contacts meteorology staff in the Modeling team and meteorology people in the VISTAS TAWG

The Modeling Team members and ASIP States and Contracting Officer hold periodic conference calls and meetings to report results, discuss project status, and modify work plans as necessary. Unscheduled meetings or conference calls are also held concerning specific issues as the needs arise. In addition, periodic project meetings and conference calls are held. In these meetings detailed technical information is exchanged, project status is discussed, and project

direction is assessed.

Written progress reports on the ASIP Emissions and Air Quality Modeling Team activities are submitted to the ASIP Contracting Officer on a monthly basis. These reports summarize project progress, results to date, problems encountered and necessary action items, and plans for the upcoming reporting period.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-1 2.0 MODEL SELECTION

This chapter introduces the regional meteorological, emissions and air quality models to

be used in the 8-hour ozone and PM 2.5 regional modeling for ASIP. The specific science configurations for each modeling system are identified and discussed briefly, where necessary. The configurations of each modeling system have been selected as the culmination of the regional modeling performed as part of the closely related VISTAS regional haze modeling

efforts.

2.1 Recommended Models

Based on the findings in the VISTAS Phase I and II modeling activities, ASIP selected the following models for use in modeling 8-hour ozone and particulate matter (PM) of size of 2.5 microns or less (PM 2.5 ):

 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, and regional haze regulatory modeling studies.

 SMOKE : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emission inputs of mobile, nonroad, area, point, fire and biogenic emission sources for photochemical grid

models.

 CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid deposition at regional scale for periods up to

one year.

Application of the MM5 for the 2002 annual period and the ASIP 36/12 km domains was performed by BAMS under contract to SESARM as part of the VISTAS Phase II activities. Details of the model application and evaluation procedures being carried out by BAMS may be

found at http://www.baronams.com/projects/VISTAS/ . For completeness, in this chapter we describe the three regional modeling systems and their intended use in the ASIP 2002 annual modeling.

2.2 MM5 Mesoscale Prognostic Model

Over the past decade, researchers at the Pennsylvania State University (PSU) and the National Center for Atmospheric Research (PSU/NCAR) have collaborated in the refinement and extension of the PSU Mesoscale Meteorological Model leading to the current version of the system, MM5 (Ver 3.6, MPP). Originally developed in the 1970s at PSU and first documentedby Anthes and Warner (1978), the MM5 modeling system maintains its status as a state-of-thescience model through enhancements provided by a broad user community (e.g., Chen andJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-2 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 investigations of severe weather. Utilization of MM5 within air quality applications is also a common practice. In recent years, the MM5 modeling system has been successfully applied in continental scale annual simulations for the years 1996 (Olerud et al., 2000), 2001 (McNally and Tesche, 2003), and 2002 (Johnson, 2003). Due to its ongoing scientific development worldwide, extensive historical applications, broad user community support, public availability, and established performance record compared with other applications-oriented prognostic models, ASIP/VISTAS selected the MM5 as the preferred meteorological model. This section provides an overview of the MM5 and its data input requirements.

2.2.1 MM5 Overview

The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a threedimensional, limited-area, primitive equation, 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 open peer-review for more than 20 years (Anthes and Warner, 1978; Anthes et al., 1987) and has been used world-wide by hundreds of scientists for a variety of mesoscale studies, including cyclogenesis, polar lows, cold-air damming, coastal fronts, severe thunderstorms, tropical storms, subtropical easterly jets, mesoscale convective complexes, desert mixed layers, urban-scale modeling, air quality studies, frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and operational mesoscale forecasting.

MM5 is based on the prognostic equations for three-dimensional wind components (u, v, and w), temperature (T), water vapor mixing ratio (q

v ), and the perturbation pressure (p'). Use of a constant reference-state pressure increases the accuracy of the calculations in the vicinity of steep terrain. The model uses an efficient semi-implicit temporal integration scheme and has a nested-grid capability that can use up to ten different domains of arbitrary horizontal and vertical resolution. The interfaces of the nested grids can be either one-way 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 pressure, or "sigma", vertical coordinate similar to that used in many operational and research models. In the non-hydrostaticMM5 (Dudhia, 1993), the sigma levels are defined according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmosphere’ air-quality models using this coordinate (e.g., CMAQ). MM5 fields can be easily used in other regional air quality models with different coordinate systems (e.g., CAMx) by performing a vertical interpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer (PBL) parameterizations are available for air-quality applications, both of which represent sub-grid-scale turbulent fluxes of heat, moisture and momentum. These parameterizations employ various surface energy budget equations to estimate ground temperature (T g ), based on the insolation, atmospheric path length, water vapor, cloud cover and longwave radiation. The surface physical properties of albedo, roughness length, moisture availability, emissivity and thermal inertia are defined as functions of land-useJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-3 for numerous categories via a look-up table. One scheme uses a first-order eddy diffusivity

formulation for stable and neutral environments and a modified first-order scheme for unstable regimes. The other uses a prognostic equation for the second-order turbulent kinetic energy, while diagnosing the other key boundary layer terms.

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

A major feature of the MM5 is its use of state-of-science methods for Four Dimensional Data Assimilation (FDDA). The theory underlying this approach and details on how it has been applied in a variety of applications throughout the country are described in depth elsewhere (Stauffer and Seaman, 1990, 1991; Seaman et al., 1992, 1997).

Results of detailed performance evaluations of the MM5 modeling system in regulatory air quality application studies have been widely reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic models such as RAMS and SAIMM. The MM5 enjoys a far richer application history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scale air quality application studies since 1995, we have generally found that MM5 model tends to produce somewhat better photochemical model inputs than alternative models. For these and other reasons set forth in the MM5 modeling protocol developed by BAMS (Olerud and Sims, 2003), MM5 was selected as the meteorological modeling system for the ASIP/VISTAS study.

2.2.2 MM5 Configuration for ASIP and VISTAS Phase II Modeling

Based on the extensive sensitivity testing carried out by Olerud and Sims (2003) as part of VISTAS, the MM5 (Ver 3.6, MMP) configuration to be used by BAMS modelers in the VISTAS Phase II modeling that will also be used by ASIP will consist of the following (see Table 2-3 for more details):

> Nested 36/12 km grids, with 34 vertical layers; > Two way nesting, no feedback; > Initialization and boundary conditions from Eta analysis fields; > Pleim-Xiu (P-X) soil model; > Asymmetric Convective Mixing (ACM) PBL model; > Kain-Fritsch 2 cumulus parameterization; > Mixed phase (Reisner 1) cloud microphysics; > Raptid Radiative Transfer Model (RRTM) radiation; > Snow effect turned on; > ETA model sea surface temperature; > 24-category USGS vegetation data sets;January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-4 > Thermal roughness by the Garratt method; and > Standard FDDA analysis nudging on 36 km and 12 km grid nests.

2.3 SMOKE Emissions Modeling System

2.3.1 SMOKE Overview

The Sparse Matrix Operator Kernel Emissions (SMOKE) Emissions Processing System Prototype was originally developed at MCNC (Coats, 1995; Houyoux and Vukovich, 1999). As with most ‘emissions models’, SMOKE is principally an emission processing system and not a

true emissions modeling system in which emissions estimates are simulated from ‘first principles’. This means that, with the exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for converting emissions inventory data into the formatted emission files required by an air quality simulation model. For mobile sources, SMOKE actually simulates emissions rates based on input mobile-source activity data, emission factors and in some cases, outputs from transportation travel-demand models.

SMOKE was originally designed to allow emissions data processing methods to utilize emergent high-performance-computing (HPC) as applied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach utilized throughout SMOKE permits both rapid and flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separated 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 also includes biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory System, Version 3 (BEIS3) (see, http://www.epa.gov/ttn/chief/software.html#pcbeis ). SMOKE has been available since 1996, and it has been used for emissions processing in a number of regional air quality modeling applications. In 1998 and 1999, SMOKE was redesigned and improved with the support of the U.S. Environmental Protection Agency (EPA), for use with EPA's Models-3/CMAQ ( http://www.epa.gov/asmdnerl/models3 ). The primary purposes of the SMOKE redesign were support of: (a) emissions processing with user-selected chemical mechanisms and (b) emissions processing for reactivity assessments.

SMOKE contains a number of major features that make it an attractive component of the VISTAS modeling system (Seppanen, 2003). The model supports a variety of input formats from other emissions processing systems and models including the Inventory Data Analyzer (IDA), Emissions Modeling System—2003 (EMS-2003), and the Emissions Preprocessor System 2.x (EPS2.x). It supports both gridded and county total land use scheme for biogenic emissions modeling. Although not necessary in ASIP, SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-5 Recent computational improvements to SMOKE include: (a) enhanced disk space requirements compared with other emissions processing software, (b) run-time memory allocation, eliminating any need to recompile the programs for different inventories, grids, or chemical mechanisms, and (c) updated I/O API libraries. A number of science features have been incorporated into the version 2.0 of SMOKE including: (a) any chemical mechanism can be used to partition pollutants to model species, as long as the appropriate input data are supplied, (b) integration with the MOBILE6.2 on-road mobile source emissions model including link based processing, (c) support of plume-in-grid (PiG) processing, (d) integration of the

BEIS3 emissions factors in SMOKE.

Notable features of SMOKE from an applications standpoint include: (a) improved control strategy input formats and designs, (b) control strategies can include changes in the reactivity of emitted pollutants, a useful capability, for example, when a solvent is changed in an industrial process, (c) no third party software is required to run SMOKE, although some input file preparation may require other software, (d) fewer SMOKE programs than the SMOKE prototype because programs were combined where possible to be used for multiple source categories, (e) integration with Models-3 file formats and settings, (f) improved data file formats,(g) support of various air quality model emissions input formats (e.g., CMAQ, MAQSIP, UAMIV, UAM-V, REMSAD and CAMx), (h) enhanced quality assurance pre- and post-processing, (h) fully integrated with Models-3, which will provide the SMOKE Tool for SMOKE input file preparation, (i) enhanced treatment of growth and control factors, (j) improved emissions reporting and QA capabilities, and (k) improved temporal allocation.

Continuing model development activities with SMOKE now occur out of the University of North Carolina (UNC) Carolina Environmental Program (CEP). SMOKE beta Version 1.5b was released 17 March 2003 and this is the version employed in the VISTAS Phase I modeling. Several patches to the model were provided during the summer of 2003, and SMOKE Version 2.0 was released on 30 Sept ’03. In 2004 SMOKE Version 2.1 was released that contained further improvements and enhancements. The VISTAS/ASIP modeling adopted SMOKE Version. 2.1 at that time. In September 2005 SMOKE Version 2.2 was released by the Community Modeling and Analysis (CMAS) center’s Model Clearinghouse at http://www.cmascenter.org/modelclear.shtml . At that time ASIP/VISTAS had expended considerable effort in setting up and performing emissions modeling using SMOKE Version 2.1. Additionally, the upgrades identified in Version 2.2 were options that ASIP/VISTAS had circumvented using existing file code and formats and switching to this latest version was felt could jeopardize the modeling schedule. Consequently, ASIP elected to continue emissions modeling using SMOKE Version 2.1. The SMOKE user’s guide is available online at the main SMOKE website, http://www.cep.unc.edu/empd/products/smoke

.

2.3.2 SMOKE Configuration for ASIP Modeling

As an emissions processing system, SMOKE has far fewer ‘science configuration’ options compared with the MM5 and CMAQ models. For a thorough characterization of the methods that will be used to exercise the SMOKE system for the annual 2002 emissions processing, see Section 5.2, “Development of Emissions Model Inputs and Resultant Inventories”. Table 2-4 summarizes the version of the SMOKE system to be used and the sources of data to be employed in constructing the required modeling inventories.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-6 2.4 CMAQ Modeling System

2.4.1 CMAQ Overview

For more than a decade, EPA has been developing the Models-3 Community MultiscaleAir Quality (CMAQ) modeling system with the overarching aim of producing a ‘OneAtmosphere’ air quality modeling system capable of addressing ozone, particulate matter (PM), visibility and acid deposition 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 Clean Air Act Amendments and EPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing state-of-science representations of atmospheric processes in a high performance computing environment (Ching, et al., 1998). EPA completed the initial stage of developmentwith Models-3 and released the Community Multi-Scale Air Quality model (CMAQ) in mid1999 as the initial operating science model under the Models-3 framework (Byun et al., 1998b). Since the initial CMAQ release in 1999, there have typically been annual [GMS1]

CMAQ consists of a core Chemical Transport Model (CTM) and several pre-processors including the Meteorological-Chemistry Interface Processor (MCIP), initial and boundary conditions processors (ICON and BCON) and a photolysis rates processor (JPROC). EPA is continuing to improve and develop new modules for the CMAQ model and typically provides a new release each year. In the past EPA has also provides patches for CMAQ as errors are discovered and corrected. EPA has funded the Community Modeling and Analysis Systems (CMAS) center to support the coordination, update and distribution of the Models-3 system.

A number of features in CMAQ’s theoretical formulation and technical implementation make the model well-suited for annual PM and 8-hour ozone modeling. In CMAQ, the modalapproach has been adapted to dynamically represent the PM size distribution using three lognormal modes (2 fine and 1 coarse). Transfer of mass between the aerosol and gas phases is assumed to be in equilibrium and all secondary aerosol (sulfate, nitrate, SOA) is assumed to be in the fine modes. The thermodynamics of inorganic aerosol composition are treated using the ISORROPIA module. Aerosol composition is coupled to mass transfer between the aerosol and gas phases. For aqueous phase chemistry, the RADM model is currently employed. This

scheme includes oxidation of SO 2 to sulfate by ozone, hydrogen peroxide, oxygen catalyzed by metals and radicals. The impact of clouds on the PM size distribution is treated empirically. For wet deposition processes, CMAQ uses the RADM/RPM approach. Particle dry deposition is included as well. CMAQ contains three options for treating secondary organic aerosol (SOA), latest being the Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 to be an reversible semi-volatile scheme whereby VOCs can be converted to condensable gases that can then form SOA and then evaporate back into condensable gases depending on atmospheric conditions. Gas-phase chemistry can be treated by the CB4, RADM or SAPRC

chemical mechanisms.

Pleim et al., (2003) describe the features implemented in CMAQ Version 4.3 (released August 2003). Many of these features are mentioned above; others pertain to details in the model’s chemistry, transport, computer implementation, and model operation. In September 2004 CMAQ Version 4.4 was released that included updates and enhancements (Pleim, 2004). CMAQ Versions 4.3 and 4.4 were used for much of the VISTAS Phase I and II testing andJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-7 model performance evaluation. In October 2005 CMAQ Version 4.5 was released (Pleim, 2005) which has been adopted for the ASIP and VISTAS modeling.

The VISTAS model evaluation indicated fairly good performance for sulfate, winter overestimation bias and summer underestimation bias for nitrate and reasonably good performance for Elemental Carbon (EC), albeit with lots of scatter and low correlation. However, Organic Carbon (OC) was underestimated with the summer OC underestimation bias being quite severe. As OC is typically the second most important PM component contribution to visibility impairment and is the most or second most important component contributing to PM 2.5 violations in the southeastern US, VISTAS and ASIP were concerned with the large OC underestimation tendency. After an intense focused analysis of the issue, VISTAS identified processes important to the formation of Secondary Organic Aerosols (SOA) that were not included in the CMAQ SOA module that may be important to OC in the Southeastern US (Morris et al., 2005). Consequently, VISTAS enhanced the CMAQ SOA module by adding several new processes. This enhancement, called SOAmods described below, was implemented in CMAQ Version 4.4 and exhibited much improved OC model performance over the standard CMAQ SOA treatment (Morris et al., 2005b).

In October 2005, the new Version 4.5 of CMAQ was released (Pleim, 2005). CMAQ Version 4.5 included several enhancements and corrections, including a correction to the CMAQ mass conservation scheme. Given the importance of mass conservation in air quality modeling, ASIP and VISTAS have adopted an enhanced version of CMAQ Version 4.5 for their modeling

and transferred the SOAmods enhancement to it.

2.4.3 CMAQ Version 4.5

CMAQ Version 4.5 has several corrections, updates and enhancements over earlier versions of

the model:

1) Aerosols - Added sea salt (fine equilibrium; non-interactive coarse mode) -- aero4 - Updated aerosol dry deposition algorithm - Updated mechanism include files to remove any aerosol species with zero

concentrations for aero4 - Updated ISORROPIA to v1.5 (25 Oct 2003) and fixed some discontinuities - Added diagnostic variables to calculate PM2.5 concentrations - Corrected bug in mode merging to reduce mode crossover

- Modified SO4 used in ISORROPIA call - Corrected inconsistency in MINL2SG (aerodepv) - Corrected the EMSULF (H2SO4 emissions) unit conversion bug

2) Chemistry - Added CB4/chlorine chemistry and associated EBI solver - Added CB4/air toxics and SAPRC99/air toxics chemistry and associated EBI

solvers - Added degradation algorithm to the generalized solvers - Corrected treatment of convergence failures in EBI solversJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-8 - No longer support the RADM2 mechanism

3) P-in-G - Improved calculation of plume centerline - Fixed bug in the aerosol species array subscripting - Corrected error in non-reactive species NH3 fluxes and dry deposition

4) PBL modeling - Updated to use PURB (% urban) for setting minimum Kz

5) Clouds - Added new sub-grid cloud mixing algorithm/module (based on ACM) - Added new cloud diagnostic variables - Corrected the interpolation times for resolved clouds (to time-step midpoint) and for subgrid clouds (to half hour)

6) Advection - Added new mass continuity scheme

7) Other - Added dynamic vertical layer allocation - Added primary carbon source apportionment capability - Added sulfate tracking capability

Thus, even though VISTAS Phase I and II modeling testing and evaluation and initial ASIP runs were performed using CMAQ Versions 4.3 and 4.4 and considerable effort has been committed to these earlier versions of CMAQ, the CMAQ Version 4.5 model updates were significant enough that VISTAS and ASIP decided to switch over to the new version (Ver 4.5) of CMAQ.

In the VISTAS testing and evaluation of the CMAQ V4.3 and V4.4, the model performance for Organic Carbon (OC) was characterized by a systematic under-prediction bias that was particularly severe in the summer. As OC is one of the two most important PM components for PM 2.5 exceedances and visibility degradation in the Southeastern US (SO4 being the other), VISTAS/ASIP were concerned with this underestimation tendency. Thus, VISTAS performed research in this area and identified several missing processes related to Secondary Organic Aerosol (SOA) formation in the CMAQ model. In particular, CMAQ failed to account for SOA from several biogenic emission sources such as sesquiterpenes and isoprene. Consequently, VISTAS enhanced the CMAQ SOA module to treat these missing processes that resulted in improved OC performance (Morris et al., 2006). This SOAmods enhancement is

discussed next.

2.4.4 SOAmods Enhancement

The formulation of the CMAQ SOA module is described in Binkowski and Roselle (2003). SOA is formed primarily from aromatic VOCs and biogenic terpenes. The biogenicJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-9 SOA precursors were modeled with the Biogenic Emissions Information System – Version 3 (BEIS3) model (Pierce et al., 2002). BEIS3 generates three biogenic VOC species: isoprene (ISOP), monoterpenes (TERP) and other biogenic VOC (OVOC). For this study, the Carbon Bond IV photochemical mechanism was used (Gery et al., 1987) that represents VOC compounds based on their carbon bond structure. The BEIS3 ISOP, TERP and OVOC species are speciated into the CB4 species for photochemical modeling in CMAQ and CAMx as follows (molar speciation):

• ISOP = ISOP (isoprene is an explicit species)

• ALD2 = 1.5 x TERP

• OLE = 0.5 x TERP

• PAR = 6.0 x TERP

• NR = 0.5 x OVOC

• OLE = 0.5 x OVOC

• PAR = 8.5 x OVOC

• TERPB = TERP

Here, ALD2, OLE, PAR and NR are the CB4 chemical mechanism representations of the biogenic VOC emissions as high molecular weight aldehydes, olefinic carbon bond, paraffin carbon bond and non-reactive functional groups. In CMAQ, the TERPB species is specified in the emissions inputs, along with its CB4 representation of ALD2, OLE and PAR, but does not participate in the photochemical mechanism and is only used in the SOA formation module. The TERPB species forms a SGTOT species based on oxidation parameters extracted from the photochemical module. SGTOT consists of the combined gaseous condensable gas (CG) plus particle SOA that are assumed to be in equilibrium. CMAQ transports the SGTOT species and splits it to a CG gaseous and particle SOA for output.

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

Polymerization : Recent measurements indicate that some SOA species may polymerize, resulting in species that are no longer volatile and cannot evaporate back to a CG. In this case, the equilibrium assumption between the CG and SOA will understate the amount of particle SOA present in the atmosphere (Kalberer et al., 2004; Jang et al., 2002).

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

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

Acid Catalyzation : Recent literature also suggests that some SOA formation may have acid catalyzed reactions (Claeys et al., 2004; Jang et al., 2005).January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-10 Heterogeneous Reactions : Recent evidence suggests that some SOA formation may occur during heterogeneous aqueous-phase chemical reactions (Yu et al., 2005).

A prototype module was added to CMAQ that accounted for the first three processes listed above. The last two processes were not included in this work because there are not enough quantitative experimental data yet to establish a parameterization. Modules were added to the CMAQ SOA module under the following constraints:

• The existing CMAQ SOA module for monoterpenes would remain unchanged;

• The same CMAQ model inputs would be used; and

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

Figure 2-1 displays how the prototype representation of new processes to represent SOA polymerization and SOA formation from sesquiterpenes and isoprene were added to the CMAQ SOA module using the existing CMAQ structure and inputs. The new components of the SOA module are indicated in bold italic, whereas the existing CMAQ SOA components (Binkowski and Roselle, 2002) use a regular font. There are several parameters that must be defined in the new elements of the enhanced SOA module: emission factors (EF), canopy escape efficiencies for gases (EEG) and aerosols (EEA) and SOA yields (Y). Based on an analysis of recent measurements, primarily from a recent biogenic emissions field study in Duke Forest, North Carolina (Stroud et al., 2005; Matsunaga et al., 2005), a range of values for the factors in Figure 2-1 were developed as shown in Table 2-1. For the initial prototype of the enhanced SOA module, we selected the mid-point of the range values for the factors from the measurements (Table 2-1). No attempt was made to optimize the parameters in Table 2-1 for OC/TCM model performance.

