BEST AVAILABLE RETROFIT TECHNOLOGY APPLICABILITY ANALYSIS

AIR QUALITY MODELING PROTOCOL

OWENS CORNING  FAIRBURN MANUFACTURING FACILITY

TRINITY CONSULTANTS

1100 Johnson Ferry Road 

Suite 685 

Atlanta, Georgia 30342 

(404) 256-1919 

August 2006 

Project 061101.0055 
OC Fairburn i Trinity Consultants 

BART Modeling Protocol 

TABLE OF CONTENTS

 

1. INTRODUCTION........................................................................................................1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS .........................2 

1.1.1 DETERMINATION OF BART-ELIGIBILITY............................................................... 2

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND BART 

APPLICABILITY ...................................................................................................... 2

1.2 ORGANIZATION OF MODELING PROTOCOL................................................................8 

2. BART-ELIGIBLE SOURCE DESCRIPTION ...............................................................9

2.1 BART-ELIGIBLE EMISSION UNITS............................................................................9 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY .....................................10 

2.2.1 ELECTRIC FURNACE PARTICULATE MATTER SPECIATION .................................. 14

2.2.2 RISER/CHANNEL AND FOREHEARTH PARTICULATE MATTER SPECIATION ......... 17

2.2.3 FORMING AND CURING PARTICULATE MATTER SPECIATION.............................. 19

2.2.4 COOLING SECTION PARTICULATE MATTER SPECIATION..................................... 21

2.3 MODELED STACK PARAMETERS AND EMISSIONS....................................................24 

3. GEOPHYSICAL AND METEOROLOGICAL DATA ....................................................26

3.1 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN ........................................26 

3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN.......................................29 

3.3 METEOROLOGICAL DATABASE ...............................................................................30 

3.3.1 MM5 SIMULATIONS............................................................................................. 30

3.3.2 MEASUREMENTS AND OBSERVATIONS ................................................................ 31

3.4 AIR QUALITY DATABASE........................................................................................32 

3.4.1 OZONE BACKGROUND CONCENTRATIONS........................................................... 32

3.4.2 AMMONIA BACKGROUND CONCENTRATIONS ..................................................... 32

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS .................... 33

3.5 NATURAL CONDITIONS AT CLASS I AREAS.............................................................33 

4. AIR QUALITY MODELING METHODOLOGY..........................................................38

4.1 PLUME MODEL SELECTION .....................................................................................38 

4.1.1 MAJOR RELEVANT FEATURES OF CALMET ....................................................... 38

4.1.2 MAJOR RELEVANT FEATURES OF CALPUFF ...................................................... 40

4.2 MODELING DOMAIN CONFIGURATION ....................................................................42 

4.3 CALMET METEOROLOGICAL MODELING..............................................................43 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS........................................44 

4.5 CALPUFF MODELING OPTION SELECTIONS ..........................................................44 

4.5.1 REPRESENTATION OF EMISSION SOURCES IN CALPUFF .................................... 45

4.6 CALPOST PROCESSING OPTION SELECTIONS FOR LIGHT EXTINCTION AND HAZE 

IMPACT CALCULATIONS..........................................................................................48 

4.7 MODELING PRODUCTS ............................................................................................49 
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BART Modeling Protocol 

5. QUALITY ASSURANCE METHODS..........................................................................50

5.1 CALMET FIELDS...................................................................................................50 

5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS ..............................................51 

5.3 PRESENTATION OF RESULTS....................................................................................52 

6. REFERENCES..........................................................................................................53

APPENDIX A. VISTAS BART MODELING PROTOCOL (REVISION 3, JULY 18, 2006) 

APPENDIX B. OZONE STATIONS
OC Fairburn 1 Trinity Consultants 

BART Modeling Protocol 

1. INTRODUCTION

Owens Corning (OC) operates a fiberglass manufacturing facility in Fairburn, Georgia located in 

Fulton County (OC Fairburn). The facility currently operates under Title V Operating Permit No. 

3296-121-0021-V-01-0, issued by the Georgia Environmental Protection Division (EPD) on 

July 31, 2003, and Title V Operating Permit Amendment No. 3296-121-0021-V-01-1, effective 

December 14, 2005. The plant manufactures wool fiberglass insulation with varying characteristics 

such as R-value (measure of resistance to heat flow), loss on ignition or LOI (percent decrease in 

weight after ignition), and size. Raw materials are received by rail car and truck and are transferred 

to one of three glass melting furnaces at the facility. Molten glass from the furnace passes through a 

conditioning riser/channel into the forehearth, which then delivers the molten glass to the forming 

section where it is formed into a downward flowing veil of fibers. The veil is sprayed with water and 

coated with a phenol/formaldehyde resin-based binder solution and then collected, formed into a 

pack, and transferred to the curing oven. The pack then enters the cooling section prior to final 

product finishing operations, which can include trimming, cutting, printing, facing application, and 

bagging or rolling. 

EPD considers the OC Fairburn facility eligible to be regulated under the Best Available Retrofit 

Technology (BART) provisions of the Regional Haze Rule. Air quality modeling was used to 

determine whether the emissions from OC’s BART-eligible sources cause or contribute to visibility 

impairment at any federally protected Class I area, and hence whether the facility is subject to BART 

and a BART determination is necessary. 

This BART modeling protocol is presented to describe the procedures, analytical techniques, data 

resources, and modeling results that OC proposes to use to make the applicability determination. 

OC’s evaluation of BART-eligibility and the modeling methods to determine applicability of BART 

as described in this modeling protocol are consistent with the following guidance documents: 

S U.S. EPA, “Regional Haze Regulations and Guideline for Best Available Retrofit 

Technology (BART) Determinations,” Federal Register Volume 70, Number 128, 

July 6, 2005. 

S U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule

(EPA-54/B-03-004), September 2003. 

S U.S. EPA, Guidance for Estimating Natural Visibility Conditions under the Regional Haze 

Rule (EPA-454/B-03-005), October 2003. 

S U.S. EPA, Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report (EPA-454/R-98-019), December 1998. 

S U.S. EPA, Guideline on Air Quality Models, 40 CFR Part 51, Appendix W (Revised, 

November 9, 2005). 

S VISTAS, Protocol for the Application of the CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART), Revision 3, July 18, 2006. 

S VISTAS and U.S. EPA, “Q&A for Source by Source BART Rule” (Draft), October 28, 2005. 
OC Fairburn 2 Trinity Consultants 

BART Modeling Protocol 

The VISTAS BART Modeling Protocol is incorporated by reference for OC’s source-specific 

applicability modeling report, and is presented in Appendix A of this document. 

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS

The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one 

or more of 26 specific industrial source categories evaluate BART if the source was “in existence” 

(i.e., built or reconstructed) before August 7, 1977 and began operation after August 7, 1962. Such 

sources are termed “BART-eligible sources.” Major sources of visibility-affecting pollutants have the 

potential to emit 250 tons per year (tpy) of one or more of oxides of nitrogen (NOX), sulfur dioxide 

(SO2), and particulate matter less than 10 micrometers in aerodynamic diameter (PM10). Glass fiber 

processing plants are one of the listed source categories, and include process units and support 

facilities that are considered to be part of major Standard Industrial Classification 32 – Stone, clay, 

glass, and concrete products. Hereafter, the “BART-eligible source” is taken to mean the collection 

of sources at a facility in existence during the relevant time period within one or more BART source 

categories that has potential emissions of one or more visibility-affecting pollutants in excess of 

250 tpy. The BART-eligible source may include multiple emission units, but need not include the 

entire facility. 

1.1.1 DETERMINATION OF BART-ELIGIBILITY

The U.S. EPA BART guidelines define the following three steps for determining which 

sources at a facility are BART-eligible: 

1. Identify the emission units in the BART source categories. 

2. Identify the start-up dates of those units. 

3. Compare potential emissions to the 250 tpy cutoff. 

EPD has determined that seventeen emission units comprise the BART-eligible sources at 

the OC Fairburn facility because the units operate at a glass fiber processing facility, were 

in existence on August 7, 1977, and began operation after August 7, 1962. This collection 

of emission units has potential emissions of 1,049 tpy NOX, 641 tpy PM10, and 

129 tpy SO2. Accordingly, the BART-eligible emission units at OC’s Fairburn facility will 

be analyzed to evaluate whether the facility is exempt from BART. Specific information 

about these emission units is provided in Section 2 of this BART modeling protocol. 

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND

BART APPLICABILITY

In its role as technical analysis coordinator, VISTAS developed a common modeling 

protocol and data resources for use by state regulatory agencies and BART-eligible 

sources. The final VISTAS BART Modeling Protocol was issued on December 22, 2005 

(and has been revised twice since, most recently on March 9, 2006), and prescribes 

modeling techniques and data resources to conduct refined analyses to assess whether a 

BART-eligible source is subject to BART. 
OC Fairburn 3 Trinity Consultants 

BART Modeling Protocol 

A BART-eligible source is determined to be subject to BART if the source causes or 

contributes to visibility impairment at a federally protected Class I area. Causation is 

defined as a single-source impact of 1.0 deciviews (dv) or more; contribution is defined as 

a single-source impact of 0.5 dv or more (each evaluated on a 24-hour average basis). The 

deciview is a metric used to represent normalized light extinction attributable to visibilityaffecting pollutants. To determine whether a BART-eligible facility causes or contributes 

to visibility impairment, U.S. EPA guidance requires the use of an air quality model, 

specifically recommending the CALPUFF modeling system, to quantify the impacts 

attributable to a single BART-eligible source. Because contribution to visibility 

impairment is sufficient cause to require a BART determination, 0.5 dv is the critical 

threshold for assessment of BART applicability. 

Regional haze is measured using the light extinction coefficient (bext), which is used to 

represent the haze index expressed in dv. The haze index (HI) is calculated as shown in the 

following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

10 ln bext HI

The impact of a BART-eligible source is determined by comparing HI for estimated 

natural background conditions with the impact of the source and without the impact of the 

source. The background extinction coefficient bext, background is affected by various chemical 

species and the Rayleigh scattering phenomenon and can be calculated as shown in the 

following equation. 

ext background bSO bNO bOC bsoil bcoarse bap bray b km = + + + + + + −

4 3

( )

1

,

where, 

[ ]

[ ]

[ ]

[ ]

[ ]

3

Ray

4 3

4 2 4

[ ] Concentration in μg/m

f(RH) relative humidity adjustment factor

b Rayleigh Scattering

0.01[Elemental Carbon]

0.0006 Coarse Mass

0.001 Soil

0.004 OC

0.003 NH NO ( )

0.003 (NH ) SO ( )

3

4

=

=

=

=

=

=

=

=

=

ap

Coarse

Soil

OC

NO

SO

b

b

b

b

b f RH

b f RH

Values for the parameters listed above specific to the natural background conditions at the 

Class I areas considered in this modeling protocol are provided on an annual average basis 

in the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions Under the 

Regional Haze Rule.

1

 VISTAS has more recently recommended calculation of the 

 

1

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 
OC Fairburn 4 Trinity Consultants 

BART Modeling Protocol 

background visibility impairment using only soil concentration and Rayleigh scattering 

based on the natural extinction. More detailed information about the natural background 

conditions particular to Class I areas potentially affected by OC’s operations at the 

Fairburn Plant are provided in Section 3.5 of this modeling protocol. 

Particulate species that affect visibility are emitted from anthropogenic sources in various 

phases and include coarse particulate matter (PMC), fine particulate matter (PMF), 

secondary organic aerosols (SOA), and elemental carbon (EC), as well as precursors to fine 

particulate matter such as SO2 and NOX. OC’s calculation of speciated visibility-affecting 

pollutant emissions is presented in Section 2 of this modeling protocol. The extinction

coefficient due to emissions of visibility-affecting pollutants from a single BART-eligible 

source bext,source is calculated using an air quality model. The extinction due to the BARTeligible source are calculated as shown in the following equation. 

( ) ext source SO NO PMC PMF SOA EC b km = b + b + b + b + b + b −

4 3

1

,

where, 

[ ]

[ ]

3

4 3

4 2 4

[ ] Concentration in μg/m

( ) relative humidity adjustment factor

0.01[ ]

0.004[ ]

0.001[ ]

0.0006[ ]

0.003 NH NO ( )

0.003 (NH ) SO ( )

3

4

=

=

=

=

=

=

=

=

f RH

b EC

b SOA

b PMF

b PMC

b f RH

b f RH

EC

soa

PMF

PMC

NO

SO

OC proposes to utilize screening and refined modeling techniques as described in the 

VISTAS BART Modeling Protocol to determine whether BART-eligible operations at the 

Fairburn manufacturing facility contribute to visibility impairment at any of these Class I 

areas. The CALPUFF modeling system will be used to compute the 24-hour average 

visibility impairment attributable to OC to assess whether the 0.5 dv contribution threshold 

is exceeded, and if so, the frequency, duration, and magnitude of any exceedance events. 

The U.S. EPA BART guidelines prescribe that the 98th percentile, 24-hour average, 

visibility impact computed in a modeling analysis that evaluates three years of 

meteorological data should be compared to the contribution threshold. However, VISTAS 

prescribes that the maximum computed visibility impact be used as the basis for 

comparison in the screening analysis and the 98th percentile impact in the refined analysis. 

To assess whether BART-eligible operations contribute to visibility impairment, OC’s 

applicability modeling analysis will quantify the top eight 24-hour average visibility 

impacts of each year modeled to illustrate the distribution (i.e., frequency, duration, and 

magnitude) of peak visibility impairment episodes attributable to the Fairburn facility. 

