Appendix D 

Modeling Protocol 
Modeling Protocol i 

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Appendix D.1 

NC Division of Air Quality 

Modeling Protocol 
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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, 2008 
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Table of Contents 

1.0 Overview of Fine Particulate Matter 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 Particulate 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 PM2.5 Episode Selection ........................................................................................................ 11 

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

3.2. PM2.5 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 Emissions 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 ......................................................................... 51 
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8.0 Model Performance Evaluation ............................................................................................ 53 

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 ................................................................................................ 59 

9.2. Control Strategy Evaluation .......................................................................................... 60 

9.3. Identification of Control Strategy Scenarios ................................................................. 60 

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

10.1. PM2.5 Model Attainment Test ................................................................................... 61 

10.2. Screening Test ........................................................................................................... 64 

10.3. Corroborative Analysis ............................................................................................. 64 

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

Attachment A: Acronyms Used .................................................................................................... 69 
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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 Environmental Protection Agency (USEPA) issued 

National Ambient Air Quality Standards (NAAQSs) for fine particulate matter (PM2.5). The 

NAAQSs include an annual standard set at 15 micrograms per cubic meter (μg/m3), based on the 

3-year average of annual mean PM2.5 concentrations and a 24-hour standard of 65 μg/m3

, based 

on the 3-year average of the 98th percentile of 24-hour concentrations. A number of events 

delayed the implementation of the PM2.5 NAAQSs. 

The new PM2.5 standards were challenged by the American Trucking Association, the 

U.S. Chamber of Commerce, and other state and 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 USEPA's authority under 

the Clean Air Act (CAA) to set NAAQSs that protect 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 several additional issues. In March 2002, the D.C. Circuit Court 

rejected all remaining legal challenges to the 1997 NAAQSs for PM2.5. 

1.2. Designations 

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

nonattainment area boundaries for the PM2.5 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 any nearby counties with sources 

contributing to fine particle pollution in those 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 PM2.5 and 8-hour ozone standards, which will help states and tribes 

facilitate future planning and implementation activities. 

In mid-February 2004, states and tribes recommended PM2.5 nonattainment area 

boundaries to the USEPA. The USEPA revised these recommendations and responded to the 

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

PM2.5 NAAQSs. Two areas in North Carolina were designated nonattainment for the annual 

PM2.5 NAAQS under Section 172 of the CAA as amended. These areas were Hickory (Catawba 

County) and Greensboro/Winston-Salem/High Point (Davidson and Guilford Counties). The 

Greensboro/Winston-Salem/High Point area is referred to as the Triad area. Figure 1.2-1 shows 

the USEPA’s final designation of PM2.5 nonattainment areas in North Carolina. 
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Figure 1.2-1: Designated Annual PM2.5 nonattainment areas in North Carolina. 

1.3. Participating Organizations 

From the conceptual model of PM2.5 formation, it is clear that PM2.5 is a regional 

problem, which results in attainment demonstration State Implementation Plan (SIP) modeling to 

be a substantial undertaking that relies on the interaction 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, government, and the private sector during the 

modeling/analysis project. As each group involved brings its own perspective, knowledge, and 

experience to the modeling process, the ability to model and develop strategies for PM2.5

reduction is greatly enhanced. The following organizations were invited to participate in 

developing the Hickory/Triad PM2.5 SIP: 

• North Carolina Division of Air Quality (NCDAQ) 

• Carolina Environmental Programs – University 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 Defense 
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• Sierra Club 

• Charlotte Department of Transportation 

• Furniture Manufacturing Representative 

• Chemical Manufacturing Representative 

• North Carolina Petroleum Council 

• North Carolina Petroleum Marketers’ Association 

• Centralina Council of Governments 

• Catawba Council of Governments 

• Representatives from city and county governments in the nonattainment areas 

Data and available expertise from participating agencies, organizations, and universities 

will be utilized in determining projected emissions and control strategies. All data and 

information will be reviewed and evaluated by the NCDAQ. All stakeholders are invited to 

contribute emissions projections and control strategy information. 

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

is initiated to address the PM2.5 standards. Various other regional modeling applications, such as 

Visibility Improvement and Tribal Association of the Southeast (VISTAS) regional haze 

modeling, will also be considered. These interactions should provide a forum for discussing the 

latest improvements and refinements to air quality modeling. 

1.3.1. Communication 

Communication between the stakeholders is an integral part of completing the 

modeling/analysis project. Stakeholders need the opportunity to review and comment on 

documentation, control strategies, and modeling 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 studies will be defined to help resolve all pertinent 

issues. Documentation will be developed concerning 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 PM2.5 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. 
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1.3.2. Protocol Modification Procedures 

The model configuration, as well as the source 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 methodologies, an issue paper stating the need for modification will 

be developed and circulated to all organizations participating in the study. The issue paper will 

be discussed at the next scheduled technical coordination meeting. 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 control 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 PM2.5 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 PM2.5 and 8-hour ozone attainment demonstration SIPs and 

VISTAS regional haze modeling effort. 

1.5. Schedule 

The NCDAQ will follow the schedule outlined 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 PM2.5 formation in North Carolina. 

• Section 3 presents details of the PM2.5 episode selection process. 

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

• Section 5 describes the model grid specifications. 

• Section 6 discusses the emission inventory development. 

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

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

• Section 9 discusses how the control strategies will be designed. 

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

• Section 11 lists the references. 

• Acronym Attachment follows the final section. 
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2.0 Conceptual Description of Fine Particulate 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 (Da) 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 Da, 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 1gram per cubic centimeter (g/cm3

). 

The division between the fine and course categories occurs in the Da size range between 1 

and 3 micrometers (μm), where concentrations are at a minimum. Particles with a Da 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 ultrafine mode is further broken down into Aitken 

mode and nucleation mode. Figure 2.1-1 illustrates the differences between the particle size 

divisions. 

Figure 2.1-1: This figure illustrates the size categories or modes 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 Da < 2.5 μm. The USEPA chose the cutoff of 2.5 μm for the 

development of a NAAQS based on the use of PM2.5 in epidemiological studies, and the desire to 

include the accumulation-mode particles, while recognizing that some coarse 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 produced by combustion, photochemical reactions, and 

can contain salt from sea spray and soil like particles. Particles are distinguished based on the 
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method of formation. Primary particles are particles directly emitted into the atmosphere and 

retain the same chemical composition as when they were released. Secondary particles are those 

formed through chemical reactions involving atmospheric oxygen (O2), water vapor, hydroxyl 

radical (OH), nitrate (NO3), sulfur dioxide (SO2), nitrogen oxides (NOx), and organic gases from 

natural and anthropogenic sources. 

Particulate matter components can include: 

• Sulfate 

For fine particles, sulfate is generally a secondary particle, and is usually found in the 

form of ammonium sulfate. 

• Nitrate 

For fine particles, nitrate is generally a secondary 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 material includes pollen, spores, and animal 

debris. 

• Crustal material 

Predominately found in coarse particle range, 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 nonattainment areas for the annual PM2.5 NAAQS, 

which are associated with the Hickory and Lexington monitoring sites located in Catawba and 

Davidson Counties, respectively. The nonattainment designations were based on the 2001-2003 

monitoring data. For that period, the Hickory monitor had an annual PM2.5 design value of 

15.5 μg/m3

 and the USEPA designated all of Catawba County as nonattainment. The Lexington 

monitor had an annual PM2.5 design value of 15.8 μg/m3

, 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 μg/m3

. The rest of the state was below the 

NAAQS, with annual averages ranging from 9.6 to 14.9 μg/m3

. Figure 2.3.1-1 shows a map of 
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North Carolina with the 2001-2003 annual PM2.5 design values. Across the majority of the 

monitoring sites, including the two violating monitors, the annual average of PM2.5 has been on 

the decline since 1999. 

Figure 2.3.1-1: 2001-2003 annual PM2.5 design values. 

Given that annual average PM2.5 concentrations at sites across the state, one can 

reasonable conclude that PM2.5 is a regional issue for North Carolina. An examination of the 

daily PM2.5 conditions across North Carolina again shows 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 PM2.5 NAAQS. 

Both nonattainment areas are in the western portion of the state known as the Piedmont 

Crescent. This portion of the state has the intersection 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 PM2.5 presents is its diurnal pattern. PM2.5 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 cools during the overnight period. Once the sun 

sets, the ground loses heat very quickly, which cools the 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 with high-pressure systems, namely calm 

winds and clear skies, contribute to the formation 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, the length of the overnight period 

greatly affects inversion formation. Winter typically has stronger and more frequent inversions, 

since the nights are longer and provide a longer period for radiational cooling to occur. 
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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 for several days. In addition, local topographical features can 

enhance the formation of inversions, especially in valley locations. 

Therefore, inversions create a very stable atmosphere where pollutants, such as PM2.5, 

become trapped near the surface. Air quality conditions under the inversion layer are greatly 

affected by the emissions from electric generating 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 second shift production from these 

facilities occurs just as the nocturnal inversion is setting up. Emissions from production are 

spewed the atmosphere where they become trapped in the very stable layer created by the 

inversion. With little mixing occurring, the pollutants begin to build up and concentrations rise 

as more pollutants are pumped into the atmosphere 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 nocturnal inversion is setting up, contributing some 

additional PM2.5 components to the load from industrial 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 PM2.5

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 PM2.5

stratified by day of the week. The black circle on the graph indicates the average concentration 

for that particular day of the week. 
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Figure 2.3.3-1: 24-hour average PM2.5 concentrations at the Hickory FRM monitor by 

days of the week. The black circle represents the average concentration for the day. 

Figure 2.3.3-2: 24-hour average PM2.5 concentrations at the Lexington FRM monitor by 

days of the week. The black circle represents the average concentration for the day. 

2.3.4. Seasonal 

Summer/Fall

High PM2.5 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 relative 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 during 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 μg/m3 during the summer, 

with spikes as high as 50 μg/m3. The season generally has its peak PM2.5 concentration in the 

July to August timeframe, with ammonium sulfate as the primary constituent of PM2.5 during this 

period. Ozone is also of great concern during the summer months. Generally, the same 

meteorological scenarios responsible for high ozone days also lead to high PM2.5 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. 
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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 results in light winds, warm temperatures and 

subsequently, high PM2.5 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 PM2.5 concentrations as the leading edge 

of the front acts to collect PM2.5, driving up concentrations along its path. During an event with 

enough steady precipitation, PM2.5 can “washout”, or remove particulates from the atmosphere, 

reducing concentrations. However, relief from high PM2.5 usually comes after frontal passage 

when a new air mass enters into the area.

Winter/Spring

PM2.5 episodes during the winter and early spring are typically lower in magnitude than 

those episodes experience in summer and early fall, and are largely driven by nitrates and to a 

lesser extent by black carbon from combustion. Peak 24-hour averages of particle pollution are 

generally around 20 to 25 μg/m3. The highest PM2.5 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 μg/m3. PM2.5 drops the next morning 

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

PM2.5 occurs when conditions are clear and calm through the nighttime hours, then cloud cover 

moves overhead at or just after sunrise, reducing convective mixing and leaving pollution 

trapped at the surface. 

Longer lasting episodes may occur when inversions are created by cold air damming 

(CAD). CAD events occur frequently during the winter and early spring and are a phenomenon 

unique to the geography of the Southeast. 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 the cold air, creating an inversion. These events are generally 

accompanied by cloudy skies, little precipitation, and 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 concentrations of particle pollution. A snow pack 

intensifies the radiational cooling 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 heating and convective mixing, preventing the 

nocturnal inversion from mixing out and keeping particle pollution at high concentrations. 
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3.0 PM2.5 Episode Selection 

A crucial step to attainment demonstration modeling is the selection of episodes to 

model. Several considerations need to be weighed before settling on not only which days to 

model, but how many days for each episode. This section details the guidance and process by 

which episodes were selected for the Hickory/Triad PM2.5 attainment demonstration SIP. 