The emission factors, EF1 and EF2, relate the monoterpene emissions estimated by BEIS3 to emissions of monoterpenes, EF1 (e.g., α -pinene), and sesquiterpenes (EF2). Table 2-1 displays the range of EF1 and EF2 factors based on recent field study data (Stroud et al., 2005). Using the midpoint of the range results in emission factors of 0.7 for EF1 and 0.4 for EF2. EF1 is assigned a value of 0.7 based on field observations that indicate that the BEIS3 terpene emission factors are likely overestimated due to a tendency of earlier measurements approaches to artificially increase the emissions due to disturbance when leaves were enclosed in the measurement system. As an initial approach for including sesquiterpene emissions, we have assigned EF2 a value of 0.4 based on the ratio of the observed sesquiterpene emission from the Duke Forest field study (Stroud et al, 2005) to the BEIS3 monoterpene emission estimate. The net result is that BEIS3 TERP emissions are increased by 10% and split 64% as monoterpenes and 36% as sesquiterpenes. The CG yields from the sesquiterpenes are assumed to partly condense into a non-volatile SOA particle that is modeled in CMAQ using the new secondary organic carbon species (SOC2) species and only some of the gas and aerosol species associated with sesquiterpenes are assumed to escape from the canopy using the mid-range of the Escape Efficiencies (EE) estimated by Stroud et al. (2005). The fraction of BEIS3 TERP emissions that are assumed to be monoterpenes (i.e., 64% of the emissions) are treated with the standard CMAQ two-product SOA module (Binkowski and Roselle, 2003) assuming equilibrium between the CG and SOA with the SOA output in the standard AORGB species (Binkowski and Roselle, 2003).January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-11 The isoprene SOA formation pathway forms a CG using the mid-point yield rate based on the range of recent measurements (Stroud et al., 2005) and a CG/SOA partitioning rate based on the mid-point of measurements from Matsunaga et al. (2003, 2005) (Table 2-1). The isoprene SOA is assumed to be volatile and is modeled as a new secondary organic carbon species in CMAQ SOAmods (SOC3). Finally, all SOA species, with the exception of the already non-volatile SOC1 (polymerized SOA) and SOC2 (sesquiterpene product) species, are assumed to partially polymerize into non-volatile particles that are stored in the SOC1 species. The polymerization rate is based on the results of Kalberer et al (2004) who found that 50% of the SOA polymerized

in 20 hours.

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

QA/QC of SOAmods Coding : The SOAmods implementation was conducted at ENVIRON. Staff at the University of California at Riverside performed independent QA/QC of the SOAmods code implementation and independent testing and evaluation.

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

Peer Review of SOAmods : The formulation of the SOAmods enhancement to the CMAQ SOA module was documented and comments were received by several parties. The results were also written up and submitted to Atmospheric Environment where it was subjected to peer review and is awaiting publication (Morris et al., 2006).

Model Performance Evaluation of SOAmods : The final level of QA of the SOAmods was comparisons of CMAQ V4.4 model performance with and without including the SOAmods enhancement. Table 2-2 displays fractional bias error for Organic Carbon (OC) IMPROVE and STN monitoring sites in the VISTAS, MRPO, MANE-VU and CENRAP states using the standard CMAQ Version 4.4 (V4.4) and then CMAQ V4.4 with the SOAmods enhancement. Whereas the standard CMAQ V4.4 underestimates OC across IMPROVE sites of from –76% (MRPO) to –102% (VISTAS), with the SOAmods enhancement the fractional biases centered on zero and ranges from –14% to +8%.

Similar results are seen for OC fractional bias across the more urban STN sites where the CMAQ V4.4 exhibits an underestimation bias of –67% to –105%, when using SOAmods the under-prediction bias is –27% to –44%. Note that the continued underestimation of OC across the urban STN sites is likely due to missing primary OC emissions and

uncertainties in the STN OC measurements.

With the release of CMAQ Versions 4.5 in October 2005, the SOAmods enhancement was added to the AERO3 aerosol module in CMAQ Version 4.5 that was compared against the standard CMAQ Versions 4.5 and SOAmods was found to produce similar improvements in OC model performance as seen with CMAQ Versions 4.4. ASIP and VISTAS are now proceeding with their regional haze and 8-hour ozone/PM 2.5 modeling using the CMAQ Versions 4.5 SOAmods.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-12 2.4.5 CMAQ Configuration for ASIP Modeling

The configuration of CMAQ used in the ASIP modeling is based on the extensive testing and evaluation of several versions and configurations of CMAQ performance as part of Phase I and II of the VISTAS modeling (Morris et al., 2004a,b; Morris et al., 2005a). As part of VISTAS, the science team has tested and evaluated CMAQ versions 4.3, 4.4beta, 4.4 and 4.5. When the CMAQ treatment of SOA was found to be incomplete, it was enhanced with the SOAmods update, first in CMAQ Version 4.4 and then in CMAQ Version 4.5. In this section we identify the main science options we recommend for 8-hour ozone and PM 2.5 modeling with CMAQ. In particular, we propose to run CMAQ Version 4.5 with the SOAmods enhancement and the configuration as shown in Table 2-5. The model would be set up and exercised on the same nested 36/12 km grid domain used in VISTAS, employing one-way grid nesting. That is, boundary conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ BCON processor. A total of 19 vertical layers would be implemented, extending up to a region top of 100 mb (approximately 15 km AGL).

The PPM horizontal advection solver will be used along with the spatially varying (Smagorinsky) horizontal diffusion approach and K-theory for vertical diffusion. The new Yamertino vertical transport scheme of CMAQ Version 4.5 will be used to correct the mass conservation problems in past versions of the model. MM5 meteorological output based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM planetary boundary layer (PBL) scheme will be used (see Table 2-3) and the recently updated CMAQ Meteorological-Chemistry Interface Processor (MCIP3.0) would process the MM5 data using the “pass through” option. The CB4 gas-phase, RADM aqueous-phase, and AERO4/ISORROPIA aerosol chemistry schemes are recommended for use in the CMAQ 2002 modeling. Treatment of reversible secondary organic aerosols would be simulated by the SORGAM implementation in CMAQ with

the SOAmods enhancement described above.

2.5 Model Limitations

All mathematical models possess inherent limitations owing to the necessary simplifications and approximations made in formulating the governing equations, implementing them for numerical solution on fast computers, and in supplying them with input data sets and parameters that are themselves approximations of the full state of the atmosphere and emissions processes. Below, we list the more important limitations of the various modeling systems to be employed in ASIP and VISTAS modeling.

2.5.1 MM5

Four different configurations of the MM5 Land Soil Model (LSM) and Planetary Boundary Layer (PBL) were evaluated as part of the 2002 meteorological modeling. Depending on the meteorological variable (e.g., winds, temperature, moisture) and location (e.g., mountains, coastal, east, west) different LSM_PBL configurations performed better. The Pleim-Xiu Asymmetric Convective Mixing PX_ACM LSM_PBL configuration was selected because it consistency was near the top performing configuration in the VISTAS region across variables and locations and was never the worst performing configuration. However, there are numerousJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-13 limitations in the MM5 with the LSM and PBL treatment being some of the most important. The MM5 PX_ACM frequently predicts very low PBL heights that can appear as “holes” in the spatial distribution of PBL heights that don’t appear physically realistic and may affect air quality modeling. Although the MM5 PX_ACM configuration model performance in the VISTAS region mostly met performance benchmarks, the performance was much worst in the western U.S. In addition, there is a stochastic component of real world meteorology that is not captured by MM5. For example, for some ozone episodes stagnation is an important attribute that MM5 fails to simulate well as it tries to organize the flow fields. The MM5 model represents approximately 20 years of development by various researchers and is showing its age. The many limitations in MM5 have spawned the development of a new meteorological model, the Weather Research Forecast (WRF) model. However, the WRF model will not be used or tested in the VISTAS/ASIP modeling.

2.5.2 SMOKE

In the VISTAS Phase I study a number of undocumented features of the SMOKE 1.5b version necessitated re-runs of the emissions processing software to overcome errors and/or ambiguities in source documentation and QA reporting. Although there were fewer problems with the SMOKE Version 2.0 and 2.1 releases, problems were encountered that were not well

documented that necessitated reruns of the model. In October 2005 Version 2.2 of SMOKE was released. However, for reasons discussed earlier, ASIP elected to keep using Version 2.1 and not transition to the new SMOKE Version 2.2. VISTAS has fully set up and evaluated SMOKE Version 2.1, including identification of problems in the modeling that have been corrected. Switching to the new Version 2.2 could not only cause a serious set back in the ASIP modeling schedule, it may result in picking up additional new undocumented errors in the new modeling system that could require rerunning scenarios. Should problems arise or issues be encountered which would require additional SMOKE runs or potential SMOKE modifications or alternate modeling methods, we will immediately notify ASIP and make recommendations for resolving the issues. Upon receipt of technical direction from ASIP, appropriate corrective action will be

taken.

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

2.5.3 CMAQ

Like all air quality models, a major limitation of CMAQ is the emissions, meteorological and IC/BC inputs. Key science limitations in the model itself include the nitrate formation and Secondary Organic Aerosol (SOA) chemistry. The VISTAS testing found the CMAQ nitrate performance suspect with winter overestimations and summer underestimations. Improvements in the ammonia emissions inventory and model formulation have improved this performance attribute, especially the winter overestimation bias. Deficiencies in the CMAQ SOA module have been partly corrected with the SOAmods enhancement. However, the current SOAmods formulation is based on very little data and more refined SOA enhancements are needed. LackJanuary 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-14 of any two-way grid nesting limits the ability of the model to properly resolve point source plumes or urban photochemistry a without a prohibitive number of grid cells. Another limitation of CMAQ is the computational requirements, including the need of excessive disk space.

2.6 Model Input Requirements

Each of the ASIP/VISTAS modeling system components has significant data base requirements. These data needs fall into two categories: those required for model setup and operation, and those required for model evaluation testing. Below, we identify the main input data base requirements for the meteorological, emissions, and air quality models.

2.6.1 MM5

The databases required to set up, exercise, and evaluate the MM5 model for the annual 2002 episode consist of various fixed and variable inputs.

 Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived from the Geophysical Data Center global data sets from the National Center for Atmospheric Research (NCAR) 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 NCAR/PSU 10 min. (~18.5 km) databases while for the 12 km grids, the United States Geological Survey (USGS) data are available.

 Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from operationally analyzed fields derived from the National Center for Environmental Predictions (NCEP) ETA (40 km resolution) following the procedures outlined by Stauffer and Seaman (1990). These 3-hr synoptic-scale initialization data the horizontal wind components (u and v), temperature (T), and relative humidity (RH) at the standard pressure levels, plus sea-level pressure (SLP) and ground temperature (T g ). Here, T g represents surface temperature over land and sea-surface
temperature over water.

 Water Temperature: Water temperatures required on both 36 km and 12 km grids canbe derived from the ETA skin temperature variable. These temperatures are bilinearly interpolated to each model domain and, where necessary, filtered to smooth out irregularities.

 Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and precipitation directly through explicit resolved-scale and parameterized sub-grid scale processes, the model does not require precipitation or cloud input. The potential for precipitation and cloud formation enters through the thermodynamic and cloud processes formulations in the model. The only precipitation-related input required is the initial mixing ratio field that is developed from the NWS and NMC data sets previously discussed.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-15  Multi-Scale FDDA : The standard “multi-scale” data assimilation strategy to be used on the 36 km and 12 km grids will objectively analyzed three-dimensional fields produced every 3-hr from the NWS rawinsonde wind, temperature, and mixing ratio data, and similar analyses generated every three hours from the available NWS

surface data.

2.6.2 SMOKE

The databases required to set up and operate SMOKE for the ASIP 2002 annual simulation are as

follows:

• Area Source emissions in IDA format

• Fugitive Dust Source emissions in IDA format

• Nonroad source emissions in IDA format

• Non-EGU Stationary Point Source emissions in IDA format

• EGU Stationary Point Source emissions in IDA format

• CEM-Based EGU Emissions, hour specific for 2002

• Prescribed, Agricultural, and Wildfire Emissions, day specific for 2002

• Onroad Motor Vehicle VMT and activity data

• MOBILE6.2 input parameters

Also required for annual modeling are data files specific for:

• Temporal allocation

• Spatial allocation

• Speciation

Chapter 5 discusses the data input requirements and data sources in detail.

2.6.3 CMAQ

As described in more detail in Chapter 5, the CMAQ Chemical Transport Model (CTM) requires the following inputs:

 Three-dimensional hourly meteorological fields that will be generated by the CMAQ MCIP3.0 processing of the BAMS MM5 output;
 Three-dimensional hourly emissions generated by SMOKE;
 Initial conditions and boundary conditions (IC/BC);
 Topographic information;
 Land use categories; and
 Photolysis rates generated by the CMAQ JPROC processor.January 2006

C:\Temp\ASIP\Sec2.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec2.doc 2-16 Table 2-1. Parameters use in enhanced SOA module (see Figure 2-1).
Parameter Mid-Point Range

EF1 0.7 0.4 ~ 1.0

EF2 0.4 0.2 ~ 0.6
EEG1 0.325 0.2 ~ 0.45

EEA1 0.2 0.05 ~ 0.35

Y2 0.875 0.75 ~ 1.0

Y1 0.11 0.06 ~ 0.16

P1 0.45 0.15 ~ 0.75 EF1 = emission factor of monoterpenes to the TERP

emissions estimated by BEIS3 EF2 = emission factor of sesquiterpenes relative to the

TERP emissions estimated by BEIS3 EEG1 = escape efficiency of gas phase precursor of

sesquiterpenes from canopy EEA1 = escape efficiency of SOA from sesquiterpenes

from canopy Y1 = SOA yield of oxidated isoprenes Y2 = SOA yield of sesquiterpenes

Table 2-2. Comparison of fractional bias performance metric for Organic Carbon (OC) using the standard CMAQ Version 4.4 (V4.4) and CMAQ V4.4 with the SOAmods enhancement.
IMPROVE OC STN OC July 2002
Fractional Bias V4.4 SOAmods V4.4 SOAmods

Southeastern U.S. -102% -2% -105% -32%
Midwestern U.S. -76% +12% -67% -24%
Northeast U.S. -82% -14% -95% -44%
Central U.S. -98% +8% -81% -27%May 2004C:\Temp\ASIP\Sec2.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec2.doc2-17Table 2-3. MM5 Meteorological Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsModel CodeMM5 Version 3.6 (MPP)Grell et al., 1994Horizontal Grid Mesh36/12 km36 km grid164 x 128 cells12 km grid180 x 189 cellsVertical Grid Mesh34 layersVertically varying; sigma pressure coord.Grid InteractionNo FeedbackIFEED=0InitializationEta first guess fields/LittleRBoundary ConditionsEta first guess fields/LittleRMicrophysicsReisner I Mixed IceLook up tableCumulus SchemeKain-Fritsch 2On 36/12 GridsPlanetary Boundary LayerACM PBLRadiationRRTMVegetation DataUSGS24 Category SchemeLand Surface ModelP-X Land Surface Model (LSM)Shallow ConvectionNoneSea Surface TemperatureEta SkinSpatially varyingThermal RoughnessGarrattSnow Cover EffectsNone4D Data AssimilationAnalysis Nudging on 36/12Integration Time Step90 secondsSimulation PeriodsAnnual 2002Other episodic periods to be definedPlatformLinux ClusterDone at BAMSMay 2004C:\Temp\ASIP\Sec2.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec2.doc2-18Table 2-4. SMOKE Emissions Model Configuration for ASIPEmissions ComponentConfigurationDetails/CommentsEmissions ModelSMOKE Version 2.1Oct '04 releaseHorizontal Grid Mesh36/12 km36 km grid148 x 112 cellsRPO Unified Grid12 km grid168 x 177 cellsVISTAS/ASIP eastern US 12 km gridArea Source EmissionsVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesOn-Road Mobile SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesPoint SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS states and stakeholdersOther States: EPA '02 NEI augmented with other 2002Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesOff-Road Mobile SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesBiogenic SourcesVISTAS Domain: VISTAS State 2002 EIUpdated '02 developed by VISTAS statesOther States: EPA '02 NEI augmented with other 2002Generated from EPA NEI02 v.1 and RPO interactionMexico/Canada Emissions:Same as Phase I w/ revisions based on available updatesTemporal AdjustmentsSeasonal, day, hourBased on latest collected information and CEM-based profilesChemical SpeciationRevised CB4 Chemical SpeciationExpected EPA release in Jan '04GriddingRevised EPA Spatial Surrogates UsedGridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/Growth and ControlsVISTAS Special Interest Team Generated Data Base Cases defined by VISTAS Planning WorkgroupQuality AssuranceQA Tools in SMOKE 2.1Independent QA with AG's Established ProtocolSimulation PeriodsAnnual 2002Other episodic periods to be definedPlatformDual Athelon 2600 \+Local 1.8 terabyte Ultra 320 RAID 5 system processing and storageMay 2004C:\Temp\ASIP\Sec2.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec2.doc2-19Table 2-5. CMAQ Air Quality Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsModel CodeCMAQ Version 4.5 with SOAmodsPleim et al., (2005)Horizontal Grid Mesh36/12 km36 km covering cont. U.S; 12 km covering easternUS36 km grid148 x 112 cellsRPO National Grid12 km grid168 x 177 cellsVISTAS/ASIP 12 km gridVertical Grid Mesh19 LayersFirst 17 layers sync'd w/ MM5Grid InteractionOne-way nestingInitial Conditions~15 days full spin-upSeparately run 4 quarters of 2002Boundary Conditions2002 3-hourly GEOS-CHEM annual runDay-specific 3-hour BCs from Global ClimateModelEmissionsBaseline Emissions ProcessingSMOKE (Ver 2.1)MM5 Meteorology input to SMOKE, CMAQDust Transport FractionApplied in emissions before SMOKETom Pace updatesNH3 Inventory AdjustmentApplied in emissions before SMOKESub-grid-scale PlumesNo Plume-in-Grid (PinG)ChemistryGas Phase ChemistryCBM-IVAerosol ChemistryAE4/ISORROPIAIncludes active Sea SaltSecondary Organic AerosolsSecondary Organic Aerosol Model (SORGAM) Schell et al., (2001)Aerosol Mass Conservation PatchYesGeorgia Institute of Technology UpdateCloud ChemistryRADM-type aqueous chemistryIncludes subgrid cloud processesN2O5 Reaction Probability0.01 – 0.001Meteorological ProcessorMCIP Version 3.0Includes dry deposition for Sea Salt and percenturban minimum Kz (PURB)Horizontal TransportEddy Diffusivity SchemeK-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approachVertical TransportAdvection SchemeYamertinoV4.5 Mass Conservation (Yamertino)Eddy Diffusivity SchemeK-theoryDiffusivity Lower LimitKzmin = 0.1 to 2.0PURB option in Ver 4.5Planetary Boundary LayerNo PatchDeposition SchemeM3dryDirectly linked to Pleim-Xiu Land Surface ModelMay 2004C:\Temp\ASIP\Sec2.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec2.doc2-20Table 2-5. CMAQ Air Quality Model Configuration for ASIPScience OptionsConfigurationDetails/CommentsparametersNumericsGas Phase Chemistry SolverEuler Backward Iterative (EBI) solverHertel et al (1993) EBI solver ~ 2x faster than MEBIHorizontal Advection SchemePiecewise Parabolic Method (PPM) schemeSimulation PeriodsAnnual 2002With~15 day spin-up in December 2001Integration Time StepDetermined by met conditions15 minute coupling time stepPlatformAthlon MP 2600+MPI using 6 processors per quarterMay 2004C:\Temp\ASIP\Sec2.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec2.doc2-21Figure 2-1.Schematic describing the addition of new SOAmods processes (bold italic) within the existing CMAQ SOA module(regular font) to treat polymerization and SOA from sesquiterpenes and isoprene (see Table 2-1 for parameters).



## Biogenic emissionsTERPISOPISPDY1P1PAROLEALD2TERPRXNA_TRP1G_TRP1A_TRP2G_TRP2ISPRXNSGTOT_ISPG_ISPA_ISPEF2 * ( EEG1 * Y2 + EEA1 )AORGBASOC2ASOC3*Y3*Y4OOO:Oxidation reaction with OH, O3, and NO3ISPRXN: The amount of reacted ISPD with oxidantsTERPRXN: The amount of reacted TERP with oxidantsDotted box: Not implemented in the initial testBold italic: New processesASOC1PolymerizationAll SOAs except SOC1 & SOC2FPOLYEF1



## Biogenic emissionsTERPISOPOVOCPAROLENRISPDY1P1PAROLEALD2SGTOT_TRP1TERPRXNSGTOT_TRP2ISPRXNSGTOT_ISPG_ISPA_ISPEF2 * ( EEG1 * Y2 + EEA1 )ASOC2ASOC3**OOOO: Oxidation reaction with OH, O3, and NO3ISPRXN: The amount of reacted ISPD with oxidantsTERPRXN: The amount of reacted TERP with oxidantsDotted box: Not implemented in the initial testBold italic: New processesASOC1PolymerizationAll SOAs except SOC1 & SOC2FPOLYEF1January 2006

C:\Temp\ASIP\Sec3.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec3.doc 3-1 3.0 EPISODE SELECTION

This chapter provides a brief overview of the process followed by the ASIP in determining the most appropriate period(s) for PM 2.5 and 8-hour ozone modeling to be used in the Southeastern

States PM 2.5 and 8-hour ozone attainment demonstrations for their State Implementation Plans (SIPs). As discussed in more detail below, one criteria in selecting modeling periods for attainment demonstration modeling is the selection of modeling periods for which databases already exist, if appropriate. This was a major criteria for the ASIP 8-hour ozone and PM 2.5 modeling that relied heavily on the VISTAS modeling databases. Thus, the ASIP episode selection was based on the analysis by the VISTAS Technical Analysis Work Group (TAWG) selection of the 2002 annual period for regional haze modeling. ASIP reviewed the 2002 annual period and also found it suitable

for PM 2.5 and 8-hour ozone modeling. While ASIP and the VISTAS TAWG plan to prepare a formal report documenting these activities, this work was not available at the time of this writing. However, much of the technical work underpinning the ASIP/VISTAS episode selection process has been published and peer-reviewed over the later stages of the VISTAS work efforts and it is this body of information that we have distilled in preparing the brief episode selection summary that follows.