CALPUFF is a refined air quality modeling system that is capable of simulating the 

dispersion, chemical transformation, and long-range transport of multiple 
OC Fairburn 5 Trinity Consultants 

BART Modeling Protocol 

visibility-affecting pollutant emissions from a single source and is therefore preferred for 

BART applicability and determination analyses. The CALPUFF modeling system is 

described in technical detail in the VISTAS BART Modeling Protocol and its use in refined 

analyses for BART applicability assessment of OC’s Fairburn facility is described in 

Sections 3, 4, and 5 of this BART modeling protocol. 

The VISTAS modeling protocol specifies that all Class I areas within 300 km of a BARTeligible source must be initially evaluated to determine whether the source contributes to 

visibility impairment. Table 1-1 summarizes the distances separating OC’s Fairburn 

Facility from all Class I areas within the VISTAS region and adjacent states. Consistent 

with the VISTAS BART Modeling Protocol, only those Class I areas within 300 km are 

considered further in the BART applicability modeling analysis. 

TABLE 1-1. DISTANCES (KILOMETERS) SEPARATING CLASS I AREAS AND

FAIRBURN FACILITY

Class I Area Distance (km) 

Breton (LA/MS) 561 

Cape Romain (SC) 466 

Chassahowitzka (FL) 563 

Cohutta (GA) 144 

Dolly Sods (WV) 759 

Everglades (FL) 913 

Great Smoky Mountains (NC/TN) 223 

Hercules Glade (AR) 826 

James River Face (VA) 642 

Joyce Kilmer-Slickrock (NC) 209 

Linville Gorge (NC) 352 

Mammoth Cave (KY) 418 

Mingo (MO) 630 

Okefenokee (GA) 351 

Otter Creek (WV) 741 

Shenandoah (VA) 718 

Shining Rock (NC) 253 

Sipsey (AL) 267 

St. Marks (FL) 380 

Swanquarter (NC) 775 

Wolf Island (GA) 392 

Figure 1-1 illustrates the location of the Fairburn facility and its location relative to the 

following federally protected Class I areas that are located within 300 km of OC’s Fairburn 

operations: 
OC Fairburn 6 Trinity Consultants 

BART Modeling Protocol 

S Cohutta Wilderness Area located approximately 144 km north of the Fairburn facility 

S Great Smoky Mountains National Park located approximately 223 km northnortheast of the Fairburn facility 

S Joyce Kilmer-Slickrock Wilderness Area located approximately 209 km northnortheast of the Fairburn facility 

S Shining Rock Wilderness Area located approximately 253 km northeast of the 

Fairburn facility 

S Sipsey Wilderness Area located approximately 267 km west-northwest of the 

Fairburn facility. 

The locations of Class I areas and receptor locations evaluated in the modeling analysis 

were determined by, and obtained from, the U.S. Forest Service, which is the FLM for 

Cohutta, Joyce Kilmer-Slickrock, Shining Rock, and Sipsey Wilderness Areas, and the 

National Park Service, which is the FLM for Great Smoky Mountains National Park.2

 

 

2

 National Park Service compilation of Class I area receptors, http://www2.nature.nps.gov/air/maps/receptors/. 
OC Fairburn 7 Trinity Consultants 

BART Modeling Protocol 

FIGURE 1-1. LOCATION OF OC’S FAIRBURN FACILITY RELATIVE TO CLASS I AREAS 

WITHIN 300 KM

The initial screening analysis will be conducted to demonstrate the maximum visibility change 

impacts at each Class I area within 300 km and whether the more distant Class I areas can be 

excluded from performance of refined analyses. 
OC Fairburn 8 Trinity Consultants 

BART Modeling Protocol 

1.2 ORGANIZATION OF MODELING PROTOCOL

The remainder of this BART modeling protocol is organized as follows. 

▲ Section 2 describes the BART-eligible emission units at OC and the emission rates modeled 

in the BART applicability analysis. 

▲ Section 3 describes the procedural and technical guidance for conducting Class I area 

analyses. 

▲ Section 4 describes the approach used for CALPUFF modeling, including the data resources 

and technical modeling options used in the CALMET, CALPUFF, and CALPOST analyses. 

▲ Quality assurance methods are discussed in Section 5. 

▲ Appendix A provides the VISTAS BART Modeling Protocol . 

▲ Appendix B contains ozone station data. 

The air quality modeling analysis methodology will generally conform to the VISTAS BART Modeling 

Protocol (Revision 3), which is provided in Appendix A of this report for reference. 
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BART Modeling Protocol 

2. BART-ELIGIBLE SOURCE DESCRIPTION

This section of the modeling protocol describes the emission units that comprise the BART-eligible 

source at OC’s Fairburn facility. Emissions and exhaust characteristics of each source are quantified 

to demonstrate how each source will be represented in the modeling analysis. 

2.1 BART-ELIGIBLE EMISSION UNITS

OC reviewed the criteria for BART-eligibility and determined that the seventeen emission units 

described in Table 2-1 comprise the BART-eligible source at the Fairburn facility. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS

Note that for the purposes of this BART applicability analysis, VISTAS and EPD have determined 

that volatile organic compounds (VOC) are not visibility-affecting pollutants. Section 4.1.3 of the 

VISTAS BART Modeling Protocol describes the regional modeling analyses showing that cumulative 

VOC emissions do not contribute to visibility impairment within the VISTAS region: 

VOC emissions from all anthropogenic point sources in each VISTAS State are being 

reduced. Given that the impact of eliminating all VOC emissions from all point 

sources in a State is less than 0.5 dv, then the impact of any one BART-eligible 

source would be less than 0.5 dv. Based on these analyses, the VISTAS States have 

concluded that VOC emissions should not be subject to BART. 

Unit ID Name Description

Date 

Built

Emits PM, NOX, 

SO2?

Emissions > 

5 tpy? Include in BART Modeling?

FG11 FG-1 Furnace Electric Cold Top Melting Furnace 1971 Yes Yes Yes

FG12 FG-1 Risers Flow Channel 1971 Yes Yes Yes

FG13 FG-1 Forehearth Flow Channel 1971 Yes Yes Yes

FG14 FG-1 Forming Section Molten Glass Spinning to Glass Fibers 1971 Yes Yes Yes

FG15 FG-1 Curing Oven Insulation Binder Curing Oven 1971 Yes Yes Yes

FG16 FG-1 Cooling Section Cooling with Ambient Air 1971 Yes Yes Yes

FG17 FG-1 Flexographic Printing Insulation Facing Flexographic Printer 1971 No No No - Emits only VOC

FG18 FG-1 Asphalt Application Asphalt Coating of Paper or Foil 1971 Yes No No - Insignificant Source

FG21 FG-2 Furnace Electric Cold Top Melting Furnace 1972 Yes Yes Yes

FG22 FG-2 Risers Flow Channel 1972 Yes Yes Yes

FG23 FG-2 Forehearth Flow Channel 1972 Yes Yes Yes

FG24 FG-2 Forming Section Molten Glass Spinning to Glass Fibers 1972 Yes Yes Yes

FG25 FG-2 Curing Oven Insulation Binder Curing Oven 1972 Yes Yes Yes

FG26 FG-2 Cooling Section Cooling with Ambient Air 1972 Yes Yes Yes

FG27 FG-2 Flexographic Printing Insulation Facing Flexographic Printer 1972 No No No - Emits only VOC

FG28 FG-2 Asphalt Application Asphalt Coating of Paper or Foil 1972 Yes No No - Insignificant Source

FG31 FG-3 Furnace Electric Cold Top Melting Furnace 1974 Yes Yes Yes

FG32 FG-3 Risers Flow Channel 1974 Yes Yes Yes

FG33 FG-3 Forehearth Flow Channel 1974 Yes Yes Yes

FG34 FG-3 Forming Section Molten Glass Spinning to Glass Fibers 1974 Yes Yes Yes

FG35 FG-3 Curing Oven Insulation Binder Curing Oven 1974 Yes Yes Yes

FG36 FG-3 Cooling Section Cooling with Ambient Air 1991 Yes Yes No - Built after applicability date
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BART Modeling Protocol 

As a result of this determination, emissions of VOC from OC’s Fairburn facility were not further 

evaluated. The seventeen emission units indicated in Table 2-1 will be considered the BART-eligible 

sources at OC’s Fairburn facility. 

In addition, OC has assumed that sources that have emission rates below the Title V insignificant 

emission unit thresholds (e.g., < 5 tpy) can be excluded from further evaluation since these units 

would not be expected to impact visibility. This exclusion of insignificant sources has been approved 

by other VISTAS states. 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY

Whereas the BART eligibility determination relies on potential emissions of visibility-affecting 

pollutants, the BART applicability analysis utilizes maximum actual 24-hour average emission rates 

of NOX, SO2, and PM10. The VISTAS BART Modeling Protocol specifies the following hierarchy of 

information resources to establish the maximum actual 24-hour average emission rate for BART 

applicability modeling over the prior three-to-five year period: 

S 24-hour maximum emissions observed using a Continuous Emission Monitor (CEM) for the 

period 2001 through 2003 

S 24-hour maximum emissions observed using a CEM for any representative period 

S Facility stack test emissions 

S Potential to emit 

S Permit allowable emissions 

OC used a combination of representative stack test data, potential emissions based on enforceable 

emissions and operating limits, and AP-42 emission factors to determine the 24-hour average 

maximum actual emission rates of visibility-affecting pollutants, which are equal to potential 

emissions. Table 2-2 summarizes these emission rates from each BART-eligible emissions unit 

considered in the applicability modeling analysis. 
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BART Modeling Protocol 

TABLE 2-2. SUMMARY OF 24-HOUR AVERAGE MAXIMUM ACTUAL EMISSION RATES

* Emissions from each line’s forming and curing sections are routed together in a mixing chamber prior to being released to the

atmosphere. Emissions are quantified at the outlet of the mixing chamber. 

Table 2-2 includes 24-hour maximum emission rates of primary sulfates (from process sources and 

combustion sources) and distinguishes the emission rates of Total PM10 (TPM10), which includes 

emissions of TPM2.5. Modeling of visibility impairment requires that the components of the exhaust 

stream be speciated because different types of particulate matter affect visibility to varying extents. 

The amount by which a mass of a certain species scatters or absorbs light is termed the extinction 

efficiency or extinction coefficient, and ranges from values of 0.6 m2

/g for coarse particulate matter to 

10 m2

/g for elemental carbon. Fine particulate matter (1 m2/g) and organic aerosols (4 m2/g) scatter 

light with intermediate efficiencies, and ammonium sulfate and ammonium nitrate (that forms from 

precursor SO2 and NOX emissions in the presence of ambient ammonia) are hygroscopic species that 

are particularly efficient light scatters in the presence of ambient water vapor (3f(RH) m2

/g, where 

f(RH) is a function of the relative humidity). The size distribution of particle species is also 

important, since smaller particles may be transported longer distances than larger particles and 

dispersed differently under prevailing ambient conditions. Figure 2-1 depicts the speciation of 

visibility-affecting pollutant emissions as represented in the VISTAS BART Modeling Protocol. 

SO2 Emissions 

NOX 

Emissions 

Total PM10 

Emissions 

Total PM2.5 

Emissions 

Primary SO4 

Emissions

Emission Unit EU ID (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

FG1 Furnace FG11 2.26 86.52 4.85 2.51 0.05

FG1 Riser/Channel FG12 0.13 0.29 7.63 3.83 0.03

FG1 Forehearth FG12 0.07 0.50 2.01 1.02 1.3E-02

FG1 Mixing Chamber - 6.23 35.81 60.00 54.00 0.38

 FG1 Forming Section FG14

 FG1 Curing Oven FG15

FG1 Cooling Section FG16 - - 5.49 4.94 0.03

FG2 Furnace - Process FG21 5.23 56.71 4.76 2.48 0.05

FG2 Riser/Channel - Process FG22 1.3E-02 - 0.25 0.12 6.8E-04

FG2 Forehearth - Process FG23 1.7E-03 - 2.8E-02 1.4E-02 7.8E-05

FG2 Mixing Chamber - 7.00 9.60 29.50 26.55 0.22

 FG2 Forming Section - Process FG24

 FG2 Curing Oven - Process FG25

FG2 Cooling Section FG26 - - 1.50 1.35 7.4E-03

FG3 Furnace - Process FG31 1.15 44.58 2.51 1.33 0.03

FG3 Riser/Channel - Process FG32 0.06 0.10 3.86 1.94 1.2E-02

FG3 Forehearth - Process FG33 0.03 0.22 1.01 0.52 6.1E-03

FG3 Mixing Chamber - 7.49 5.53 24.00 21.60 0.16

 FG3 Forming Section - Process FG34

 FG3 Curing Oven - Process FG35
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BART Modeling Protocol 

FIGURE 2-1. PARTICULATE MATTER SPECIATION

(AFTER FIGURE 4-3 OF THE VISTAS BART MODELING PROTOCOL) 

While few data are available to estimate speciated emissions, OC has reviewed what data are 

available to arrive at a conservative, yet reasonable estimate of speciated emissions. However, it 

should be noted that the data quality on PM speciation is inadequate for setting regulatory emission 

limits and are provided here solely as the best estimated data for a scientific analysis of potential 

impacts on visibility impairment at Class I areas using CALPUFF modeling. The following analysis 

does not represent source test results for specific sources at the Fairburn facility. 