3.1. Overview of the USEPA Guidance on Particulate Matter 

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

PM2.5 and Regional Haze (EPA, 2001) sets out specific criteria for the selection of episodes for 

modeling the attainment of the PM2.5 NAAQS. First, episodes should include days 

encompassing a variety of meteorological conditions. Episodes should also be chosen around 

days for which there are extensive database air quality and meteorology measurements, including 

measurements speciated data, and upper air measurements. Finally, a sufficient number of days 

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

In addition to these primary criteria, the USEPA also suggests a set of secondary criteria 

that may be used in the selection of episodes. This set of criteria allows states to give preference 

to previously modeled episodes. This is a very valuable consideration, as the USEPA points out, 

since it can save modeling resources and effort. The USEPA also recommends selecting 

episodes that occur during the period corresponding to the 5 year current design value, 2000-

2004. Additional considerations include selecting episodes that include weekends and the 

selection of episodes meeting primary and secondary criteria in all other nonattainment areas, 

when participating in regional modeling. Using these criteria laid out by the USEPA, the 

NCDAQ systematically examined the data available to determine the best episodes for modeling. 

The USEPA suggests three approaches to PM2.5 modeling. The first, and preferred 

approach, is to use a photochemical model to model an entire year (or more). As an alternative 

to the preferred modeling, states can model a minimum 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. PM2.5 Episode Selection 

With the advances in computing and storage technologies, and aided by regional 

modeling efforts, the NCDAQ will model an entire year for the Hickory/Triad PM2.5 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 PM2.5 concentrations. 

Efforts were made to determine an appropriate year to model. One of the secondary 

criteria would suggest using episodes drawn from 2001-2003, as this period corresponds to the 

design value period for which nonattainment designations 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 adjusted to correspond to a different base case year 
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during modeling efforts. Differences between the base case and base line (typical) inventories 

are explained in Section 6. 

Additionally, the selection of 2002 as the base case year also fulfills the secondary 

criteria, which suggests states give 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 PM2.5. The VISTAS modeling employs “one atmosphere” 

modeling, or modeling of all atmospheric constituents, including particulate matter and ozone. 

This modeling is done in parallel to capture interactions between various compounds. Since 

PM2.5, along with ozone and regional haze, is being modeled as part of the VISTAS modeling 

efforts, its data can easily be extracted from the modeling results. 

The USEPA guidance suggests that when selecting a representative year, one should 

examine the annual mean concentration and the pattern 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 sites. 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 concentration for the 2002 calendar year is generally 

close to either design value, usually within ± 0.5 μg/m3

. When examining the patterns of the 

mean quarterly concentrations, the representative year should follow a similar pattern. 

Table 3.2-2 shows the mean quarterly concentrations 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; specifically the third quarter has the highest mean 

concentration, with the lowest concentrations in the first or fourth quarter. 

Table 3.2-1: Annual mean concentration and design value for sites in nonattainment areas 

Monitoring 

Site County Annual Mean Design Values 2002 – 

DV(01-03)

2002 – 

2001 2002 2003 2004 2001-2003 2002-2004 DV(02-04)

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 difference 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 quarter, red numbers indicate highest quarter. 
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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 PM2.5 values generally coincide with high ozone values since both need similar 

atmospheric condition to accumulate. Both PM2.5 and ozone thrive in stagnant air masses in the 

summer, which result from reduced wind conditions that limit vertical mixing in the atmosphere. 

The limited mixing allows pollutants to collect near the surface, driving the concentration of both 

pollutants up. The 2002 season was examined to verify that it was representative of the nature of 

PM2.5 formation in the Hickory and Triad areas to further support its use in modeling. The 

following section details the results of the study. 

3.3. Episode Classification 

Since the NCDAQ is moving towards modeling an entire calendar year, a general 

discussion of episodes is presented in this section. The same categories of meteorological 

scenarios exist through out the year. 

3.3.1. Definition of a PM2.5 Episode 

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

for PM2.5 is violated; making it is difficult to define PM2.5 episodes. Further complicating the 

issue is the fact that North Carolina rarely has days with a 24-hour average PM2.5 concentration 

greater then 40.5 μg/m3, which is the lower end cutoff of the Code Orange range of the Air 

Quality Index (AQI). As an arbitrary method to classify episodes to ensure various 

meteorological scenarios were selected, days with a 24-hour PM2.5 concentration greater than 

15 μg/m3 (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 PM2.5 concentrations for the year, with each block as a 

separate month. Days with a 24-hour concentration greater than 15 μg/m3, but less than 30 

μg/m3, are shade light gray. Days greater than 30 μg/m3 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 monitors that report every three days. The 

Mendenhall is a Tapered Element Oscillating Microbalance (TEOM) monitoring site and reports 

everyday. 
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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 3rd day. White blocks denote PM2.5 < 15 μg/m3

, light gray 15-

30 μg/m3

, dark gray > 30 μg/m3, 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 NC 
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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 Concentrations 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 3rd day. White blocks denote PM2.5 < 15 μg/m3

, light gray 15-

30 μg/m3

, dark gray > 30 μg/m3, 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 NC
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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 becomes apparent the months of May through 

October have the days with the highest PM2.5 concentrations. The peak concentrations of the 

year occur during the months of July and August, and remain below 40.7 μg/m3

. There is one 

isolated episode on December 7, 2002, which had a 24-hour PM2.5 concentration of 49.2 and 

43.7 μg/m3

 at the Mendenhall and Lexington monitoring sites, respectively. These high values 

are likely in association with a winter storm that struck North Carolina on December 4th and 5th, 

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

The column labeled “Met” contains a classification 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 systems. Systems are defined as either over North 

Carolina, to the south of the State, 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 PM2.5, as well as a front stalled near/over 

North Carolina, slow frontal approach, 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 scenarios that occur less frequently, but have an 

impact on PM2.5 concentrations in North Carolina. Tropical influence is noted in combination 

with the main surface feature on land. The following 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 month for the identified episodes. Several 

seasonal patterns can be ascertained from Table 3.3.1-1. 

Table 3.3.2-1: Count based on the meteorological scenarios associated with elevated PM2.5

High Pressure

Stagnation under high pressure is responsible for, roughly 57% of the noted PM2.5 

episodes in 2002 (see Table 3.3.2-1). High pressure builds into the southeast, settling nearly 

over North Carolina. The high leads to clear and calm conditions across the state and most of the 

region. With light or calm winds and clear skies, 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 10 143
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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 heating to mix out the inversion. Highest particle 

pollution may occur when clouds build in shortly after sunrise. The clouds diminish the daytime 

surface heating and reduce the amount of atmospheric mixing, allowing the nighttime inversion 

layer to persist well into the daylight hours. High-pressure episodes may be quickly followed by 

a frontal approach (see section below), which can lead to a further rise in particle pollution. If 

high pressure can remain overhead for 2 to 3 days, PM2.5 can rise to 20 to 25 μg/m3 in the winter, 

and as high as 40 μg/m3 in the summer. 

A milder rise in PM2.5 may occur if high pressure is positioned further south, over 

northern Florida or to the east of Jacksonville. A west to southwest flow develops over North 

Carolina. The flow has a long history over land, originating from the central Gulf coast region to 

the lower Mississippi River valley. Since the high pressure is centered further south and/or east, 

winds remain light to moderate, preventing a strong nocturnal inversion from developing. The 

winds also mix pollutants within a deeper layer of the atmosphere. Under this scenario, PM2.5

generally reaches a maximum between 15 to 20 μg/m3

. 

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 the 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 scenarios of the 2002 season involved frontal 

passage. Approximately 31% of the elevated episodes of 2002 were either classified as frontal 

approach, front stalled near/over North Carolina, slow frontal approach, or high pressure 

followed by a frontal approach (See Table 3.3.2-1). 

North Carolina experiences numerous frontal passages during the year. Mass 

convergence ahead of a cold front typically yields 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 moving front may cause a rise 

in PM2.5 for a few hours, causing a spike in 1-hour concentrations and a slight 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 PM2.5 concentration, leading to elevated 24-hour concentrations. In 

North Carolina, a fronts tend to slow when a long-wave upper level trough is located over the 

eastern United States, between the Appalachian Mountains and the Mississippi River. As a cold 

front plunges southward from Canada into Alabama and Mississippi, it then slows down as it 

becomes oriented parallel to the upper level flow. In this situation, North Carolina remains 

within the dirtier air just to the east of the frontal boundary, where mass convergence leads to a 

buildup of particle pollution. Eventually the cold front pushes south and east of North Carolina, 

bringing cleaner air into the state. An example of this type of event occurred on November 25, 

2002. 
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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 region, the upper level trough weakens, leaving no upper 

level dynamics to support the frontal 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 pressure cause particle pollution concentrations to 

remain elevated, and can lead to increased concentrations. 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 coast, 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 the Appalachian Mountains and Piedmont. In the wake of the 

departing storm, high pressure builds 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 albedo of the snow suppresses surface heating and 

convective mixing, preventing the nocturnal inversion from mixing out and keeping particle 

pollution at high concentrations. 

Especially severe winter storms may cause extreme rises in PM2.5. 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 experienced a severe ice storm on December 4, 

2002. Power was knocked out for several days across a wide swath of the Piedmont. Wood 

combustion for both residential heating and debris removal caused PM2.5 to top out above 40 

μg/m3. Due to the anomalous nature of severe winter storms, it is not appropriate to include 

PM2.5 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 northeast will push colder air southward to the 

east of the Appalachian mountains, down into the Carolinas. 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 stratus cloud layer usually forms as the warm air overrides the cold air. 

Particle pollution is able to build within the inversion layer, 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 (see Table 3.3.2-1). The presence of tropical 

system offshore during the late summer to early fall can degrade PM2.5 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 prevail far out from the system’s center, allowing 

pollutants to build up. The subsidence 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 PM2.5 concentration to levels between 15 and 25 μg/m3

. 
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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 PM2.5, with concentrations on the order of 18 μg/m3

. 

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, leading to the formation of an inversion layer. 

The low-pressure system approach by morning shrouds the area in cloud cover, making it 

difficult to erode the inversion layer. The pollutants are trapped at the surface by the inversion 

layer, but dissipate after the low-pressure system moves east. 
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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 configuration of regional meteorological, 

emissions processing, and photochemical air quality models used by the VISTAS regional haze 

modeling study. The underlying science behind each component of the overall modeling system 

are identified and discussed briefly in this section. Although the configuration of each of the 

modeling components has been selected as the culmination of intensive study by VISTAS, there 

remains the possibility that certain algorithms and parameter settings may still be updated prior 

to the running of the final annual 2002 base case 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 determine appropriate refinements to the model 

codes, input databases, and post-modeling analysis procedures. Notable limitations of the 

models, relevant to their intended 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 PM2.5 attainment 

demonstration omodeling should utilize the following suite of models: 

• MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, 

prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate matter, and regional haze regulatory 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 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, visibility, and acid deposition 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 (NCAR) 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 through enhancements provided by a broad user 

community (e.g., Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 
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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. 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 applicationsoriented prognostic models, MM5 has been selected as the preferred meteorological model for 

this effort. This section provides 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, prognostic model that has been used widely in 

regional air quality model applications (Seaman, 2000). The basic model has been under 

continuous development, improvement, testing, and has been openly peer-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 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 – 

zonal wind component, v – meridional wind component, 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 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-hydrostatic 

MM5 (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., 

Comprehensive Air Quality Model with Extensions - 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 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-use for 
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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 scheme 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 

studies have involved comparisons with other prognostic models such as the Regional 

Atmospheric Modeling System (RAMS) and the Systems Application International Mesoscale 

Model. The MM5 enjoys a far richer application history in regulatory modeling studies 

compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 

recent regional scale air quality application studies 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 modeling, Barons Advanced Meteorological Systems, 

LLC (BAMS) (Olerud and Sims, 2003), MM5 was 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 used by BAMS modelers will consist of the 

following: 

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

• Two way nesting, no feedback 

• Initialization and boundary conditions from Eta 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 method 
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• 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 modeling exercise. Some of 

the plots and statistics to be generated include: 

• Spatial plots of model predictions with the 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 temperature, mixing ratio, wind speeds, wind 

direction, and cloud cover. These will include time series of modeled/observed 

means, bias/error, and index of agreement. 