3.1 Overview of EPA Guidance

EPA’s current draft guidance on PM 2.5 /Regional Haze modeling (EPA, 2001) and final guidance for 8-hour ozone modeling (EPA, 2005) identifies specific goals to consider when selecting one or more episodes for use in demonstrating attainment of the PM 2.5 and 8-hour ozone NAAQS. There is much in common with selecting episodes for annual and episodic PM 2.5 and 8-hour ozone attainment demonstrations, as well as regional haze, EPA’s guidance addresses all three in a consistent fashion in their guidance documents (EPA, 2001; 2005). As an update to the draft PM 2.5 and regional haze modeling guidance, EPA has published an updated summary of PM 2.5 and Regional Haze Modeling Guidance (Timin, 2002) that serves, in some respects, as in interim placeholder until the final guidance is issued as part of the PM2.5/regional haze NAAQS implementation process that is expected during 2006.

EPA recommends that episode selection derive from three principal criteria:

 A variety of meteorological conditions that lead to exeedances of the PM 2.5 and 8- hour ozone NAAQS should be covered;

 To the extent possible, the modeling data base should include days for which extensive data bases (i.e. beyond routine aerometric and emissions monitoring) are available; and

 Sufficient days should be available such that relative reduction factors (RRFs) can be based on several days (> 15 days, with at least 5 days being essentially mandatory).

For regional haze and annual PM 2.5 modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year (EPA, 2001, pg. 188). EPA also listsJanuary 2006

C:\Temp\ASIP\Sec3.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec3.doc 3-2 several ‘other considerations’ to bear in mind when choosing potential PM/regional haze and 8-hour ozone modeling episodes including: (a) choose periods which have already been modeled; (b) choose periods which are drawn from the years upon which the current design values are based; (c) include weekend days among those chosen; and (d) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment areas as possible.

ASIP and VISTAS adopted a logical, stepwise approach in implementing the EPA guidance in order to identify the most preferable, representative year for PM 2.5 , regional haze and 8-hour modeling. These steps are summarized briefly in this chapter.

3.2 Episode Selection Methodology

The episode selection methodology entailed coordinated investigations by ASIP and VISTAS contractors and members of the VISTAS Technical Analysis Workgroup (TAWG). To begin, Olerud (2003b) identified important meteorological characteristics and data sets in the Southeastern U. S. directly relevant to the evaluation of candidate annual modeling episodes. A separate detailed aerometric analysis and pattern recognition study was carried out by ICF (Douglas et al., 2003) to characterize the extent to which days from January-March 2002 and January-March 2003 represent the type of meteorological conditions that are most frequently associated with high and low values of

a haze index and PM 2.5 concentrations. Using the standard Classification and Regression Tree(CART) analysis software, these researchers characterized each of the days within these two threemonth periods relative to haze and PM 2.5 observations as well as other relevant meteorological factors. This work was based on previous episode characterization work carried out in support of the SEARCH and MARAMA research projects (Douglas et al., 2003). The analysis was also supported by climate summaries provided by the North Carolina Division of Air Quality (NCDAQ).

In parallel with the CART episode characterization analyses, collaborative investigations by ASIP and VISTAS TAWG participants (e.g., NCDAQ, Georgia DNR, FL DEP) intensively studied the availability of PM 2.5 ozone, meteorological, and emissions data and representativeness of alternative Baseline modeling periods from a regulatory standpoint (Boylan et al., Brewer et al., 2003). Daily average speciated PM 2.5 monitoring data in the Southeastern US were review intensively, by site and by monitoring network (e.g., IMPROVE, SEARCH, STN, FRM). 8-hour ozone data for the nonattainment areas were also analyzed. In addition to analyzing data representativeness, consideration was given to the timeliness with which new data could be obtained for the ASIP and VISTAS modeling. Also, data availability from parallel meteorological and emissions database acquisition efforts was considered both for the Southeastern US states as well as

for other states and countries in the 36 km domain.

To assess the representativeness of the five year baseline 2000-2004 period that is used in the 8-hour ozone, PM2.5 and regional haze projections (see Chapter 8), temperature and precipitation records were examined over the 108 yr period of record and additional high-resolution meteorological analyses were considered (e.g., CART analyses for SEARCH and MARAMA sites). For each PM2.5 monitoring site in the VISTAS domain, and for each component of PM 2.5, monthly means and deviations (from the monthly mean) were calculated for the months within the 2002-2003 period of record. Daily, monthly, and annual trends of PM 2.5 concentrations across the three yearJanuary 2006

C:\Temp\ASIP\Sec3.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec3.doc 3-3 period were subsequently analyzed (Boylan et al., 2003). Equally as important, the methodologies and decisions underpinning the episode selection processes carried out by other RPOs were also considered (several had already chosen CY-2002 as the modeling year).

3.3 Selection of CY 2002 For ASIP and VISTAS

After a lengthy process of integrated studies, the episode selection process culminated in the selection of calendar year (CY) 2002 (1 January through 31 December) as the most current, representative, and pragmatic choice for VISTAS regional haze and ASIP 8-hour ozone and PM 2.5 modeling. All of the EPA criteria for 8-hour ozone, PM 2.5 and PM/regional haze episode selection were directly considered in this process together with many other pragmatic considerations (e.g., timing of new emissions or aerometric data deliveries by EPA or the states to the modeling teams).January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-1 4.0 MODELING DOMAINS AND DATA AVAILABILITY

This chapter summarizes the model domain definitions including the model domain, resolution, map projections and nesting schemes for high resolution sub-domains.

4.1 Horizontal Modeling Domain

The ASIP horizontal domains for each of the models will be identical to those used in the VISTAS Phase I and II modeling. As in VISTAS, as well as the CENRAP and WRAP RPOs, a coarse grid continental United States (US) domain with a 36 km horizontal grid resolution will be used (the Inter-RPO domain). The CMAQ domain is nested in the MM5 domain. The selection of the MM5 domain is described in the VISTAS MM5 modeling protocol (Olerud, 2003). Figure 4-1 shows the MM5 horizontal domain as the outer most, blue grid. Also shown in Figure 4-1 is the CMAQ 36 km domain nested in the MM5 domain. To achieve finer spatial resolution in the Southeastern US States are also using a one-way nested high resolution grid with a 12 km grid resolution. Figure 4-2 shows the 36 km CMAQ continental grid and the high resolution, nested 12-km grid in the VISTAS states. Figure 4-3 shows in more detail the 12 km grid for the Southeaster US region that is the focus of ASIP and VISTAS.

Both MM5 and CMAQ employ the Regional Planning Organization (RPO) unified griddefinition for the 36 km continental domain. The RPO unified grid consists of a LambertConformal map projection using the map projections parameters listed in Table 4-1.

Table 4-1. RPO Unified Grid Definition.

PARAMETER VALUE projection Lambert-conformal alpha 33 degrees
beta 45 degrees

x center 97 degrees y center 40 degrees

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

Table 4-2 lists the number of rows and columns and the definition of the X and Y origin (i.e., the southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are reversed. In Table 4-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells whileJanuary 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-2 “cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Finally, we note that the grid definition for the CMAQ Meteorology Chemistry Interface Processor (MCIP) and CMAQ Chemical Transport Model (CCTM) are identical.

Table 4-2. Grid Definitions For MM5 and CMAQ.

MODEL COLUMNS

DOT(CROSS) ROWS

DOT(CROSS) XORIGIN YORIGIN

MM5 36km 129 (128) 165 (164) -2952000 -2304000

CMAQ 36km 149 (148) 113 (112) -2736000 -2088000

MM5 12km 190 (189) 181 (180) 7200 -1656000

CMAQ 12km 169 (168) 178 (177) 108000 -1620000

4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. Table 4-3 lists the layer definitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the computational cost of the CMAQ simulations. The effects of layer averaging were evaluated in the VISTAS Phase I modeling effort and found to have a relatively minor effect on the model performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to ambient monitoring data.January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-3 Figure 4-1 . Nesting of 36-km CMAQ Grid in the MM5 36-km Grid.January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-4 Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid

.January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-5 Figure 4-3. Domain Definition for High Resolution 12-km Grid.January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-6 Table 4-3. Vertical Layer Definition For MM5 Simulations (Left Most Columns), And Approach For Reducing CMAQ Layers By Collapsing Multiple MM5 Layers (Right Columns).

MM5 CMAQ 19L Layer Sigma Pres(mb) Height(m) Depth(m) Layer Sigma Pres(mb) Height(m) Depth(m)

34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 0 1.000 1000 0 0January 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-7 4.3 Data Availability

The CMAQ modeling system requires emissions, meteorological, initial and boundary condition (IC/BC) and ozone column data for defining the inputs to operate the CMAQ Chemical Transport Model and air quality data with which to evaluate the CMAQ CTM concentrations and deposition estimates.

4.3.1 Emissions Data

The base year emissions inventory for Phase II of the VISTAS modeling will be the basis for the ASIP modeling. These data are founded on 2002 Consolidated Emission Reporting Rule (CERR) inventories submitted to VISTAS by participating state or local agencies and compiled by VISTAS emission inventory contractors in NEI Input Format (NIF) 3.0. These emissions were reviewed by VISTAS stakeholders and considered complete in January of 2004, with minor

modifications submitted since that time. Non-VISTAS state emissions will be based on inventories obtained by the Study Team from the other RPOs or EPA and determined to be representative of the 2002 episode year. Mexican and Canadian emissions will be based on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. For purposes of air quality model validation, actual 2002 calendar year emissions for EGU and fire activity will be used. For strategy and future year emission runs, “typical year” emissions for these categories will be processed for 2002 and the future years.

A final revised 2002 VISTAS state emission inventory is expected in February 2006 and will be used in the final model performance demonstration and configuration expected to begin in spring 2006. Non-VISTAS state emissions are expected to be based on RPO updated base year emissions augmented with additional data provided by RPO, State, and international sources. As in the initial revised modeling, actual 2002 calendar year emissions will be modeled for EGU and fires for base case model performance evaluation, while “typical year” emissions for these categories for 2002 and the future years will be processed during the strategy runs.

All emissions will be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for air quality modeling using Version 2.1 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal and speciation profiles and cross-reference data provided with the version 2.1 release of the model augmented with any recommended and approved emission profile data provided by the emissions inventory contractor, obtained from EPA, or prepared by the Study Team prior to initial emissions modeling. Spatial allocation of the emissions will be based on profiles and spatial allocation factors developed for the National RPO grid. Additional description of emissions processing is described in Chapter 5 and emissions QA is described in Chapter 6.

4.3.2 Air Quality

Data from ambient monitoring networks for both gas and aerosol species are used in the model performance evaluation. Ambient monitoring data are described in detail in the report: “Review and Assessment of Available Ambient Air Quality Data to Support Modeling and Modeling Performance Evaluation for the Three VISTAS Phase I Episodes” (ENVIRON, UCRJanuary 2006

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-8 and Alpine 2003) so are not repeated here. Table 4-4 summarizes ambient monitoring networks. Data have been compiled for all networks except the PAMS and PM Supersites.

Of particular note for the ASIP 8-hour ozone and PM 2.5 attainment demonstration modeling are the locations of the key ozone monitors and key FRM monitors where ozone and PM has been determined to be in nonattainment. Also important are the STN speciated PM monitor that is associated with each FRM PM monitor. ASIP is currently formulating its strategy on how the PM 2.5 attainment demonstration will be performed using associated FRM and STN PM monitoring sites.May 2004C:\Temp\ASIP\Sec4.docZ:\VISTAS Phase II\ASIP\Protocol\Draft#2\Sec4.doc4-9Table 4-4.Overview of Ambient Data Monitoring Networks.Monitoring NetworkChemical Species MeasuredSampling PeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24 hraveragehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (see speciesmappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10, Pb Typically hourlyaveragehttp://www.epa.gov/air/data/PM2.5Federal Reference Method(FRM)Total PM2.5Mass1 in 3 days; 24-hour averagesSpeciation Trends Network(STN)Speciated PM24-hour average http://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (SO4, NO3, NH4, elements);Hourly PM2.5 (Mass, SO4, NO3,NH4, EC, TC); and Hourly gases (O3,NO, NO2, NOy, HNO3, SO2, CO)Hourly or 24-houraverage,depending onparameter.Electric Power Research Institute (EPRI), SouthernCompany, and other companies.http://www.atmospheric-research.comEPA Particulate Matter SupersitesSpeciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htmMay 2004

C:\Temp\ASIP\Sec4.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec4.doc 4-10 4.3.3 Ozone Column Data

Additional data used in the air quality modeling include the Total Ozone Mapping Spectrometer (TOMS). TOMS data is available for 24-hour average and is obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html . The day-specific TOMS data is used in the CMAQ radiation model (JPROC) to calculate photolysis rates. The TOMS data were missing or bad for several periods in 2002: August 2-12; June 10; and November 18-19. Thus, the TOMS data for August 1, 2002 was used for August 2-7 and TOMS data for August 13 was used for August 8- 12. Similarly, TOMS data for June 9 was used for June 10 and data for August 17 was used for August 18-19.

4.3.4 Meteorological Data

Meteorological data are being generated using the MM5 prognostic meteorological model by Baron Advanced Meteorological Systems (BAMS). BAMS is operating the MM5 at 5-day increments for 2002 on the 36 km and 12 km grid with a 14 day spin up period for the end of December 2001. Details on the VISTAS Phase II 2002 MM5 modeling can be found at the

BAMS VISTAS website: http://www.baronams.com/projects/VISTAS/

.

4.3.5 Initial and Boundary Conditions Data

The CMAQ default Initial Concentrations (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 Inter-RPO 36 km grid and the ASIP simulations will be based on day-specific 3-hourly averages from a 2002 GEOS-CHEM global simulation model output. Boundary conditions for the 12 km grid will be based on CMAQ results from the 36 km rid processed with the CMAQ BCON boundary condition processor.January 2006

C:\Temp\ASIP\Sec5.doc 5-1

5.0 MODEL INPUT PREPARATION PROCEDURES

In this section we describe the procedures to be used to develop the CMAQ model inputs

for the ASIP 2002 annual 36/12 km model simulations to address 8-hour ozone and PM 2.5 nonattainment in the Southeastern US States. The development of the CMAQ meteorological and emissions inputs are discussed first followed by the science options to be used by CMAQ. The procedures for developing the initial and boundary conditions and photolysis rates inputs are then discussed along with the model application procedures. With the exception of using a 2009 future-year and performing daily mobile source emissions modeling, the procedures used in the ASIP CMAQ 36/12 km modeling are identical to those used in VISTAS (ENVIRON, 2004; Morris et al., 2004a,b).

5.1 Meteorological Inputs to Emissions and Air Quality Models

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

The National Center for Atmospheric Research (NCAR)/Pennsylvania State University (PSU) Fifth-Generation Mesoscale Model (MM5) (v3.6) is being used by the VISTAS meteorological modeling contactor, Baron Advanced Meteorological Systems (BAMS) (Olerud, 2004a-d) to simulate meteorology at a 36-km resolution for calendar year 2002 over the entire continental United States and including portions of Canada, Mexico, and the Atlantic and Pacific Oceans. MM5 is also being applied over the southeastern U.S. using a 12 km resolution grid. The MM5 is a three-dimensional prognostic meteorological model that is used not only for meteorology studies but also for air quality studies. Some of the physics used in the simulation include one-way nesting; nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and mixing ratio; explicit treatment of moisture; cumulus cloud parameterization; vertical mixing of momentum in the mixed layer; PBL process parameterization; atmospheric radiation; sea ice treatment; and snow cover (see Chapter 2 for more details).

After the MM5 simulation is completed, the MM5 output files are transferred to the emissions and air quality modeling team and analyzed by the Meteorological Gatekeeper. The Meteorological Gatekeeper performs two main roles; (1) to provide an independent evaluation of

the 2002 MM5 simulation that also serves to determine whether the MM5 data have been transferred correctly from the VISTAS meteorological modeling contractor and (2) to process the 2002 MM5 output using Version 2.2 of the Models-3 CMAQ Meteorological-Chemical Interface Processor (MCIP) to generate meteorological fields that will be used for emissions processing and air quality simulation.January 2006

C:\Temp\ASIP\Sec5.doc 5-2 5.1.1 MCIP Reformatting Methodology

The Models-3 Community Multiscale Air Quality (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particles) air quality problems. But before running the CMAQ Chemical Transport Model (CCTM), the MM5 generated meteorological data must be pre-processed and converted to Models-3 consistent data structures. MCIP Version 3.0 will be used to preprocess the MM5 meteorological output. The “pass through” option in MCIP will be used in the modeling. One of MCIP’s functions is to translate meteorological parameters from the output of the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Modeling System Generation 5 (MM5) to the Models-3 input/output applications program interface (I/O API format) which is required for operation of Models-3 CMAQ processors. Some other necessary parameters not available from the meteorological model are estimated with appropriate diagnostic algorithms in the program. The key functions of MCIP include:

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

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

The MCIP processor transforms the data into I/O API format while also calculating several new data fields (e.g. low, middle, and high cloud fractions) that are not readily available in the raw MM5 output. It also interpolates temperature and wind speed to observation height (1.5m and 10m, respectively). The MCIP processor culls a minimum of six cells about the domain periphery to minimize edge effects in the MM5 simulation. MCIP can be used to further reduce the rows or columns in the MM5 data so that the domain definition for the MCIP output files precisely matches the domain used in the air quality modeling. MCIP also allows MM5 layers to be “collapsed” (i.e., some layers can be aggregated). When feasible it is desirable to use the same layer structure in the air quality model as in the MM5 to prevent errors associated with aggregating layer data and to maintain consistency between data produced by the meteorological model and those used by the chemistry-transport model. However, due to computational costs associated with using large number of vertical layers, vertical layer collapsing is typically used to reduce the total number of layers used by the CCTM. In the ASIP and VISTAS modeling we will collapse from 34 layers in MM5 output into 19 layers for the CMAQ air quality simulations. The first 8 layers of CMAQ, up to approximately 450 m AGL, will match the MM5 vertical layer structure exactly. The MM5 layers are then “doubled up” in CMAQ, up to a height of approximately 3,500 m AGL. The region top for CMAQ is the same as used by MM5, 100mb (approximately 15 km AGL). The 36 km analysis domain contains 148 columns, 112 rows, and 19 layers. The 12 km analysis domain covers 168 columns, 177 rows, and 19 layers. More details on the CMAQ modeling domain definitions are provided in Chapter 4 with the vertical layer structure of MM5 and MCIP/CMAQ shown at Table 4-3.January 2006

C:\Temp\ASIP\Sec5.doc 5-3 5.1.2 Products of the Meteorological Input Development Process

The meteorological input development process produces three two-dimensional and four three-dimensional daily meteorological and geophysical output data in the Models-3 I/O API format. These CCTM-ready meteorological input files are used in both emissions processing and

the CCTM simulations. The met fields are 36 km and 12 km horizontal resolution on a Lambert Conformal Projection (LCP) coordinate system with 19 vertical sigma layers extending from the surface to the 100 mb pressure level. The data files include three-dimensional gridded fields of u- and v-wind components, vertical velocity, temperatures, Jacobian, Jacobian weighted air density, total air density, water vapor, cloud water content, rain water content, ice and snow mixing ratio, layer heights, and vertical exchange coefficients. Two-dimensional gridded fields of latitude and longitude, squared map-scaled factor, surface temperatures and pressures, 1.5 and 10 meter temperature, planetary boundary heights, rainfall, total cloud fraction, snow cover, deposition velocities, u* and w*, surface roughness length, as well as dominant land use category are also developed.

Table 5-1 shows the configuration to be used in MCIP Version 3.0 for processing the 2002 MM5 output to produce CCTM-ready meteorology input files.

Table 5-1. MCIP V3.0 Configuration used In the ASIP Modeling. Module or option Values or setting Additional Information

PBL value computation option 1 Use PBL value from input meteorology

Radiation fields 1 Use radiation fields from input meteorology

Dry deposition option 2 Use Models-3 (Pleim) dry deposition
routine
Use PURB Kz_min Option True Calculate Kz_min as a function of percent urban land use
Sea Salt Deposition True Output dry deposition parameters for
Na and Cl
Output interval 60 Unit is in minutes
Vertical layer structure 19 layers See Chapter 4

5.2 Development of Emissions Model Inputs and Resultant Inventories

The base year emissions inventory for ASIP modeling are founded on 2002 Consolidated Emission Reporting Rule (CERR) inventories submitted to VISTAS by participating state or local agencies and compiled by VISTAS emission inventory contractors in NEI Input Format (NIF) 3.0. These emissions were reviewed by VISTAS stakeholders and considered complete in January of 2004, with minor modifications submitted since that time.

Non-VISTAS state emissions are based on inventories obtained by the Study Team and determined to be representative of the 2002 episode year. Base year 2002 emission inventories for non-mobile source categories were obtained from each RPO in the U.S. domain. These dataJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-4 were supplemented with EPA based VMT and MOBILE6 input files necessary to develop onroad mobile source emissions domain-side. Additionally, an inventory of point source resolved agricultural fire emissions were provided by the western state RPO (WRAP) and utilized in the modeling.

Mexican and Canadian emissions are based on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. At this time, these

inventories are the same as in Phase I.

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

A final revised 2002 VISTAS state emission inventory is expected in February 2006 and will be used in the final model performance demonstration and configuration expected to begin in spring 2006. Non-VISTAS state emissions are expected to be based on RPO updated base year emissions augmented with additional data provided by RPO, State, and international sources. As in the initial revised modeling, actual 2002 calendar year emissions will be modeled for EGU and fires for base case model performance evaluation, while “typical year” emissions for these categories for 2002 and the future years will be processed during the strategy runs.

These emissions will then be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for air quality modeling using Version 2.1 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal, spatial, and speciation profiles and cross-reference data currently provided with the 2.1 release of the model augmented with any recommended and approved emission profile data provided by the emissions inventory contractor or obtained from EPA prior to initial emissions modeling. The processing will be adjusted for each run to account for the specific air quality model (AQM) input required by CMAQ.

5.2.1 Emissions Modeling Methodology

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

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

The study team will acquire emissions estimate data from Emissions Inventory Contractor, in the NIF 3.0 format for purposes of generating the emission inventory base yearJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-5 files necessary for the ASIP contract. These data will be augmented with highway mobile source data submitted to the ASIP emission inventory contractors from the VISTAS participating States.