NOX emissions from the Fairburn facility’s BART-eligible sources result from process and 

combustion operations and are largely based on permit limits, source testing, and/or AP-42 emission 

factors. Similarly, SO2 emissions emanate from both process and combustion sources, and are mostly 

based on source testing and/or AP-42 emission factors. Primary emissions of sulfuric acid mists or 

vapors, if any, are assumed to occur as only a small percentage of the primary PM2.5 emissions using 

the SMOKE PM2.5 speciation factors as given on the VISTAS website.3

 Because of the condensable 

nature of such emissions and the distinct effect on visibility caused by sulfates, primary sulfate is 

evaluated as a distinct, speciated particulate fraction. Further speciation of PM2.5 emissions using the 

SMOKE PM2.5 profiles was used to further partition both condensable and filterable emissions. 

Table 2-3 gives definitions for the nomenclature used herein. 

 

3

 http://www.vistas-sesarm.org/BART/calpuff.asp 
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BART Modeling Protocol 

TABLE 2-3. NOMENCLATURE FOR EMISSIONS SPECIATION ANALYSIS

 

Nomenclature Description 

 

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μm 

PM10 Filterable particulate matter with an aerodynamic diameter < 10 μm 

PM6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μm 

PM2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μm 

PM2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μm 

PM1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

PM1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

PM0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PM0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μm 

PM<0.5 Filterable particulate matter with an aerodynamic diameter < 0.5 μm 

CPM Condensable particulate matter (organic and inorganic) 

POC Primary organic condensable emissions 

PIC Primary inorganic condensable emissions 

POA Primary organic aerosol 

TPM10 Filterable PM10 + CPM 

TPM2.5 Filterable PM2.5 + CPM 

These PM classifications are necessary in the Class I visibility analysis because each type of PM has a 

different effect on visibility as defined by the extinction efficiency. The emission rates of each of 

these particulate phases and size categories are modeled in CALPUFF and grouped according to 

visibility-affecting characteristics as was illustrated in Figure 2-1. Elemental carbon (EC), if emitted, 

typically results from unburned carbonaceous fuel and is distinguished from other PM types because 

of its light extinction characteristics. It is assumed that all EC falls within two particle size bins: 

PM<0.5 and PM0.5-0.625.

4

 Coarse PM (PMC) comprises PM2.5-6 and PM6-10. Fine PM (PMF) comprises 

PM<0.5, PM0.5-0.625, PM0.625-1, PM1-1.25, and PM1.25-2.5. CPM comprises both organic and inorganic 

species. The organic fraction of CPM is represented in CALPUFF as primary organic condensable 

(POC) emissions, which are direct emissions but are sometimes referred to as secondary organic 

aerosols (SOA) by convention and due to the representation of their visibility-affecting characteristics 

in the light extinction equation. Primary emissions of inorganic CPM (PIC) may contain hygroscopic 

sulfates (SO4) and nitrates (NO3), as well as other salts (e.g., carbonates) that may be hygroscopic to a 

lesser degree, and hence are considered in a manner similar to PMF (i.e., as soil) in terms of light 

extinction.5

 Therefore, it is important to distinguish inorganic CPM since certain hygroscopic species 

(i.e., sulfate and nitrate species) will have a greater extinction coefficient than non-hygroscopic (i.e., 

non-sulfate and non-nitrate) species. Even the distinction between primary sulfate and nitrate 

emissions is important since primary nitrate emissions will be affected by the partitioning of nitrate 

and nitric acid in the presence of ambient ammonia, which is modeled explicitly in CALPUFF and 

can be corrected when the ammonia limiting method (as described in Section 4 of this protocol) is 

 

4

 Seinfeld, John H. and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate 

Change, John Wiley & Sons, Inc., 1998, page 707. 

5

 The U.S. EPA’s Guidance for Tracking Progress under the Regional Haze Rule identifies carbonates, 

magnesium oxides, and sodium oxides as components of the soil mass concentration when analyzed to assess natural 

background visibility (Malm 1994). 
OC Fairburn 14 Trinity Consultants 

BART Modeling Protocol 

applied. OC distinguishes primary emissions of sulfates and nitrates, which would be assigned to the 

appropriate modeled PM type (i.e., SO4 and NO3, respectively), from non-hygroscopic species (e.g., 

carbonates), which would be modeled as non-hygroscopic PIC species. Inorganic condensable 

emissions are assumed to be PIC unless a specific emission factor for primary sulfate or nitrate 

emissions is available. 

Table 2-4 summarizes the grouping of PM species and extinction coefficient of each component. 

A discussion of the PM speciation methodologies for each of the BART-eligible sources at the 

Fairburn facility is presented in the following sections of this protocol. 

TABLE 2-4. ASSIGNMENT OF EMITTED PM SPECIES TO MODELED PM CATEGORIES

 

Modeled 

PM 

Category† Components 

Output 

Category‡

Extinction 

Coefficient 

(m2/g) 

PMC Filterable coarse particles (PM6-10, PM2.5-6) PMC 0.6 

PMF Filterable fine particles (PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625, PM<0.5) SOIL 1 

PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 

SO4 Primary inorganic condensable emissions of sulfates SO4 3f(RH) 

NO3 Primary inorganic condensable emissions of nitrates* NO3 3f(RH) 

POC Primary organic condensable emissions SOA 4 

EC Uncombusted carbonaceous fuel EC 10 

 

* In the screening analyses, all condensable, non-sulfate inorganic emissions will be represented as PIC emissions. The refined 

analysis, if necessary, would distinguish between primary nitrate and other primary condensable inorganic emissions. 

† Modeled PM Category denotes the input of emissions data into CALPUFF. 

‡ Output Category denotes the assignment of modeled emissions in POSTUTIL for the visibility calculations in CALPOST. 

2.2.1 ELECTRIC FURNACE PARTICULATE MATTER SPECIATION

To speciate PM emissions from the electric glass melting furnaces, the PM2.5 portion of 

PM10 emissions was first calculated based on engineering estimates made by OC 

personnel.6

 Speciation factors from SMOKE PM2.5, as provided by VISTAS, were then 

used to divide PM2.5 emissions into various categories. It was assumed that emissions 

within a particular PM category are equally distributed to all size bins within that category. 

The relevant size bins are given within the “Revised BART Speciation Template” available 

on the VISTAS website.7

 Table 2-5 summarizes the applicable data for this source. 

 

6

 E-mail communication from Mr. Franco Vigo (OC) to Ms. Melissa Antoine (Trinity Consultants) on 

July 23, 2006. 

7

 http://www.vistas-sesarm.org/BART/calpuff.asp 
OC Fairburn 15 Trinity Consultants 

BART Modeling Protocol 

TABLE 2-5. ELECTRIC GLASS MELTING FURNACE SPECIATION DATA

Using the information presented in Table 2-5, OC first calculated process TPM10 emissions 

from each furnace by multiplying the maximum hourly TSP emission rate of 0.75 lb/ton of 

glass pulled by the glass pull rate of each furnace, which for the FG-1 Furnace, yields 

4.69 lb/hr. For the glass melting furnaces, OC personnel estimate that approximately 50% 

of PM10 emissions are PM2.5. Multiplying PM10 emissions by 50% results in PM2.5

emissions from the FG-1 Furnace of 2.34 lb/hr. 

Next, PM2.5 is speciated into the categories given in Table 2-5. An example is provided 

below for process PEC emissions from the FG-1 Furnace. 

hour

lb PEC allsize categories 0.05

TPM

PEC 2%

hr

lb TPM 2.34 2.5

2.5 ⎟ =

⎠

⎞ ⎜

⎝

⎛ ⎟ ×

⎠

⎞ ⎜

⎝

⎛

Emissions are calculated for the remaining PM2.5 categories listed in Table 2-5 in a similar 

manner. 

The glass melting furnaces at the Fairburn facility are equipped with back-up natural gas 

burners. Normally, these burners do not operate; however, for conservatism in estimating 

maximum 24-hour emissions, it is assumed that the burners are firing at their rated capacity 

throughout a single 24-hour period. Combustion PM emissions are speciated using factors 

from SMOKE PM2.5, as available on the VISTAS website. Table 2-6 presents the 

speciation factors for all combustion sources at the Fairburn facility. 

Speciation Data Value Reference

PM2.5 as a % of PM10 50.00% Mr. Franco Vigo (OC)

Primary EC as a % of PM2.5 2.00% SMOKE PM2.5, Speciation Profile 22033

Primary PM2.5 as a % of PM2.5 63.30% SMOKE PM2.5, Speciation Profile 22033

Primary NO3 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033

Primary OA as a % of PM2.5 33.60% SMOKE PM2.5, Speciation Profile 22033

Primary SO4 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033
OC Fairburn 16 Trinity Consultants 

BART Modeling Protocol 

TABLE 2-6. SPECIATION FACTORS FOR COMBUSTION SOURCES

Table 2-7 presents speciated PM emissions for the glass melting furnaces at the Fairburn 

facility. 

TABLE 2-7. GLASS MELTING FURNACES SPECIATED PM EMISSIONS

Figures 2-2, 2-3, and 2-4 present a graphical representation of the PM speciation for the 

glass melting furnaces at the Fairburn facility. 

FIGURE 2-2. FG-1 FURNACE TPM10 SPECIATION (LB/HR) 

Speciation Data Value Reference

PM2.5 as a % of PM10 100.00% AP-42, Chapter 1.4, Table 1.4-2

Primary EC as a % of PM2.5 0.00% SMOKE PM2.5, Speciation Profile 22004

Primary PM2.5 as a % of PM2.5 19.45% SMOKE PM2.5, Speciation Profile 22004

Primary NO3 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22004

Primary OA as a % of PM2.5 60.00% SMOKE PM2.5, Speciation Profile 22004

Primary SO4 as a % of PM2.5 20.00% SMOKE PM2.5, Speciation Profile 22004

FG-1 Furnace

0.01

0.05 0.89

2.34

1.52

0.05

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

Total PM10 Total PM2.5 PM10 - PM2.5 PEC PPM2.5 PNO3 POA PSO4 

Emission Unit EU ID (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

FG1 Furnace FG11 4.85 2.51 2.34 0.05 1.52 0.01 0.89 0.05

FG2 Furnace FG21 4.76 2.48 2.27 0.05 1.48 0.01 0.89 0.05

FG3 Furnace FG31 2.51 1.33 1.19 0.02 0.78 0.01 0.48 0.03
OC Fairburn 17 Trinity Consultants 

BART Modeling Protocol 

FIGURE 2-3. FG-2 FURNACE TPM10 SPECIATION (LB/HR) 

FIGURE 2-4. FG-3 FURNACE TPM10 SPECIATION (LB/HR) 

2.2.2 RISER/CHANNEL AND FOREHEARTH PARTICULATE MATTER SPECIATION

To speciate PM emissions from the risers/channels and forehearths, the PM2.5 portion of 

PM10 emissions was first calculated based on engineering estimates made by OC 

personnel.8

 Speciation factors from SMOKE PM2.5, as provided by VISTAS, were then 

used to divide PM2.5 emissions into various categories. It was assumed that emissions 

within a particular PM category are equally distributed to all size bins within that category. 

The relevant size bins are given within the “Revised BART Speciation Template” available 

on the VISTAS website.9

 Table 2-8 summarizes the applicable data for this source. 

 

8

 E-mail communication from Mr. Franco Vigo (OC) to Ms. Melissa Antoine (Trinity Consultants) on 

July 23, 2006. 

9

 http://www.vistas-sesarm.org/BART/calpuff.asp 

FG-2 Furnace

0.01

0.05 0.89

2.27

1.48

0.05

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

FG-3 Furnace

0.01

0.03 0.48

1.19

0.78

0.02

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)
OC Fairburn 18 Trinity Consultants 

BART Modeling Protocol 

TABLE 2-8. ELECTRIC GLASS MELTING FURNACE SPECIATION DATA

Using the information presented in Table 2-8, OC first calculated process TPM10 emissions 

from each riser/channel and forehearth by multiplying the applicable maximum hourly TSP 

emission rate of by the glass pull rate of the corresponding glass melting furnace, which for 

the FG-1 Riser/Channel, yields 7.61 lb/hr, and for the FG-1 Forehearth, yields 1.97 lb/hr. 

Similar to the glass melting furnaces, OC personnel estimate that approximately 50% of 

PM10 emissions from the riser/channel and forehearth are PM2.5. Multiplying process PM10

emissions by 50% results in PM2.5 emissions from the FG-1 Riser/Channel of 3.81 lb/hr 

and from the FG-1 Forehearth of 0.98 lb/hr. 

Next, PM2.5 is speciated into the categories given in Table 2-8. An example is provided 

below for process PEC emissions from the FG-1 Riser/Channel. 

hour

lb PEC allsize categories 0.08

TPM

PEC 2%

hr

lb TPM 3.81 2.5

2.5 ⎟ =

⎠

⎞ ⎜

⎝

⎛ ⎟ ×

⎠

⎞ ⎜

⎝

⎛

Emissions are calculated for the remaining PM2.5 categories listed in Table 2-8 in a similar 

manner. Combustion emissions from the risers/channels and forehearth are calculated 

using the previously described methodology and the speciation profile given in Table 2-6. 

Table 2-9 presents speciated PM emissions for the risers/channels and forehearths at the 

Fairburn facility. 