• Daily accumulated precipitation plots of modeled versus observed. 

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

domains. 

• Comparison of satellite versus modeled cloud images. 

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

• Comparison of profiler observations with modeled winds 

4.2.4. Meteorological Data 

Meteorological data are being generated using the MM5 prognostic meteorological model 

by 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. 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 data to be visualized on the model fields via 

Package for Analysis and Visualization of Environmental 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 match 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 derived primarily from rain gauges. 

For aloft analyses, standard sounding observations from the National Center for 

Environmental Predictions (NCEP) ds353.4 archive are processed. These observations are 

quality controlled and used to produce model/observation skewT sounding plots for selected 

sites. Additionally, the observations are integrated into sigma levels that match the MM5 

specifications and subsequently can be statistically analyzed for performance at sigma levels 9, 

17, and 22 (~500m, ~1600m, ~3400m, respectively). Qualitative profiler plots showing 
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model/observed hourly winds are also created based upon the data stored at the Forecast Systems 

Lab. 

4.3. SMOKE Emissions Modeling System 

The SMOKE Emissions Processing System Prototype was originally developed at the 

Micro-computing Center of North Carolina (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 

outputs from transportation travel-demand models. 

SMOKE was originally designed to allow emissions data processing methods to utilize 

emergent high-performance-computing 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 includes 

biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory System, 

version 3 (BEIS3). 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 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 major features that make it an attractive component of the 

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), and the Emissions Preprocessor System 2.x (EPS). 

It supports both gridded and county total land use scheme for biogenic emissions modeling. 

Although it is not necessary for our purposes, SMOKE can accommodate emissions files from 

up to 10 countries and any pollutant can be processed by the system. 

Recent computational improvements to SMOKE include: 

• Enhanced disk space requirements compared with other emissions processing 

software 
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North Carolina Attainment Demonstration August 21, 2009 

• Run-time memory allocation, eliminating any need to recompile the programs for 

different inventories, grids, or chemical mechanisms 

• Updated Input/Output Applications Programming Interface libraries 

A number of science features have been incorporated into the latest version of SMOKE 

(version 2.0), including: 

• Any chemical mechanism can be used to partition 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 emissions 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 solvent 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 file 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 Quality Assurance (QA) capabilities 

• Improved temporal allocation 

The Carolina Environmental Program at the University of North Carolina is continuing model 

development activities with SMOKE. The emissions modeling will employ the SMOKE version 

2.0, released on September 30, 2003. The SMOKE executables, 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, fine particulate matter, 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 
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from the challenges posed by the 1990 CAA as amended and the USEPA’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). The USEPA completed the initial stage of development with Models-3 and 

released the CMAQ model in mid 1999 as the initial operating science model under the Models3 framework (Byun et al., 1998b). The most recent rendition is CMAQ version 4.4, which was 

released in October 2004. 

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). 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 distribution of the Models-3 system. 

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

ozone and PM2.5 attainment demonstrations SIPs, as well as the regional haze SIP, having a 

model that can handle both ozone and fine particulate 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 particulate matter size distribution using 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 SO2 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 Model 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 a 

reversible semi-volatile scheme whereby VOC emissions can be converted to condensable gases 

that can then form SOA and then evaporate 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 domain, 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 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. MM5 
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North Carolina Attainment Demonstration August 21, 2009 

meteorological output based on the Pleim-Xiu 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 Bond version 4 (CB4) gas-phase, RADM aqueousphase, and AERO3/ISORROPIA aerosol chemistry 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 evaluated three photochemical mechanisms: CB4, CB4-

2002 and SAPRC99. CB4-2002 produced nearly identical results as CB4 but took much longer 

to run since it is only implemented in the slower SMVGEAR (Sparse Matrix Vectorized Gear) 

chemistry solver, compared to CB4 that is also implemented in the faster Euler Backward 

Iterative chemistry solver. Thus, CB4-2002 was dropped from consideration. Comparisons of 

CB4 and SAPRC99 found they produced mostly similar but different model performance. 

However, no one mechanism performed better than any other mechanism across all species, 

sites, and periods. The testing only evaluated the mechanism’s base 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 influence of the ICs. The CMAQ Boundary Conditions 

(BCs) for the initial simulations will be based on seasonal averages 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 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 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, temperature, moisture) and location (e.g., mountains, 

coastal, east, west) different LSM_PBL configurations 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 locations and was never the 

worst-performing configuration. However, there are numerous 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 do not appear physically realistic and may affect air quality modeling. Although the 

MM5 PX_ACM configuration model performance in the southeastern United States mostly met 
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North Carolina Attainment Demonstration August 21, 2009 

performance benchmarks, the performance was much worse in the western United States. 

Additionally, there is a stochastic component of real world meteorology that is not captured by 

MM5. For example, for some air pollution episodes stagnation is an important attribute that 

MM5 fails to simulate well as it tries to organize the flow fields. However, the MM5 model 

represents approximately 20 years of development by various researchers. 

4.5.2. SMOKE 

In early testing, a number of undocumented features of the SMOKE 1.5b version 

necessitated re-runs of the emissions processing software to 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 newly-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 which would require additional SMOKE runs or 

potential SMOKE modifications or alternate modeling methods, the NCDAQ will immediately 

notify stakeholders and make recommendations for resolving the issues. Upon receipt of 

technical direction from the stakeholders, 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.), SMOKE does not include as many 

features. The NCDAQ will keep abreast of SMOKE 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 limitations in the model itself include the nitrate 

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

overestimations and summer underestimations. Other science limitations in the current version 

of CMAQ include inadequate treatment of sea salt and the assumption that all 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, such 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 categories: 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, exercising, and evaluating the MM5 model for the 

2002 season consist of various fixed and variable inputs. 
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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 datasets from the 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 PSU/NCAR 10 min. (~18.5 km) databases while for the 

12-km grids, the USGS data are available. 

• Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from 

operationally analyzed fields derived from the NCEP ETA (40 km resolution) following 

the procedures outlined by Stauffer and Seaman (1990). These 3-hr synoptic-scale 

initialization data include the horizontal wind components (u and v), temperature, and 

relative humidity at the standard pressure levels, plus sea-level pressure and ground 

temperature. Here, ground temperature represents 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, 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 National Weather Service (NWS) and NMC 

datasets previously discussed. 

• Multi-Scale FDDA: The standard "multi-scale" data assimilation 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 year 
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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 fields that will be generated by the CMAQ 

MCIP2.3 processing of the BAMS MM5 output 

• Three-dimensional hourly emissions generated by SMOKE 

• Initial conditions and boundary conditions 

• Topographic information 

• Land use categories 

• Photolysis rates generated by the CMAQ JPROC processor 
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5.0 Grid Specifications and Modeling Domains 

This chapter summarizes the model domain definitions including the model domain, 

resolution, map projections and nesting schemes 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 the MM5 horizontal domain as the outer most, 

blue grid with the CMAQ 36-km domain nested in the MM5 domain. To achieve finer spatial 

resolution in the VISTAS states, the NCDAQ will also use a one-way nested high resolution grid 

with a 12-km grid resolution. Figure 5.1-2 shows 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 the outer most, blue grid, with the CMAQ 36-

km domain nested in the MM5 domain. 
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North Carolina Attainment Demonstration August 21, 2009 

Figure 5.1-2: The 36-km CMAQ continental 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. 
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Both MM5 and CMAQ employ the RPO unified grid definition for the 36-km continental 

domain. The RPO unified grid consists of a Lambert-Conformal map projection using the map 

projections parameters listed in Table 5.1-1. 

Table 5.1-1: RPO Unified Grid Definition. 

The MM5 36-km grid includes 164 cells in the 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 the MM5 coarse grid is also nested in the Eta 

grid, 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 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 suggestion 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” refers to the grid mesh defined at the vertices of the grid cells 

while “cross” refers to the grid mesh defined by the grid cell centers. Thus, 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 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 5.2-1 lists the layer 

PARAMETER VALUE 

projection Lambert-conformal 

1st true latitude (alpha) 33 degrees 

2nd true latitude (beta) 45 degrees 

x center - 97 degrees 

y center 40 degrees 
NCDAQ 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 simulations. The effects of layer averaging were 

evaluated in conjunction with the VISTAS modeling effort and were 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. 

Table 5.2-1: 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 1.000 1000 0 0 
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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 emission inventory source classifications; stationary point sources 

area sources, off-road mobile sources, on-road mobile sources, and biogenic sources. Stationary 

point sources are those sources that emit greater than a specified tonnage per year and the data is 

provided at the facility level. Stationary area sources are those sources whose emissions are 

relatively small but due to the large number of these sources, the collective emissions could be 

significant (i.e., dry cleaners, service stations, etc.) Off-road mobile sources are equipment that 

can move but do not use the roadways, i.e., lawn mowers, construction equipment, railroad 

locomotives, aircrafts, etc. On-road mobile sources 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 formatted for processing through the SMOKE emissions processing 

system, which formats the data into air quality model ready files. On-road mobile source 

emissions are estimated within the SMOKE system, which uses the USEPA’s MOBILE6.2 

model, with modeling meteorology and various mobile inputs. The biogenic emissions are also 

estimated within the SMOKE system, using the USEPA’s BEIS model, with modeling 

meteorology. 

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

inventories. The first is the actual base year inventory. This inventory is the base year emissions 

that correspond to the meteorological data, for this modeling effort is 2002. These emissions are 

used for evaluating the air quality model performance. 

The second type of inventory is the typical base year inventory. 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 inventory is an inventory developed for some future year for which 

attainment of the PM2.5 standard is needed. For this modeling project, the future year inventory 

will be 2009, the last complete year for which the standard must be attained. It is the future year 

base inventory that control strategies and sensitivities are applied to determine what controls, to 

which source classifications, must be made in order to attain and maintain the PM2.5 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 for splitting the point source inventory is that the EGU sources 

account for the majority of the point source NOx emissions and hour specific data is available for 

these sources through the USEPA’s acid rain database. Using this more refined data will help 
NCDAQ 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 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. 

6.1.1. Electric Generating Units 

Actual Base Year Inventory

For EGU sources with the USEPA reported 2002 CEM data or with 2002 hourly 

emissions provided by stakeholders, actual hourly data will be used. For the sources where the 

USEPA 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. The annual emission 

values that have been provided will be maintained, but will be distributed using hourly to annual 

profiles. For sources where hour-specific data was 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 NOx, SO2, and heat input 

data will be collected from the 2002 CEM datasets, and used to develop unit-level temporal 

distributions. The hourly, day of week, and monthly specific temporal profiles will be used in 

conjunction with the emissions inventory supplied emissions data to calculate hourly EGU 

emissions by unit. 

Typical Base Year Inventory

Since the NOx emissions from EGU sources are a significant part of the emissions 

inventory, a typical base year emissions inventory was developed for these sources to avoid 

anomalies in emissions due to variability in meteorology, economic and outage factors in 2002. 

This approach is consistent with the USEPA’s modeling guidance. 

To develop a typical year 2002 emissions inventory for EGU sources, for each unit the 

average CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to 

generate a unit specific normalizing factor. This normalizing 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 monitoring 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 was used. If a unit was shutdown in 2002, but not 

permanently shutdown, the emissions and heat inputs 2001 (or 2000) were used in the 

normalizing calculations. 

Future Base Year Inventory

As part of the VISTAS modeling, VISTAS and the Midwest Regional Planning 

Organization (MRPO) contracted with ICF Resources, L.L.C., to generate future year emission 

inventory for the electric generating sector of the contiguous United States using the IPM. IPM 
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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 projection of the behavior of the power system 

over a specified future period. The optimization logic determines the least-cost means of 

meeting electric generation and capacity requirements while complying with specified 

constraints including air pollution regulations, transmission bottlenecks, and plant-specific 

operational constraints. The versatility 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 the 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 applications 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 analysis is the USEPA’s National Electric 

Energy Data System Database, with changes based upon the comments and technical 

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

installations of NOx, SO2 and particulate matter controls, NOx emission rates, SO2

emission limits, capacity of existing units, heat rate and unit identifications 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 selected years within the study horizon known 

as run years. Each run year represents several calendar years in the study horizon, and all 

calendar years within the study horizon are mapped to their representative run years. 