Contacts with ASIP’s emission inventory contractors and the U.S. EPA will be established and formal requests made for inventory corrections, updates and ancillary data pertinent to the modeling of emissions in their jurisdictions. Where feasible and consistent with project resources and schedule, these updated data sets will be acquired and will be used to create day-specific modeling inventories specific to the ASIP domain for the base year episodes

to be modeled.

5.2.2 Set-up of SMOKE Over the ASIP Domain

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

To produce an emissions inventory to support annual modeling, representative time periods will be selected and modeled. Area, nonroad, and point sources will be modeled as a block of Thursday, Friday, Saturday, Sunday, Monday one per month (total of 60 days modeled). For 36-km modeling, onroad motor vehicles will be represented by an entire single week for each month. This selection criteria allows for the representation of day-of-the-week variability in the on-road motor vehicles, and models a representation of the meteorological variability in each month. For 12-km runs, onroad motor vehicles will be run for every day of the year. Holidays will be modeled as Sundays. A list of modeled holidays is provided in Table 5-2. The biogenic emissions will be modeled on a day specific basis (365 days).

Table 5-2. SMOKE Modeled Holidays.
Date Julian Day Holiday Description January 1, 2002 2002001 New Year's Day March 29, 2002 2002089 Good Friday May 27, 2002 2002147 Memorial Day July 4, 2002 2002185 July 4th September 2, 2002 2002245 Labor Day November 28, 2002 2002332 Thanksgiving Thurs November 29, 2002 2002333 Thanksgiving Fri December 24, 2002 2002358 Christmas Eve December 25, 2002 2002359 Christmas Day

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

purposes.January 2006

C:\Temp\ASIP\Sec5.doc 5-6 The domain for the Phase II episode will be identical to the Phase I domain, which is based on the EPA’s 36-km national CMAQ domain, illustrated in Figure 5-1 below (details on the modeling domains are provided in Chapter 4).

Figure 5-1. EPA 36-km National CMAQ Domain.

The parameters for the SMOKE runs are as follows:

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

Future Years : To be determined.

Output Time Zone : Greenwich Mean Time (zone 0)

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

Domain :

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

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

Layer Structure : The CMAQ layer structure will be 19 layers, with specific layer positions defined in the meteorology files (see Chapter 4).January 2006

C:\Temp\ASIP\Sec5.doc 5-7 CMAQ Model Species : The CMAQ initial configuration will be for the CB-IV chemical mechanism with PM. The model species in the emission input files will be: CO, NO, NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO3, POA, PSO4, and PMC.

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

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

sources.

Producing 365 day-specific input files for all source categories places a burden on available computing facilities, data management systems, and would adversely affect the ASIP schedule. Selecting representative model days for some or all of the source categories reduces the processing and file handling requirements to a more manageable level and in most cases does not compromise the accuracy of the emissions files.

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

Biogenic emissions will be modeled for each episode day, using the daily meteorology. Point sources, including CEM-based and fire emissions will be modeled for each episode day to take advantage of the available day-specific emissions and meteorology. Area sources, including nonroad mobile and dust emissions do not utilize meteorological data, and are temporally allocated by monthly, daily and hourly profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved by selecting representative Thursday, Friday, Saturday, Sunday, and Monday profiles for each month.

Motor vehicle emissions are influenced by meteorological variability, but the processing requirements for daily motor vehicle emissions were determined to be prohibitive under the current schedule. Rather than utilizing averaged meteorological data or pre-calculated motor vehicle emissions, for the 36-km domain, a single week per month was selected for modeling. This week was selected from mid-month, to try to best represent the average temperature ranges for the month, and also adjusted to exclude holidays that would require atypical processing. As noted above, in the 12-km domain runs, daily modeling will be conducted. The area source modeling dates were also selected from these ranges to simplify data handling procedures.January 2006

C:\Temp\ASIP\Sec5.doc 5-8 2002 36-km modeling of Onroad Mobile Sources Represented by the Following Weeks : January 13-19 February 10-16

March 10-16 April14-20 May 12-18
June 9-15 July 14-20 August 11-17 September 15-21

October 13-19

November 10-16

December 15-21

2002 12-km onroad Mobile Source Modeling was Performed for Every Day

5.2.3 Development of Point Source Emissions

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

For the ASIP initial baseline modeling, we will be separating the point source emissionsinto EGU and non-EGU categories. The non-EGU category will not be using any day or hourspecific emissions. All non-EGU point source emissions will be temporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allocation factors. These factors will be based on the cross-reference and profile data supplied with the SMOKE 2.1 version and will be supplemented with relevant data provided to the study team by ASIP or its

contractors.

For EGU sources with EPA reported CEM data or with hourly emissions provided by stakeholders, actual hourly data will be used to temporally allocate emissions. For those sources where EPA CEM data are utilized, NOx, SO2, and heat input-based hour-specific profiles were developed and applied to NOx, SO2, and all other emissions, respectively. This ensured that the annual emission values provided by the emissions inventory contractor were maintained, but distributed using hourly to annual profiles. For sources providing hour-specific data and where they were approved by the State in which they operated, those data were substituted for EPA

CEM-based distributions.January 2006

C:\Temp\ASIP\Sec5.doc 5-9 To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input data were collected from the 2002 Continuous Emissions Monitoring (CEM) datasets, and used to develop unit-level temporal distributions. The hour, day of week, and monthly specific temporal profiles will be used in conjunction with the emissions inventory supplied emissions data to calculate hourly EGU emissions by unit.

All point sources will be spatially allocated in the domain based on the stationary source geographic coordinates. If a point source is missing its latitude/longitude coordinates, the source will be placed in the center of its respective county.

5.2.4 Development of Area and Non-Road Source Emissions

All area and non-road source emissions will be temporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allocation factors. These factors will be based on the cross-reference and profile data supplied with SMOKE Version 2.1 and will be supplemented with relevant data provided to the study team by ASIP or itscontractors. Area and non-road sources will be spatially allocated in the domain based on SCCbased spatial allocation factor files. If an area or non-road source SCC does not have an existing cross-reference profile assigned to it, the county-level emissions will be allocated by population density in the respective county.

A crustal PM transport factor will be applied to fugitive dust emission sources that have been identified in U.S. EPA modeling to have only a portion of its mass transportable from the source of the emission generation. The EPA’s studies indicate that 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simulation. For this reason, we will apply county-specific fugitive dust emissions transport factors to these sources in the modeling files to adjust PM emissions accordingly. These factors can be located at http://www.epa.gov/ttn/chief/emch/invent/statusfugdustemissions_082203.pdf

.

5.2.5 Development of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE will be used to develop the base year on-road mobile source emissions estimates for CO, NOx, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates will be combined with gridded, episode-specific temperature data to calculate the gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially allocated based on link location and subsequently summed to the grid cell level, the off-network emissions estimates are spatially allocated based on a combination of the FHWA Version 2.0 highway networks and population. For the ASIP 36/12 km modeling, no link based data will be used. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by the meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:January 2006

C:\Temp\ASIP\Sec5.doc 5-10

• Hourly and daily minimum/maximum temperatures;

• Facility speeds;

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

• Adjustments for running losses;

• Splitting of evaporative and exhaust emissions into separate source categories;

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

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and maintenance program in effect, type of oxygenated fuel program in effect, alternative vehicle mix profiles, RVP of in-use fuel, operating mode) that

direct the calculation of the MOBILE6 emissions factors.

5.2.6 Development of Biogenic Source Emissions

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

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

• Emission factors – many updates including some recent NARSTO modifications,

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

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

For this particular application of BEIS-3, Version 0.9 as currently incorporated in the SMOKE processor will be used. This means that: 1) soil NO emissions shall be prepared without the input of specific soil moisture and precipitation data and 2) MEOH emissions will not be modeled explicitly. Otherwise, the modeling should be identical 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 estimate biogenic emissions in this study. The BEIS model also requires as input hourly, gridded temperature and solar radiation data to estimate biogenic emissions, and these data will be derived from the MM5 predictions.January 2006

C:\Temp\ASIP\Sec5.doc 5-11 5.2.7 Wildfires, Agricultural, and Prescribed Burns, Wind Blown Dust and Sea Salt
Source Emissions

Wildfires, Agricultural, and Prescribed Burns

Wildfire, agricultural, and prescribed burn emissions will be handled separately from the standard area source input files. The study team expects to receive monthly estimates of fire emissions from the emissions inventory contractor, which include burn acreage and biomass loading information for the VISTAS states. Depending on the completeness and quality of the data received, attempts will be made to calculate spatial and temporal distributions of the fire emissions, rather than relying on standard distribution profiles. Also, the study team will attempt to calculate vertical distribution of the fire emissions, based on fire size and biomass involvement. The SMOKE 2.1 can model fire plume rise if provided with the following

variables:

PTOP – Top of the fire plume profile (meters above ground level) PBOT – Bottom of the fire plume profile (meters above ground level) Lay1 – The percent of the emissions entrained in the first modeling layer

The WRAP Fire Emissions Joint Forum Emissions Inventory Report (FEJF, 2002) has documented an approach to calculating these plume descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to calculate representative hourly plume profiles. These profiles are then used by SMOKE 2.1 to distribute the vertical emissions for the fires. To successfully model fires as elevated point sources, the data provided by the emissions inventory contractor will need to include both the day or days on which the fire occurs, and a spatial identifier of the fire location. At a minimum, a latitude and longitude of the fire location can be used, while a polygon coverage would be preferable.

In addition, wildfire and prescribed burn data, including emissions estimates and plume rise distributions, will be obtained from other RPOs and used to supplement the inventory for the

non-VISTAS states.

Windblown Dust

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

Sea Salt

CMAQ currently treats sea salt as an inert PM species. That is, the sea salt is not allowed to chemically interact with other species, such as producing particulate sodium nitrate. There are plans to update CMAQ to have chemically active sea salt, but it is unclear whether such an update will occur during the ASIP modeling. Accordingly, the initial modeling will be conducted without any sea salt emissions. If CMAQ is updated to treat chemically active Sea Salt, or if CAMx is run using its full-science options, then Sea Salt emissions will be generated using appropriate procedures (e.g., as was done in VISTAS Phase I).January 2006

C:\Temp\ASIP\Sec5.doc 5-12 5.2.8 Speciation and Reformatting of Emissions

SMOKE will be run to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM-IV, CB-IV or CB4). The SMOKE model will also reformat the emissions estimates for use in CMAQ modeling. For each model-readyemissions inventory, SMOKE will produce at a minimum five (5) separate air quality modelready files: low-level point source, area source, elevated point source, mobile source, and biogenics. Other source categories, such as EGU and fire emissions may also be handled as separate air quality model-ready files.

5.2.9 Development of Modeling Inventories

The emissions inventories modeled for the VISTAS ASIP can be grouped into four distinct types: (1) 2002 actual annual emission inventories, (2) 2002 typical annual emission inventories; (3) 2009 base case emission inventories; and (4) 2009 control strategy emission inventories. In all cases, the Study Team expects to receive the emissions inventory data for the ASIP states from the emissions inventory contractor, add non-ASIP states and Canadian and Mexican data acquired from alternate EPA and/or RPO sources, and produce the CMAQ ready

emissions files.

5.2.9.1 2002 Annual Inventories

2002 36/12 km actual and typical annual inventories will be developed under ASIP for CMAQ modeling. These inventories will be identical to the 2002 36/12 km actual and typicalinventories developed under VISTAS, only the 2002 12 km ASIP inventories used everyday onroad mobile source emissions modeling.

5.2.9.2 2009 Annual Future Year Inventories

2009 36/12 km annual emission inventories will be develop for an On-the-Books (OTB)base case emissions scenario and, as needed, control strategy emission scenarios. The 2009 Onthe-Books base case will include growth and controls that have been promulgated, such as the following regional rules:

• NOx SIP Call

• Tier 2/Low Sulfur

• Heavy Duty Diesel

• Non-Road Engine

• Clean Air Interstate Rule (CAIR)

Not included in the 2009 OTB base case emissions scenario are Regional Haze controls including Best Available Retrofit Technology (BART) controls and any additional controls

needed to attain the 8-hour ozone and PM 2.5 standards. Control strategies to be modeled will be determined during the course of the study.

5.2.10 Products of the Emissions Inventory Development ProcessJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-13 In addition to the CMAQ-ready input files generated for each hour of the days modeled in the ASIP annual run, a number of quality assurance (QA) files may be prepared and used to check for gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distribution of the emission provides insight into the quality and accuracy of the emissions inputs.

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

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

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

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

C Spot check vertical allocation of point sources using PAVE.

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

process.

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

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

The quantitative QA analyses often reveal significant deficiencies in the input data or the model setup. It may become necessary to tailor these procedures to track down the source of each major problem. As such, we can only outline the basic quantitative QA steps that we will perform in an attempt to reveal the underlying problems with the inventories or processing. Following are some of the reports that may be generated to review the processed emissions:

C State and county totals from inventory for each source category

C State and county totals after spatial allocation for each source category

C State and county totals by day after temporal allocation for each source category for representative daysJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-14 C State and county totals by model species after chemical speciation for each source
category

C State and county model-ready totals (after spatial allocation, temporal allocation, and chemical speciation) for each source category and for all source categories combined

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

sources

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

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

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

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

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

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

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

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

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

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

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

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

sources

5.3 Model Configuration and Modeling Approach

5.3.1 CMAQ Science Configuration

This section described the model configuration and science options to be used in the ASIP modeling effort. The recommendations are based on testing and model evaluations of several models or model configurations carried out in the VISTAS study, as well as related studies including WRAP, CENRAP, BRAVO, CAIR and other studies. Table 5-3 summarizes the proposed configuration for CMAQ. The latest version of CMAQ is currently Version 4.5 that was released October 2005. CMAQ Version 4.5 includes several enhancements and corrections over precious versions of the model, including corrections to the mass conservation algorithms and active treatment of Sea Salt. VISTAS has updated CMAQ Version 4.5 with theJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-15 SOAmods secondary organic aerosol (SOA) module enhancements (see Section 1). ASIP has adopted CMAQ Version 4.5 with SOAmods as its primary model for demonstrating attainment

of the 8-hour ozone and PM 2.5 standards.

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

The PPM advection solver would be used along with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion. The minimum eddy diffusion constant (Kz_min) will be based on the new CMAQ Version 4.5 PURB option that depends on the percent of urban land use in the grid cell with Kz_min ranging from 0.1 m 2 /s to

2.0 m 2 /s when the grid cell is 100% urban.

The MCIP3.0 will be used to process the MM5 data using the “pass through” option and outputting parameters to treat Sea Salt dry deposition and the PURB Kz_min option.

The AERO4/ISORROPIA aerosol chemistry scheme will be used for inorganic aerosol thermodynamics. During the VISTAS testing of the CMAQ Version 4.3 and Version 4.4 models

it was noted that the AERO3/ISORROPIA aerosol modules failed to conserve sulfur and nitrogen mass, an examination of the AERO4/ISORROPIA module revealed that this is also likely true for the new version in CMAW Version 4.5. A mass conservation patch developed by the Georgia Institute of Technology (GIT) was added to the model that renormalized the total sulfur, reactive nitrogen and reduced nitrogen species after the call to the aerosol modules to conserve sulfur and nitrogen mass in the model. Sensitivity simulations with and without the mass conservation patch showed it has a very small effect and does not affect model performance, however mass conservation is a fundamental characteristic of the real-world atmosphere so ASIP and VISTAS have retained the mass conservation patch for the CMAQ aerosol modules in the modeling. Note that the GIT mass conservation patch is different that the corrections to the CMAQ mass conservation in CMAQ Version 4.5, the GIT mass conservation patch corrects for sulfur and nitrogen mass loss or gained within the CMAQ aerosol modules, whereas the CMAQ Version 4.5 mass conservation update corrects mass conservation problems in earlier versions of CMAQ in the advection algorithms. The SORGAM scheme, which includes a reversible thermal equilibrium, will be used for secondary organic aerosols. The CB4 gas-phase mechanism will be used in the ASIP and VISTAS modeling.

Table 5-3. ASIP Model Configuration for the CMAQ Chemical Transport Model.

Model Option CMAQ

Model Version Version 4.5 (October 2005)

Horizontal Resolution 36/12 km No. Vertical Layers NZ = 19
Horizontal Advection PPM
Vertical Advection PPM
Horizontal Diffusion Spatially Varying Vertical Diffusion (Kz) K V (Eddy Diffusion)
Minimum Kz PURB 0.1 to 2.0 m 2 /s MM5 Configuration Pleim-Xiu/ACMJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-16

Model Option CMAQ MM5 Processing MCIP3.0 Pass Through Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver EBI/Hertel Secondary Organic Aerosol SORGAM w/ SOAmods Aqueous-Phase Chemistry RADM Aerosol Chemistry AE4/ISORROPIA Dry Deposition Pleim-Xiu
Plume-in-Grid Off
Initial Concentrations CMAQ Default w/ ~15 day spin-up Boundary Conditions 3-Hourly 2002 GEOS-CHEM

Emissions 2002 VISTAS States
2002 RPO Inventories

5.3.2 Spin-Up Initialization

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

5.3.3 Boundary Conditions

Boundary Condition (BC) concentrations along the lateral edges of the 36 km Inter-RPO modeling domain were based on a 2002 simulation of the GEOS-CHEM global chemistry model (Jacob, Park and Logan, 2005). The 2002 GEOS-CHEM output were processed to the CMAQ 36 km domain boundary cells and vertical layer structure (Byun, 2004). The resultant BCs were 3-hourly day-specific based on the 2002 GEOS-CHEM simulation.

5.3.4 Photolysis Rates

Several chemical reactions in the atmosphere are initiated by the photodissociation of various trace gases. To accurately represent the complex chemical transformations in the atmosphere, accurate estimates of these photodissociation rates must be made. The Models-3 CMAQ system includes the JPROC processor, which calculates a table of clear-sky photolysis rates (or J-values) for a specific date. JPROC uses default values for total aerosol loading and provides the option to use default column O3 data or to use TOMS data for total column O3.

JPROC produces a "look-up" table provides the photolysis rates as a function of latitude, altitude, and time (in terms of the number of hours of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six latitudinal bands (10o, 20o, 30o, 40o, 50o, and 60o N), seven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), and hourly values up to " 8 hours of deviation from local noon. During model calculations, photolysis rates for each model grid cell are estimated by first interpolating the clear-skyJanuary 2006

C:\Temp\ASIP\Sec5.doc 5-17 photolysis rates from the look-up table using the grid cell latitude, altitude, and hour angle, followed by applying a cloud correction factor.

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

ranges.

During the VISTAS modeling errors were found in the TOMS data for a few short periods that affected the CMAQ photolysis rates input. The days with the bad or missing TOMS ozone column data were identified (June 10, August 2-12; and November 18-19) and the following approach was used to fill the bad data:

• June 9 data were used for June 10;

• August 1 data were used for August 2-7;

• August 13 data were used for August 8-12; and

• November 17 data were used for November 18-19.January 2006

C:\Temp\ASIP\Sec6.doc 6-1

6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assurance procedures that will be used in the ASIP modeling. More details are provided in the ASIP Quality Assurance Project Plan (QAPP; Morris and Stella, 2005).

6.1 Quality Assurance Objectives

In December 2002, the USEPA publish extensive guidance 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 EPA guidance suggests that a QAPP should include the following elements:

• a systematic planning process including identification 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 parameterization that is detailed enough so others can fully understand the model output;

• input data and parameters that are accurate and appropriate for the problem;

• output data that can be used to help inform decision making;

• documentation of any changes from the original quality assurance plan;

Moreover, the EPA guidance specifies that different levels of QAPP may be required depending on the intended application of the model, with a modeling study designed for regulatory purposes requiring the highest level of quality assurance.

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

The modeling team has already developed QA documents and procedures in the VISTAS effort (Morris, Tonnesen and Tesche, 2003) and ASIP modeling (Morris and Stella, 2005).

6.2 Emissions Model Inputs and Outputs

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical step in performing air quality modeling studies. Because emissions processing is tedious, time consuming and involves complex manipulation of many different types of large data sets, errorsJanuary 2006

C:\Temp\ASIP\Sec6.doc 6-2 are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected.

As part of the VISTAS and ASIP QA effort, an “Emissions Gatekeeper” function was implemented to QA the emission inventories. The role of this Gatekeeper is to perform quality assurance activities on the following emission inventory (EI) data:

(1) EI data obtained from the VISTAS and ASIP emissions inventory contractors; and (2) The emission inventory to be used for modeling outside of the States in the Southeastern US region.

Specifically, the Emissions Gatekeeper will review the content and format of the provided emission inventories ensuring an appropriate appraisal of the emissions data and estimates for the Southeastern US States. Other tasks will include any additional translation from mass emissions files into the emissions modeling input file structure necessary for modeling. The Study Team will supplement these activities with QA checks on the intermediate and model output files using internal and public domain visualization and diagnostic packages.

For ASIP, we propose to continue with multistep emissions the QA/QC approach applied in the VISTAS modeling. This includes the initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the air quality modeler of the processed model ready emission files. This multistep process with 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.

6.2.1 Emissions Modeling QA/QC

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

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

SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of processed emissions as daily totals according to species, source category and county and state boundaries. These summaries will then be compared with summary data prepared for the pre-processed emissions, e.g., state and county totals for emissions from the augmented emissions data.January 2006

C:\Temp\ASIP\Sec6.doc 6-3 6.2.2 QA of the Model-Ready Emissions Impacts

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

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

Vertical Profile : For point sources the emissions total for each layer will be summed and plotted to show the vertical distribution of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The objective of this step is to identify possible errors in vertical

distribution of emissions.

Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared that display the diurnal variation in total hourly emissions. The objective of this step is to identify errors in temporal profiles.

Long Term Temporal Summary : The total domain emissions for each day will be accumulated and displayed as time series plots that show the daily total emissions across the domain as a function of time. The objective of this step is to identify particular days for which emissions appear to be inconsistent with other days for no reason (e.g., not a weekend) and compare against the general trend.