TABLE 2-9. RISER/CHANNEL AND FOREHEARTH SPECIATED PM EMISSIONS

Speciation Data Value Reference

PM2.5 as a % of PM10 50.00% Mr. Franco Vigo (OC)

Primary EC as a % of PM2.5 2.00% SMOKE PM2.5, Speciation Profile 22033

Primary PM2.5 as a % of PM2.5 63.30% SMOKE PM2.5, Speciation Profile 22033

Primary NO3 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033

Primary OA as a % of PM2.5 33.60% SMOKE PM2.5, Speciation Profile 22033

Primary SO4 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033

Total PM10 Total PM2.5 PM10 - PM2.5 PEC PPM2.5 PNO3 POA PSO4 

Emission Unit EU ID (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

FG1 Riser/Channel FG12 7.63 3.83 3.81 0.08 2.41 0.02 1.29 0.03

FG2 Riser/Channel FG22 0.22 0.11 0.11 2.2E-03 0.07 6.0E-04 0.04 6.0E-04

FG3 Riser/Channel FG32 3.86 1.94 1.93 0.04 1.22 0.01 0.65 0.01

FG1 Forehearth FG12 2.01 1.02 0.98 0.02 0.63 0.01 0.35 0.01

FG2 Forehearth FG23 0.03 0.01 0.01 2.8E-04 9.0E-03 7.8E-05 4.8E-03 7.8E-05

FG3 Forehearth FG33 1.01 0.52 0.50 0.01 0.32 0.00 0.18 0.01
OC Fairburn 19 Trinity Consultants 

BART Modeling Protocol 

FG-1 Riser/Channel & Forehearth

0.03

0.04 1.65

4.79

3.04

0.10

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

Figures 2-5, 2-6, and 2-7 present a graphical representation of the riser/channel and 

forehearth PM speciation at the Fairburn facility. 

FIGURE 2-5. FG-1 RISER/CHANNEL & FOREHEARTH TPM10 SPECIATION (LB/HR) 

FIGURE 2-6. FG-2 RISER/CHANNEL & FOREHEARTH TPM10 SPECIATION (LB/HR) 

FG-2 Riser/Channel & Forehearth

0.00

0.00 0.04

0.12

0.08

0.00

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)
OC Fairburn 20 Trinity Consultants 

BART Modeling Protocol 

FG-3 Riser/Channel & Forehearth

0.01

0.02 0.83

2.43

1.54

0.05

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

FIGURE 2-7. FG-3 RISER/CHANNEL & FOREHEARTH TPM10 SPECIATION (LB/HR) 

2.2.3 FORMING AND CURING PARTICULATE MATTER SPECIATION

Air flow from the forming and curing sections are first routed together through a mixing 

chamber prior to being released to the atmosphere. As such, emissions from these process 

units are quantified together. To speciate PM emissions from the mixing chamber, the 

PM2.5 portion of PM10 emissions was first calculated based on engineering estimates made 

by OC personnel.10 Speciation factors from SMOKE PM2.5, as provided by VISTAS, were 

then used to divide PM2.5 emissions into various categories. It was assumed that emissions 

within a particular PM category are equally distributed to all size bins within that category. 

The relevant size bins are given within the “Revised BART Speciation Template” available 

on the VISTAS website.11 Table 2-10 summarizes the applicable data for this source. 

TABLE 2-10. MIXING CHAMBER SPECIATION DATA

 

10 E-mail communication from Mr. Franco Vigo (OC) to Ms. Melissa Antoine (Trinity Consultants) on 

July 23, 2006. 

11 http://www.vistas-sesarm.org/BART/calpuff.asp 

Speciation Data Value Reference

PM2.5 as a % of PM10 90.00% Mr. Franco Vigo (OC)

Primary EC as a % of PM2.5 2.00% SMOKE PM2.5, Speciation Profile 22033

Primary PM2.5 as a % of PM2.5 63.30% SMOKE PM2.5, Speciation Profile 22033

Primary NO3 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033

Primary OA as a % of PM2.5 33.60% SMOKE PM2.5, Speciation Profile 22033

Primary SO4 as a % of PM2.5 0.55% SMOKE PM2.5, Speciation Profile 22033
OC Fairburn 21 Trinity Consultants 

BART Modeling Protocol 

Using the information presented in Table 2-10, OC first calculated process TPM10

emissions from each mixing chamber by multiplying the applicable maximum hourly TSP 

emission rate of by the glass pull rate of the corresponding glass melting furnace, which for 

the FG-1 Mixing Chamber, yields 60.0 lb/hr. Note that because emissions are based on 

stack testing, this emission rate includes combustion emissions from the forming section 

fiberizers, the curing ovens burners, and the incinerator burners. OC personnel estimate 

that approximately 90% of PM10 emissions from the mixing chambers are PM2.5. 

Multiplying process PM10 emissions by 90% results in PM2.5 emissions from the FG-1 

Mixing Chamber of 54.0 lb/hr. 

Next, PM2.5 is speciated into the categories given in Table 2-10. An example is provided 

below for process PEC emissions from the FG-1 Mixing Chamber. 

hour

lb PEC allsize categories 1.08

TPM

PEC 2%

hr

lb TPM 54.0 2.5

2.5 ⎟ =

⎠

⎞ ⎜

⎝

⎛ ⎟ ×

⎠

⎞ ⎜

⎝

⎛

Emissions are calculated for the remaining PM2.5 categories listed in Table 2-10 in a 

similar manner. Table 2-11 presents speciated PM emissions for the mixing chambers at 

the Fairburn facility. 

TABLE 2-11. MIXING CHAMBER SPECIATED PM EMISSIONS

Figures 2-8, 2-9, and 2-10 present a graphical representation of the PM speciation for the mixing 

chambers at the Fairburn facility. 

Total PM10 Total PM2.5 PM10 - PM2.5 PEC PPM2.5 PNO3 POA PSO4 

Emission Unit EU ID (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

FG1 Mixing Chamber - 60.00 54.00 6.00 1.07 34.00 0.30 18.25 0.38

FG2 Mixing Chamber - 29.50 26.55 2.95 0.52 16.65 0.15 9.02 0.22

FG3 Mixing Chamber - 24.00 21.60 2.40 0.43 13.58 0.12 7.32 0.16
OC Fairburn 22 Trinity Consultants 

BART Modeling Protocol 

FIGURE 2-8. FG-1 MIXING CHAMBER TPM10 SPECIATION (LB/HR)

FIGURE 2-9. FG-2 MIXING CHAMBER TPM10 SPECIATION (LB/HR) 

FG-1 Mixing Chamber

0.30

0.38 18.25

6.00

34.00

1.07

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

FG-2 Mixing Chamber

0.15

0.20 8.96

2.95

16.63

0.52

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)
OC Fairburn 23 Trinity Consultants 

BART Modeling Protocol 

FIGURE 2-10. FG-3 MIXING CHAMBER TPM10 SPECIATION (LB/HR) 

2.2.4 COOLING SECTION PARTICULATE MATTER SPECIATION

To speciate PM emissions from the cooling section, the PM2.5 portion of PM10 emissions 

was first calculated based on engineering estimates made by OC personnel.12 Speciation 

factors from SMOKE PM2.5, as provided by VISTAS, were then used to divide PM2.5

emissions into various categories. It was assumed that emissions within a particular PM 

category are equally distributed to all size bins within that category. The relevant size bins 

are given within the “Revised BART Speciation Template” available on the VISTAS 

website.13 Table 2-12 summarizes the applicable data for this source. 

Using the information presented in Table 2-12, OC first calculated process TPM10

emissions from each applicable cooling section by multiplying the applicable maximum 

hourly TSP emission rate of by the glass pull rate of the corresponding glass melting 

furnace, which for the FG-1 Cooling Section, yields 5.49 lb/hr. OC personnel estimate that 

approximately 90% of PM10 emissions from the cooling section are PM2.5. Multiplying 

process PM10 emissions by 90% results in PM2.5 emissions from the FG-1 Cooling Section 

of 4.94 lb/hr. 

Next, PM2.5 is speciated into the categories given in Table 2-12. An example is provided 

below for process PEC emissions from the FG-1 Cooling Section. 

hour

lb PEC allsize categories 0.11

TPM

PEC 2%

hr

lb TPM 5.49 2.5

2.5 ⎟ =

⎠

⎞ ⎜

⎝

⎛ ⎟ ×

⎠

⎞ ⎜

⎝

⎛

 

12 E-mail communication from Mr. Franco Vigo (OC) to Ms. Melissa Antoine (Trinity Consultants) on 

July 23, 2006. 

13 http://www.vistas-sesarm.org/BART/calpuff.asp 

FG-3 Mixing Chamber

0.12

0.14 7.24

2.40

13.55

0.43

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)
OC Fairburn 24 Trinity Consultants 

BART Modeling Protocol 

Emissions are calculated for the remaining PM2.5 categories listed in Table 2-12 in a 

similar manner. Table 2-13 presents speciated PM emissions for the applicable cooling 

sections at the Fairburn facility. 

TABLE 2-12. COOLING SECTION SPECIATED PM EMISSIONS

Figures 2-11 and 2-12 present a graphical representation of the PM speciation for the FG-1 and FG-2 

cooling sections at the Fairburn facility. 

FIGURE 2-11. FG-1 COOLING SECTION TPM10 SPECIATION (LB/HR) 

FIGURE 2-12. FG-2 COOLING SECTION TPM10 SPECIATION (LB/HR) 

FG-1 Cooling Section

0.03

0.03 1.66

0.55

3.13

0.10

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

FG-2 Cooling Section

0.01

0.01 0.45

0.15

0.85

0.03

PIC, Nitrate (0.5-1.0 microns)

PIC, Sulfate (0.5-1.0 microns)

POC (Equally distributed between Bins 5-7)

PMC (Equally distributed between Bins 1-2)

PMF (Equally distributed between Bins 3-7)

EC (Equally distributed between Bins 6-7))

Size Bins

Bin 1: (6-10 microns)

Bin 2: (2.5-6 microns)

Bin 3: (1.25-2.5 microns)

Bin 4: (1.0-1.25 microns)

Bin 5: (0.625-1.0 microns)

Bin 6: (0.5-0.625 microns)

Bin 7: (<0.5 microns)

Total PM10 Total PM2.5 PM10 - PM2.5 PEC PPM2.5 PNO3 POA PSO4 

Emission Unit EU ID (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

FG1 Cooling Section FG16 5.49 4.94 0.55 0.10 3.13 0.03 1.66 0.03

FG2 Cooling Section FG26 1.50 1.35 0.15 0.03 0.85 7.4E-03 0.45 7.4E-03
OC Fairburn 25 Trinity Consultants 

BART Modeling Protocol 

2.3 MODELED STACK PARAMETERS AND EMISSIONS

Actual stack parameters will be input to the CALPUFF model to represent the point of visibilityaffecting pollutant emissions. The location of each point source will be represented consistently in 

the Lambert Conformal Coordinate system used for the screening and refined meteorological data 

analyses prepared by VISTAS. Each exhaust discharges vertically without obstruction. The effects 

of building downwash will not be considered in the BART applicability analyses. Table 2-14 

summarizes the stack parameters for BART-eligible emission units at OC’s Fairburn facility. 

TABLE 2-14. STACK PARAMETERS FOR BART-ELIGIBLE EMISSION UNITS

UTM 

East UTM North

UTM 

Zone

Stack 

Height

Base 

Elevation Diameter

Gas Exit 

Velocity

Stack 

Gas Exit 

Temp.

Stack ID Source Description km km ft ft ft ft/s deg F

FG11A FG-1 Furnace Electric Cold Top Melting Furnace - Stack A 721.400 3713.442 16 68.63 1,031 4.00 10.96 102

FG11B FG-1 Furnace Electric Cold Top Melting Furnace - Stack B 721.407 3713.434 16 68.63 1,031 4.00 8.29 112

FG1MCa FG-1 Mixing Chamber Mixing Chamber 721.364 3713.399 16 61.01 1,031 9.50 48.71 165

FG12 FG-1 Riser/Channel Flow Channel

FG13 FG-1 Forehearth Flow Channel

FG14 FG-1 Forming Section Molten Glass Spinning to Glass Fibers

FG15 FG-1 Curing Oven Insulation Binder Curing Oven

FG16 FG-1 Cooling Section Cooling with Ambient Air 721.341 3713.377 16 51.10 1,031 3.17 40.55 195

FG21A FG-2 Furnace Electric Cold Top Melting Furnace - Stack A 721.416 3713.417 16 70.58 1,031 4.00 8.05 110

FG21B FG-2 Furnace Electric Cold Top Melting Furnace - Stack B 721.422 3713.409 16 70.58 1,031 4.00 8.72 111

FG2MCa FG-2 Mixing Chamber Mixing Chamber 721.371 3713.378 16 65.33 1,031 10.50 25.06 165

FG22 FG-2 Riser/Channel Flow Channel

FG23 FG-2 Forehearth Flow Channel

FG24 FG-2 Forming Section Molten Glass Spinning to Glass Fibers

FG25 FG-2 Curing Oven Insulation Binder Curing Oven

FG26 FG-2 Cooling Section Cooling with Ambient Air 721.348 3713.358 16 51.10 1,031 2.62 40.55 170

FG31 FG-3 Furnace Electric Cold Top Melting Furnace 721.435 3713.399 16 68.75 1,031 4.00 10.59 107

FG3MCa FG-3 Mixing Chamber Mixing Chamber 721.407 3713.372 16 80.66 1,031 8.00 22.14 245

FG32 FG-3 Riser/Channel Flow Channel

FG33 FG-3 Forehearth Flow Channel

FG34 FG-3 Forming Section Molten Glass Spinning to Glass Fibers

FG35 FG-3 Curing Oven Insulation Binder Curing Oven

a. The Mixing Chamber includes emissions from the Forming and Curing Sections. It is assumed that emissions from the Riser/Channel and Forehearth are also routed through the Mixing Chamber.
OC Fairburn 26 Trinity Consultants 

BART Modeling Protocol 

3. GEOPHYSICAL AND METEOROLOGICAL DATA

Section 3 of this BART modeling protocol for OC’s Fairburn facility describes the geophysical and 

meteorological data that will be used in the screening and refined analyses. The information in this 

section is largely adapted from the VISTAS BART Modeling Protocol, which is presented in 

Appendix A of this source-specific modeling protocol for reference, and sample model files made 

available on the VISTAS technical contractor website.14

CALMET requires geophysical data about the domain to characterize the terrain and land use 

parameters that potentially affect dispersion. Terrain features affect flows and create turbulence in 

the atmosphere and are potentially subjected to higher concentrations of elevated puffs, and different 

land uses exhibit variable characteristics such as surface roughness, albedo, Bowen ratio, and leafarea index that also affect turbulence and dispersion.