Although results are only reported for the run years, IPM takes into account all years in 

the study horizon while developing the projections. 

iii) The Duke Power and Progress Energy control technology investment strategies for 

complying with North Carolina’s Clean Smokestacks 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 CFR Parts 51 et. al., Supplemental Proposal for 

the Rule to Reduce Interstate Transport of Fine Particulate Matter and Ozone, proposed 

on June 10, 2004. Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, 

Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, 

Michigan, Minnesota, Mississippi, Missouri, New Jersey, New York, North Carolina, 

Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, 

Wisconsin are the states affected by the CAIR SO2 and the CAIR annual NOx policies 

starting 2010. Connecticut is affected by an ozone season NOx policy. The CAIR plants 

affected by the annual NOx policy are capped at 1.6 million tons starting 2010 and 1.33 

million tons starting 2015. The power plants affected by the CAIR SO2 policy have to 

surrender two Title IV SO2 allowances for every ton of SO2 emitted starting 2010 and 

three Title IV SO2 allowances for every ton of SO2 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-EGU category will use annual emissions, which will 

be temporally allocated to month, day, and hour using source category code (SCC) based 

allocation factors. These factors will be based on the cross-reference 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 (CERR) 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 year data is to use recently updated growth 

and control data consistent with 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 the 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 NOx Control Measures Adopted by 

States and Nonattainment Areas for 1999 NEI Base Case Emissions Projection Calculations. 

The report identified and compiled a listing of the VOC and NOx 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 several counties in Virginia), 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 NOx Reasonably Available Control Technology 

(RACT) in 1-hour Ozone SIP areas were implemented prior to 1999. These reductions should 

already be accounted for in the VISTAS 2002 inventory since the 2002 inventory was based on 

2002 actual emissions submitted by the States. 

NOx SIP Call

For non-EGU sources, Phase I of the NOx SIP call applies to large industrial boilers and 

turbines, and cement kilns. States in the VISTAS region affected by the NOx SIP call have 

developed rules for the control of NOx 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 ozone season allowances. 

For the sources within North Carolina, the NCDAQ has decided to use the 2007 emission 

allowances for the 2009 future year inventory. The 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 NCDAQ has established emissions 

caps for three facilities affected by the Phase II NOx SIP call rule and will apply these caps to the 

future year inventory. 

For the other states in the VISTAS region, affected units were identified using the same 

methodology as was used by the USEPA in the proposed 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 level of 82 percent for natural gas-fired internal combustion engines 

and 90 percent for diesel or dual fuel categories. Therefore, these control levels were applied to 

the identified sources. 

Maximum Achievable Control Technology Regulations

The USEPA anticipates reductions in particulate matter and SO2 as a result of the 

Industrial Boiler/Process Heater Maximum Achievable Control Technology (MACT) standard. 

The methods used to account for these reductions are the same as those used for the Interstate 

Air Quality Transport Rule. Since the attainment demonstration is utilizing one atmosphere 

modeling, the reductions for these pollutants 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 staff. 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 already 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 inventory does not include the NOx 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 are affected by two October 2003 Clean Air Act 

settlements under the USEPA Petroleum Refinery Initiative. The refineries are: (1) the Chevron 

refinery in Pascagoula, Mississippi; (2) the Ergon refinery in Vicksburg, Mississippi; and (3) the 

Ergon refinery in Newell, West Virginia. Although these sources are not within North Carolina 

or South Carolina, the expected emission reductions will be accounted for in the 2009 modeling. 

NOx RACT in 8-hour Ozone SIP

The NCDAQ will make every effort to include NOx RACT controls for 8-hour ozone 

nonattainment areas in the VISTAS region. However, since cost is a factor of consideration in a 

RACT determination, it may not be known at the time of the final modeling which sources will 

be subject to actual controls. 

Clean Air Interstate Rule

As stated in the preamble to the CAIR rule, 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 relatively 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 indicator of collective activity, such as fuel 

usage, number of household or population. Thus, a variety of activity level data is collected, 

including, United States Census economic data, forestry and agriculture agency data, and other 

data sources. Stationary area source emissions are estimated on the county level. 

Actual Base Year Inventory

A portion of the area source 2002 base year inventory for North Carolina was developed 

by the NCDAQ and provided to the 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 husbandry, 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 approaches were used to calculate area source 

emissions. Emission factors may be grouped as per capita emission factors; commodity 

consumption-related emission factors; and level-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 collective activity for each source category 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 are gathered from various sources (government 

reports, census, trade groups, employment data, direct surveys, etc.) as appropriate. 

For the animal husbandry and fertilizer application 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 particulate matter in North Carolina and therefore, do not 

significantly impact the PM2.5 nonattainment issues and would not affect the PM2.5 attainment 

modeling demonstration. 

For wildland fires, i.e., wildfires and prescribed 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 the fire emissions, rather than relying on standard distribution 

profiles. Data will be obtained through consultation with stakeholders that participate in the 

VISTAS Fire Special Interest Work Group. The fire data will be split into two groups, small 

fires estimated on a county level and treated as an area source; and large fires that will be treated 

as a point source. 

Typical Base Year Inventory

The actual base year inventory will serve as the typical base year inventory for all area 

source categories except for wildland fires. For this source category, development of a typical 

year fire inventory provided the 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 Fire Special Interest Work Group was consulted and 

decided to use State level ratios 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 develop 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 approach used to calculate the future base year 

emissions for stationary area sources was as follows: 

• Use the final 2002 VISTAS base year inventory as 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 the 2002 base year inventory based on existing 

controls reported for the 2002 base year inventory. 

• Controls (including control efficiency, rule effectiveness and rule penetration) provided 

by the States or originally developed for use in estimating projected emissions for the 

USEPA’s Heavy Duty Diesel rulemaking emission projections and used in the CAIR 

projections were then used to calculate controlled 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 controlled emissions to the appropriate 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 either 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 locomotives, 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 USEPA’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 inventory will be used for both the actual and 

typical base year emissions inventories. All non-road mobile source emissions, except for 

aircraft engines, commercial marine vessels and railroad locomotives, were estimated using the 

USEPA NONROAD2005 model. This model predicts the emissions for non-road equipment 

based upon the year inputted into the model. 

For railroad locomotive emissions, emission factors were supplied by the Procedures for 

Emission Inventory Preparation Volume IV: Mobile 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 of 
NCDAQ 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 airports 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) Emissions and Dispersion Modeling System version 

4.2, when there is sufficient detail to employ it. 

Commercial marine emissions are estimated 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 takes into consideration 

rules that are in effect that could impact the emissions from these source categories. For the four 

largest airports in North Carolina, the FAA’s Terminal Area Forecast will be used to project 

growth in aircraft emissions. 

For the commercial marine, railroad locomotives and the remaining airport emissions, the 

VISTAS contractor will project the future base year emissions using the following guidelines: 

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

inventory. 

• Detailed inventory data (both before and after controls) for 1996 and 2010 will be 

obtained from the USEPA’s Clean Air Interstate Rule Technical Support Document. 

Straight-line interpolations between 1996 and 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 NOx emissions in North Carolina, and 42 percent of the NOx

emissions in South Carolina. Emissions from motor vehicles occur throughout the day while the 

vehicle is in motion, at idle, parked, and during refueling. Each of these emissions sources needs 

to be estimated in order to properly 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 traveled (VMT) as provided by the North Carolina 

Department of Transportation (NCDOT), 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 develop the on-road mobile source emissions 

estimates for carbon monoxide (CO), NOx, 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 those vehicles are traveling on, and any control 

programs (e.g., emissions inspection programs). Inputs are combined with gridded, day-specific 

temperature data to calculate the gridded, temporalized emission estimates. Of note, whereas the 

on-network emissions estimates are spatially 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 Federal Highway Administration version 2.0 highway networks and 

population. For the North Carolina 36/12-km modeling, 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 the 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 collect hourly speeds per functional class for this 

modeling effort. Information from Travel Demand 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 registration data is provided. The only areas with “area 

specific” registration data include the Charlotte/Gastonia, Raleigh/Durham and Greensboro/ 

Winston-Salem areas. The latest available age distribution 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 MOBILE6.2 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 will be used to generate the vehicle mix for all road types except 

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

Version 2 of the SMOKE model uses the MOBILE5 eight vehicle classification format 

for the vehicle mix. Therefore, the current vehicle mix format used by the NCDAQ had to be 

converted from the sixteen MOBILE6 vehicle classification format to correlate to the MOBILE5 

eight vehicle classification system. This was done using the guidance provided by the USEPA. 
NCDAQ Modeling Protocol 45 

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6.4.4. Temperature Assumptions 

MOBILE6 in the SMOKE emissions model uses the gridded (modeled) meteorology data 

to calculate temperature. Spatial and temporal temperature averaging 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 counties. This program tests emissions 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. The program was implemented in the original nine 

tailpipe test counties and expanded 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 tests, two separate programs must be incorporated into the 2002 

input files. The implementation dates of each program are also included in the input files. 

6.4.6. RVP Assumptions 

Reid Vapor Pressure (RVP) reflects a gasoline’s volatility. North Carolina has adopted 

the Phase II RVP of 7.8 psi during June-September as a control 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 data will be provided by the NCDOT. 

6.5. Biogenic Source Emissions 
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A revised version of a commonly used biogenic emissions model, the Biogenic 

Emissions Inventory System, has recently been developed and tested by the USEPA 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 North American Research 

Strategy for Tropospheric Ozone (NARSTO) modifications, 

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

A series of sensitivity modeling simulations has been completed and concluded that the 

more recent BEIS-3 methodology will impact base case 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 control 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 precipitation 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 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. 

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 the most up to date available. The temporal profiles used to 

disaggregate the annual emissions to the appropriate month, day and hour are the latest available 

profiles provided with the SMOKE model with the exception of the EGU profiles, which will be 

developed based on CEM data. 

For each model-ready emissions inventory, separate air quality model-ready files will be 

created for the EGU point sources, non-EGU point sources, area sources, dust, low-level fires, 

elevated fires, non-road mobile sources, on-road mobile sources, and biogenic emissions. 
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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 combined efforts to be employed by VISTAS and the 

NCDAQ. The VISTAS contractors will perform QA on modeling inputs and outputs for the 

modeling region as a whole. The NCDAQ will perform QA on their respective emission 

inventories, as well as look at near state data for reasonableness. Additionally, the NCDAQ will 

review the modeling outputs for reasonableness. 

7.1. Quality Assurance Objectives 

In December 2002, the USEPA published 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 USEPA 

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 USEPA 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 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 focus primarily on documenting data sources and 

QA of data processing performed by the model team. The QA objectives for specific aspects of 

the project are discussed below, and 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 the most critical steps in performing air 

quality modeling studies. Emissions processing can be time consuming and involves complex 

manipulation of many different types of large datasets. If errors are made and rigorous QA 
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measures are not in place, these errors may remain 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 from 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 appropriate appraisal of the emissions data and 

estimates for the VISTAS States. Other tasks will include any additional translation from mass 

emissions files into the emissions modeling input file structure necessary for modeling. The 

VISTAS 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. 

This multistep emissions QA/QC approach 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. 

7.2.2. Emissions Modeling QA/QC 

Modeling QA involves performing data quality checks, assuring simulation accuracy, and 

recognizing and identifying problems as they happen; it is the process of looking for glaring 

faults in the model input and output data (I/O) and determining whether the input data are 

producing the desired results. Scrutiny of the I/O using standard statistical analyses can reveal 

problems in the data and/or the model setup. Using 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 data 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/her 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. 
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Lead modelers will oversee the entire modeling 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, organize 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 periodic reviews/audits of the data and modeling process. Outside 

reviewers will consist of peers, co-workers not working directly on the inventory in question, 

state inventory contacts and stakeholders. 

7.2.3. SMOKE Log Files 

Each of the programs that make up SMOKE produces a log at run time. Stored in a 

single directory for each unique simulation, the logs 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 (collectively called "flags") that occurred during model execution. 