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

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

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

6.3 Meteorological Model Outputs

As part of the VISTAS effort, a “Meteorological Gatekeeper” function was implemented. As ASIP is using the exactly same 2002 36/12 km meteorological fields as VISTAS, the VISTAS meteorological QA/QC procedures are also applicable to ASIP. The task of the VISTAS Gatekeeper was to provide an independent review and quality assurance of the meteorological modeling and related data sets developed by the VISTAS meteorological modeling contractor (BAMS) and used subsequently by the emissions and air quality modeling teams. This Gatekeeper QA review serves two specific purposes: (a) to ensure that any potential problems with the data sets (should they exist) are identified and corrected in a timely manner,January 2006

C:\Temp\ASIP\Sec6.doc 6-4 and (b) to provide the study team with information to support ongoing CMAQ model performance testing and sensitivity analyses. In the case of meteorology, the Gatekeeper’s independent QA analysis of the MM5 meteorological data sets serves to provide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model performance and sensitivity analyses.

The Meteorological Gatekeeper also has personal responsibility for the quality and chain of custody of the meteorological data sets. In performing the Gatekeeper quality assurance activity, one of the first steps is to conduct an independent operational evaluation on the MM5 model results at 36 km and 12 km grid scale. This evaluation covers surface and aloft wind direction, temperature, mixing ratio, precipitation, and planetary boundary layer (PBL) depths on a continental scale (36 km) and subregional scale (12 km) basis. The specific techniques to be used are described in the MM5 model performance protocol prepared for EPA for annual modeling (McNally and Tesche, 2002). The Gatekeeper will also perform supplemental, ad hoc analysis of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to exercise the Meteorological Chemistry Interface Processor (MCIP) Version 2.2 is to read the MM5 outputs from BAMS and produce binary input files for the CMAQ Chemical Transport Model (CCTM) to provide the complete set of parameters necessary in the emissions processing and air quality modeling.

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

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

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

 We will select several days of the MM5 output and reprocess the MM5 files with MCIP v2.2 using the predetermined MCIP options. We will then compare the MCIP files with those provided by BAMS to verify that we obtain identical results from the MCIP processing.

 We will create horizontal and vertical plots of temperature, pressure, precipitation, modeled flow patterns, PBL heights, etc. to assess whether the MCIP output fields are reasonable;

 The VISTAS 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the WRAP 2002 MM5 simulation as an independent QA and evaluation of the database.January 2006

C:\Temp\ASIP\Sec6.doc 6-5  We will make the plots available on the VISTAS website for viewing and download.

 We will re-process the MM5 output using MCIP3.0 so that ASIP and VISTAS can use the latest (Version 4.5) version of the CMAQ model that includes active treatment

of Sea Salt emissions.

6.4 Air Quality Model Inputs and Outputs

Key aspects of QA for the CMAQ input and output data include the following:

• Verification that correct configuration and science options are used in compiling and running each model of the in the CMAQ modeling system, where these include the MCIP, JPROC, ICON, BCON and the CCTM.

• Verification that correct input data sets are used when running each model.

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

• Processing of ambient monitoring data for use in the model performance evaluation.

• Evaluation of the CCTM results against concurrent observations.

• Backup and archiving of critical model input data.

The most critical element in the QA plan for CMAQ simulations is the QA/QC of the meteorological and emissions input files. The major QA issue specifically associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files were used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the name of key science options or inputs are included in the name of CMAQ executable program, in the name of the CMAQ output files, and in the name of the directory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files. For example, if a model simulation is performed using the CB4 mechanism, all compile and run scripts contain the variable definition “$MECH = CB4”, and this variable is hard coded into the script for the executable name, the output file name, and the output directory name. This procedure produces long file/directory names but it effectively prevents mistakes or makes mistakes readily apparent if they do occur.

A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the original runs scripts and directory structure that were used in performing a model simulation. For example, if we perform simulation with the SAPRC mechanism, instead of editing the original scripts to specify “$MECH = SAPRC” we will create a parallel directory structure with a new set of scripts to perform the SAPRC simulations. This provides a permanent archive of the scripts that were used in performing model simulations. In addition, output from the model simulation will be directed to a log file that provides a record of input file names, warning messages etc that

will be archived.January 2006

C:\Temp\ASIP\Sec6.doc 6-6 We will also perform a post-processing QA of the CMAQ output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case minus the base case. Often, errors in the emissions inputs can be discovered by viewing the animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This provides a summary that can be useful for more quickly comparing various

model simulations.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-1 7.0 MODEL PERFORMANCE EVALUATION

7.1 OVERVIEW

A critical component of every air quality modeling study is the model performance evaluation where the modeled estimates for the current year base case are compared against observed values to access the model’s accuracy and provide an indication of its reliability. As noted previously, the ASIP modeling database, models and modeling approach are intricately linked to the VISTAS modeling. Chapter 11 presents a summary of the current Scope of Work (SOW) for the ASIP Emissions and Air Quality Modeling activities. This ASIP SOW delineates the tasks, approach and schedule for carrying out the various technical activities described in greater detail in this Modeling Protocol. Although the ASIP SOW does not explicitly include the model performance evaluation (MPE) component of the modeling that is being carried out under VISTAS, the MPE is a critically important component of a modeling study so the approaches to be used in the model evaluation of the ASIP/VISTAS databases are described in this section of the ASIP Modeling Protocol.

Consistent with the spirit of a Modeling Protocol for regulatory decision-making, this section lays out the ‘roadmap’ for achieving an adequately tested modeling system for regulatory usage. But, obviously, this does not mean that every analysis identified in this chapter will be carried out or is indeed even possible given the ASIP and VISTAS schedule and resources, the existing aerometric data bases, and present technology constraints. The roadmap guides the way to the desired destination – in this case, an evaluated, operational PM/regional haze/ozone modeling system – but does not commit the driver to exploring every side street and back country road along the way. Indeed, one expectation of the ASIP and VISTAS is a close working relationship with the modeling team to ensure that the available resources and schedule are applied most efficiently in reaching the aforementioned goal.

This chapter describes a range of model testing methodologies potentially available to the Emissions and Air Quality Modeling study team in its efforts to adequately evaluate the performance of the CMAQ air quality modeling system for the 2002 annual period. The final 2002 actual base case modeling will likely be conducted in early 2006. Preliminary 2002 base case simulations and abbreviated model performance evaluation has uncovered several performance issues that have been attempted to be addressed through improvements in model inputs, formulation or both. In this Section we set forth a broad range of methods and techniques that may be brought to bear in examining CMAQ model performance. We identify the core operational evaluation procedures, recommended in EPA (2001) PM 2.5 and regional haze modeling guidance that has been and will continue to be performed as part of the VISTAS modeling efforts. We also describe a broad range of additional performance testing methods that may be worth considering, if deemed needed and

resources and time are available.

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

 The VISTAS/ASIP SOWs places clear limits on the resources available to perform model evaluation analyses. Accordingly, some evaluation steps, while desirable, simply may not be possible given current funding levels;January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-2  The VISTAS/ASIP SOW places stringent schedule demands on the model evaluation. A number of the model performance evaluation methods introduced in this chapter (e.g. Weight of Evidence analyses, diagnostic testing with individual measurement networks, PM indicator species and ratios analyses could very likely require more time to carry out given their quasi-research nature. Since VISTAS/ASIP is not a model research and development effort, but rather an operational evaluation of existing modeling systems for regulatory decision-making, some interesting, but time consuming analyses simply may not be possible given the present schedule; and

 To conform to the EPA PM guidance documents requirements for PM model testing, it may not be necessary to conduct many of the diagnostic and Weight of Evidence tests identified in this protocol. Indeed, an adequate evaluation of the VISTAS/ASIP modeling system may be possible through straightforward application of the core operational performance evaluation procedures identified in EPA’s 2001 draft PM 2.5 and regional haze and final 2005 8-hour ozone guidance.

At a minimum, the evaluation of the CMAQ modeling system for the annual 2002 simulation will be consistent with EPA’s draft guidance on PM model testing and final 8-hour ozone modeling guidance. EPA’s guidance essentially calls for an operational evaluation of the model focusing on a specific set of gas phase and aerosol chemical species and a suite of statistical metrics for quantifying model response over the annual cycle. The emphasis is on assessing: (a) How accurately the model predicts observed concentrations? and, (b) How accurately does the model predict responses of predicted air quality to changes in inputs? States are encouraged to utilize the evaluation procedures set forth in the earlier 1991 guidance document (EPA, 1991) for gas phase species and the newer (2001 and 2005) guidance for PM and ozone species. Thus, in carrying out the initial operational evaluation and the subsequent final evaluation, we will implement the suggested EPA performance testing methodologies for the key gas phase and aerosol species. Since these methods are explicitly presented in EPA’s guidance documents, there is no need to repeat them here.

We conclude by again emphasizing that most important goal of the CMAQ evaluation is to determine whether the aggregate modeling system (model codes plus input data sets andobservational data for testing) offers sufficiently reliable and accurate results that public decisionmakers may have reasonable confidence in using the model to help choose between alternative emission control strategies designed to regional haze reduction scenarios. If the CMAQ model evaluation, as outlined in this chapter, provides sufficient evidence that the modeling system is operating reliably and in conformance with measurements and scientific expectations, then specific justifications explaining why the model is acceptable for developing 8-hour ozone, PM 2.5 and regional haze strategies. Conversely, should the evaluation determine that the modeling system suffers from important flaws or errors that undermine its reliability or use, these findings will also be documented, together with recommendations regarding the use of alternate methods, steps to improve the model and/or data base, or other approaches that can be used in the analysis.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-3 7.2 Context for the Model Performance Evaluation

We begin the discussion of the CMAQ 2002 modeling evaluation methodology by reviewing how the model output is used to project future year 8-hour ozone and PM 2.5 levels.

When designing a model performance evaluation, it is important to understand how the modeling results will ultimately be used. EPA has published two versions of draft guidance for fine particulate and regional haze modeling (EPA, 2000; 2001), utilizing a Fine Particulate Guidance Workgroup to provide technical input in the development of both documents 1 . More recently, EPA has provided an informal update on the PM/regional haze modeling guidance (Timin, 2002) and conducted a PM model evaluation workshop (see, for example, Timin, 2004; Boylan, 2004) shedding additional light on what the final PM 2.5 and regional haze guidance document might contain. After issuing several draft guidance documents for 8-hour ozone modeling (EPA, 1999, 2005a, EPA issued the final 8-hour ozone modeling guidance in 2005 (EPA, 2005b). These modeling guidance documents, along with the 1-hour ozone guidance (EPA, 1991) provide a framework for the VISTAS/ASIP model performance evaluation approach.

A key concept in EPA’s guidance for addressing 8-hour ozone and PM 2.5 issues and regional haze is that the modeling results should be used in a relative sense to scale or roll back the observed 8-hour ozone and individual particulate matter (PM) species concentrations. The modeled derived ratios of future-year to current-year scaling factors used to project future-year 8-hour ozone and PM species components are called relative reduction factors (RRFs). As 8-hour ozone concentrations are made up as an average of 1-hour ozone concentrations and there is a wealth of model performance evaluation for 1-hour ozone, then the model performance for 1-hour ozone is important also. Since the model is used to project future year PM 2.5 species components rather than total PM 2.5 mass, then the model performance for each of the components that make up PM 2.5 is actually more important

than for total PM 2.5 mass which the standard was written for. These components are:

• Sulfate (SO4);

• Nitrate (NO3);

• Ammonium (NH4);

• Organic Carbon (OC);

• Elemental Carbon (EC); and

• Other Inorganic fine Particulate (IP or Soil).

The VISTAS/ASIP model testing will concentrate on an operational evaluation of those model predictions that are most necessary for estimating PM 2.5 (i.e., NH 4, SO 4, NO 3, OC, EC and IP) and 8- hour ozone (i.e., 1-hour and 8-hour ozone) concentrations. Where feasible and supported by sufficient measurement data, we will also evaluate the modeling system for its ability to accurately estimate coarse mass (CM) PM and other gas-phase precursor, product and indicator species. The correct simulation of gas-phase oxidant species is needed for PM since correct, unbiased simulation of gas-phase photochemistry is a necessary element of reliable secondary PM predictions. This evaluation will be carried out across the full Southeastern US domain for the entire year and also on

1 Members of the VISTAS modeling team participated on this work group over the two-year span of

its activities.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-4 subdomains (e.g., ozone and PM 2.5 nonattainment areas) and month-by-month to daily basis to help build confidence that the modeling system is operating correctly. With this context in mind, we next turn to the philosophy of the model evaluation process.

7.3 Multi-Layered Model Testing Process

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) and final guidance for 8-hour ozone modeling (EPA, 2005b) affirms the recommendations of numerous modeling scientists over the past decade (see, for example, Dennis et al., 1990; Tesche et al., 1990, 1994; Seigneur et al., 1998, 2000; Russell and Dennis, 2000; Arnold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a comprehensive, multi-layered approach to model performance testing should be performed, consisting of the four components: operational, diagnostic, mechanistic (or scientific) and probabilistic. As applied to PM 2.5 and 8-hour ozone modeling, this multi-layered framework may be viewed conceptually as follows:

> Operational Evaluation : Tests the ability of the model to estimate PM concentrations, the components at PM 10 and PM 2.5 (i.e., sulfate, nitrate, ammonium, organic carbon,

elemental carbon and other PM 2.5 ) and 1-hour and 8-hour ozone concentrations. This evaluation examines whether the measurements are properly represented by the model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”;

> Diagnostic Evaluation: For fine PM, this step tests the ability of the model to predict PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 ) and associated oxidants (e.g., ozone and nitric acid); PM size distribution; temporal variation; spatial variation; mass fluxes; and components of light extinction (i.e., scattering and absorption). For 8-hour ozone the diagnostic evaluation tests the models ability to predict the temporal and spatial variations in ozone, ozone precursor species (e.g., VOC, NOx and CO) and key indicator species that provide indication of key photochemical regimes (e.g., NOx/ozone, HNO3/H2O2, NOy, etc.);

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

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

Within the constraints of the VISTAS/ASIP schedule and resources, the VISTAS/ASIP model evaluation effort will attempt to include elements of each of these components. The operational evaluation will obviously receive the greatest attention since this is the primarily thrust of EPA’s

2001 PM 2.5 and 2005 8-hour ozone guidance. However, we will consider, where feasible and appropriate, diagnostic and mechanistic tests (e.g., use of probing tools, indicator species and ratios, aloft model evaluations, urban vs. rural performance analyses), traditional sensitivity simulations to explore uncertainty, and comparison of the VISTAS/ASIP CMAQ performance with those from other groups (e.g., MANE-VU, MRPO, CENRAP) some of which the use alternative science platforms (e.g., CAMx). The scope of these additional diagnostic and mechanistic tests will beJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-5 shaped by the resources available and the timing of when such analyses are commissioned relative to

the VISTAS/ASIP schedule and resources.

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

7.4 Development of Consistent Evaluation Data Sets

7.4.1 Surface Measurements

The ground-level model evaluation database will be developed using several routine and research-grade databases. The first is the routine gas-phase concentration measurements for ozone, NO, NO 2 and CO archived in EPA’s Aerometric Information Retrieval System (AIRS/AQS) database. Other sources of information come from the various PM monitoring networks in the U.S. These include the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends Network (CASTNet), (c) Southeastern Aerosol Research and Characterization (SEARCH), (d) EPA PM 2.5 and PM 10 Mass Networks (EPA-FRM), (e) EPA Speciation Trends Network (STN); (f) National Acid Deposition Network (NADP) and (g) EPA Supersites (EPA-SPEC) networks. Typically, these networks provide ozone, other gas phase precursors and product species, PM mass and species, and visibility measurements. Noteworthy for the VISTAS/ASIP evaluation is the 24-hour average and continuous speciated PM 2.5 and continuous gas-phase concentration measurements available from the SEARCH network for 2002 modeling period. For 2003, additional continuous PM speciated data will be available from the FOCUS network and in the Midwest U.S. ammonia measurements are available. However, since these data were not collected during the 2002 modeling year, their use in the evaluation will be more qualitative and diagnostic.

As an example, the IMPROVE network gives daily (24-hour) average mass concentrations every 3 days for SO 4 , NO 3 , organic carbon (OC), elemental carbon (EC), soil (IP) and total, PM 2.5 and PM 10 mass from which CM can be derived. Some IMPROVE sites also have gas-phase species measurements (e.g., ozone and Great Smokey and Shenandoah National Parks). These data are available at approximately 38 sites in the VISTAS/ASIP 12 km domain. In addition, hourly values of light extinction and deciview are available at several of these sites. The SEARCH network provides 24-hour as well as continuous (hourly) speciated measurements of PM 2.5 components and other specifics from 8 stations, depending on the time period (Hansen et al., 2003). Of key importance for the ASIP modeling is the ozone and PM performance of the CMAQ model at the, respectively, AIRS/AQS and FRM, and nearby STN, sites that are measuring violations of the 8-hour

ozone and PM 2.5 NAAQS. We will use data from these and the other observational databases listed in Table 7-1, for CMAQ model performance testing.

Ozone measurements are highly accurate and precise, but can be highly influenced by local conditions, such as local NOx sources that can suppress the observed ozone concentrations and make them less representative of the surrounding community. An important consideration in evaluating models for PM is that different PM monitoring networks may use different measurement approaches that “measure” different amounts of the same species that are also different from the modeledJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-6 species. For example, the IMPROVE network only speciates PM 2.5 so any sulfate or nitrate in the coarse mode (PM 2.5-10 ) is included in the Coarse Mass (CM) species “measurement” (where CM =

PM 10 – PM 2.5 mass). Because the different monitoring networks may use different measurement technology that results in different “measured” values for the same PM component, the CMAQ model will be evaluated separately for each monitoring network. Ozone is made up of a single species so the mapping of the measured to modeled species is easily accomplished. However, there is sometimes ambiguity in the mapping of modeled PM species to measurements. For example, PM measurements only measure the carbon component of OC, whereas in the model the entire Organic Mass Carbon (OMC) is simulated that includes carbon as well as other elements attached to the carbon (e.g., hydrogen and oxygen). Thus, a factor is assumed to adjust the measured OC to OMC. In the past a 1.4 OMC/OC factor has been used based on urban scale measurements of fresh OC emissions and this is the factor used in the current IMPROVE reconstructed mass equation (Malm, 2000). However, this OMC/OC factor is likely too low, especially for aged OC compounds, and OMC/OC ratios of 1.4 to 2.2 have been observed (Turpin and Lim, 2001) with the current average OMC/OC ratio value of 1.8 being recommended.

The VISTAS Phase I air quality data assessment report (ENVIRON, 2003c) provides more details on the ambient monitoring data available for the VISTAS/ASIP model performance evaluation modeling.

7.4.2 Aloft Measurements

In recent years, the use of instrument aircraft in support of regulatory monitoring and research programs has become much more commonplace. Indeed, in the upper Midwest, the Lake Michigan Air Directors Consortium (LADCo) has been centrally involved in aircraft programs to support model development and applications studies for seventeen (17) years, beginning with pioneering flights in 1987. Supplementing the long-term sampling performed by LADCo in the Midwest, there have been other occasional intensive airborne sampling campaigns throughout the eastern U.S. (e.g., the 1999 SOS field program which provided aloft data for our evaluation of CMAQ for the July ’99 episode), that have produced very useful information for air quality model performance testing. Fortunately, during CY-2002, there were at least two mature airborne field programs underway in the eastern U.S. One was centered over the Midwest, the other on the mid-Atlantic coast. A brief characterization of these potentially valuable CMAQ model evaluation data sets is given here. Note that the advanced modeling evaluation using non-routine data sets like aircraft data are not currently planned under the VISTAS/ASIP work efforts. However, their inclusion in the evaluation would provide valuable information on the model accuracy and reliability.

During 2002, the Wisconsin Department of Natural Resources (WDNR) and the Midwest RPO (MRPO) (who funded the Jacko aircraft) collaborated on the support of airborne sampling using two aircraft that, along with ground-based measurements, provided a 3-dimensional representation of air pollution concentrations across the upper Midwest with some flight paths extending south to include the Mammoth Cave, KY and Dolly Sods, WV Class I areas in the VISTAS domain. The goal of the WDNR/MRPO flights was to collect aloft air quality and meteorological data to support model evaluation and data analyses. The aircraft flights were aimed at: (1) characterizing high fine particle and ozone episodes, (2) characterizing air quality over theJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-7 Class I areas in the upper Midwest (Isle Royale National Park and Seney National Wildlife Refuge in northern Michigan) on both clean and hazy days, and (3) characterizing urban areas in the Midwest.

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

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

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

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

7.5 Model Evaluation Tools

This section introduces the various statistical measures, graphical tools, and related analytical procedures that have proven useful over the years in evaluating grid-based chemical transport models. Many of the methodologies mentioned below have been utilized in the VISTAS Phase I preliminary evaluation and have been refined during the course of the VISTAS and other (e.g., WRAP, MRG, CENRAP) studies. While we plan on calculating a rich variety of statistical performance metrics, only a very limited subset of these measures will actually be relied upon to form judgments concerning model acceptability and in the final reporting.

7.5.1 Statistical Performance Metrics

EPA’s 1991 and 2005 ozone and draft 2001 PM and regional haze guidance documents suggests a suite of metrics for use in evaluating model performance. EPA’s 1-hour ozone guidance lists three statistical measures with performance goals that a model should achieve before being used

to demonstrate ozone attainment in a SIP:

• Mean Normalized Bias (MNB) < + 15%

• Mean Normalized Gross Error (MNGE) < 35%

• Unpaired Peak Accuracy (UPA) < + 20%January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-8 The 1-hour ozone MNB and MNGE performance measures are typically calculated using an observed hourly ozone cutoff threshold of 60 ppb (40 ppb is also used sometimes). These performance measures have also been used for 8-hour ozone model performance evaluation. The newer 8-hour ozone (EPA, 2005b) and draft PM2.5/regional haze (EPA, 2001) modeling guidance focuses more on a holistic model evaluation approach that assesses not only how well the model matches the observation but also whether the model is correctly simulating the processes that produces the elevated ozone and PM concentrations, including a comparison against a conceptual model. In fact some performance measures and goals in earlier versions of the 8-hour ozone guidance (e.g., most daily maximum 8-hour ozone concentrations near the monitor matching to

within + 20%; EPA, 1999) were not included in the final version (EPA, 2005b).

Table 7-3 lists a standard set of statistical performance measures that will be used to evaluate fine particulate and ozone models. These performance measures will be calculated using several model performance evaluation software tools for ozone and PM species concentrations, including:

UCR Analysis Tool operates on a Linux platform, performs species and temporal matching of the predictions and observations and generates statistical performance measures, scatter plots and time series plots for user specified subdomains and across all sites and all days, for each site and all days and for each day across all sites.