3.1 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN

Terrain elevations within the modeling domain were processed from SRTM-GTOPO30 digital terrain 

data format with 30-arcsec resolution. SRTM30 is a digital elevation data set that spans the globe 

from 60° north latitude to 56° south latitude, approximately from the southern tip of Greenland to 

below the southern tip of South America. It has a horizontal grid spacing of 30 arc-seconds 

(approximately 1 kilometer). GTOPO30 is a global digital elevation model with a horizontal grid 

spacing of 30 arc-seconds (approximately 1 kilometer) that was derived from several raster and vector 

sources of topographic information that include U.S. Geological Survey digital elevation models. 

The VISTAS technical contractor used data preprocessors to format and assimilate these data into a 

single geophysical data file for processing by CALMET. The representation of terrain in the regional 

screening analysis resolves the terrain onto the 12-km regional grid depicted in Figure 3-1. 

Figure 3-2 shows terrain for the 4-km sub-domain 4. 

 

14 http://src.com/verio/download/download.htm#VISTAS_VERSION. 
OC Fairburn 27 Trinity Consultants 

BART Modeling Protocol 

FIGURE 3-1. TERRAIN REPRESENTATION IN THE 12-KM REGIONAL SCREENING GRID

(AFTER FIGURE 4-2 OF THE VISTAS BART MODELING PROTOCOL)
OC Fairburn 28 Trinity Consultants 

BART Modeling Protocol 

FIGURE 3-2. TERRAIN REPRESENTATION IN THE 4-KM SUB-DOMAIN 4
OC Fairburn 29 Trinity Consultants 

BART Modeling Protocol 

3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN

Land use and land cover (LULC) within the modeling domain was assimilated by the VISTAS 

technical contractor into a single geophysical data file for processing by CALMET using Composite 

Theme Grid (CTG) data archived by the U.S. Geological Survey at a resolution of 200 meters. LULC 

in each grid cell was used by CALMET to compute the micrometeorological parameters (i.e., surface 

roughness, Bowen ratio, albedo, soil heat flux) that affect turbulent dispersion in the boundary layer. 

Figure 3-3 shows the land use for the 4-km sub-domain 4. 

FIGURE 3-3. LAND USE REPRESENTATION IN THE 4-KM SUB-DOMAIN 4 
OC Fairburn 30 Trinity Consultants 

BART Modeling Protocol 

3.3 METEOROLOGICAL DATABASE

CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields 

from mesoscale model (MM) output, raw observations of surface and upper air conditions, 

precipitation measurements, and geophysical parameters into a single hourly, gridded data set for 

input to CALPUFF. The federal Guideline for CALPUFF processing provides the following 

recommendations for the meteorological data period at Section 9.3.1.2: 

Less than five, but at least three, years of meteorological data (need not be 

consecutive) may be used if mesoscale meteorological fields are available, as 

discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used 

in conjunction with available standard [National Weather Service] NWS or 

comparable meteorological observations within and near the modeling domain. 

The VISTAS BART Modeling Protocol describes a regional domain and a set of pre-computed 

regional CALMET meteorological files with 12 km grid size for the years 2001, 2002, and 2003, 

prepared by the VISTAS technical contractor to allow any Class I areas within the VISTAS area to be 

evaluated with a consistent meteorological database and consistent CALPUFF modeling options. In 

addition, the VISTAS technical contractor also prepared sub-domains of the regional grid in a similar 

fashion to the regional screening domain with 4 km grid size for the years 2001, 2002, and 2003. The 

CALMET modeling output files in the form of CALPUFF-ready three-dimensional meteorological 

files were made available on external hard drives to the States and other parties. These data were 

obtained by OC’s technical contractor and were utilized in the 12-km and 4-km analyses. Note that 

sub-domain Number 4 will be utilized for 4-km modeling. 

3.3.1 MM5 SIMULATIONS

MM5 data are used as “observed” or “first-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional 

grid. The following three years of MM5 meteorological data have been assembled by 

VISTAS for use in the regional CALPUFF modeling effort: 

▲ 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA) 

▲ 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS) 

▲ 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization).

These data sets were provided to the VISTAS technical contractor, which produced annual 

CALMET meteorological files for the 12-km grid resolution in the regional domain and 

4-km grid resolution sub-domains. The development of the 12-km CALMET 

meteorological fields from MM5 data were conducted in No-Observations (“No-Obs”) 

mode since the MM5 data already reflect assimilation of observational data and are likely 

to adequately characterize regional wind patterns that are consistent with the 12-km scale. 

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain 

adjustments were turned off since the grid resolution of the MM5 data is the same as the 
OC Fairburn 31 Trinity Consultants 

BART Modeling Protocol 

CALMET grid and the terrain adjustments on the 12-km grid scale will already be 

reflected in the MM5 dataset. In this case, the MM5 winds will be interpolated by 

CALMET to the CALMET layers and CALMET’s boundary layer modules will compute 

mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. For 2003, the 36-km MM5 data will be used as CALMET’s initial 

guess field and then the CALMET diagnostic terrain adjustments (see Section 3.1.1 of the 

VISTAS BART Modeling Protocol) will be applied to reflect terrain on the scale of the 

CALMET grid (i.e., 12 km). 

For refined analyses at higher resolution grid spacing (i.e., 2 km or 1 km), MM5 data will 

be processed in CALMET using the same model control options as were used by the 

VISTAS technical contractor to prepare the 4-km refined grid. 

3.3.2 MEASUREMENTS AND OBSERVATIONS

The finer grid (4 km) CALMET simulations, which OC anticipates utilizing as part of a 

refined analysis, were run by VISTAS technical contractor in hybrid mode, using both 

MM5 data to define the initial guess fields and NWS meteorological observational data in 

the Step 2 calculations. In this manner, actual observations of three-dimensional 

meteorological conditions can be used in the model to smooth the coarse MM5 resolution 

to better represent areas in which terrain features may have an important effect on 

meteorological conditions, but not be well resolved in the mesoscale model. Surface, 

upper air, and precipitation observation points are readily available for use in CALMET. 

The following generally describes the use of NWS observations in Step 2 of the CALMET 

analyses.

Parameters affecting turbulent dispersion that are observed hourly at surface stations 

include wind speed and direction, temperature, cloud cover and ceiling, relative humidity, 

and precipitation type. Surface data would be selected from the available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations were 

obtained from the National Climatic Data Center (NCDC), quality assured, and merged 

using the SMERGE pre-processor to create a single assimilated data file of surface 

observations for each year analyzed. 

Observations of meteorological conditions in the upper atmosphere provide a profile of 

turbulence from the surface through the depth of the boundary layer in which dispersion 

occurs. Upper air data are collected by balloons launched simultaneously across the 

observation network at 0000 Greenwich Mean Time (GMT) (7 o’clock PM in South 

Carolina) and 1200 GMT (7 o’clock AM in South Carolina). Sensors observe pressure, 

wind speed and direction, and temperature (among other parameters) as the balloon rises 

through the atmosphere. The upper air observation network is less dense than surface 

observation points since upper air conditions vary less and are generally not as affected by 

local effects (e.g., terrain or coastlines). Upper air data were extracted from the NCDC’s 

available data inventory to optimize spatial coverage and representation of the domain, and 

utilization from year to year may vary due to availability and data quality. 
OC Fairburn 32 Trinity Consultants 

BART Modeling Protocol 

The effects of wet deposition processes on ambient pollutant concentrations are an 

important part of the BART applicability analysis. Therefore, it is necessary to include 

observations of precipitation in the CALMET analysis. Precipitation data were collected 

from selected surface meteorological data stations included in the analysis, plus 

Cooperative Observation Network (COOP) stations nearer to or within the domain. 

Precipitation data were extracted from among the NCDC’s available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations from these 

stations were quality assured and merged using the PMERGE pre-processor to create a 

single assimilated data file of precipitation observations. 

In refined analyses for OC’s BART applicability modeling analysis, hourly surface 

meteorological observations, precipitation observations, and twice-daily upper air sounding 

data will be provided.

For refined analyses at higher resolution grid spacing (i.e., 2 km or 1 km), NWS surface, 

upper air, and precipitation data will be processed in CALMET using the same model 

control options and data resources as were used by the VISTAS technical contractor to 

prepare the 4-km refined grid. 

3.4 AIR QUALITY DATABASE

The CALPUFF model is capable of simulating linear chemical transformation effects by using 

pseudo-first-order chemical reaction mechanisms for the conversions of SO2 to SO4, and NOX, which 

consists of nitrogen oxide (NO) and nitrogen dioxide (NO2), to nitrate (NO3) and nitric acid (HNO3). 

In this study, chemical transformations involving five species (SO2, SO4, NOX, HNO3, and NO3) will 

be modeled using the MESOPUFF II chemical transformation scheme. Ambient concentrations of 

ammonia and ozone concentrations as represented in the model affect the MESOPUFF II chemical 

transformation simulation. 

3.4.1 OZONE BACKGROUND CONCENTRATIONS

Both screening and refined analyses will utilize observed ozone data for 2001 through 

2003 from non-urban CASTNet and AIRS stations compiled by the VISTAS technical 

contractor for the regional domain. Monthly average ozone background values will be 

computed based on daytime average ozone concentrations from the OZONE.DAT file 

(6am-6pm average ozone concentrations computed by month) for substitution should all 

observations be missing for a particular hour of the dataset. A list and plot of ozone 

stations used in the analysis is provided in Appendix B. 

3.4.2 AMMONIA BACKGROUND CONCENTRATIONS

In the screening and refined analyses, a constant background value (0.5 ppb) for ammonia 

will be utilized. The revised VISTAS BART Modeling Protocol prescribes that 

postprocessing to repartition HNO3 and NO3 using the ammonia limiting method (ALM) in 

POSTUTIL be used only with the 0.5 ppb background level. 
OC Fairburn 33 Trinity Consultants 

BART Modeling Protocol 

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS

The initial VISTAS BART Modeling Protocol envisions the use of modeled boundary 

conditions of ammonia and sulfates in the refined analysis of chemical transformations 

involving these species and nitrates. However, VISTAS’ Technical Analysis Workgroup 

has since concluded that modeled background and boundary conditions should not be 

utilized for BART modeling purposes because “EPA and FLM recommend that ALM 

approach using CMAQ concentration data (SOX, NOX, total NH3) be reviewed by EPA 

Modeling Clearinghouse before being used in a regulatory application.” Accordingly, such 

background data will not be utilized in OC’s BART applicability modeling analyses. 

3.5 NATURAL CONDITIONS AT CLASS I AREAS

The visibility goal of the Clean Air Act is both the remedying of existing visibility impairment, and 

prevention of future visibility impairment. In its BART Implementation Guidance, U.S. EPA affirms 

that it interprets the goal to mean return atmospheric conditions to “natural visibility conditions.” For 

the purposes of BART analyses, the U.S. EPA has determined that it “did not intend to limit States to 

the use of the 20% best visibility days...States may use 20% best visibility days or annual average.”15 

The July 18, 2006 revision to the VISTAS BART Modeling Protocol indicates that the annual average 

visibility may be considered as the reference natural background condition, and the initial screening 

analyses presented by the VISTAS contractor used only the annual average natural background 

conditions to assess visibility impacts attributable to OC’s Fairburn facility. Accordingly, OC will 

conduct the refined analyses using the annual average natural background. 

For the five Class I areas within 300 km of the Fairburn facility and potentially affected by OC’s 

operations, Table 3-1 summarizes the default natural background conditions as tabulated in 

Appendix B of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule. 

 

15 U.S. EPA Memorandum from Mr. Joseph Paisie to Ms. Kay Prince, as Attachment A to a proposed settlement 

agreement between the Utility Air Regulatory Group and U.S. EPA, published at 71 Federal Register No. 84, pp. 25,838-

25,840, May 2, 2006. 
OC Fairburn 34 Trinity Consultants 

BART Modeling Protocol 

TABLE 3-1. NATURAL BACKGROUND CONCENTRATIONS

FOR CLASS I AREAS POTENTIALLY AFFECTED BY THE FAIRBURN FACILITY

 

Class I Area bext (Mm-1) 

Annual Average 

Haze Index (dv) 

Best Days Haze 

Index (dv) 

Worst Days Haze 

Index (dv) 

Cohutta 21.39 7.60 3.76 11.44 

Great Smoky Mtns. 21.39 7.60 3.76 11.44 

Slickrock 21.40 7.61 3.77 11.45 

Shining Rock 21.40 7.61 3.77 11.45 

Sipsey 21.28 7.55 3.71 11.39 

* As tabulated in Appendix B of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze 

Rule (2003). 