Generated as text files, the logs are named according to the program that created them and the 

emissions source modeled by the simulation, and the names include identifiers that distinguish 

the simulation from all others. The logs are usually 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 SMOKE. One set is created by the program 

Smkmerge and the other by the program Smkreport. While both programs allow users to 

configure the content of the reports, Smkreport is a more powerful reporting program that was 

designed specifically as a QA tool. Controlled by configuration files, Smkreport can create text 

reports at every step in the emissions 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 assigned to different temporal, spatial, and 

chemical profiles; normalize emissions by population; and report the amount of emissions 

allocated to each vertical layer per model-hour. Smkreport also allows the targeted reporting of 

emissions at specific sources, plants, grid cells, or subdomains. 

The program Smkmerge creates either state- or county-level reports at each of the major 

steps in the emissions generation process (spatial 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 report biogenic emissions totals, 

whereas Smkreport can create reports only for anthropogenic emissions sources. 

7.2.5. Visualization Tools 

Visualization is an important part of the QA/QC procedure. Viewing bar charts and pie 

charts of the data verifies that more populous urban counties have greater emissions then the 

rural counties. Additionally, the PAVE visualization tool is used to graphically view the data to 

make sure that the data appears reasonable both spatially and temporally. 
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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 modeling results to ensure that the modeling results 

look reasonable. 

7.2.6. Document Tracking 

 In order to keep track of the details of modeling, certain notes and files will be 

maintained. Notes will be kept of files produced on desktop computers as to origin and purpose. 

These notes may be maintained in a logbook or by using the file 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 "Meteorological Gatekeeper" will be tasked with 

providing an independent review and quality assurance of the meteorological modeling and 

related datasets developed by the VISTAS meteorological modeling contractor (BAMS) and 

used subsequently by the emissions and air quality 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 case of meteorology, the Gatekeeper’s independent 

QA analysis of the MM5 meteorological datasets 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. 

 

In addition to having personal responsibility for the quality and chain of custody of the 

meteorological datasets supplied by other VISTAS contractors, the Meteorological Gatekeeper 

will be responsible for ensuring and maintaining the integrity of the data files uploaded to the 

project website. This website, hosted by UCR (University California – Riverside), serves as the 

repository of data for the ENVIRON/UCR/Alpine modeling centers and for the VISTAS 

Technical Analysis Workgroup participants. 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 PBL depths on a continental 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 exercise MCIP version 2.3 to read the MM5 outputs 

from BAMS and produce binary input files for the 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 2.3 output files from BAMS, the NCDAQ 

will verify the integrity of the file transfer (i.e., no missing and/or corrupted 

files). 
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• Since the CMAQ modeling domain is a subset 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 MM5 files with MCIP v2.3 using the 

predetermined MCIP options. The MCIP files will then be compared with those 

provided by BAMS to verify that identical results from the MCIP processing 

were obtained. 

• Horizontal and vertical plots of temperature, pressure, precipitation, modeled flow 

patterns, PBL heights, etc. will be created 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 simulation as an independent QA and 

evaluation of the database. 

• Plots constructed by the VISTAS Gatekeeper will be made available on the 

VISTAS website for viewing and download 

(http://pah.cert.ucr.edu/vistas/vistas2/index.shtml). 

7.4. 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 CMAQ modeling 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 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, a system of naming conventions was employed which uses environment variables in the 

compile and run scripts that guarantee that correct inputs and science options are used. A 

redundant naming system is also used so that the 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 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 
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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 original runs scripts 

and directory structure that were used in performing a model simulation). For example, if a 

simulation is performed with the SAPRC mechanism, instead of editing the original scripts to 

specify “$MECH = SAPRC,” a parallel directory structure with a new set of scripts to perform 

the SAPRC simulations will be created. 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. 

Post-processing QA of the CMAQ output files similar 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, errors in the emissions inputs 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. 
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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, PM2.5 and Regional 

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

• How well the model is able to replicate observed concentrations of PM2.5

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

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

PM2.5 to changes in emissions. 

Each component suggests a different type of evaluation procedure, with the first being 

“operational evaluation,” and the second being “diagnostic evaluation.” Since 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 evaluate model performance. Working with the 

knowledge that many states involved with the VISTAS regional haze work would want to apply 

some of the work to their individual SIPs for 8-hour ozone and PM2.5, plans were put in place to 

perform exhaustive analysis of all atmospheric 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 evaluating model performance. The standard set of 

statistical performance measures suggested by the USEPA for evaluating PM2.5 models includes: 

normalized bias, normalized gross (unsigned) error, fractional bias, fractional gross error, and 

fractional bias in standard deviations. Several other 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 modeling 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 constituents, including the major components of PM2.5. 

Typically, the statistical metrics are calculated 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 computed for the appropriate averaging times: 1 hr 

for ozone, and gas-phase precursors such as NO, nitrogen dioxide (NO2), CO, SO2, 8-hour for 

ozone, and 24-hour for sulfate, nitrate, PM2.5, and other aerosol species. 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, the statistics will 
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North Carolina Attainment Demonstration August 21, 2009 

be aggregated 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) the 20% haziest/cleanest days, in order 

to help elucidate model performance problems. For the purposes of the Hickory/Triad PM2.5

SIP, only the statistics for PM2.5 and its component species will be reported. The statistics for the 

pollutants and precursors will be reviewed internally for reasonableness. 

Table 8.1-1: Statistical Metrics 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical Expression Notes 

Accuracy of 

Paired Peak 

Ap

peak

peak

O

P − O

Coefficient of 

Determination 

r

2

( )( )

∑ ∑

∑

= =

=

− −

⎥

⎦

⎤ ⎢

⎣

⎡ − −

N

i

N

i

i i

N

i

i i

P P O O

P P O O

1 1

2 2

2

1

( ) ( )

Pi= prediction at time and 

location i; 

Oi = observation at time and 

location i; 

P = arithmetic average of 

Pi, i = 1, 2, . . ., N; 

O = arithmetic average of 

Oi, i = 1, 2, . . ., N; 

Normalized 

Mean Error 

NME 

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

1

1

Reported as % 

Root Mean 

Square Error 

RMSE 

( ) 2

1

1

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

Pi Oi N

Reported as % 

Fractional Gross 

Error 

FE

∑= +

− N

i i i

i i

P O

P O

N 1

2 Reported as % 

Mean Absolute 

Gross Error 

MAGE ∑=

−

N

i

Pi Oi N 1

1

Mean 

Normalized 

Gross Error 

MNGE 

∑=

N −

i i

i i

O

P O

N 1

1 Reported as % 

Mean Biased MB ∑( ) =

−

N

i

Pi Oi N 1

1 Reported as concentration 

Mean 

Normalized Bias 

MNB ( ) ∑=

− N

i i

i i

O

P O

N 1

1 Reported as % 

Mean 

Fractionalized 

Bias (Fractional 

Bias) 

MFB 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i i i

i i

P O

P O

N 1

2

Reported as % 
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North Carolina Attainment Demonstration August 21, 2009 

Table 8.1-1: Statistical Metrics (Continued) 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical Expression Notes 

Normalized 

Mean Bias 

NMB ( )

∑

∑

=

=

−

N

i

i

N

i

i i

O

P O

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 evaluation will utilize numerous graphical 

displays to facilitate quantitative and qualitative comparisons between CMAQ predictions and 

measurements. Together with the statistical 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 tools are intended to depict the model’s ability 

to predict the observed gaseous species, such as ozone, and fine particulate species 

concentrations. The core graphical 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 concentration maps (i.e., tile plots) 

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

• Soccer and bugle plots 

• 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 

• 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 improvement wherein a variety of diagnostic probing 

tools and sensitivity testing methods are used to identify, analyze, and then attempt to remove the 
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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 challenges that may necessitate focused diagnostic 

and sensitivity testing in order for them to be resolved. It is hoped that these diagnostic and/or 

sensitivity tests can be adequately carried out within the resources and schedule. Where 

practical, diagnostic or sensitivity analyses, if needed, could be performed on selected episodes 

within the annual cycle, thereby avoiding the time-consuming task of running CMAQ for the full 

2002 period. Below, the types of diagnostic and sensitivity testing methods that might be 

employed in diagnosing inadequate model performance and devising appropriate methods for 

improving the model response are identified. 

8.2.1. 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. These levels are: 

• Level I: Model algorithm evaluation and configuration testing 

• Level II: Model performance testing, uncertainty analysis and compensatory 

error diagnosis 

• Level III: Investigation of model output response (e.g., ozone, aerosol, 

deposition) to changes in precursors as part of emissions control scenario 

analyses. 

The Level I and Level II cover the aspect of 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, given that open community nature of 

CMAQ and the frequent science updates 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 compensatory error analysis 

• To reveal the parameters (or inputs) that dominate (or do not dominate) the 

model’s operation 

• To reveal propagation of errors through the model 

• To provide guidance for model refinement and data collection programs 

The merits of performing Level II sensitivity testing will depend upon whether 

performance problems are encountered. In addition, the number of tests possible, should 

performance difficulties arise, will be limited by the available schedule and resources. From past 

experience with CMAQ and other models, it is possible to identify examples of sensitivity runs 

that could be useful in model performance improvement exercises with the annual 2002 CMAQ 

simulation. These include: 
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The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix D.1 

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• Modified biogenic emissions estimates 

• Modified on-road motor vehicle emissions 

• Modified air quality model vertical grid structure 

• Modified boundary conditions 

• Modified fire emissions 

• Modified EGU emissions 

• Modified ammonia emission estimates 

• Modified aerosol/Nitric Pentoxide/Nitric acid (HNO3) chemistry 

• Modified ammonia and HNO3 deposition velocities 

Note that in a few cases [e.g., vertical grid structure, ammonium (NH4) emissions estimates], 

some sensitivity experimentation has already been carried out by VISTAS. To the extent that 

this information can help guide the future diagnostics analyses, this earlier work will be used. 

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

control scenario identification 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) traditional or “brute force” testing, (b) Decoupled Direct 

Method, (c) Ozone Source Apportionment Technology and Particulate Matter Source 

Apportionment Technology, and (d) Process Analysis. 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 air quality model in simulating the atmosphere’s 

response to significant emissions changes. 

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

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO2 emissions 

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point 

source NOx emissions 

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level 

NOx emissions 

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

The need to perform sensitivity experimentation (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 diagnostic experiments will hinge on the availability 

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. 

8.3. Air Quality and Ozone Column Data 

Data from ambient monitoring networks for both gas and aerosol species are used in the 

model performance evaluation. Table 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. 
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Additional data used in the air quality modeling include the Total Ozone Mapping 

Spectrometer (TOMS). TOMS data provides ozone 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 Ambient Data Monitoring Networks. 

Monitoring Network Chemical Species Measured Sampling 

Period Data Availability/Source 

The Interagency 

Monitoring of 

Protected Visual 

Environments 

(IMPROVE) 

Speciated PM2.5 and PM10 (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 PM2.5, 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, NO2, O3, SO2, PM2.5, 

PM10, 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 PM2.5 (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) 

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 PM2.5 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; SO4, 

NO3, HNO3, NH4, SO2), O3, 

meteorological data 

Hourly http://www2.nature.nps.gov/ard/gas/netdata1.ht

m 
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9.0 Control Strategy 

It is important to remember that photochemical models are tools; they do not make 

decisions. The results from photochemical models are one of several pieces of information that 

decision-makers must consider when adopting control strategies. To ensure that the modeling 

analyses provide information that meets the needs of the decision 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 section outlines the process behind developing and 

evaluating emission control strategies to be employed for the PM2.5 attainment demonstration. 

9.1. Control Strategy Design 

9.1.1. Emission Sensitivity Test 

To begin the process of control strategy design a series of simulations using across-theboard reductions of direct PM2.5 emissions and PM2.5 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 decisions 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, isopleths relating uniform reductions of PM2.5

precursor emissions to PM2.5 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 spatial 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 standards. 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 defensible control strategies. In addition, isopleths of population 

exposure can be prepared and used to assess proposed control measures in an integrated manner. 