Alpine Geophysics MAPS Software also operates on a Linux platform generating statistical measures, scatter plots, time series plots and spatial comparisons of predictions and observations. MAPS also spatial averaged performance summaries (e.g., time series of bias and error) that are useful for synthesizing model performance.

PAVE by MCNC is used on a Linux platform to generate spatial maps (tile plots) of model predictions with super imposed observations as colored symbols.

ENVIRON Performance Software calculates performance statistics and exports them along with the predictions and observations to be used with macros operating standard Windows software such as Excel and SURFER to generate graphical displays of model performance that can be customized by the user where the data are also available for further analysis if

desired.

The VISTAS/ASIP ozone and PM evaluation of the 2002 36/12 km CMAQ base case simulation will strive to use each of these evaluation packages to some extent to elucidate model performance. Although procedures for assessing ozone model performance are well established since ozone SIPs using photochemical grid modeling in the attainment demonstration have been developed for over a decade (e.g., dating back to the 1994 SIPs; see Morris, 1995). Procedures forevaluating PM models, however, are much less established and research is ongoing. Morris and coworkers (2005) summarize some of the newer PM model evaluation techniques for assessing regional haze models as part of VISTAS, enhancements to these techniques with a focus on urban PM performance will be needed for the ASIP PM2.5 evaluation.

Typically, the statistical metrics are calculated at each monitoring site across the full computational domain for all simulation days. In the VISTAS/ASIP CMAQ evaluation, we will stratify the performance statistics across relevant space and time scales. As part of the operationalJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-9 evaluation, the gas-phase and aerosol statistical measures shown in Table 7-3 will be computed for the full 36 km and 12 km domains, as well as for the individual RPOs (VISTAS, WRAP, CENRAP, MRPO and MANE-VU) and on other subdomains as appropriate (e.g., 8-hour ozone and PM 2.5 nonattainment areas). Temporally, we will compute the statistical measures for the appropriate averaging times: 1-hour and 8-hour for ozone and hourly for other gas-phase precursors such as NO,

NO 2 , CO, SO 2 ; usually 24-hour for sulfate, nitrate, EC, OC, PM and other aerosol species, although some SEARCH sites have continuous PM species and the CASTNet monitoring network measures weekly PM species; and weekly for wet deposition of sulfate, nitrate and ammonium from the NADP network. Where appropriate these results will then be averaged over annual, monthly, and seasonal periods for display, further analysis, and reporting. Should it become necessary as part of model performance diagnosis, we will consider aggregating the statistics in other ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipitation vs. non-precipitation days, (d) month of the year, and (e) exceedance events, in order to help elucidate model performance problems. The amount of these supplemental time/space analyses would depend on available resources. In subregional performance testing, the focus would likely be on the nonattainment areas, Class I areas and sites where enhanced monitoring (EPA STN and FRM locations, the hourly/daily SEARCH sites) within the VISTAS 12 km domain (Hansen et al., 2003) is available.

As part of the operational evaluation, the metrics defined in Table 7-3 will be calculated for each gas phase species and each fine particulate species in the extinction equation as well as separately for SO 4 , NO 3 and ammonium (NH 4 ) on both the 36 km and 12 km domains. In any diagnostic evaluations that are performed, we will examine the model’s ability to estimate the gaseous species listed above from EPA’s guidance (EPA, 1991; 2001; 2005). However, in reality ambient gaseous species in 2002 are principally available for ozone, NO 2 , SO 2 , and CO.

7.5.2 Graphical Representations

The VISTAS/ASIP operational air quality model evaluation will utilize numerous graphical displays to facilitate quantitative and qualitative comparisons between CMAQ predictions and measurements, many of which were used in the VISTAS Phase I Final Report (ENVIRON, 2004) and summarized by Morris and co-workers (2005). Together with the statistical metrics listed in Table 7-3, the graphical procedures are intended to help: (a) identify obviously flawed model simulations, (b) guide the implementation of performance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ simulations. These graphical tools are intended to depict the model’s ability to predict the observed fine particulate and gaseous species concentrations.

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

> Spatial mean concentration time series plots; > Time series plots at monitoring locations; > Ground-level gas-phase and particulate concentration maps (i.e., tile plots); > Concentration scatterplots stratified by station, by time, and by network; > Bias and error stratified by concentration;January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-10 > Bias and error stratified by time; > Histogram plots of the statistical metrics, stratified by day, by pollutant, by subregion (e.g., 12 km vs. 36 km, by RPO), and by monitoring network; and > Quantile-Quantile (Q-Q) plots.

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

7.5.3 MAPS/Flying Data Grabber Routines

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

The principal challenge in using the aircraft data for meteorological and photochemical model evaluations relates to the ‘incommensurability’ of Lagrangian aircraft observations with Eulerian (i.e., fixed location) volume-averaged model estimates (see, for example, Hanna, 1994). Aircraft data are essentially continuous, high frequency Lagrangian samples having response times on the order of 30 seconds or less. In contrast, CMAQ model estimates represent hourly-averaged values. Thus, the aircraft data must be averaged in some manner to yield quantities that are at least qualitatively comparable to the air quality fields estimated by CMAQ in the grid volume(s) through which the aircraft passes. The objective is to develop hourly-average time series of measurements and model estimates that are as nearly comparable as possible.

The procedures proposed for processing the aloft meteorological and air quality observations and CMAQ model predictions have been described in several science reports prepared in connection with the Lake Michigan Ozone Study (Tesche and McNally, 1993a-d, 2001) and more recently the Houston-Galveston 1-hr ozone SIP modeling that utilized the TexAQS 2000 data base (Tesche and Jeffries, 2002). These methods, formalized within AG’s Flying Data Grabber (FDG ) model, were employed in Phase I for the 13-21 July 1999 episode. The methods used are substantial extensions of the techniques pioneered a decade ago by Schere and Wayland (1989) for the Regional Oxidant Model (ROM2.0) evaluation against the NEROS database and by Barchet and Dennis (1990) for the RADM/ADOM evaluation (Dennis et al., 1990).

For a typical aircraft (or helicopter flight), the Flying Data Grabber first identifies the specific time interval during which the aircraft was located in a given CMAQ model grid cell along the flight path. The observations are then integrated to produce mean, standard deviation, bias and error estimates for the variable measured within each grid cell of the flight path. This averaging process produces an observed, averaged time series for the above statistical quantities along the flight path. Note, that these time intervals are characteristically much smaller than the one-hour model averaging time. Flight path statistics, together with the mean modeled and observed horizontal winds along theJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-11 flight paths are also produced by the FDG. The maximum and minimum values during each time segment within a grid cell are also recorded.

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

7.5.4 Use of Multiple Evaluation Packages

In VISTAS Phase I model evaluation (ENVIRON, 2004) relied principally on the UCR evaluation package that was originally developed for WRAP and then enhanced by VISTAS. This package produce scatter plots by site, day or all sites and days, time series plots and statistical measures In the VISTAS/ASIP model evaluation we will attempt to augment the UCR Analysis Tool evaluation software with other software evaluation packages as discussed previously.

7.5.5 Probing Tools and Allied Methods

The VISTAS/ASIP CMAQ model evaluation will employ routine operational evaluation methods and standard statistical metrics (Table 7-4) and graphical displays to support the assessment of whether the model is shown to perform with sufficient accuracy and reliably for its intended purpose. Ideally, this operational evaluation will confirm that the modeling system is performing consistent with its scientific formulation, technical implementation, and at a level that is at least as reliable as other current state-of-science methods. Should unforeseen model performance problems arise in the initial or refined year 2002 model simulations, it may be necessary to draw into the evaluation supplemental diagnostic tools to aid in model testing. These diagnostic techniques are loosely referred to as “probing tools”. The actual need for their use, if any, can only be determined once the initial 2002 CMAQ operational evaluation is completed. Should such diagnostic methods actually be needed, their usage would require additional resources not currently allocated under VISTAS/ASIP. Below, we identify the types of probing tools that could be brought to bear under should their use become necessary.

Current ‘One-Atmosphere” models such as CMAQ and CAMx have been outfitted with a number of “probing tools” that have proven to be very useful in testing and improving model performance and in evaluating emissions control strategies. Among the probing tools available in one or both models are: (a) ozone source apportionment technology (OSAT) algorithms, (b) PM Source Apportionment Technology (PSAT) and Tagged Species Source Apportionment (TSSA) PM source apportionment techniques; (c) process analysis (PA), and (d) the direct decoupled method (DDM) for sensitivity analysis.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-12 Source Apportionment Techniques: CAMx contains a suite of “source attribution” methods for ozone and PM that use reactive tracers that operate in parallel to the host model. The Ozone Source Apportionment Technology (OSAT) tracks ozone formation from user defined source regions and categories based on how the group’s ozone precursors contributed to ozone formation. Thus, OSAT decides whether ozone formation is NOx or VOC limited in each grid cell at each time step, and bases ozone attributions on the relative amounts of the limiting precursor from different sources that are present in that grid cell at that time step. These incremental ozone attributions are integrated throughout the model run. The method is generally applicable and has been widely used to aid model diagnosis in the performance testing phase, to guide control strategy development and for ozone culpability assessments (e.g., NOx SIP Call and CAIR). The PM Source Apportionment Technology (PSAT) also uses reactive tracers to rack PM species formation back to user defined source regions and categories based on the primary precursor to the PM species (e.g., sulfate is traced back to SO2 emissions, nitrate is traced back to NOx emissions, etc.). A Tagged Species Source Apportionment (TSSA) approach has also been implemented in CMAQ and tested for sulfate and nitrate (Tonneson, 2004, personal communication). However, TSSA contained unexplained PM source apportionment that has been attributable to mass conservation errors in CMAQ that has been fixed in the latest (Version 4.5, October 2005) of CMAQ (Pleim, 2005).

Decoupled Direct Method (DDM) : Various forms of the Decoupled Direct Method (DDM)have been installed in CMAQ and CAMx, based on the original work of Dunker and coworkers (Dunker, 1981; 1984; Dunker et al., 2002) and researchers at Georgia Institute of Technology (GIT). In general, the DDM method: (a) calculates first order sensitivities dC/dP where C is a concentration output and P an input parameter 2 , (b) promotes accuracy by using consistent numerical methods and the same time steps for concentrations and sensitivities, (c) optimizes the code for efficiency, but not at expense of accuracy, and (d) calculates sensitivities with respect to parameters representing pollutant sources – emissions, BCs and ICs. Finally, the DDM provides a flexible and powerful user interface for defining various sensitivities including:

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

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

2 Recent research by Prof. Russell and coworkers at GIT has led to the extension of the CMAQ DDM method to include second order sensitivity coefficients (see, Hakami et al., 2003).January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-13 Process Analysis (PA): Photochemical air quality model simulations are usually evaluated primarily in terms of their ability to simulate observed O 3 data. There is an increasing awareness that chemical mechanisms and air quality models must also be evaluated in terms of their ability to simulate the fundamental chemical processes that control O 3 formation and the sensitivity of O 3 to emissions reductions (Arnold et al., 1998). Process analysis is a method for explaining model simulations by adding algorithms to the AQM to store the integrated rates of species changes due to individual chemical reactions and other sink and source processes (Jeffries and Tonnesen, 1994; Tonnesen, 1995). By integrating these rates over time and outputting them at hourly intervals, process analysis provides diagnostic outputs that can be used to explain a model simulation in terms of the budgets of free radicals, production and loss of odd oxygen and O 3 , and conversion of NOx to inert forms, as well as the effects of transport and other sink and source terms. Of particular importance to the VISTAS modeling, process analysis can also improve model diagnosis and performance evaluation efforts by identifying processes that are ‘out of balance’ (Tesche and Jeffries, 2002), by identifying situations for which the model formulation and/or implementation should not be expected to apply and by suggesting how ambient data can be used to evaluate model accuracy for key terms in the chemical processing of VOC and NOx (e.g., Imre et al., 1998). Process Analysis (PA) is implemented in both CMAQ and CAMx and each model supports three complementary aspects of the method: (a) the integrated process rate (IPR), (b) integrated reaction rate (IRR) and (c) chemical process analysis (CPA). Several versions of process analysis (PA) have been implemented in air quality models (AQMs) including both trajectory models (Tonnesen, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and Dennis, 2000; Arnold et al., 1998; and Wang, 1997). The fundamental approach in all versions of PA is similar: The AQM is modified to calculate the integral over time of the individual sink and source processes and each chemical reaction. These integrated sink/source process rates (IPR) and integrated reaction rates (IRR) can then be stored to a file and analyzed using a post-processor, or some processing can be performed internally in the model and a more limited set of process diagnostic information is output directly by the AQM. Chemical process analysis (CPA) is an improvement on the IRR method whereby some of the processing of IRR information is internalized within the AQM to output chemically meaningful parameters directly (e.g., budget terms for O 3 , NOx and odd oxygen).

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

Because application of all three of these probing tools--source apportionment, DDM, and Process Analysis—are computational intensive and require a fair amount of analysis time to reap the benefits of using the methods, they do not lend themselves directly to annual simulations. However, each method has potential for use in addressing key episodic periods or geographical locations in the VISTAS/ASIP domain where performance in the 2002 simulation may present a problem or where particular attention needs to be focused on emissions controls (a specific nonattainment area forJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-14 example). In such focused applications, one or more of these probing tools may indeed serve a purpose and will be considered where appropriate.

7.6 Model Evaluation Procedures

EPA draft PM modeling guidance (EPA, 2001, pg. 227) suggests that the performance evaluation focus on two aspects (similar suggestions are contained in the 8-hour ozone modeling guidance, EPA, 2005b):

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

PM 2.5 , total observed mass of PM 2.5 ? and

> How accurately does the model characterize the sensitivity of changes in component

concentrations to changes in emissions?

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

consideration.

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

Given that PM 2.5 mass is the sum of the individual components of fine particulate matter and

the PM 2.5 attainment demonstration test involves the separate projection of each PM component, the model should be evaluated separately for each of the key fine particulate matter components that make up PM 2.5 mass. Current EPA draft PM modeling guidance suggests that the model should also be evaluated for several key gas-phase species that are important for fine particulate modeling. For particulate species this includes SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated

with NO 3 , elemental carbon (EC), organic carbon (OC), IP, mass of individual constituents of IP, and coarse matter (CM). The gaseous species include ozone (O 3 ), HNO 3 , NO 2 , PAN, NH 3 , NOy, SO 2 , CO, and H 2 O 2

.

For ozone modeling, EPA guidance (EPA, 2001; 2005b) recommends evaluating the model for ozone as well as ozone precursor (e.g., VOC, NOx and CO) as well as key indicator species (e.g., NOy, NOz, HNO3, H2O2, etc.). As noted previously, the 1-hour ozone modeling guidance includes model performance goals, whereas more recent 8-hour ozone (EPA, 2005b) and PM 2.5 (EPA, 2001) guidance stresses more confirmatory and corroborative techniques and processed based evaluation to assure that the model is getting the right answer for the right reason, in addition to demonstrating that the model exhibits skill in predicting the observed 8-hour ozone and PM 2.5 concentrations.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-15 At some of the IMPROVE sites there are also direct measurements of hourly extinction using transmissometer or nephelometer instruments that will provide another measure of performance for fine particulate Thus, it would be scientifically interesting to evaluate the model estimated extinction with the hourly measured values at these sites.

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

include:

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

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

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

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

establish correlations.

 For ozone, comparisons against observed hourly and 8-hour ozone concentrations in

nonattainment areas.

The types of analysis that could be performed as part of the VISTAS/ASIP CMAQ diagnostic model

evaluations that could be considered are:

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

> Evaluate how well the model simulates various physicochemical processes by: (a) examining observed and modeled correlations between various species pairs, and (b) comparing model-predicted ratios of various species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

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

> Create scatter plots of modeled vs. observed data and hourly and 24-hour averages by site and subregion to help identify any site-specific biases.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-16 > Create time series plots of predicted and observed concentrations stratified by key variables as appropriate.

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

> Compare observed versus modeled mass fractions of PM constituents at various sites that are characterized by their proximity or remoteness relative to sources, or by specific meteorological conditions (e.g., frontal passage, stagnation, precipitation); these will enable identification of trends in the model of over- or under-prediction of specific PM

constituents under these conditions.

> Calculate the measured and predicted relative abundance of key PM components and

compare with EPA guideline recommendations and emergent alternative science recommendations (e.g., removing the soil component from the calculations, use of alternative relative importance equations [i.e., Boylan, 2004]).

> Evaluate for ozone precursors and key indicator species and ratios (e.g., HNO3/H2O2) as well as product species.

The suite of statistical metrics and graphical tools identified in the previous section for the core operational evaluation efforts that would likely also be used to diagnose performance problems with the CMAQ simulations should they exist and to highlight differences between model runs. Experience in ozone/PM modeling is the best basis upon which to identify obviously flawed simulation results. Efforts to improve the CMAQ model’s base case performance will be made, where necessary, warranted (i.e., to reduce the discrepancies between model estimates and observations), and consistent with the project resources and schedule; however, these model performance improvements efforts must be based on sound scientific principles. “Curve-fitting”

exercises will be avoided.

7.6.2 Assessment of Aloft Gas-Phase and Aerosol Species

A substantial number of aircraft flights were conducted during 2002 over the Midwest and Eastern U.S. Should VISTAS elect to fund the optional aloft model performance evaluation, we willendeavor to obtain this information and use it in a scientific performance evaluation of aloft gasphase and aerosol species (see section 7.3.2).

7.7 Performance Goals and Benchmarks

Establishment of performance goals and benchmarks for regulatory modeling is a necessary but difficult activity. Here, performance goals refer to targets that we believe a good performing model should achieve, where as performance benchmarks are based on historical model performance measures for the best performing simulations. Performance goals are necessary in order to provide consistency in model applications and expectations across the country and to provide standardization in how much weight may be accorded modeling study results in the decision-making process. It is a problematic activity, though, because many areas present unique challenges (e.g., Houston, SanJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-17 Joaquin Valley, Los Angeles) and no one set of performance goals is likely to fit all needs. Equally concerning is the very real danger that modeling studies will be truncated when the ‘statistics look right’ before full assessment of the model’s reliability is made. This has the potential from breeding built-in compensating errors (Reynolds et al., 1996) as modelers strive to get good statistics as opposed to searching for the explanations for poor performance and then rectifying them. A NARSTO review of more than two-dozen urban-scale ozone SIP applications found this tendency to be all too prevalent in the regulatory modeling of the 1990s. (Roth et al, 1997). In fact more recent

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

state:

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

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

EPA’s 1999 draft 8-hour ozone modeling guidance adopted the 1-hour performance goals and added additional performance goals related to 8-hour ozone model performance. For example, the draft 8-hour ozone guidance lists a performance goal to match the observed daily maximum 8-hour

ozone concentrations near the monitor to within + 20%.

However, when these evaluation metrics and goals were later applied to evaluate PM species, difficulties arose because performance statistics that divide by low concentration observations become much less useful. Indeed, some observed PM species approach zero (e.g., NO 3 ) which results in the MNB and MNGE performance metrics approaching infinity. In time, this has led to the introduction of the fractional and normalized mean bias and error metrics (see Table 7-4) in addition to the mean normalized bias and gross error (MNB and MNGE) metrics and related performance expectations based on these alternative measures.

While the 1-hour ozone metrics and goals still have value in interpreting ozone and some gasphase species performance, it has been necessary to develop new performance metrics and goals for fine particulates. EPA’s PM guidance document (EPA, 2001) guidance document identifiesJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-18 particulate matter components of interest to include: SO 4 and/or S, NH 4 , NO 3 , mass associated with

SO 4 , mass associated with NO 3 , EC, OC, IP, and mass of individual constituents of inorganic primary particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO 3 , NO 2 , PAN,

NH 3 , NO y , SO 2 , CO, and H 2 O 2 . In addition, EPA guidance identifies several potentially useful statistical measures including: (a) accuracy of spatially averaged concentrations near a monitor, (b) fractional bias in means and standard deviations of predictions and observations, (c) normalized bias, (d) normalized gross error, (e) unpaired comparisons between predicted and observed peak

concentrations.

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

< 50% for Phoenix (ENVIRON, 1998) have been used. As correctly pointed out by Seigneur and coworkers (2003), the current ability of regional PM models to predicting regional PM and visibility is an area of research with improvements needed for characterizing meteorology and emissions as well as PM models themselves. To this list we would add the need for improvements in model evaluation methodologies as well.

When EPA’s draft guidance was developed nearly four (4) years ago, an interim set of fine particulate modeling performance goals were suggested for aggregated mean normalized gross error and mean normalized bias as follows (EPA, 2001):

Pollutant Gross Error Normalized Bias

PM 2.5 ~30-50% ~ " 10%
Sulfate ~30-50% ~ " 20-30%
Nitrate ~20-70% ~ " 15-50%
EC ~15-60% NA
OC ~40-50% ~ " 38%

Because regional-scale fine particulate and regional haze modeling is an evolving science, and considerable practical application and performance testing has transpired in the intervening years since these goals were postulated, we consider them general guidelines. As part of the VISTAS preliminary model performance evaluation efforts along with the model evaluation studies conducted by WRAP (Tonnesen et al., 2004; CENRAP (Morris et al., 2005), MRPO (Baker, 2005), and other studies has developed model performance goals and criteria for PM species. These goals and a summary of model evaluation display techniques are summarized by Morris and co-workers (2005) and consist of the following:

Fractional
Bias Fractional

Error Comment

≤ ±15% ≤ 35% Ozone model performance goal for which PM model performance would be considered good.

≤ ±30% ≤ 50% A level of model performance that we would hope each PM species could meet

≤ ±60% ≤ 75% At or above this level of performance indicates fundamental problems with the modeling system.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-19 We regard the above goals and criteria not as a pass/fail test, but rather as a basis of intercomparing model performance across studies, sensitivity tests and models.