To represent natural conditions in the absence of anthropogenic sources of sulfates and nitrates, the 

monthly background extinction coefficient is expressed in terms of Rayleigh scattering and scattering 

due to soils (i.e., fine particles) based on the annual average background, and is calculated from the 

tabulated annual average values and the following equations. 

⎟

⎠

⎞ ⎜

⎝

⎛ = 10

10 exp

HI bback , 

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total bback for the annual 

average natural background at the relevant Class I areas, including the Rayleigh scattering coefficient, 

is calculated as shown in the following equations. 

For Cohutta and Great Smoky Mountains: 

21.39 Mm 10 Mm 11.39 Mm

10

7.60 10 exp −1 −1 −1 ⎟ = = + = + ⇒ = ⎠

⎞ ⎜

⎝

⎛ back = ray soil soil soil b b b b b

For Slickrock and Shining Rock: 

21.40 Mm 10 Mm 11.40 Mm

10

7.61 10 exp −1 −1 −1 ⎟ = = + = + ⇒ = ⎠

⎞ ⎜

⎝

⎛ back = ray soil soil soil b b b b b

For Sipsey: 

21.28 Mm 10 Mm 11.28 Mm

10

7.55 10 exp −1 −1 −1 ⎟ = = + = + ⇒ = ⎠

⎞ ⎜

⎝

⎛ back = ray soil soil soil b b b b b

Alternatively, Table 3-2 summarizes the default natural background conditions using average natural 

concentrations of sulfate, nitrate, and particulate species for areas in the Eastern U.S. as tabulated in 

Table 2-1 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule. 
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BART Modeling Protocol 

TABLE 3-2. NATURAL BACKGROUND CONCENTRATIONS OF

VISIBILITY-AFFECTING POLLUTANTS

The values presented in Table 3-2 are more appropriate for the determination of natural background 

conditions, since this approach includes all visibility-affecting species and does not rely only on soil 

dust concentrations to define the natural background conditions. Ammonium sulfates and nitrates as 

well as organic and elemental carbon are appropriate to represent as part of the natural background 

due to emissions from naturally occurring biogenic sources (e.g., vegetation and wildfire biomass 

burning). Accordingly, OC will compute the light extinction change relative to background 

conditions using both definitions of the annual average concentration representative of the natural 

background. 

As is described in Section 4 of this protocol, the effects of relative humidity to amplify the visibility 

impairment of hygroscopic sulfates and nitrates will be characterized using “Method 6,” which 

computes ∆bext using a monthly average relative humidity adjustment particular to each Class I area 

applied to background and modeled sulfate and nitrate. Table 3-3 summarizes the monthly average 

humidity values that will be applied for the five Class I areas considered in this analysis, as tabulated 

in Table A-3 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the 

Regional Haze Rule. 

TABLE 3-3. MONTHLY AVERAGE f(RH) FOR SELECTED CLASS I AREAS* 

 

Class I Area January February March April May June July August September October November December 

Cohutta 3.3 3.1 3.0 2.8 3.4 3.8 4.0 4.2 4.2 3.8 3.4 3.5 

Great Smoky 

Mtns. 3.3 3.0 2.9 2.7 3.2 3.9 3.8 4.0 4.2 3.8 3.3 3.4 

Slickrock 3.3 3.1 2.9 2.7 3.3 3.8 4.0 4.2 4.2 3.8 3.3 3.5 

Shining Rock 3.3 3.0 2.9 2.7 3.4 3.9 4.1 4.5 4.4 3.8 3.3 3.4 

Sipsey 3.4 3.1 2.9 2.8 3.3 3.7 3.9 3.9 3.9 3.6 3.3 3.4 

* As tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule (2003). 

Dry Extinction 

West East Error Efficiency 

Component (μg/m3

) (μg/m3

) Factor (m2

/g) 

Ammonium sulfate 0.12 0.23 2 3 

Ammonium nitrate 0.1 0.1 2 3 

Organic carbon mass 0.47 1.4 2 4 

Elemental carbon 0.02 0.02 2-3 10 

Soil 0.5 0.5 11⁄2 - 2 1 

Coarse Mass 3 3 11⁄2 - 2 0.6 
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Natural background conditions for each Class I area will be calculated using the data summarized in 

Tables 3-1, 3-2, and 3-3, and the default IMPROVE light extinction formula, which is summarized in 

the following equation. 

ext background bSO bNO bOC bsoil bcoarse bap bray b km = + + + + + + −

4 3

( )

1

,

where, 

[ ]

[ ]

[ ]

[ ]

[ ]

3

Ray

4 3

4 2 4

[ ] Concentration in μg/m

f(RH) relative humidity adjustment factor

b Rayleigh Scattering

0.01[Elemental Carbon]

0.0006 Coarse Mass

0.001 Soil

0.004 OC

0.003 NH NO ( )

0.003 (NH ) SO ( )

3

4

=

=

=

=

=

=

=

=

=

ap

Coarse

Soil

OC

NO

SO

b

b

b

b

b f RH

b f RH

As noted in the revised VISTAS BART Modeling Protocol, the U.S. EPA and the Regional Planning 

Organizations (including VISTAS) are evaluating whether refinements are warranted to the methods 

recommended in U.S. EPA’s guidance to calculate default estimates of natural background visibility. 

In addition, the Interagency Monitoring of Protected Visual Environments (IMPROVE) work group 

has recently approved an alternative to the default formula used to estimate extinction from particle 

concentration measurements.16 Refinements in the revised IMPROVE formula include adding a sitespecific Rayleigh scattering term to the formula. Values have been calculated by IMPROVE for most 

Class I areas. 

For the purposes of calculating current, future, and natural background visibility at Class I areas as 

part of its reasonable progress analyses, VISTAS intends to present regional air quality modeling 

results using both the current U.S. EPA recommended assumptions and the newly revised IMPROVE 

light extinction formula. Accordingly, in refined BART applicability analyses, OC will evaluate the 

results of the analysis using both the standard light extinction calculation (which excludes sea salt 

concentrations and uses the default 10 Mm-1 value of Rayleigh scattering), as well as applying a 

correction to the Rayleigh scattering value. Note that OC is not proposing to use the revised 

IMPROVE light extinction equation, rather only to utilize corrections to Rayleigh scattering that can 

be utilized in the existing CALPOST algorithms for refined BART Applicability Modeling Analyses. 

The default Rayleigh scattering coefficient of 10 Mm-1 represents light scattering due to air molecules 

at a reference elevation condition of approximately 5,000 feet elevation. The 2005 IMPROVE work 

group report describes that Rayleigh scattering depends on the density of the air and thus varies with 

temperature and pressure, and accordingly tabulates Class I-area specific values of Rayleigh 

 

16 Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005. Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering Committee, 

November 2005. 
OC Fairburn 37 Trinity Consultants 

BART Modeling Protocol 

scattering corrected for these effects. A value of 11.4 Mm-1 is recommended for Cohutta, Great 

Smoky Mountains, Slickrock, and Shining Rock, while a value of and 11.3 Mm-1 is recommended for 

Sipsey.

 
OC Fairburn 38 Trinity Consultants 

BART Modeling Protocol 

4. AIR QUALITY MODELING METHODOLOGY

Section 4 of this BART applicability modeling protocol for OC’s Fairburn facility describes the air 

quality modeling methodology that will be used in the screening and refined analyses. The 

information in this Section 4 is largely adapted from the VISTAS BART Modeling Protocol, which is 

presented in Appendix A of this source-specific protocol for reference, and sample model files made 

available on the VISTAS technical contractor website.17

Section 2.2 of the VISTAS BART Modeling Protocol summarizes recommendations for the air quality 

modeling analyses required to assess applicability of BART by determining whether OC’s Fairburn 

facility contributes to visibility impairment at the Cohutta (144 km), Great Smoky Mountains 

(223 km), Joyce Kilmer-Slickrock (209 km), Shining Rock (253 km) and/or the Sipsey (267 km) 

Class I areas. The CALPUFF V5.754 modeling system is recommended as the preferred modeling 

approach for use in the BART analyses. 

4.1 PLUME MODEL SELECTION

CALPUFF and its meteorological model, CALMET, are designed to handle the complexities posed 

by the complex terrain, the large source-receptor distances, chemical transformation and deposition, 

and other issues related to Class I visibility impacts. The CALPUFF modeling system has been 

adopted by the U.S. EPA as a Guideline model for source-receptor distances greater than 50 km, and 

for use on a case-by-case basis in complex flow situations for shorter distances. CALPUFF is 

recommended for Class I impact assessments by FLAG and IWAQM. The final BART guidance 

recommends CALPUFF as “the best modeling application available for predicting a single source’s 

contribution to visibility impairment.” As a result of these recommendations, the VISTAS BART 

Modeling Protocol is based on the use of CALPUFF for its BART determinations. Specifically, 

VISTAS CALMET Version 5.724 and CALPUFF Version 5.754 were used in the CALPUFF 

analyses for BART applicability assessment. 

This source-specific BART modeling protocol for OC’s Fairburn facility incorporates by reference 

the VISTAS BART Modeling Protocol, which is provided in Appendix A of this document. The 

following sections present a brief summary of major features of the CALMET and CALPUFF 

models, and further detailed information should be obtained from the VISTAS BART Modeling 

Protocol and documentation referenced therein. 

4.1.1 MAJOR RELEVANT FEATURES OF CALMET 

The CALMET meteorological model consists of a diagnostic wind field module and 

boundary layer micrometeorological modules for overwater and overland boundary layers. 

Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to 

compute hourly gridded fields of the sensible heat flux, surface friction velocity, MoninObukhov length, and convective velocity scale. Mixing heights are determined from the 

 

17 http://src.com/verio/download/download.htm#VISTAS_VERSION. 
OC Fairburn 39 Trinity Consultants 

BART Modeling Protocol 

computed hourly surface heat fluxes and observed temperature soundings using a modified 

Carson (1973) method based on Maul (1980). The model also determines gridded fields of 

Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates. 

The diagnostic wind field module uses a two-step approach to the computation of the wind 

fields (Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted 

for kinematic effects of terrain, slope flows, and terrain blocking effects to produce a 

Step 1 wind field. Gridded MM5 can be used to define the initial guess field. The second 

step consists of an objective analysis procedure to introduce observational data into the 

Step 1 wind field to produce a final wind field.

Development of the Step 1 wind field begins with the initial guess field defined by the 

MM5 prognostic meteorological model. Normally, the CALMET computational domain is 

specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used 

to develop the initial guess field on a 4-km or even a 1- or 2-km CALMET grid. The Step 

1 algorithms in CALMET described below apply terrain adjustments to the initial guess 

field on the fine-scale CALMET grid. Thus, the CALMET winds are adjusted to respond 

to fine-scale terrain features not necessarily seen by the coarser scale MM5 model. 

The approach of Liu and Yocke (1980) is used to evaluate the effects of the kinematic 

terrain on the wind field. The initial guess field winds are used to compute a terrain-forced 

vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme 

is applied iteratively until the three-dimensional divergence is less than a threshold value. 

The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 1997) 

based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

advective-gravity and equilibrium flow regimes. At night, the slope flow model 

parameterizes the flow down the sides of the valley walls into the floor of the valley, and 

during the day, upslope flows are parameterized. The magnitude of the slope flow depends 

on the local surface sensible heat flux and local terrain gradients. The slope flow wind 

components are added to the wind field adjusted for kinematic effects. 

The thermodynamic blocking effects of terrain on the wind flow are parameterized in terms 

of the local Froude number (Allwine and Whiteman, 1985). If the Froude number at a 

particular grid point is less than a critical value and the wind has an uphill component, the 

wind direction is adjusted to be tangent to the terrain. 

The wind field resulting from the preceding adjustments of the initial-guess wind is the 

Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An 

inverse-distance squared interpolation scheme is used which weights observational data 

heavily in the vicinity of the observational station, while the Step 1 wind field dominates 

the interpolated wind field in regions with no observational data. The resulting wind field 
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BART Modeling Protocol 

is subject to smoothing, an optional adjustment of vertical velocities based on the O’Brien 

(1970) method, and divergence minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also 

possible to run the model in “no observations” (No-Obs) mode, which involves the use 

only of MM5 gridded data for the initial guess field followed by fine-scale terrain 

adjustments by CALMET. In No-Obs mode, observational data are not used in the Step 2 

calculations. The No-Obs mode is appropriate when the MM5 simulations adequately 

characterize the regional wind patterns and when local observations, especially surface 

observations, reflect local conditions on a scale smaller than that of the CALMET domain 

and hence their spatial representativeness may be limited. Such situations are most likely to 

occur when the CALMET grid scale is relatively large i.e., coarser than the scale of 

variation of the true wind field, which is particularly likely to occur in complex terrain or 

along the seashore. The No-Obs mode will be used for the 12-km screening grid. 

As was described in Section 3.3.1 of this modeling protocol, when the 12-km MM5 (2001 

and 2002) data are used, the diagnostic CALMET terrain adjustments will be turned off 

since the grid resolution of the MM5 data is the same as the CALMET grid and the terrain 

adjustments on the 12-km grid scale are already be reflected in the MM5 dataset. In this 

case, the MM5 winds will be interpolated by CALMET to the CALMET layers and 

CALMET’s boundary layer modules will compute mixing heights, turbulence parameters 

and other meteorological parameters that are required by CALPUFF. For 2003, the 36-km 

MM5 data will be used as CALMET’s initial guess field and then the CALMET diagnostic 

terrain adjustments (see Section 3.1.1 of the VISTAS BART Modeling Protocol) will be 

applied to reflect terrain on the scale of the CALMET grid (i.e., 12 km). Refined analyses, 

if required, will utilize the MM5 data as the first-guess wind field, apply the diagnostic 

algorithms to create the Step 1 winds, and use NWS data for smoothing in Step 2. 