9.1.3. Ranking Control Strategies 

Control strategies should be implemented in an ordered fashion that reduces both PM2.5

concentrations and population exposure. Emission controls that affect multiple pollutants should 

be sorted separately from single pollutant controls. Estimates of control levels that are expected 

in future years should be made. An attempt to reduce population exposure to a minimum each 

year while reducing PM2.5, can be made by looking at all potentially available controls. 
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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 analysis will be performed to 

investigate the predicted impact of each strategy on air quality and population exposure. The 

results of these analyses will be summarized both in tabular and graphical form to allow 

systematic comparison and contrast of all strategies. 

To assist decision makers in fully understanding the impact of proposed control 

strategies, the following products may be prepared as a part of the control strategy evaluation: 

1. Total PM2.5 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 PM2.5 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 control 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 PM2.5 formation on an urban 

and regional scale. 

Mandated controls will be modeled first (inspection and maintenance programs, NOx SIP 

Call, North Carolina’s Clean Smokestacks Act legislation, federal engine standards, federal fuel 

standards, etc.). If attainment of the annual PM2.5 NAAQS is not shown, additional alternative 

control strategies identified by the preceding steps, will be modeled until attainment is reached. 

A “frozen” future year dataset will be available 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 these 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. 
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10.0 Demonstration of Attainment 

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

the annual PM2.5 NAAQS. An attainment demonstration consists of (a) analyses which estimate 

whether selected emissions reductions will result in ambient concentrations that meet the 

NAAQS, and (b) an identified set of measures that will result in the required emissions 

reductions. Determining necessary emission reductions 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. PM2.5 Model Attainment Test 

The PM2.5 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 response factors (RRFs). A RRF is 

calculated for each constituent of PM2.5 and is then used to calculate the projected PM2.5

concentration for the future modeling year. Since the attainment test for PM2.5 utilizes both total 

PM2.5 and the individual component species, the test is referred to the Speciated Model 

Attainment Test (SMAT). In its entirety, SMAT consists of four basic steps. 

First, the observed quarterly mean PM2.5 and quarterly mean composition for each 

monitor is calculated. This is achieved by multiplying the monitored quarterly mean 

concentration of PM2.5 from FRM monitors by the monitored fractional composition of PM2.5

species for each quarter (e.g., (20% sulfate) x (15.0 μg/m3

 PM2.5 mass) = 3.0 μg/m3 sulfate 

mass). 

The monitored quarterly mean concentration of PM2.5 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 year. The fractional composition of PM2.5 species is derived 

from STN monitoring site data that has been processed by the “sulfate, adjusted nitrate, derived 

water, inferred carbonaceous material balance approach”, or SANDWICH method, so STN and 

FRM masses are equivalent. The mean composition derived from the SANWICH method 

includes the percent of PM2.5 that can be attributed to SO4, NO3, OC, EC, other primary 

inorganic particulates (or crustal materials), NH4, 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 = ([Cj, projected]/[Cj, current])i Equation 10.1-1

Where: 

Cj, current is the quarterly mean concentration predicted at or near the monitoring site with 

emissions characteristic of the period used to calculate the baseline design value for annual 

PM2.5. 

Cj, projected is the future year quarterly mean concentration predicted at or near the monitoring site. 
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For the third step, the component specific RRFs are applied to the observed air quality 

concentrations to projected quarterly species estimate. For each quarter, the current quarterly 

mean component concentration (step 1) are multiplied 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 components 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 μg/m3

) 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 

PM2.5 nonattainment test. 

Example 10.1: 

Step 1: Site X has the following observed quarterly mean PM2.5 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 quarterly 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 quarterly speciation profile yields the following 

quarterly mean composition: 
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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, SO4, and NO3 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 the weighted quarterly 

average species concentrations from table 10.1-4 to derive future year 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 using the following formula: 

NH4future = DON * SO4future + 0.29*NO3future 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 AmmoniumFuture= 0.280 * 3.35 + 0.29 * 0.64 = 1.007 μg/m3, etc. 

The NH4future, SO4future, and NO3future concentrations can then be used 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 Component 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 0.764
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Step 4: These quarterly components 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 μg/m3 for monitoring site X. This DVF at site X is less than 15 

μg/m3

; therefore, the site passes the attainment test. 

10.2. Screening Test 

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

determine if attainment of the annual PM2.5 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 that area predicted not to meet the PM2.5 NAAQS. The 

NCDAQ will implement this tool, should it be available in time to contribute to a timely SIP 

submittal. Should a peer reviewed MATS release 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 suggested and is the logical course of action that 

should be taken. 

10.3. Corroborative Analysis 

After the completion of the attainment test, the USEPA PM2.5 modeling guidance 

suggests additional measures should be taken to further 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 when the predicted future design values are between 

14.5 and 15.5 μg/m3

. Analysis can include a wide variety of tests and analyses, including the 

application and results of air quality models, observed air quality trends and estimated emissions 

trends, and the outcome of observational models. 

Should the area, clearly demonstrate attainment (DVF < 14.5 μg/m3), then basic 

supplemental analysis will be performed to further support the test’s findings. If either the 

attainment or screening tests are greater than 15.5 μg/m3

, it is doubtful that the more qualitative 

arguments made in a weight of evidence determination can be sufficiently convincing to 

conclude that the NAAQS for PM2.5 will be attained. 
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For DVFs between 14.5 and 15.5 μg/m3, a weight of evidence determination will be 

preformed to supplement the conclusion that the area is expected to attain the NAAQS. The end 

product of a weight of evidence determination is a document which describes analyses 

performed, data used, key assumptions and outcomes of each analysis, and why the State 

believes that the evidence, viewed as a whole, supports a conclusion that the area will attain the 

annual NAAQS for PM2.5. 
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Planning and Standards, prepared by Alpine Geophysics, LLC, Arvada, CO. 

Olerud, Donald, K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the 

United States with MM5. Prepared for Normal Possiel, Client Representative, U.S. 

EPA/OAQPS. EPA Task Order Number CAA689805. 

Olerud, D and A. Sims. 2003. “MM5 Sensitivity in Support of VISTAS (Visibility Improvement 

– State and Tribal Association): Task 2e Deliverables.” Baron Advanced Meteorological 

Systems, LLC, Research Triangle Park, NC. (available at: 

http://www.baronams.com/projects/VISTAS/#reports). December 4. 
NCDAQ Modeling Protocol 68 

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Pleim, J., Gipson, G., Roselle, S., Young, J., 2003, New features of the 2003 release of the 

CMAQ model. 2nd Annual CMAS Models-3 Conference, Research Triangle Park, NC. 

Seaman, N.L. 2000. “Meteorological Modeling for Air Quality Assessments,” Atmos. Environ., 

Vol. 34, No. 12-14, 2231-2260. 

Seaman, N.L., D.R. Stauffer, and T.W. Tesche. 1992. "The SARMAP Meteorological Model: A 

Four-Dimensional Data Assimilation Technique Used to Simulate Mesobeta-Scale 

Meteorology During a High-Ozone Episode in California,” International Specialty 

Conference on Tropospheric Ozone Nonattainment and Design Value Issues, U.S. 

EPA/AWMA, 27-30 October, Boston, MA. 

Seaman, N. L., et al., 1997. "The Use of the San Joaquin Valley Meteorological Model in 

Preparation of a Field Program in the South Coast Air Basin and Surrounding Regions of 

Southern California: Volume II -- Numerical Modeling Studies for the Development and 

Application of a Guidance Technique to Support of the Design of the 1997 Southern 

California Ozone Study Field Program,” prepared for Alpine Geophysics, LLC and the 

California Air Resources Board, prepared by the Department of Meteorology, 

Pennsylvania State University, University Park, PA. 

Seppanen, C., 2003. “Status of SMOKE,” 2003 Models-3 Users’ Workshop October 27-29, 

Research Triangle Park, NC. 

Stauffer, D.R. and N.L. Seaman, 1990. “Use of Four-Dimensional Data Assimilation in a 

Limited-Area Mesoscale Model. Part I: Experiments with Synoptic Data.” Mon. Wea. 

Rev., 118, 1250-1277. 

Stauffer, D.R., N.L. Seaman and F.S. Binkowski, 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. “Ozone Modeling Protocol for the Peninsular Florida Ozone Study 

(Version 1.0),” prepared for the Florida Department of Environmental Protection, 

prepared by Alpine Geophysics, LLC, Ft. Wright, KY. 

Tesche, T. W., et al., 2003. “Peninsular Florida Ozone Study Final Report,” prepared for the 

Florida Department of Environmental Protection, prepared by Alpine Geophysics, LLC, 

Ft. Wright, KY. 

Xiu, A., and J.E. Pleim, 2000: Development of a land surface model. Part I: Application in a 

mesoscale meteorology model. Journal of Applied Meteorology, 40, 192-209.
NCDAQ Modeling Protocol 69 

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Attachment A: Acronyms Used 

μg/m3 Microgram per meter cubed 

μm 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 Inventory 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 

Da 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 

HNO3 Nitric Acid 

I/O input/output 
NCDAQ Modeling Protocol 70 

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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 Thermodynamics/Partitioning: Model 

that calculates the composition and phase state of an 

ammonia-sulfate-nitrate-chloride-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-Chemistry Interface Processor 

MCIP2.3 Meteorological-Chemistry Interface Processor (ver. 2.3) 

MM5 Mesoscale Meteorological Model, 5 refers to the version 

number 

MNDHL Mendenhall air quality monitoring site 

MOBILE6.2 USEPA vehicle emission factor 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 

NH4 Ammonium 

NMC National Meteorological Center 

NO Nitric Oxide 

NO2 Nitrogen Dioxide 

NO3 Nitrate 

NOx Oxides of Nitrogen 
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NOx 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 

NOx, one of the precursors of ozone. 

NOy Total Available Nitrogen 

NWS National Weather Service 

O2 Atmospheric Oxygen 

O3 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 

PM2.5 Particulate Matter with a diameter less than 2.5 μm 

PSU Pennsylvania State University 

PSU/NCAR Pennsylvania State University/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 Available 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 Sulfate, Adjusted Nitrate, Derived Water, Inferred 

Carbonaceous material balance approach

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 

SO2 Sulfur Dioxide 

SOA Secondary Organic Aerosol 

SORGAM Secondary Organic Aerosol Model 

STN Speciation Trends Network 

TEOM Tapered Element Oscillating Microbalance 
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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 California at Riverside and Davis 

USEPA United States Environmental Protection Agency 

USGS United States Geological Service 

VISTAS Visibility Improvement State and Tribal Association of the 

Southeast 

VMT Vehicle Miles Traveled 
Appendix D.2 

Association for Southeastern Integrated 

Planning 

Modeling Protocol 
ASIP Modeling Protocol i 

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(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 PM2.5 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 415.899.0700 

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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 .............................................................................................................. 2-14

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 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 

7.0 

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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 PM2.5 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........................................................................................... 9-3 

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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. ........................................................................................ 7-26 

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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. PM2.5 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................................................................. 5-6 

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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 PM2.5 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 PM2.5 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. 

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(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 (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 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 base 

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the PM2.5 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 PM2.5 modeling for the 

Southeastern United States that will be the basis of the regional modeling component for the 

Southeastern States 8-hour ozone and PM2.5 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 PM2.5 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 PM2.5 (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 PM2.5 nonattainment areas, South Carolina includes 8-hour ozone nonattainment 

counties (near Charlotte) but no PM2.5 nonattainment areas. 

The states need to submit the 8-hour ozone State Implementation Plans (SIPs) to EPA by 

June 2007; the PM2.5 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. 

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

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Figure 1-2. PM2.5 nonattainment counties designed by EPA in December 2004. 

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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 (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 modeling and document the relevant sensitivity analyses, model 

configuration testing, and performance evaluations that have been performed 

(ENVIRON, 2003b); 

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 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 track 

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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. 

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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 PM2.5 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 PM2.5 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. 

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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. 

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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 SO2 control strategy is being analyzed and there are 

changes in emissions for other pollutants or for SO2 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. 

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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, 

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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 minor 

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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% (SO4) 

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/m3. 