7-8. Diagnostic and Sensitivity Testing

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance expectations. Indeed, our experience has been that initial simulations that ‘look very good’, usually do so as the result of compensating errors. The norm 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 attempt to remove the causes of inadequate model performance. This is invariably the most technically challenging and time consuming phase of a modeling study. We anticipate that the annual CMAQ model base case simulations will present some performance challenges that may necessitate focused diagnostic and sensitivity testing in order for them to be resolved. Hopefully, these diagnostic and/or sensitivity tests can be adequately carried out within the resources and schedule of Tasks 4a/4b. If not, then it may be necessary to draw upon the Optional Task 14 (Enhanced Model Performance Evaluation) and/or Optional Task 15 (Contingency) resources to conduct the necessary work. Where practical, diagnostic or sensitivity analyses, if needed, could be performed on selected episodes within the annual cycle, thereby avoiding the time-consuming task of running CMAQ for the fully 2002 period. Below we identify the types of diagnostic and sensitivity testing methods that might be employed in diagnosing inadequate model performance and devising appropriate methods for improving the model response.

7.8.1 Traditional Sensitivity Testing

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

These levels are:

> Level I

. Model algorithm evaluation and configuration testing;

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

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

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

> To reveal internal inconsistencies in the model; > To provide a basis for compensatory error analysis;January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-20 > To reveal the parameters (or inputs) that dominate (or do not dominate) the model’s operation; > To reveal propagation of errors through the model; and > To provide guidance for model refinement and data collection programs.

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

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

> Modified ammonia emission estimates.

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

> Modified NH 3 and HNO 3 deposition velocities.

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

If necessary, Process Analysis extraction outputs can be included in these Level II diagnostic sensitivity simulations in order to provide insight into why the model responds in a particular way to each input modification. Again, the number, complexity, and importance of these types of traditional sensitivity simulations can only be determined once the initial CMAQ annual 2002 simulation(s) are

executed.

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

further detail in section 7.9 below.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-21 Based on experience in other regional studies, examples of Level III monthly or annual sensitivity runs for Phase II might include:

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

source NO

x emissions; > Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level NO

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

Of course, traditional ‘brute force’ sensitivity experiments are just one way of quantifying these or other Level III sensitivities. Other methods that can be applies include DDM, OSAT, or PSAT

simulations.

The need to perform sensitivity experimentation (Levels I, II, or III) will depend on the outcome of the initial operational performance evaluations. If such a need arises, the ability to actually carry out selected sensitivity and/or diagnostic experiments will hinge on the availability of resources and sufficient time to carry out the analyses. Clearly, selection of the specific analysis method will depend upon the nature of the technical question(s) being addressed at the time. Note that as part of VISTAS modeling, Georgia Institute of Technology will be performing emissions sensitivities with CMAQ.

7.8.2 Diagnostic Tests

A rich variety of diagnostic probing tools are available for investigating model performance issues and devising appropriate means for improving the model and/or its inputs. Previously, in section 7.4.4 we introduced the suite of ‘probing tools’ available for use in the CMAQ and CAMx modeling system for use in Phase II. Where the need exists (i.e., if performance problems are encountered) and assuming VISTAS elects to fund the use of the probing tool applications, these techniques could be employed as appropriate to assist in the model performance improvement efforts associated with the annual 2002 CMAQ basecase development. Here we describe an additional diagnostic method – indicator species and species ratios -- that is potentially useful not only in model performance improvement activities but also in judging the models reliability in estimating the impacts on air quality from future emissions. This method involves the use of so-called ‘indicator species’ and species ratios. If, during the conduct of Phase II, we determine that application of indicator species and species ratio techniques would be beneficial to the study (and if existing project resources allow), we will discuss with the VISTAS TAWG and ASIP the merits of including this additional probing tool as part of the evaluation effort.

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

x , NO

z /NO y (a measure ofJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-22 chemical aging), and O 3 /NO

z (a measure of the ozone production efficiency per NO

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

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

7.9 Corroborative and Weight of Evidence Modeling Analyses

This section identifies additional modeling analyses that might be worth pursuing to add strength to the core model evaluation efforts already planned as part of the VISTAS/ASIP efforts.

7.9.1 Corroborative Models

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

As we discovered in the VISTAS/Phase I CMAQ/CAMx inter-comparisons, the general levels of difference revealed between the two model’s ozone and PM predictions is typical of what one encounters when inter-comparing alternative state-of-science regional models. These differences provide some insight into the current limits of predictability and reproducibility of today’s best photochemical/PM models. In light of these understandable differences in modeling results between state-of-science “One Atmosphere” models, not only is the issue of model selection for

VISTAS/ASIP critical 3, but the procedures for the selected model (CMAQ in this instance) and interpreting its output are important as well. Thus, recognizing the uncertainty that attends even the most sophisticated models, the EPA’s draft PM/regional haze modeling guidance explicitly addresses the issue of modeling uncertainty by recommending that alternative models (photochemical and observation-based) be considered in the attainment demonstration ‘weight of

3 Model selection is a key issue that all RPOs have addressed and some such as WRAP and VISTAS have chosen CMAQ. Others, such as the MRPO have adopted CAMx instead. Still other RPO’s have yet to decide between these two or another model. Clearly, there does not appear to be any one ‘right’ selection at the present time.January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-23 evidence’ analyses. Indeed, the Relative Reduction Factor (RRF) approach in the formal attainment demonstration process is designed to address the fact that no model is fully capable of giving precise predictions. In fact the RRF approach was developed in response to the findings of many modelers that alternative, comparable models give somewhat divergent ozone and secondary aerosol predictions.

7.9.2 Weight of Evidence Analyses

EPA’s guidance recommends three general types of ‘weight of evidence’ analyses in support of the attainment demonstration: (a) use of air quality model output, (b) examination of air quality and emissions trends, and (c) the use of corroborative modeling such as alternative models including observation-based (OBM) or observation-driven (OBD) models. We will consider the use of one or more methods in conducting the CMAQ modeling because it could significantly strengthen the credibility and reliability of the modeling available to the states for their subsequent use. The exact details of the ‘weight of evidence’ analyses must wait until ASIP evolves further. It is premature to prescribe which, if any of the WOE analyses would be performed since the model’s level of performance with the 2002 episode is obviously not known at this time and the time and remaining project resources available to support WOE analyses is unknown as well. Also, how much of this WOE analyses will be performed by the modeling team verses states or other has not been determined. Nonetheless, we outline below our thoughts regarding what would likely be considered should the operational CMAQ model evaluation need to be bolstered with WOE analyses.

Use of Air Quality Models

. As just discussed, we recommend augmenting the CMAQ annual 2002 and episodic simulations with the use of CAMx to provide additional information on model uncertainty and sensitivity. More specifically the use of CAMx on a somewhat limited basis to corroborate the key model performance evaluation results and emissions control findings of the primary model, CMAQ would be useful. Second, applyingthe DDM and OSAT/PSAT methods to develop corroborative information on sourcereceptor relationships and model sensitivities would strengthen the analyses. These supplemental calculations would be performed with one or both models for one or more key periods within the annual 2002 cycle. The results of this additional modeling would be used directly in the ‘weight of evidence’ analyses to quantify the degree of modeling uncertainty and to corroborate appropriateness of the subsequent PM emissions reductions scenarios.

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

Use of Corroborative Observational Modeling

. While regulatory modeling studies for ozone attainment demonstrations have traditionally relied upon photochemical models to evaluate ozone control strategies, there has recently been growing emphasis on the use of data-driven models to corroborate the findings of air quality models. As noted, EPA’s guidance now encourages the use of such observation-based or observation-driven models (OBMs/ODMs). As part of VISTAS/ASIP, we will consider the merits of using theseJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-24 techniques as supportive weight of evidence. While the OBD/OBM models cannot predict future year air quality levels, they do provide useful corroborative information on the extent to which specific subregions may be VOC-limited or NOx-limited, for example, or where controls on ammonia or SO2 emissions might be most influential in reducing PM 2.5

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

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes

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

These include:

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

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

 Use of predicted and observed ratios of ‘chemical indicator species’.

We note that in some urban-scale analyses, the use of weekday/weekend information has been helpful in assessing the model’s response to emissions changes.

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

However, recent analytical and numerical modeling studies have demonstrated how the use of ambient data and indicator species ratios can be used to corroborate the future year control strategy estimates of Eulerian air quality models. Blanchard et al., (1999), for example used data from environmental (i.e., smog) chambers and photochemical models to devise a method for evaluating the 1-hr ozone predictions of models due to changes in precursor NOx and VOC emissions. Reynolds et al., (2003) followed up this analysis, augmented with process analysis, to assess the reliability of SAQM photochemical model estimate of 8-hr ozone to precursor emissions cutbacks. With respect to secondary aerosol PM, the recent CMAQ evaluation by Arnold et al. (2003) clearlyJanuary 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-25 demonstrated how the use of indicator species analysis could be use to develop insight into the expected reliability and adequacy of a photochemical/PM model for simulating the effects of emissions control scenarios. These researchers used three indicator ratios (or diagnostic ‘probes’) to quantify the model’s response to input changes:

> The ozone response surface probe [O 3 /NO

x ]; > The chemical aging probe [NO

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

z ].

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

Thus, the extension of these techniques to address CMAQ predictions for secondary aerosols will doubtless be quite challenging, but the use of indicator species (e.g., ammonia or HNO 3 limitation for nitrate particle formation) and species ratios appears to offer, at this time, the only real opportunity to quantify the expected reliability of the air quality model to correctly simulate the effects of emissions changes. In the CMAQ model evaluation, we will remain alert to opportunities to extend the indicator species ratio analyses to the problem of fine particulate and regional haze. This is one area where technical collaboration between the Emissions and Air Quality Modeling team and the VISTAS and ASIP States and Stakeholders can be especially fruitful in terms of identifying and testing emergent methods for challenging the model’s ability to correctly simulate the effects of future year emissions changes. Finally, we note that this is truly a current research area and as such falls outside the scope of the current modeling effort. However, given its importance, we will remain alert to opportunities to utilize newly available methods should this prove feasible within

resources and schedule.

Table 7-1 . Ground-Level Ambient Data Monitoring Networks and Stations Available in VISTAS 12
Km Domain for CY-2002. Monitoring
Network Chemical Species Measured Sampling Frequency;
Duration Approximate

Number of
Monitors

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

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

SEARCH 24.hr PM25 (FRM Mass, OC, BC, SO4,

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

NADP WSO4, WNO3, WNH4 Weekly 100

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

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

AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly 6,407January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-26 Table 7-2 . Aircraft Sampling Programs Performed in the VISTAS 12 Km Domain for CY-2002. Aircraft Program Meteorological Parameters &

Chemical Species Measured Sampling Program & Flight Duration Approximate

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

Midwest RPO &
Wisconsin DNR Meteorology : WS, WD, Temp,

RH, dew point, pressure Air Quality : O 3 , NO, NO 2 , NO

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

Table 7-3. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIRON, 2003b,d for details).

Statistical
Measure Shorthand

Notation Mathematical Expression Notes

Accuracy of paired peak ( A p ) Paired_Peak

peak peak 

## O OP



## − P peak = paired (in

both time and space) peak prediction

Coefficient of determination ( r

2 ) Coef_Determ



# ∑ ∑

∑

= = =



## − − 

    

−−

N

i N

i i i N

i i i



## OOPP OOPP

1 1 2

2 2

1



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

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

O = arithmetic average of O i , i=1,2,...,N
Normalized Mean Error ( NME

) Norm_Mean_Err



# ∑

∑

= = 

## −

N

i i N

i ii



## O OP

1 1 R e p orted as %

Root Mean Square Error ( RMSE

) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

## 1 

    

− 

# ∑

= N

i ii 

## OP N Reported as %January 2006

C:\Temp\ASIP\Sec7.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec7.doc 7-27 Statistical
Measure Shorthand

Notation Mathematical Expression Notes

Fractional Gross Error ( F

E ) Frac_Gross_Err 

# ∑

= 

## + − N

i ii ii 

## OP OP

N 1 

## 2 R e p orted as %

Mean Absolute Gross Error ( MAGE

) Mean_Abs_G_Err 

# ∑

= 

## − N

i ii 

## OP N 1 

## 1

Mean Normalized Gross Error ( MNGE

) Mean_Norm_G_Err 

# ∑

= 

# − N

i i ii 

# O OP

N 1 

# 1 Re p orted as %

Mean Bias ( MB

) Mean_Bias 

# ( )



# ∑

= 

## − N

i ii 

## OP N 1 

## 1 Rep orted as
concentration (e.g., μg/m 3 )

Mean Normalized Bias ( MNB

) Mean_Norm_Bias 

# ( )



# ∑

= 

## − N

i i ii 

## O OP

N 1 

## 1 Re p orted as %

Mean Fractionalized Bias (Fractional Bias,
MFB ) Mean_Fract_Bias 

# ∑

= 

## 

      

+ − N

i ii

ii 

## OP OP

N 1 

## 2 Re p orted as %

Normalized Mean
Bias ( NMB

) Norm_Mean_Bias



# ∑

∑

= = 

## −

N

i i N

i ii



## O OP

1 1 

## )( R e p orted as %

Bias Factor ( BF

) Bias Factor

1 

## 1 N

i

i i 

## P

N O

= 

##       

# ∑ Reported as BF:1 or

1: BF or in fractional notation (BF/1 or 1/BF).January 2006

C:\Temp\ASIP\Sec8.doc 8-1

8.0 OZONE AND PM 2.5 ATTAINMENT DEMONSTRATION

This chapter provides a summary of how the modeling results will be used to demonstrate

attainment of the 8-hour ozone and PM 2.5 standards. The procedures for demonstrating

attainment of the 8-hour ozone standard follow those outlined in EPA’s final 8-hour ozone modeling guidance (EPA, 2005b). Whereas the procedures for demonstrating attainment of the

PM 2.5 National Ambient Air Quality Standard (NAAQS) follow those given in EPA’s draft PM 2.5 and regional haze modeling standard (EPA, 2001) with updates from the Clean Air Interstate Rule (CAIR; EPA, 2005c) augmented by more recent procedures that use the SANDWICH (Frank, 2005) and Speciated Model Attainment Test (SMAT; Timin, 2005). These procedures are being refined and may be updated during the course of the study including the expected

release of EPA’s final PM 2.5 and regional haze modeling guidance in 2006.

8.1 8-Hour Ozone Attainment Demonstration

The procedures for demonstrating attainment of the 8-hour ozone standard are outlined in EPA’s final 8-hour ozone modeling guidance document (EPA, 2005b). These procedures include a modeled attainment demonstration test along with performing additional analysis that can be used to support and corroborate the modeled attainment demonstration.

8.1.1 Modeled 8-Hour Ozone Attainment Demonstration Test

The 8-hour ozone modeled attainment demonstration procedures use the modeling results in a relative sense to scale or project the current baseline observed 8-hour ozone Design Values (DVB) to estimate a future-year 8-hour ozone Design Value (DVF) that is compared with the 8- hour ozone NAAQS to determine whether attainment has been demonstrated (DVF < 85 ppb) or not (DVF > 85 ppb). The ratio of future-year to current year modeling results is called the Relative Reduction Factor (RRF) and the basic Design Value projection approach for a site i is expressed as follows:

DVF i = RRF i x DVB i

Where,

DVB i = the baseline (current year) 8-hour ozone Design Value (ppb) at monitor i;

DVF i = the future-year projected 8-hour ozone Design Value (ppb) at monitor i; and

RRF i = the relative reduction factor, calculated near site i that is the ratio of future to baseline year predicted concentrations near the monitoring site averaged over multiple days.

Although the basic equation for projecting future-year 8-hour ozone Design Values is conceptually simple, there are several assumptions and issues that need to be resolved as follows:

How is the site-specific baseline Design Value [DVB i ] calculated? The baseline 8-hour ozone Design Value is calculated as the three–year average of 8-hour ozone Design ValuesJanuary 2006

C:\Temp\ASIP\Sec8.doc 8-2 centered on the 2002 modeling year. As an 8-hour ozone Design Value is the three-year average of the fourth highest daily maximum 8-hour ozone concentration at a site, the DVB “5-year Design Value” will weigh the fourth highest 8-hour ozone concentration from 2000 and 2004 once, 2001 and 2003 twice and 2002 three times.

In calculating the site-specific RRF i , what is meant by “near the monitor” and which predicted 8-hour ozone concentrations “near” the monitor should be used? Near the monitor is roughly defined as the highest estimated daily maximum 8-hour ozone concentration within 15 km of the monitor. This is obtained by looking for the highest estimated daily maximum 8-hour ozone concentration in an array of NX by NY grid cells centered on the grid cell containing the monitor. EPA guidance presents defaults for the number of cells to use in the array as a function of grid resolution (EPA, 2005b, pg. 16) and for the ASIP 12 km and 36 km resolution grid array sizes centered on the monitor of, respectively, 3 x 3 and 1 x 1 (i.e., just the grid cell containing the monitor) will be used. Thus for each modeling day under consideration, the highest daily maximum 8-hour ozone concentrations near the monitor is extracted for the baseline-year base case simulation out of the 3 x 3 array of cells centered on the monitor for the 12 km simulation. Similarly, the highest daily maximum 8- hour ozone concentrations in the 3 x 3 array of 12 km cells is extracted from the future-year simulation; note that the baseline-year and future-year cell extractions may not be from the same grid cell.

How are future-year projected 8-hour ozone Design Values calculated in unmonitored areas? EPA’s 8-hour modeling guidance lists a multistep procedure for projecting future-year 8- hour ozone Design Values away from the monitors:

• Interpolate the observed 8-hour ozone Design Values to generate spatial fields of 8- hour ozone Design Values;

• Adjust the spatial fields using the gridded model output ozone concentrations gradients for the baseline year simulation;

• Apply gridded RRFs to the model adjusted spatial fields of current-year Design Values to obtain gridded fields of future-year projected 8-hour ozone Design Values;

and

• Compare spatial fields of projected 8-hour ozone Design Values with the standard to determine whether attainment has been demonstrated across the region. EPA notes that the Design Value projections in the unmonitored areas is more uncertain and recommends it be done as a separate test to the monitor-based Design Value projections. The procedures for interpolating the observed 8-hour ozone Design Values using modeled ozone gradient predictions is not well defined at this time and EPA is in the process of developing software so that a uniform unmonitored ozone attainment test can be defined. Consequently, initially we will just perform the monitor-based attainment test for the ASIP 8-hour ozone modeling and as a sensitivity we will examine the spatial distribution of the RRFs in each

nonattainment area.

Which days should be used in calculating the RRFs? For the ASIP modeling, RRFs will be based on the ratio of the average highest daily maximum 8-hour ozone concentrations near the monitor of the future-year to baseline year for all days in which the baseline year modeled estimated highest daily maximum 8-hour ozone concentration is greater or equal to a threshold-ozone concentration. Initially, a threshold of 85.0 ppb will be used. However, if this results in less than 10 modeling days in the RRF calculation, then the threshold will beJanuary 2006

C:\Temp\ASIP\Sec8.doc 8-3 successively lowered by 1 ppb until at least 10 modeling days are used in the RRFs or when a minimum threshold of 70.0 ppb is reached. If using a threshold of 70 ppb still results in less than 10 days, then the data will be flagged and discussed with ASIP, especially if less than 5 days are being utilized.

8.1.2 Additional Analysis to Support the Attainment Demonstration

EPA’s guidance (EPA, 2005b) lists a series of additional analysis that can be conducted to support and corroborate the modeled ozone attainment demonstration test listed above. EPA notes that cases when the future-year projected 8-hour ozone Design Value is well below (< 82 ppb) the ozone NAAQS less supporting evidence is required then if the projected Design Value is closer to the NAAQS. In fact, EPA recommends that a complete suite of Weight of Evidence (WOE) supporting analysis be performed when the future-year projected 8-hour ozone Design Value is in the 82 to 87 ppb range. EPA notes that with projected Design Values of 88 ppb or above it is very unlikely that supporting analysis could be sufficiently convincing to conclude that the NAAQS will be attained given the results of the modeled attainment test.

EPA lists several additional modeling metrics, supplemental modeling analysis, use of observational models and analyzing emissions and air quality trends data that should be performed as part of the supporting analysis. With the possible exception of calculating additional modeling metrics, most of this additional supporting and WOE analysis will be carried out by the States, although the ENVIRON/Alpine Team may be recruited to assist in this matter

at a future date.

8.2 PM 2.5 Attainment Demonstration

Currently, only the annual average PM 2.5 NAAQS is violated in the ASIP States so only it will be addressed. However, EPA has proposed to lower the 24-hour PM 2.5 standard to it may have to be addressed in the future. EPA has issued draft modeling guidance that describes procedures for combining PM monitoring data with modeling results to project future-year PM 2.5 Design Values for comparison with the NAAQS in an attainment demonstration (EPA, 2001). These procedures are called the Speciated Modeled Attainment Test (SMAT). A preliminary version of the SMAT was applied as part of the proposed Clean Air Interstate Rule (CAIR; EPA, 2004) with the approach refined in the final CAIR (EPA, 2005c,d). Like the ozone projection procedures described above, SMAT uses the modeling results in a relative sense to scale baseline

PM 2.5 Design Values to estimate future-year projected PM 2.5 Design Values. However, unlike ozone, PM 2.5 consists of several different components (e.g., sulfate, nitrate, ammonium, etc.). The SMAT develops site-specific separate RRFs for each PM component, projects each PM component to the future-year and then recombines all of the PM components to obtain total

PM 2.5 mass for comparisons with the PM 2.5 NAAQS.

The SMAT procedures consists of two components: (1) the combination of the PM 2.5 mass measurements from the Federal Reference Method (FRM) with the speciated PM 2.5 measurements, such as those from the Speciated Trends Network (STN); and (2) the combination of the modeling results with the speciated FRM PM 2.5 Design Values to obtain future-year projected PM 2.5 Design Values.January 2006

C:\Temp\ASIP\Sec8.doc 8-4 8.2.1 Speciation of FRM PM 2.5 Mass Measurements

PM 2.5 attainment/nonattainment can only be determined from the FRM PM 2.5 measurements that measure just total PM 2.5 mass. The FRM PM 2.5 measurements are used to develop the PM 2.5 Design Values that determine attainment classification and are used as the starting point for projecting future-year PM 2.5 Design Values for demonstrating attainment. Thus, representative speciated PM 2.5 measurements need to be mapped to the FRM measurements. For most FRM sites there is a speciated PM 2.5 in the same general area that can be used in the mapping. However, in some cases, there is no nearby speciated PM 2.5 site so speciated PM 2.5 data must be interpolated from sites surrounding the FRM site.