4.1.2 MAJOR RELEVANT FEATURES OF CALPUFF 

By its puff-based formulation and through the use of three-dimensional meteorological 

data developed by the CALMET meteorological model, CALPUFF can simulate the 

effects of time- and space-varying meteorological conditions on pollutant transport from 

sources in complex terrain. The major features and options of the CALPUFF model are 

summarized in Table 3-2 of the VISTAS BART Modeling Protocol. Some of the technical 

algorithms are briefly described as follows. 

S Complex Terrain: The effects of complex terrain on puff transport are derived from 

the CALMET winds. In addition, puff-terrain interactions at gridded and discrete 

receptor locations are simulated using one of two algorithms that modify the 

puff-height (either that of ISCST3 or a general “plume path coefficient” adjustment), 

or an algorithm that simulates enhanced vertical dispersion derived from the 

weakly-stratified flow and dispersion module of the Complex Terrain Dispersion 

Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms 

rely on the receptor elevation (relative to the elevation at the source) and the height of 

the puff above the surface. The enhanced dispersion adjustment relies on the slope of 

the gridded terrain in the direction of transport during the time step. 
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S Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG 

treats terrain features that are not resolved by the gridded terrain field, and is based 

on the CTDMPLUS (Perry et al., 1989). Plume impingement on subgrid-scale hills 

is evaluated at the CTSG subgroup of receptors using a dividing streamline height 

(Hd) to determine which pollutant material is deflected around the sides of a hill 

(below Hd) and which material is advected over the hill (above Hd). The local flow 

(near the feature) used to define Hd is taken from the gridded CALMET fields. As in 

CTDMPLUS, each feature is modeled in isolation with its own set of receptors. 

S Puff Sampling Functions: A set of accurate and computationally efficient puff 

sampling routines is included in CALPUFF, which solve many of the computational 

difficulties encountered when applying a puff model to near-field releases. For 

near-field applications during rapidly-varying meteorological conditions, an 

elongated puff (slug) sampling function may be used. An integrated puff approach 

may be used during less demanding conditions. Both techniques reproduce 

continuous plume results under the appropriate steady state conditions. 

S Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are 

both incorporated into CALPUFF. An option is provided to use either model for all 

stacks, or make the choice on a stack-by-stack and wind sector-by-wind sector basis. 

Both algorithms have been implemented in such a way as to allow the use of wind 

direction specific building dimensions. The PRIME building downwash model 

(Schulman et al., 2000) is also included in CALPUFF as an option. 

S Dispersion Coefficients: Several options are provided in CALPUFF for the 

computation of dispersion coefficients, including the use of turbulence measurements 

(σv and σw), the use of similarity theory to estimate σv and σw from modeled surface 

heat and momentum fluxes, or the use of Pasquill-Gifford (PG) or McElroy-Pooler 

(MP) dispersion coefficients, or dispersion equations based on the CTDM. Options 

are provided to apply an averaging time correction or surface roughness length 

adjustments to the PG coefficients. In Version 5.8 of CALPUFF being used by 

VISTAS, an option is provided to use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most recent approach to dispersion in EPAapproved regulatory modeling. In addition, turbulence advection is included. For 

additional details on these features, see Scire et al. (2005). 

S Overwater and Coastal Interaction Effects: Because the CALMET meteorological 

model contains both overwater and overland boundary layer algorithms, the effects of 

water bodies on plume transport, dispersion, and deposition can be simulated with 

CALPUFF. The puff formulation of CALPUFF is designed to handle spatial changes 

in meteorological and dispersion conditions, including the abrupt changes that occur 

at the coastline of a major body of water. 

S Dry Deposition: A resistance model is provided in CALPUFF for the computation of 

dry deposition rates of gases and particulate matter as a function of geophysical 

parameters, meteorological conditions, and pollutant species. For particles, sourcespecific mass distributions may be provided for use in the resistance model. Of 

particular interest for BART analyses is the ability to separately model the deposition 

of fine particulate matter (< 2.5 μm diameter) from coarse particulate matter (2.5-10 

μm diameter). 
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S Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that 

allows vertical wind shear effects across individual puffs to be simulated. 

Differential rates of dispersion and transport among the “new” puffs generated from 

the original, well-mixed puff can substantially increase the effective rate of 

horizontal spread of the material. Puffs may also be split in the horizontal when the 

puff size becomes large relative to the grid size, to account for wind shear across the 

puffs. 

S Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF 

to compute the depletion and wet deposition fluxes due to precipitation scavenging. 

The scavenging coefficients are specified as a function of the pollutant and 

precipitation type (i.e., frozen vs. liquid precipitation). 

S Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOX, HNO3, and 

NO3

-

) employed in the MESOPUFF II model or a set of user-specified, diurnallyvarying transformation rates. The MESOPUFF II scheme is recommended by 

IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from 

emissions of SO2 and NOX and thus allows analyses of visibility impacts. Ambient 

ozone concentrations are used in the parameterized chemical transformation module 

as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent 

equilibrium relationship to partition nitric acid and nitrate on an hour-by-hour and 

receptor-by-receptor basis. 

4.2 MODELING DOMAIN CONFIGURATION

The VISTAS regional modeling domain was illustrated in Figure 3-1 in the preceding section of this 

protocol, and was designed to allow any Class I areas within the VISTAS area to be evaluated with a 

single meteorological database and consistent CALPUFF modeling options. The horizontal domain 

is comprised of grid cells, each containing a central grid point at which meteorological and 

computational parameters are calculated at each time step. For the initial regional analysis, a grid 

spacing interval of 12 km was selected. Given this interval, the domain consists of 160 by 172 grid 

cells. A Lambert Conformal Coordinate projection system is used to describe the horizontal grid, 

with origin at 40 degrees North latitude and 97 degrees West longitude. Standard parallels for the 

projection were set at 33 degrees North and 45 degrees North. 

Table 4-1 summarizes the vertical grid structure selected for this analysis, which comprises ten 

vertical layers. The cell face height of each layer indicates its vertical extent. The vertical domain is 

composed of terrain-following grid cells, the number and size of which are chosen so as to constrain 

the boundary layer in which dispersion and chemical transformations take place. The highest cell 

face was selected to be 4,000 meters to constrain the default maximum mixing height of 3,000 meters. 
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TABLE 4-1. VERTICAL GRID STRUCTURE

 

Vertical Grid Cell 

Cell Face Height 

(meters) 

1 20 

2 40 

3 80 

4 160 

5 320 

6 640 

7 1,200 

8 2,000 

9 3,000 

10 4,000 

Refined analyses will be conducted using the appropriate subregional 4-km grid that the VISTAS 

technical contractor has provided. Additional runs at higher resolution (e.g., 2 km or 1 km) may be 

performed and the modeling subdomain would be selected appropriately from the VISTAS regional 

grid. 

4.3 CALMET METEOROLOGICAL MODELING

CALMET meteorological modeling for the initial regional screening analysis will be conducted over 

the entire VISTAS regional domain described in section 4.2. The major features of CALMET were 

described in Section 4.1.1 of this protocol, and the geophysical and meteorological databases were 

described in Section 3. CALMET processing will be conducted generally in accordance with the 

recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long 

Range Transport Impacts, with the following exceptions and/or specifications of non-default values. 

S Modeling period: 2001 through 2003 

S Meteorological inputs: MM5 data provide initial guess fields in CALMET 

S CALMET mode: No-Observations mode including option to read overwater data directly from 

MM5 

S Diagnostic options: IWAQM default values, except as follows: diagnostic terrain blocking and 

slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but no diagnostic 

terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

S CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

S TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 2003 

simulations). Values of ~10-20 km will be tested, and a value of 15 km was selected by the 

VISTAS technical contractor.

S Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature allows 

the temperature field over large bodies of water such as the Atlantic Ocean and the Great lakes to 

be properly characterized by buoy observations.
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S Mixing height averaging parameter (MNMDAV) was determined by the VISTAS technical 

contractor to be 1 grid cell for regional simulations based on sensitivity tests. The purpose of the 

testing was to optimize the variable to allow spatial variability in the mixing height field, but 

without excessive noise. 

Refined analyses will be prepared using appropriate CALMET model settings as described by the 

VISTAS technical contractor for the 4 km sub-regional grid utilized (Domain 4). 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS

CALPUFF analyses to assess the visibility impacts attributable to OC’s Fairburn facility will be 

performed on a computational domain that is a subset of the VISTAS regional domain. The size of 

the domain will be selected to encompass the Fairburn facility and the five relevant Class I areas, and 

to extend at least 50 km beyond in all directions. The size of the domain allows for the possible 

recirculation of puffs beyond the facility and areas being evaluated. 

Class I receptors: Use FLM Class I receptor list with receptor elevations provided (available from the 

NPS). Ambient impacts will be predicted at receptors specified by the FLM to represent the Cohutta, 

Great Smoky Mountains, Joyce Kilmer-Slickrock, Shining Rock, and Sipsey Class I areas as depicted 

in Figure 1-2 of this protocol.18

4.5 CALPUFF MODELING OPTION SELECTIONS

The CALPUFF analysis will be conducted generally in accordance with the recommendations of 

IWAQM Phase 2 Summary Report and Recommendations for Modeling Long Range Transport 

Impacts, with the following exceptions and/or specifications of non-default values.

S Chemical mechanism: MESOPUFF II module 

S Species modeled: SO2, SO4, NOX, HNO3, NO3 and particulate matter in size categories of <0.625 

μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic diameters. 

S Emission rates for modeling based on U.S. EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological period 

modeled (excluding days where start-up, shutdown or malfunctions occurred sometime during the 

day). Note that potential emissions are used to determine if a source is BART-eligible, but 24-

hour average maximum emissions are used for modeling purposes (70 FR 39162). Pollutants 

considered include SO2, H2SO4, NOX and PM10. The basis for modeled emission rates was 

described in Section 2 of this protocol. 

S Condensable emissions are considered as primary fine particulate matter and allocated equally to 

the three sub-micrometer particle size classes. SMOKE PM2.5 speciation factors were utilized to 

analyze the phase, size, and character of PM emissions as described in Section 2 of this protocol. 

S Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine PM 

< 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. SMOKE PM2.5

 

18 http://www2.nature.nps.gov/air/maps/Receptors/index.htm 
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speciation factors were utilized to analyze the phase, size, and character of PM emissions as 

described in Section 2 of this protocol. 

S CALPUFF model options: Use IWAQM guidance, including Pasquill-Gifford (ISC-like) 

dispersion coefficients. 

S Ozone dataset: use observed ozone data for 2001 through 2003 from CASTNet and AIRS 

stations. Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly 

average ozone (backup) background values are to be computed based on daytime average ozone 

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations computed by 

month). 

S Background ammonia concentration: In refined CALPUFF analyses, use constant (0.5 ppb) 

values for ammonia. 

S Puff representation: integrated puff sampling methodology. 

S Building downwash: Building downwash will not be considered due to the distance separating 

the Fairburn facility from all Class I areas. 

4.5.1 REPRESENTATION OF EMISSION SOURCES IN CALPUFF 

Sample model processing files provided by VISTAS and its technical contractor 

demonstrate modeling of a single point source using CALPUFF, POSTUTIL, and 

CALPOST to assess visibility change.19 The sample approach simulates actual emissions 

of each of three gaseous pollutants (SO2, SO4, and NOX) and unit emissions (e.g., 1 g/s) of 

each of seven generic particle categories distinguished and designated by size: PM800, 

PM425, PM187, PM112, PM081, PM056, and PM025 to represent PM6-10, PM2.5-6, 

PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625, and PM<0.5, respectively. The size distribution is 

the only distinguishing feature of these particle categories. Table 4-3 summarizes the 

relevant model input parameters for each size category.

TABLE 4-3. REPRESENTATION OF PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter 

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PM800 Particle 8.00 0 1.0 % 10-4 3.0 % 10-5

PM425 Particle 4.25 0 1.0 % 10-4 3.0 % 10-5

PM187 Particle 1.87 0 1.0 % 10-4 3.0 % 10-5

PM112 Particle 1.12 0 1.0 % 10-4 3.0 % 10-5

PM081 Particle 0.81 0 1.0 % 10-4 3.0 % 10-5

PM056 Particle 0.56 0 1.0 % 10-4 3.0 % 10-5

PM025 Particle 0.25 0 1.0 % 10-4 3.0 % 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean 

diameter. 

 

19 http://www.src.com/verio/download/sample_files.htm#EXAMPLE_BART 
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Because unit emission rates were modeled from the single point source in the sample 

approach, actual emission rates were used in POSTUTIL to combine PM types and sizes 

into light scattering groups by scaling the modeled concentrations up or down by the 

multiplicative factor of the actual emission rate of each PM size category, which includes 

multiple types of PM (e.g., coarse filterable PM, fine filterable PM, organic condensable 

PM, inorganic condensable PM, and elemental carbon). The output concentration file from 

POSTUTIL was then input to CALPOST to calculate visibility change attributable to 

emissions from the point source. 