• 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 meet 

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≤±60% ≤75% At or above this level of performance indicates 

fundamental problems with the modeling system. 

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Figure 1-3a. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.

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Figure 1-3b. Concluded. Data flow and quality assurance steps in the ASIP Emissions and Air Quality Modeling.

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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. 

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Table 1-2. ASIP Emissions and Air Quality Modeling project participants and contacts. 

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 

Raleigh, NC 27699-1641

(919) 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.net

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1.8 ASIP Project Manager and Co-Principal Investigator 

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 program 

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• 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: 

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• 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 

develop the regional modeling component for 8-hour ozone and PM2.5 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 PM2.5 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 modeling 

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 PM2.5 nonattainment areas in the southeastern 

United States. Specific activities of the ASIP States include:

• Oversee 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. 

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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. 

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2.0 MODEL SELECTION 

This chapter introduces the regional meteorological, emissions and air quality models to 

be used in the 8-hour ozone and PM2.5 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 (PM2.5): 

 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 documented 

by 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 and 

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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 (qv), 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-hydrostatic 

MM5 (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 (Tg), 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-use 

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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; 

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 > 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. 

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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. 

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2.4 CMAQ Modeling System 

2.4.1 CMAQ Overview 

For more than a decade, EPA has been developing the Models-3 Community Multiscale 

Air 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 development 

with 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 modal 

approach 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 SO2 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 and 

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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 PM2.5

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 solvers 

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 - 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 PM2.5 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 biogenic 

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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). 

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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). 

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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/PM2.5 modeling using the CMAQ Versions 4.5 SOAmods. 

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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 PM2.5 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 numerous 

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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. Lack 

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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 (Tg). Here, Tg represents surface temperature over land and sea-surface 

temperature over water. 

 Water Temperature: Water temperatures required on both 36 km and 12 km grids can 

be derived from the ETA skin temperature variable. These 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. 

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 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. 

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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. 

July 2002 IMPROVE OC STN OC 

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% 

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Table 2-3. MM5 Meteorological Model Configuration for ASIP 

Science Options Configuration Details/Comments 

Model Code MM5 Version 3.6 (MPP) Grell et al., 1994 Horizontal Grid Mesh 36/12 km  36 km grid 164 x 128 cells  12 km grid 180 x 189 cells Vertical Grid Mesh 34 layers Vertically varying; sigma pressure coord. Grid Interaction No Feedback IFEED=0 Initialization Eta first guess fields/LittleR Boundary Conditions Eta first guess fields/LittleR Microphysics Reisner I Mixed Ice Look up table Cumulus Scheme Kain-Fritsch 2 On 36/12 Grids Planetary Boundary Layer ACM PBL Radiation RRTM Vegetation Data USGS 24 Category Scheme Land Surface Model P-X Land Surface Model (LSM) Shallow Convection None Sea Surface Temperature Eta Skin Spatially varying Thermal Roughness Garratt Snow Cover Effects None 4D Data Assimilation Analysis Nudging on 36/12 Integration Time Step 90 seconds Simulation Periods Annual 2002 Other episodic periods to be defined Platform Linux Cluster Done at BAMS 

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Emissions Component

Table 2-4. SMOKE Emissions Model Configuration for ASIP 

Configuration Details/Comments 

Emissions Model SMOKE Version 2.1 Oct '04 release Horizontal Grid Mesh 36/12 km  36 km grid 148 x 112 cells RPO Unified Grid  12 km grid 168 x 177 cells VISTAS/ASIP eastern US 12 km grid Area Source Emissions VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

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

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Point Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states and stakeholders 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Off-Road Mobile SourcesVISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Biogenic Sources VISTAS Domain: VISTAS State 2002 EI Updated '02 developed by VISTAS states 

Other States: EPA '02 NEI augmented with other 2002 Generated from EPA NEI02 v.1 and RPO interaction Mexico/Canada Emissions: Same as Phase I w/ revisions based on available updates 

Temporal Adjustments Seasonal, day, hour Based on latest collected information and CEM-based profiles Chemical Speciation Revised CB4 Chemical Speciation Expected EPA release in Jan '04 Gridding Revised EPA Spatial Surrogates Used Gridding of surrogates from http://www.epa.gov/ttn/chief/emch/spatial/Growth and Controls VISTAS Special Interest Team Generated Data Base Cases defined by VISTAS Planning Workgroup Quality Assurance QA Tools in SMOKE 2.1 Independent QA with AG's Established Protocol Simulation Periods Annual 2002 Other episodic periods to be defined Platform Dual Athelon 2600 \+ Local 1.8 terabyte Ultra 320 RAID 5 system processing and storage 

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Table 2-5. CMAQ Air Quality Model Configuration for ASIP 

Science Options Configuration Details/Comments 

Model Code CMAQ Version 4.5 with SOAmods Pleim et al., (2005) Horizontal Grid Mesh 36/12 km US 

36 km covering cont. U.S; 12 km covering eastern 

 36 km grid 148 x 112 cells RPO National Grid  12 km grid 168 x 177 cells VISTAS/ASIP 12 km grid Vertical Grid Mesh 19 Layers First 17 layers sync'd w/ MM5 Grid Interaction One-way nesting Initial Conditions ~15 days full spin-up Separately run 4 quarters of 2002 Boundary Conditions 2002 3-hourly GEOS-CHEM annual run Model 

Day-specific 3-hour BCs from Global Climate 

 Baseline Emissions Processing Emissions 

SMOKE (Ver 2.1) MM5 Meteorology input to SMOKE, CMAQ 

 Dust Transport Fraction Applied in emissions before SMOKE Tom Pace updates  NH3 Inventory Adjustment Applied in emissions before SMOKE  Sub-grid-scale Plumes No Plume-in-Grid (PinG)  Gas Phase Chemistry Chemistry 

CBM-IV 

 Aerosol Chemistry AE4/ISORROPIA Includes active Sea Salt  Secondary Organic Aerosols Secondary Organic Aerosol Model (SORGAM) Schell et al., (2001)  Aerosol Mass Conservation Patch Yes Georgia Institute of Technology Update  Cloud Chemistry RADM-type aqueous chemistry Includes subgrid cloud processes N2O5 Reaction Probability 0.01 – 0.001 Meteorological Processor MCIP Version 3.0 urban minimum Kz (PURB) 

Includes dry deposition for Sea Salt and percent 

 Eddy Diffusivity Scheme Horizontal Transport 

K-theory with Kh grid size dependence Multiscale Smagorinsky (1963) approach 

 Advection Scheme Vertical Transport 

Yamertino V4.5 Mass Conservation (Yamertino) 

 Eddy Diffusivity Scheme K-theory  Diffusivity Lower Limit Kzmin = 0.1 to 2.0 PURB option in Ver 4.5 Planetary Boundary Layer No Patch Deposition Scheme M3dry Directly linked to Pleim-Xiu Land Surface Model 

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Table 2-5. CMAQ Air Quality Model Configuration for ASIP 

Science Options Configuration Details/Comments 

parameters 

 Gas Phase Chemistry Solver Numerics 

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

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

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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).

Biogenic emissions TERP

ISOP ISPD Y1

P1

ALD2

OLEPAR

TERPRXN

A_TRP2G_TRP2A_TRP1G_TRP1

ISPRXN SGTOT_ISP

A_ISP

G_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) ASOC2

AORGB

ASOC3

* Y3*Y4

O

O

O: Oxidation reaction with OH, O3, and NO3

Bold italic: New processesDotted box: Not implemented in the initial testTERPRXN: The amount of reacted TERP with oxidantsISPRXN: The amount of reacted ISPD with oxidants

ASOC1 Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1

emissions Biogenic

OVOC

TERP

ISOP

NR

OLEPAR

ISPD Y1

P1

PAROLE

ALD2 _SGTOT TRP1

TERPRXN

_TRP2

SGTOT

ISPRXN SGTOT_ISP

A_ISP

G_ISP

EF2 * ( EEG1 * Y2 + EEA1 ) ASOC2

ASOC3

**

O O

O

IO: Oxidation reaction with OH, O3, and NO3

SPRXN: The amount of reacted ISPD with oxidants

Bold italic: New processesDotted box: Not implemented in the initial testTERPRXN: The amount of reacted TERP with oxidants

ASOC1 Polymerization All SOAs except SOC1 & SOC2 FPOLY

EF1

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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 PM2.5 and 8-hour ozone modeling to be used in the Southeastern 

States PM2.5 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 PM2.5 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 PM2.5 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 PM2.5/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 PM2.5 and 8-hour ozone NAAQS. 

There is much in common with selecting episodes for annual and episodic PM2.5 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 PM2.5 

and regional haze modeling guidance, EPA has published an updated summary of PM2.5 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 PM2.5 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 PM2.5 modeling, the guidance goes further by suggesting that the 

preferred approach is to model a full, representative year (EPA, 2001, pg. 188). EPA also lists 

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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 PM2.5, 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 year 

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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 PM2.5

modeling. All of the EPA criteria for 8-hour ozone, PM2.5 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). 

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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 grid 

definition 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 while 

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“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. 

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Figure 4-1. Nesting of 36-km CMAQ Grid in the MM5 36-km Grid. 

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Figure 4-2. Nesting of 12-km Grid in the CMAQ 36-km Grid. 

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Figure 4-3. Domain Definition for High Resolution 12-km Grid.

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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 0

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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, UCR 

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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 PM2.5 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 PM2.5 attainment demonstration will be performed using associated FRM and 

STN PM monitoring sites. 

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Table 4-4. Overview of Ambient Data Monitoring Networks. 

Monitoring Network Chemical Species Measured Sampling Period Data Availability/Source (Protected Visual Environments The Interagency Monitoring of 

IMPROVE) 

species mappings) Speciated PM25 and PM10 (see 

average 1 in 3 days; 24 hr 

VE/improve_data.htm

http://vista.cira.colostate.edu/improve/Data/IMPRO

Network (Clean Air Status and Trends 

CASTNET) mappings) 

Speciated PM25, Ozone (see species 

week average Approximately 1- http://www.epa.gov/castnet/data.html

Program (National Atmospheric Deposition 

NADP) 

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

Air Quality System (AQS) Aka System (Aerometric Information Retrieval 

AIRS) 

CO, NO2, O3, SO2, PM25, PM10, Pb Typically hourly 

average 

http://www.epa.gov/air/data/

PM2.5 Federal Reference Method (FRM) 

Total PM2.5 Mass 

hour averages 1 in 3 days; 24-

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/amtic/amticpm.html and Characterization (Southeastern Aerosol Research 

SEARCH) 

NO, NO2, NOy, HNO3, SO2, CO) NH4, EC, TC); and Hourly gases (O3, Hourly PM2.5 (Mass, SO4, NO3, coarse (SO4, NO3, NH4, elements); SO4, NO3, NH4, Elem.); 24-hr PM 24-hr PM25 (FRM Mass, OC, BC, 

parameter. depending on average, Hourly or 24-hour 

http://www.atmospheric-research.com

Company, and other companies. Electric Power Research Institute (EPRI), Southern 

EPA Particulate Matter Supersites Speciated PM25 http://www.epa.gov/ttn/amtic/supersites.htmlMonitoring Stations (Photochemical Assessment 

PAMS) 

Varies for each of 4 station types. http://www.epa.gov/ttn/amtic/pamsmain.html

Pollutant Monitoring Network National Park Service Gaseous 

meteorological data HNO3, NH4, SO2), O3, Acid deposition (Dry; SO4, NO3, Hourly http://www2.nature.nps.gov/ard/gas/netdata1.htm

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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. 

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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 PM2.5

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. 

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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. 

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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 data 

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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 year 

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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. 

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 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). 

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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. 

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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 emissions 

into 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. 

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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 its 

contractors. 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: 

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• 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. 

 

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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

PM10 and PM2.5 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). 

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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-ready 

emissions 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 typical 

inventories 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 PM2.5 standards. Control strategies to be modeled will be 

determined during the course of the study. 

5.2.10 Products of the Emissions Inventory Development Process

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 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 days 

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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 the 

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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 PM2.5 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 m2/s to 

2.0 m2/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) KV (Eddy Diffusion) 

Minimum Kz PURB 0.1 to 2.0 m2/s 

MM5 Configuration Pleim-Xiu/ACM 

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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-sky 

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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. 