Speciated PM 2.5 measurements are routinely collected on the same 1:3 day sampling frequency as used by the FRM network at two monitoring networks in the US: the Speciated Trends Network (STN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. Each of these three monitoring networks use different measurement technologies each of which have their own measurement artifacts. For example, FRM uses a single Teflon filter and includes water in the measurement (after equilibration at ~35% RH) and just measures total PM 2.5 on the filter. Particulate nitrate may volatilize off of the FRM Teflon filter. The STN measurement technology uses Teflon, Nylon and Quartz filters for measuring the speciated data and does not measure the water component of the PM 2.5 . The STN Quartz

filters are also not blank corrected which results in inaccurate OC measurements. IMPROVE also uses multiple filters and does not include ammonium in its measurements.

As the FRM is the de facto regulatory definition of PM 2.5 , EPA has developed procedures for adjusting the STN and IMPROVE speciated PM 2.5 measurements to account for the measurement artifacts of the different networks and make the speciated PM measurements

consistent with the FRM PM 2.5 mass measurements. These adjustments include the following:

• Adjust nitrates downward to account for volatilization off of the FRM nylon filter;

• Add particle bound water (PBW) that are associated with nitrate and sulfate in the FRM measurements; and

• Estimate total carbonaceous mass accounting for lack of blank correction in the

STN measurements.

The resultant fine particle chemical speciation approach has been named the Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous mass and estimated aerosol acidity (H+), or SANDWICH. Details on the SANDWICH procedures is given in Frank (2005).

8.2.2 Speciated Modeled Attainment Test (SMAT)

The SMAT procedures have been applied for the proposed and final CAIR analysis (EPA, 2004; 2005c) each time with refinements. A SMAT Tool has been developed using the SAS programming language that is populated with 1999-2003 FRM and SANDWICH speciated

PM 2.5 data. Use of 1999-2003 PM 2.5 data is appropriate for the CAIR modeling since it is centered on the 2001 CAIR modeling year. However, for the ASIP PM 2.5 projections, FRM andJanuary 2006

C:\Temp\ASIP\Sec8.doc 8-5 speciated PM data are needed for the 2000-2004 period so that it is centered on the ASIP 2002 modeling year.

Because the CAIR SMAT projections were looking at all FRM monitors, including many without nearby associated speciated PM 2.5 monitors, the SANDWICH speciated PM 2.5 data were interpolated to obtained gridded fields of PM 2.5 speciation that were then used to speciate the FRM data. EPA used a Voroni Nearest-neighbor Approach (VNA) to interpolate the

SANDWICH PM 2.5 speciation components for sulfate, nitrates, OC, EC, crustal/other and a Degree of Neutralization (DON). DON was interpolated instead of ammonium because the IMPROVE network does not measure ammonium and using the DON ammonium can be backed out of the interpolated sulfate values.

Updating the CAIR SMAT Tool with the 2004 data in a consistent fashion and extending it to the 12km grid would be a major undertaking. The interpolation procedures are not well defined. Furthermore, the ENVIRON/Alpine Team has applied the CAIR SMAT Tool and been unable to exactly duplicate EPA’s CAIR results, which has been attributable to different versions of SAS used by EPA (PC) and the ENVIRON/Alpine Team (Linux).

ASIP has reviewed the FRM monitors in nonattainment areas in the Southeast US and found that most have an associated STN monitor. However, there is a small subset of FRM sites that the ASIP States did not link to a nearby STN site. In these cases, the State of North Carolina will revise parts of the existing EPA SMAT Tool to get an approximated speciation mix for the FRM site. The State of North Carolina will work with the ASIP States to identify the FRM-STN pairs and process the STN data for 2000-2004 using the SANDWICH procedures so that the FRM PM 2.5 mass measurements can be speciated for the application of the SMAT. Once we

have the FRM and associated SANDWICH PM 2.5 speciation data for the 2000-2004 period, the SMAT procedures involves the following steps:

1. Derive quarterly mean average concentrations for each of the major components of PM 2.5 and each of the quarters from the 2000-2004 baseline period. This is done by applying the fractional contribution of each major component of PM 2.5 from the SANDWICH

PM 2.5 speciation data for the same quarter and year. Major components are as follows:

• Sulfate (SO4);

• Nitrate (NO3);

• Ammonium (NH4);

• Elemental Carbon (EC);

• Organic Mass Carbon (OMC);

• Final Crustal/Other; and

• Particle Bound Water (PBW).

2. Use the model estimated PM 2.5 components near each monitor and for each of the four quarters of 2002 from the 2009 and 2002 simulations to develop monitor-, quarter- and

PM 2.5 species-specific Relative Reduction Factors (RRFs) using the ratio of the 2009 to 2002 quarterly average modeling results.January 2006

C:\Temp\ASIP\Sec8.doc 8-6 3. Apply the monitor-, quarter- and species-specific RRF to each quarterly average observed

PM 2.5 species concentrations from 2000-2004 to obtain 5-years of quarterly average

PM 2.5 species concentrations representative of 2009 conditions.

4. Recalculate the Particle Bound Water (PRB) component from the 2009 projected quarterly average sulfate and nitrate concentrations.

5. Average the four quarterly mean 2009 species concentrations to make an annual average

PM 2.5 species concentrations at each monitor and for each of the 5 years (2000-2004). Sum the annual average species components for each of the five years and each monitor to obtain five years of annual average total PM 2.5 mass concentrations.

6. Calculate the PM 2.5 Design Values from the five years of 2009 projected annual average

PM 2.5 concentrations and compare against the NAAQS PM 2.5 in the attainment test.

As in the ozone projections, there are a few issues that need to be resolved and defined to apply

the above PM 2.5 attainment test.

What quarterly average model estimated PM 2.5 species components are used “near” the

monitor? As in the ozone projections, a grid resolution dependent array of cells centered on the monitor is used (i.e., 3 x 3 for 12 km grid and 1 x 1 for 36 km grid). However, for

the PM 2.5 projections the average of the estimated PM 2.5 species across the array of cells is used, rather than the highest value that is used in the ozone projections.

What PM 2.5 Design Values should be used in the projections? An average of the 2001,

2002 and 2003 PM 2.5 Design Values will be used in the PM 2.5 projections. As a Design Value is a three-year average of annual values, then this three year average of PM 2.5 Design Values will weigh the annual average PM 2.5 concentrations from the years 2000 and 2004 once, 2001 and 2003 twice and 2002 three times.

When is PM 2.5 attainment demonstrated? The SMAT attainment test is passed when the 2009 projected 3-year average Design Value is 14.9 μ g/m 3 or lower. [should this be 15.4 ug/m3???]

8.2.3 Additional Supporting Analysis

Additional supporting analysis to the SMAT modeled PM 2.5 attainment test will be conducted to corroborate the modeling analysis. The exact definition of the supporting analysis is being analyzed but would like consist of additional modeling metrics as well as emissions and air quality trends and alternative modeling and projection approaches. The supporting analysis to be performed as part of the ASIP PM 2.5 attainment demonstration will be better defined once

EPA has released their final PM 2.5 modeling guidance. Currently, this additional supporting analysis is planned to be carried out by the ASIP States, although the ENVIRON/Alpine Team may assist at a future date.January 2006

C:\Temp\ASIP\Sec9.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec9.doc 9-1 9.0 DATA MANAGEMENT

Data management and data security procedures are critical components of the ASIP regional fine particulate and 8-hour ozone modeling. Very large data files are used in each component of the modeling process, including processing of the meteorology data, emissions processing, and PM 2.5 and ozone modeling with CMAQ. An annual simulation on the ASIP 36- km domain requires approximately 2 Terabytes (Tb) of disk storage, whereas an annual simulation on the ASIP 12-km domain requires over 3 Tb of disk storage. This chapter describes data management practices that will be used in the ASIP 8-hour ozone and PM 2.5 modeling analyses.

For all critical files we will maintain backup copies either on tapes, storage disks or redundant disk systems. In addition, because ASIP model simulations will be performed separately by the ENVIRON and AG modeling centers, each institution will maintain its own copy and backup of critical input and output files. Because there are differences in system configurations at each of the modeling centers the data backup and archiving are discussed separately for each center, below. Some of the ASIP States will also maintain copies of the modeling data files relevant to their particular nonattainment areas.

CMAQ generates large output files of which most information is rarely used. For example, model output for layers other than the surface layer are typically only used to define boundary conditions for finer grid simulations. Thus, once the boundary condition files have been generated for the ASIP States 12 km and 4 km refined modeling domain, the 3-D CONC files of instantaneous concentrations do not have to be archived. We do not plan redundant archiving for model output files (including output from MCIP and SMOKE) except for key final model outputs (e.g., 2002 and 2009 model-ready emissions) because these files can be regenerated by repeating model simulations, and this is approach more efficient and more cost effective than redundant archiving.

To promote efficient, reliable communication among project participants, the modeling

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

States are a subset of the VISTAS states the same listservs will also be used for ASIP. The

listservs are:

• vistas-all@cert.ucr.edu: general project information for all interested persons.

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

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

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

These separate listservs are aimed at providing better organization of communications and allowing for detailed discussions of specific topics such as emissions QA.January 2006

C:\Temp\ASIP\Sec9.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec9.doc 9-2 9.1 Project Website

Depending on the amount of data generated by ASIP, a project website will be established to facilitate the distribution of information. Whether the ASIP website will be part of the VISTAS website or its own has yet to be determined.

9.2 Data Transfer

Data transfer among the modeling centers and between other ASIP participants or contractors will be accomplished using a combination of email, ftp downloads and portable disk drives depending on the size of the data transfer. For data files smaller than a few MB email typically works well and is most efficient. For data files of less than about 500 MB file transfer protocol (ftp) is typically the fastest and most efficient method. ENVIRON and AG each maintain webpages and ftp pages that can be used for exchanging data. In addition, each modeling center has several portable disk drives with both USB2 and firewire interfaces that can be FedEx among project participants to exchange large data sets. Portable disk drives range in size from 80 to 300 GB and are adequate for all large files data transfers. The approach described here has been used throughout the VISTAS project, as well as WRAP, CENRAP and MRPO, and has proven to be economical and efficient.

9.3 Data Backup and Archiving

Data backup and archiving will be performed at each of the modeling centers. Copies of critical project data will be maintained at each modeling center to provide redundant backup of key project data. Each modeling center stores key model inputs on RAID systems that have self re-generation capabilities in case a disk drive fails. In addition, each modeling center will perform backups of key project data to tape or redundant disk storage systems. Data storage and back up resources at each modeling center are described next.

9.3.1 ENVIRON

Over 20,000 Gigabytes (>20 Terabytes, Tb) of disk storage are available to the UNIX/Linux workstations. All of the workstations are networked together and are accessible from each employee’s desktop PC. All workstations have CD-ROM drives and can access DLT, 4mm DAT and 8mm Exabyte tape drives for data backup and data transfer. ENVIRON can also create CDs (CD-R and CD-RW) and DVDs (DVD+ and DVD-) for data backup and distribution. For ASIP modeling, all CMAQ simulations will be performed on one of two 13 node Beowulf Linux Clusters that included one master node and 12 processing nodes. Each node consists of two AMD Athlon 2600+ processors. The master nodes have 2 Gb of memory and are connected to a ~3-4 Tb RAID disk system. Each secondary processing node includes 1 Gb of memory. The ENVIRON Novato computing center also includes approximately 10 dual processor Linux workstations with processing speeds of 1700+ to 3000+. Three older Unix workstations are also available, SUN, DEC and SGI. The Linux computer systems are located in their own room with their own dedicated air conditioning (AC) system. The room includes a temperature sensitive power shut off device that will shut off the power to all computers in case the AC breaks downJanuary 2006

C:\Temp\ASIP\Sec9.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec9.doc 9-3 so that catastrophic failure due to too high temperatures does not occur. Backups are made on IDE disk drives that are removed from the computer and stored on a shelf to protect against power surges destroying the backup data.

9.3.2 Alpine Geophysics

Alpine Geophysics' computing facilities consist of SUN Microsystems SPARCstation computers and a very powerful array of over 20 multiprocessor Linux-based workstations. Disk storage systems include ~20 Tb of aggregate disk space and over 8 Tb of SCSI and IDE Raid-5 protected space. All client data is stored on at least one RAID-5 protected disk array. To further protect client data, two of our main servers backup their disk drives on a weekly basis to a second server that is physically disconnected from the power supply when not doing an active transfer. This way a catastrophic power failure will not compromise the ability for Alpine Geophysics to deliver.January 2006

C:\Temp\ASIP\Sec10.doc Z: \ VISTAS Phase II \ ASIP \ Protocol \ Draft#2 \ Sec10.doc 10-1 10.0 DOCUMENTATION

This section describes the documentation that be provided during ASIP and the potential for modifications to this Modeling Protocol and Quality Assurance Project Plan (QAPP) that might become necessary as this phase of the study unfolds.

10.1 Planned Documentation

Documentation associated with the ASIP emissions and air quality modeling will include all relevant input data bases and scripts associated with the pre- and post-processing associated with model input development, model application, sensitivity and diagnostic analyses, and performance evaluations. At this time the deliverables consists of disk drives with modeling results and databases for the ASIP States and PowerPoint presentations of the 2009 projections of 8-hour ozone and PM 2.5 Design Value Projections. Deliverables under the current ASIP modeling contract are as follows:

• ASIP Modeling Protocol (this document)

• ASIP Quality Assurance Project Plan (QAPP)

• CMAQ-ready 2002 36/12 km Typical emissions with every day mobile emissions on the 12 km grid

• CMAQ-ready 2009 36/12 km Typical emissions with every day mobile emissions on the 12 km grid

• PowerPoint presentations of 2009 8-hour ozone and PM 2.5 Design Value projections

• Disk drive of modeling results and boundary conditions for the NC, ALGA and VA

subdomains

• Archive drives of modeling results

Additional deliverables by the modeling team may be added by ASIP at a later date. Chapter 11 contains the current Scope of Work (SOW) for the ENVIRON/Alpine team under the ASIP work

effort and the schedule for the deliverables.

10.2 Procedures for Updating Modeling Protocol and QA Plan

One of the underlying realizations stemming from the VISTAS modeling activities was the awareness that the science of ‘One-Atmosphere’ PM/regional haze modeling is advancing very rapidly. Part of this stems from the parallel activities being carried out by the RPOs; some if it is due to other ongoing 8-hour ozone and PM modeling studies being performed by various states. In addition, EPA is in the process of revising its PM 2.5 and regional haze guidance documents. Collectively, it is quite likely that there will be new opportunities to strengthen the modeling algorithms, input data sets, and evaluation procedures throughout the duration of the ASIP and VISTAS modeling efforts. Moreover, the ASIP emissions and air quality modeling activities involve the collaboration with the ASIP States as well as other ASIP contractors who will provide the required emissions and meteorological inputs to the SMOKE and CMAQ models. Given the ongoing model refinement activities and the need for strong coordination with other ASIP contractors, it may be necessary to modify certain aspects of this modeling protocol. In this event, modification will be made in consultation with the ASIP project coordinator and the revised protocol will be submitted to the ASIP States for approval.January 2006

C:\Temp\ASIP\Sec11.doc Z: \ VISTA S Phase II \ ASIP \ Protocol \ Draft#2 \ Sec11.doc 11-1 11.0 ASIP STATEMENT OF WORK AND SCHEDULE

This section describes the current ASIP Statement of Work (SOW) for the ENVIRON/Alpine Modeling Team, the schedule and deliverables under the ASIP work effort. This work currently includes four Tasks that focus on the emissions and air quality modeling for the 2002 calendar year using a 36/12 km grid for two emission scenarios: (1) a 2002 Typical Base Case; and (2) a 2009 Base Case. Unlike VISTAS, ASIP is performing everyday on-road mobile source modeling on the 12 km grid (on the 36 km grid and for VISTAS 36/12 km modeling on-road mobile source emissions modeling are performed for one week from each month). The 2002 and 2009 modeling results will be used to project 2009 8-hour ozone and

PM 2.5 Design Values for the attainment demonstration. The ENVIRON/Alpine Team may be charged with additional Tasks for ASIP in the future at which time this Modeling Protocol will be updated accordingly.

11.1 Task 1: Project Management

The objective of this task is to manage project activities, participate in conference calls, manage the ASIP contract and subcontractors, general oversight and overall quality assurance and the preparation of the Quality Assurance Project Plan (QAPP) and Modeling Protocol (this document).

The management structure for the ASIP Emissions and Air Quality Modeling is similar to what was used in VISTAS with Ralph Morris of ENVIRON serving as Project Manager and Ralph Morris and Gregory Stella of Alpine Geophysics serving as Co-Principal Investigators (Co-PIs) and managing the activities in the ENVIRON and Alpine Geophysics modeling centers, respectively. Section 1.7 provides more details of the ASIP Emissions and Air Quality Modeling management structure. Under this task we are performing all management activities for the ASIP modeling study, including:

• Participation in scheduled conference calls to be held approximately once per month as well as expected ad hoc conference calls to be held as needed.

• Preparation and review of contracts between SESARM and ENVIRON and subcontracts between ENVIRON and Alpine.

• Develop and refine the Scope of Work and conduct contract discussions with the ASIP modeling team and the ASIP technical and project representatives.

• Preparation of monthly reports, invoicing to SESARM and payments to

subcontractors.

• Internal project conference calls and discussions among the ENVIRON/Alpine project team.

• Develop and implement the ASIP Quality Assurance Project Plan (QAPP).January 2006

C:\Temp\ASIP\Sec11.doc Z: \ VISTA S Phase II \ ASIP \ Protocol \ Draft#2 \ Sec11.doc 11-2 • Develop the ASIP Modeling Protocol.

Table 11-1 summarizes the deliverables and expected delivery dates under Task 1 of the ASIP regional ozone and PM 2.5 SIP support modeling analysis.

Table 11-1. ASIP Task 1 Project Management task deliverables and due dates.
Item Description Due Date 1 ASIP Quality Assurance Project Plan (QAPP) 11/30/05 2 ASIP Modeling Protocol 12/15/05
3 Conference Calls with ASIP States Monthly; 11/05-2006 4 Monthly Reports and Invoices Monthly; 11/05-2006

11.2 Task 2: 2002/2009 Emissions Modeling

The objective of this task is to perform SMOKE emissions modeling to generate 2002 36/12 km Typical “Base F4” and 2009 36/12 km “Base F4” CMAQ-ready emission inputs using the every day mobile source emissions modeling approach.

Version 2.1 of the SMOKE emissions modeling system will be used to generate 2002 Typical 12 km on-road mobile sources emissions using daily meteorology and the SMOKE MOBILE module. VISTAS has generated 2002 Typical Base F emissions using weekly MOBILE model run for 36 km and 12 km grid as well as generating 2002 Typical Base F 36/12 km emissions for the other source categories (e.g., area, point, non-road, etc.). Under ASIP this task will provide supplemental 2002 daily MOBILE modeling for the 12-km grid. The 2002 12 km Typical daily mobile source emissions will be merged with the other source categories to generate 2002 Typical 12 km emissions inputs based on daily mobile source modeling that will be used in the ASIP ozone and PM modeling.

Also under this task we will process the 2009 Base F annual emissions through the SMOKE emissions processor for all anthropogenic emission source categories to prepare the emissions for use in the CMAQ air quality model. Base F emissions include outputs of the Integrated Planning Model (IPM) for utilities and all inventory revisions from VISTAS and other RPOs that are available by July 2005. Daily 2009 MOBILE run will be used in SMOKE for the 12 km grid and weekly MOBILE run for the 36 km grid.

Table 11-2. ASIP Task 2 2002/2009 Emissions Modeling task deliverables and due dates.

Item Description Due Date
1 2002 12 km Typical Base F daily mobile source SMOKE emissions modeling 11/28/05 2 2002 36/12 km Typical Base F CMAQ-ready emissions w/daily mobile source

emissions 12/3/05

3 2009 36/12 km Base F CMAQ-ready emissions w/daily mobile sources 12/15/05January 2006

C:\Temp\ASIP\Sec11.doc Z: \ VISTA S Phase II \ ASIP \ Protocol \ Draft#2 \ Sec11.doc 11-3 11.3 Task 3: 2002/2009 36/12 km Base F CMAQ Modeling

The objective of this task is to run the CMAQ model on the 36 km and 12 km grid for the typical 2002 and 2009 Base F emission scenarios using every day mobile source emissions.

The CMAQ model is applied for the 2002 and 2009 Base F emission scenarios with weekly mobile emissions modeling for the 36 km grid and every day mobile sources emissions on the 12 km grid. QA/QC is performed on the runs including comparisons of the modeling

results to the observed PM concentrations. Extractions are made at the ozone and PM monitoring sites, where values at the monitoring site are extracted from the 36 km results and a 3 x 3 grid cell average concentration centered on the monitoring site is extracted for the 12 km modeling results. The 12 km modeling results will be processed to generate boundary condition inputs for the Alabama/Georgia (ALGA) and North Carolina (NC) subdomains and provided to the states of North Carolina and Georgia. Table 11-3 list the deliverables for the ASIP Task 3 CMAQ modeling.

Table 11-3. ASIP Task 3 2002/2009 36/12/ km Base F CMAQ Modeling.
Item Description Due Date

1 CMAQ 36/12 km simulations for 2002 Typical Base F w/everyday mobile
sources emissions 12/21/05

2 CMAQ 36/12 km simulations for 2009 Base F w/everyday mobile sources
emissions 1/15/06 3 Disk with boundary conditions for ALGA and NC subdomains 1/31/06

11.4 Task 4: Data Analysis and Data Management

The objective of this task is to analyze the results of the Task 2 emissions and Task 3 CMAQ air quality modeling, archive and distribute the modeling results.

The SMOKE emissions and CMAQ air quality modeling results will be analyzed. 8-hour ozone projections and annual PM 2.5 projections will be made at all monitoring sites in and adjacent to the VISTAS states using both the 36 km and the 12 km modeling results. These results will be documented in PowerPoint presentations and presented ASIP states (Table 11-4). The results from the emissions and air quality modeling will also be archived on USB portable

disks and delivered to the ASIP states. Summaries of the results will also be available on the

Internet.

Table 11-4. ASIP Task 4 2002 CMAQ Modeling task deliverables and due dates.
Item Description Due Date 1 PowerPoint Presentation on 2009 8-hour ozone projections 1/31/06
2 PowerPoint Presentation on 2009 PM 2.5 projections 1/31/06 3 Archive disks of ASIP 2002/2009 modeling results 2/10/06January 2006

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