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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, errors 

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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. 

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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 

SO2 control strategy is being analyzed and there are changes in emissions for other pollutants or 

for SO2 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, 

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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. 

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 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. 

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 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. 

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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) PM2.5 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; 

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 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 PM2.5 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 and 

observational 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, PM2.5 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. 

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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 PM2.5 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 documents1. 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 PM2.5 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 PM2.5 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 PM2.5 species components rather than total PM2.5 mass, then 

the model performance for each of the components that make up PM2.5 is actually more important 

than for total PM2.5 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 PM2.5 (i.e., NH4, SO4, NO3, 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. 

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subdomains (e.g., ozone and PM2.5 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 PM2.5 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 PM2.5 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 PM10 and PM2.5 (i.e., sulfate, nitrate, ammonium, organic carbon, 

elemental carbon and other PM2.5) 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 NH3) 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 PM2.5 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 be 

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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, NO2 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 PM2.5 and PM10 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 PM2.5 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 SO4, NO3, organic carbon (OC), elemental carbon (EC), soil (IP) and total, PM2.5

and PM10 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 PM2.5 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 PM2.5 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 modeled 

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species. For example, the IMPROVE network only speciates PM2.5 so any sulfate or nitrate in the 

coarse mode (PM2.5-10) is included in the Coarse Mass (CM) species “measurement” (where CM = 

PM10 – PM2.5 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 the 

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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, O3, NO, NO2, NOx, NOy, speciated VOCs, carbonyls, HNO3, NH3, Hg, SO4, OC, EC, 

PM2.5, 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 O3, NO, CO, SO2 

samplers, as well as a NO2 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% 

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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 for 

evaluating 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 operational 

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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 PM2.5 

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, 

NO2, CO, SO2; 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 SO4, NO3 and ammonium (NH4) 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, NO2, SO2, 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; 

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> 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 the 

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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. 

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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 parameter2, (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). 

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Process Analysis (PA): Photochemical air quality model simulations are usually evaluated 

primarily in terms of their ability to simulate observed O3 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 O3 formation and 

the sensitivity of O3 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 O3, 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 O3, 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 for 

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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 

PM2.5, total observed mass of PM2.5? 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 PM2.5 mass is the sum of the individual components of fine particulate matter and 

the PM2.5 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 PM2.5 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 SO4 and/or S, NH4, NO3, mass associated with SO4, mass associated 

with NO3, elemental carbon (EC), organic carbon (OC), IP, mass of individual constituents of IP, and 

coarse matter (CM). The gaseous species include ozone (O3), HNO3, NO2, PAN, NH3, NOy, SO2, 

CO, and H2O2. 

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 PM2.5 (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 PM2.5 concentrations. 

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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 SO2, SO4, NO3, EC, OC, PM2.5, and 

PM10, 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, SO4/SOx). 

> 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. 

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> Create time series plots of predicted and observed concentrations stratified by key 

variables as appropriate. 

> Evaluate for total sulfur (SO2 + SO4), nitrate (HNO3 + NO3) and ammonia (NH3 + NH4). 

> 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 will 

endeavor 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, San 

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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., NO3) 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 identifies 

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particulate matter components of interest to include: SO4 and/or S, NH4, NO3, mass associated with 

SO4, mass associated with NO3, EC, OC, IP, and mass of individual constituents of inorganic 

primary particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO3, NO2, PAN, 

NH3, NOy, SO2, CO, and H2O2. 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, PM10 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 

PM2.5 ~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. 

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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; 

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> 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/N2O5/HNO3 chemistry; and 

 > Modified NH3 and HNO3 deposition velocities. 

Note that in a few cases (e.g., vertical grid structure, NH4 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. 

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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 SO2 emissions; 

> Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point 

source NOx emissions; 

> Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level NOx

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 O3/NOx, NOz/NOy (a measure of 

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chemical aging), and O3/NOz (a measure of the ozone production efficiency per NOx 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 critical3, 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. 

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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, applying 

the 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 these 

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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 PM2.5. 

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) clearly 

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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 [O3/NOx]; 

 > The chemical aging probe [NOz/NOy]; and 

 > The ozone production efficiency probe [O3/NOz]. 

 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 HNO3

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 PM2.5 and PM10 1 in 3 days; 24 hr 38 

CASTNET Speciated PM2.5, 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 (PM2.5) 1 in 3 days; 24 hr 313 (?)

EPA-SPEC Speciated PM2.5 Varies; Varies 43 (?)

AIRS/AQS CO, NO, NO2, NOx, O3 Hourly; Hourly 6,407 

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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: O3, NO, NO2, CO, 

SO2, 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: O3, NO, NO2, NOx, 

NOy, speciated VOCs, carbonyls, 

HNO3, NH3, Hg, SO4, OC, EC, 

PM2.5, 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 (Ap) 

Paired_Peak 

peak

peak

O

− OP

Ppeak = 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

)()(

))((

Pi = prediction at 

time and 

 location i; 

Oi = observation at 

time and location i; 

P = arithmetic 

average of Pi, 

i=1,2,..., N; 

O = arithmetic 

average of Oi, 

i=1,2,...,N 

Normalized Mean 

Error (NME) 

Norm_Mean_Err 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

Reported as % 

Root Mean Square 

Error (RMSE) 

Rt_Mean_Sqr_Err 

( )

2

1

1

1 2











 ∑ −

=

N

i

OP ii N

Reported as % 

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Statistical 

Measure 

Shorthand 

Notation 

Mathematical 

Expression Notes 

Fractional Gross 

Error (FE) 

Frac_Gross_Err 

∑= +

−

N

i ii

ii

OP

OP

N 1

2

Reported as % 

Mean Absolute 

Gross Error (MAGE) 

Mean_Abs_G_Err 

∑=

−

N

i

OP ii N 1

1

Mean Normalized 

Gross Error (MNGE) 

Mean_Norm_G_Err

∑=

−

N

i i

ii

O

OP

N 1

1

Reported as % 

Mean Bias (MB) Mean_Bias 

∑( )

=

−

N

i

OP ii N 1

1

Reported as 

concentration 

(e.g., μg/m3) 

Mean Normalized 

Bias (MNB) 

Mean_Norm_Bias ( ) ∑=

−

N

i i

ii

O

OP

N 1

1

Reported as % 

Mean Fractionalized 

Bias (Fractional Bias, 

MFB) 

Mean_Fract_Bias 

∑=

















+

−

N

i ii

ii

OP

OP

N 1

2

Reported as % 

Normalized Mean 

Bias (NMB)

Norm_Mean_Bias 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

)(

Reported 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). 

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8.0 OZONE AND PM2.5 ATTAINMENT DEMONSTRATION 

 This chapter provides a summary of how the modeling results will be used to demonstrate 

attainment of the 8-hour ozone and PM2.5 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 

PM2.5 National Ambient Air Quality Standard (NAAQS) follow those given in EPA’s draft PM2.5

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 PM2.5 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: 

DVFi = RRFi x DVBi

Where, 

 DVBi = the baseline (current year) 8-hour ozone Design Value (ppb) at monitor i; 

 DVFi = the future-year projected 8-hour ozone Design Value (ppb) at monitor i; and 

RRFi = 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 [DVBi] calculated? The baseline 8-hour 

ozone Design Value is calculated as the three–year average of 8-hour ozone Design Values 

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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 RRFi, 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 be 

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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 PM2.5 Attainment Demonstration 

Currently, only the annual average PM2.5 NAAQS is violated in the ASIP States so only it 

will be addressed. However, EPA has proposed to lower the 24-hour PM2.5 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 PM2.5

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 

PM2.5 Design Values to estimate future-year projected PM2.5 Design Values. However, unlike 

ozone, PM2.5 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 

PM2.5 mass for comparisons with the PM2.5 NAAQS. 

The SMAT procedures consists of two components: (1) the combination of the PM2.5

mass measurements from the Federal Reference Method (FRM) with the speciated PM2.5

measurements, such as those from the Speciated Trends Network (STN); and (2) the combination 

of the modeling results with the speciated FRM PM2.5 Design Values to obtain future-year 

projected PM2.5 Design Values. 

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8.2.1 Speciation of FRM PM2.5 Mass Measurements 

PM2.5 attainment/nonattainment can only be determined from the FRM PM2.5

measurements that measure just total PM2.5 mass. The FRM PM2.5 measurements are used to 

develop the PM2.5 Design Values that determine attainment classification and are used as the 

starting point for projecting future-year PM2.5 Design Values for demonstrating attainment. 

Thus, representative speciated PM2.5 measurements need to be mapped to the FRM 

measurements. For most FRM sites there is a speciated PM2.5 in the same general area that can 

be used in the mapping. However, in some cases, there is no nearby speciated PM2.5 site so 

speciated PM2.5 data must be interpolated from sites surrounding the FRM site. 

Speciated PM2.5 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 PM2.5 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 PM2.5. 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 PM2.5, EPA has developed procedures 

for adjusting the STN and IMPROVE speciated PM2.5 measurements to account for the 

measurement artifacts of the different networks and make the speciated PM measurements 

consistent with the FRM PM2.5 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 

PM2.5 data. Use of 1999-2003 PM2.5 data is appropriate for the CAIR modeling since it is 

centered on the 2001 CAIR modeling year. However, for the ASIP PM2.5 projections, FRM and 

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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 PM2.5 monitors, the SANDWICH speciated PM2.5 data were 

interpolated to obtained gridded fields of PM2.5 speciation that were then used to speciate the 

FRM data. EPA used a Voroni Nearest-neighbor Approach (VNA) to interpolate the 

SANDWICH PM2.5 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 PM2.5 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 PM2.5

and each of the quarters from the 2000-2004 baseline period. This is done by applying 

the fractional contribution of each major component of PM2.5 from the SANDWICH 

PM2.5 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 PM2.5 components near each monitor and for each of the four 

quarters of 2002 from the 2009 and 2002 simulations to develop monitor-, quarter- and 

PM2.5 species-specific Relative Reduction Factors (RRFs) using the ratio of the 2009 to 

2002 quarterly average modeling results. 

 

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3. Apply the monitor-, quarter- and species-specific RRF to each quarterly average observed 

PM2.5 species concentrations from 2000-2004 to obtain 5-years of quarterly average 

PM2.5species 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 

PM2.5 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 PM2.5 mass concentrations. 

6. Calculate the PM2.5 Design Values from the five years of 2009 projected annual average 

PM2.5 concentrations and compare against the NAAQS PM2.5 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 PM2.5 attainment test. 

What quarterly average model estimated PM2.5 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 PM2.5 projections the average of the estimated PM2.5 species across the array of cells 

is used, rather than the highest value that is used in the ozone projections. 

What PM2.5 Design Values should be used in the projections? An average of the 2001, 

2002 and 2003 PM2.5 Design Values will be used in the PM2.5 projections. As a Design 

Value is a three-year average of annual values, then this three year average of PM2.5

Design Values will weigh the annual average PM2.5 concentrations from the years 2000 

and 2004 once, 2001 and 2003 twice and 2002 three times. 

When is PM2.5 attainment demonstrated? The SMAT attainment test is passed when the 

2009 projected 3-year average Design Value is 14.9 μg/m3 or lower. [should this be 15.4 

ug/m3???] 

8.2.3 Additional Supporting Analysis 

Additional supporting analysis to the SMAT modeled PM2.5 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 PM2.5 attainment demonstration will be better defined once 

EPA has released their final PM2.5 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. 

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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 PM2.5 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 PM2.5 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. 

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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 down 

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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. 

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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 PM2.5 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 PM2.5 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. 

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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 

PM2.5 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). 

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• Develop the ASIP Modeling Protocol. 

Table 11-1 summarizes the deliverables and expected delivery dates under Task 1 of the ASIP 

regional ozone and PM2.5 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 12/15/05 

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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 PM2.5 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 PM2.5 projections 1/31/06 

3 Archive disks of ASIP 2002/2009 modeling results 2/10/06 

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