The preceding approach is reasonable for modeling a single point source; however, for 

facilities with multiple emission point sources, the preceding approach is not appropriate. 

Specifically, for sources with multiple emission points that have different exhaust 

characteristics (e.g., stack height, diameter, velocity, and temperature) and/or different 

emissions profiles of speciated PM, the use of unit emission rates is not appropriate since 

the CALPUFF output concentrations at particular receptors do not distinguish which 

source(s) contribute to the PM concentrations. Therefore, the POSTUTIL source profile 

technique cannot be applied. One alternative to this approach for sources with multiple 

emission points is to model each emission point individually using unit emission rates, then 

run the CALSUM postprocessing utility prior to running POSTUTIL, which combines the 

PM concentrations at each receptor for each modeled emission point. This approach, 

though conceptually appropriate, is undesirable due to substantial additional computer 

runtime required to process and post-process each emission point individually.

As a computationally efficient alternative to the preceding approaches, this applicability 

analysis will be conducted by explicitly modeling in CALPUFF the actual emission rate of 

each of 15 particle species defined as described in Table 4-4. 

TABLE 4-4. EXPLICIT MODELING OF PM TYPES AND SIZE CATEGORIES

 

Modeled PM 

Category Components 

 

PMC800 

PMC425 

Filterable coarse particles divided between two size categories 

(PM6-10, PM2.5-6) 

PMF187 

PMF112 

PMF081 

PMF056 

PMF025 

Filterable fine particles divided among five size categories 

(PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625, PM<0.5) 

POC081 

POC056 

POC025 

Primary condensable organic emissions divided between three size categories 

(PM0.625-1, PM0.5-0.625, PM<0.5) 

PIC081 

PIC056 

PIC025 

Primary condensable inorganic emissions divided between two size categories 

(PM0.625-1, PM0.5-0.625, PM<0.5) 

EC056 

EC025 

Primary elemental carbon emissions divided among two size categories 

(PM0.5-0.625, PM<0.5) 
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So that explicit modeling of the 15 particle species and sizes can be conducted equivalently 

with the unit emissions approach, identical model processing options for each PM size 

category will be enabled as summarized in Table 4-5. 

TABLE 4-5. REPRESENTATION OF EXPLICITLY MODELED PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter 

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PMC800 Particle 8.0 0 1.0 % 10-4 3.0 % 10-5

PMC425 Particle 4.25 0 1.0 % 10-4 3.0 % 10-5

PMF187 Particle 1.87 0 1.0 % 10-4 3.0 % 10-5

PMF112 Particle 1.12 0 1.0 % 10-4 3.0 % 10-5

PMF081 

POC081 

PIC081 

Particle 0.81 0 1.0 % 10-4 3.0 % 10-5

PMF056 

POC056 

PIC056 

EC056 

Particle 0.56 0 1.0 % 10-4 3.0 % 10-5

PMF025 

POC025 

PIC025 

EC025 

Particle 0.25 0 1.0 % 10-4 3.0 % 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean 

diameter. 

To post-process the CALPUFF output concentrations that result from explicitly modeled 

multiple emission points, POSTUTIL will be used only to group modeled PM into light 

extinction groups. Unit scaling factors will be used in POSTUTIL and there will be no 

adjustment to the explicitly modeled emission rate. Table 4-5 summarizes the POSTUTIL 

grouping of modeled PM species into light extinction groups, and the light extinction 

coefficient subsequently used in CALPOST to compute light extinction due to the multiple 

emission points at the source. 
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TABLE 4-6. ASSIGNMENT OF MODELED PM SPECIES TO LIGHT EXTINCTION GROUPS

 

Modeled 

Components 

CALPOST Light 

Extinction Group 

Extinction Coefficient 

(m2

/g) 

PMC800 

PMC425 

PMC 0.6 

PMF187 

PMF112 

PMF081 

PMF056 

PMF025 

PIC081 

PIC056 

PCI025 

SOIL 1 

POC081 

POC056 

POC025 

SOA 4 

EC056 

EC025 

EC 10 

 

Implementation of the explicit modeling approach requires minor changes to parameter 

declaration file and re-compilation of the CALPUFF model executable file. Note that this 

approach does not require changes to the FORTRAN model code, only the parameter 

declaration limits. Explicit modeling of the 15 PM types and sizes plus the SO2, SO4, NOX, 

HNO3, and NO3 species results in a total of 20 modeled species, which is within the default 

parameter limit of 20 species modeled (MXSPEC). However, the parameter for particle 

species deposited (MXPDEP) will be increased from 9 to 20 to accommodate the greater 

number (17, including SO4 and NO3) of particle species simulated in the model. No 

changes to the maximum number of particle size intervals used (9) to calculate effective 

deposition velocity (MXNINT) will be necessary since the same size categories will be 

used in the explicit modeling approach.

4.6 CALPOST PROCESSING OPTION SELECTIONS FOR

LIGHT EXTINCTION AND HAZE IMPACT CALCULATIONS

The following postprocessing techniques will be used to compute the 24-hour average visibility 

impacts at the Class I area located within 300 km of OC’s Fairburn facility. 

▲ Species to be considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM 

▲ Visibility Method 6 will be used with Class I area-specific, monthly average, relative 

humidity values as described in Section 3.5 of this modeling protocol using both the 20% best 

days and annual average natural concentrations of visibility-affecting pollutants. 

▲ Natural background light extinction will be represented at the Class I Areas as described in 

Section 3.5 of this modeling protocol. 
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▲ Ammonia Limiting Method: No ammonia limiting methods will be utilized in the screening 

or refined analyses. 

The initial run results will be based on the highest change in light extinction (deciviews) from natural 

conditions over the three-year modeling period for each Class I area considered. Predicted changes 

exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid CALPUFF modeling 

analysis. To assess whether BART-eligible operations contribute to visibility impairment, OC’s 

applicability modeling analysis will demonstrate the top eight 24-hour average visibility impacts of 

each year modeled to illustrate the distribution (i.e., frequency, duration, and magnitude) of peak 

visibility impairment episodes attributable to the Fairburn facility. The 98th percentile 24-hour 

average visibility impact (eighth-highest impact of each year or 22nd highest impact over three years) 

will be evaluated in the refined analysis. 

4.7 MODELING PRODUCTS

OC will prepare and submit a BART applicability analysis result describing the modeling procedures, 

data resources, and results of screening and refined modeling (if necessary) used to assess whether the 

Fairburn facility is subject to BART. The presentation of modeling results will generally conform to 

the expectations described in the VISTAS BART Modeling Protocol and described as follows. The 

Results section of the CALPUFF modeling report should contain the following information: 

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source 

3. A discussion of the number of Class I areas with visibility impairment from the source on 98th

percentile days in each year greater than 0.5 dv (total visibility impairment minus impairment 

on 20% best days for natural background visibility equals delta-dv, the visibility impact 

attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below the 

98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in the 

Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report same 

results as provided for 12-km initial exemption modeling. 
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5. QUALITY ASSURANCE METHODS

OC will conduct quality assurance of CALMET, CALPUFF, and CALPOST analyses in a manner 

that generally conforms to the VISTAS BART Modeling Protocol. A description of the quality 

assurance methods and products (e.g., test case simulations, graphic representations of model fields 

and performance) will be provided in OC’s BART Applicability Modeling Report. 

The following sections describe techniques that can be used to visualize and quality assure 

performance of each model component as described in the VISTAS BART Modeling Protocol. 

5.1 CALMET FIELDS 

Section 4 of the VISTAS BART Modeling Protocol describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or group 

of sources is subject to BART controls. In the initial application, the regional CALPUFF-ready 

meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required in 

developing source-specific meteorological fields, as described below. Also, VISTAS is planning to 

provide five subregional CALMET meteorological datasets in a CALPUFF-ready format. The 

development of these CALMET datasets will be subject to a QA program as part of their 

development, so the necessary quality assurance activity of end-users is again reduced from what 

would be required in the development of the dataset. It is not expected that the quality assurance 

steps in the development will be repeated in each application. The VISTAS-provided regional and 

subregional meteorological fields will include a test case simulation for demonstrating that expected 

modeling results are obtained on the user’s computer platform. OC will execute this test case 

simulation to demonstrate that the expected results can be reproduced. 

The critical CALMET input parameters depend on the mode in which the model is run (i.e., 

observations mode, hybrid mode, or no-observations mode), and the location and spatial 

representativeness of any observational data. In a site-specific protocol involving the development of 

a meteorological dataset, the elements of the QA process include preparation of wind rose (using 

observed, MM5 and CALMET-derived data), including examination of the data as a function of 

season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, and presentation of 

2-D vector plots illustrating terrain effects or other features of the flow expected to occur within the 

domain. For example, 2-D vector plots produced during light wind speed stable conditions (e.g., 

early morning such as 4 am) are good for assessing the performance of the CALMET model 

configuration and switches in reproducing terrain effects because these conditions are likely to 

maximize the terrain impacts in the model. Customization of the QA process for the individual sitespecific domain based on the availability of data and the physical processes expected to be important 

at that location will be conducted as part of the site-specific QA plan development. 
OC Fairburn 51 Trinity Consultants 

BART Modeling Protocol 

5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of the 

VISTAS BART Modeling Protocol or in a source-specific protocol, the IWAQM guidance should be 

used in setting up the CALPUFF simulations. The CALPUFF model obtains the switch settings from 

an ASCII “control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. 

CALPOST is run separately for each Class I area in order to obtain the necessary visibility statistics 

for evaluating compliance with the BART screening and finer grid modeling thresholds. The inputs 

to CALPOST involve selection of the visibility method (Method 6 in the standard EPA BART 

guidance), entry of Class I area-specific data for computing background extinction and monthly 

relative humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that 

allow subsets of a receptor network modeling in CALPUFF to be selected for processing in a given 

CALPOST run. This is how receptors within a single Class I area are selected for processing from a 

CALPUFF output file that may contain receptors from several Class I areas. CALPOST contains 

options for creating plot files that will help in the confirmation that the proper receptor subset is 

extracted. 

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be selected in 

each CALPOST run so that each CALPOST run represents the impacts at a single Class I area. For a 

screening assessment, the peak value of the change in extinction is shown at the bottom of the 

visibility table. For a finer grid simulation, the 98th percentile value (8th highest day) is used for 

comparison against the BART threshold of 0.5 deciviews. It is necessary to import the results of the 

CALPOST table into a sorting program such as a spreadsheet to rank the daily change in extinction 

values such as is presented in Table 4-2. 

The CALPOST inputs that will be carefully checked as part of the CALPOST quality assurance 

include the following: 

S Visibility technique (Method 6) 

S Monthly Class I-specific relative humidity factors for Method 6 

S Background light extinction values 

S Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, organics), coarse 

and fine particulate matter, and elemental carbon. 

S Appropriate species names for coarse PM used 

S Extinction efficiencies for each species 

S Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area specific 

value for finer grid modeling) 

S Screen to select appropriate Class I receptors for each CALPOST simulation. 
OC Fairburn 52 Trinity Consultants 

BART Modeling Protocol 

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF simulations 

into a total concentration file. In addition, it contains options to scale the concentrations from 

different modeled species (e.g., different particle sizes) into species-dependent size distributions for 

the particulate matter. For example, PM is often simulated with unit emission rates for each particle 

size category and, in the POSTUTIL stage, the contributions of each size category based on the 

species being considered (e.g., elemental carbon, coarse particulate matter, etc.) are combined to form 

the species concentrations for input into CALPOST. This process, although simple, requires a careful 

review of the weighting factors for each source. POSTUTIL also allows a repartitioning of nitric acid 

and nitrate to account for the effects of ammonia limiting conditions. 

 

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be evaluated. 

The use of alternative ammonia concentration data (e.g., CMAQ output rather than derived ammonia 

based on aerosol measurements) will require an evaluation of the model performance relative to the 

techniques in the VISTAS common protocol. 

5.3 PRESENTATION OF RESULTS

Results tables will be developed based on the CALPOST output file. The results from CALPOST 

will be copied into a spreadsheet and organized for presentation. Tables presented to the agency will 

be reviewed for accuracy against the CALPOST files. 
OC Fairburn 53 Trinity Consultants 

BART Modeling Protocol 

6. REFERENCES

The following guidance documents, regulations, and technical publications were referenced in 

preparation of this BART Applicability Modeling Protocol for OC and the VISTAS BART Modeling 

Protocol. 

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex 

Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest 

Laboratory, Richland, Washington. 

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. 

Boundary-Layer Meteorol., 56, 261-274. 

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed 

layer and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323. 

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast 

Cycle: the RUC, Mon. Wea. Rev., 132, 495-518. 

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary 

layer. Quart. J. Roy. Meteor. Soc., 99, 450-467. 

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC 

and VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466. 

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of 

the Montana Department of Environmental Quality. 

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air 

Resources Board, Sacramento, California. 

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: 

Causes, Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze 

Rule; EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions 

under the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 
OC Fairburn 54 Trinity Consultants 

BART Modeling Protocol 

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling 

(IWAQM). EPA-454/R-98-019, U.S. Environmental Protection Agency, RTP, NC. 

Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol 

Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on 

the Applications of Air Pollution Meteorology, Norfolk, VA. May 2002. 

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: Bulk parameterization of 

air-sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate, 16, 571-591. 

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis 

of PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. 

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I 

Report. U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service. 

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn 

State/NCAR MM5, NCAR TN-398-STR, NCAR Technical Note. 

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary 

Organic Aerosols. Atmos. Environ., 23, 1733-1747. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY. 

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface 

fluxes from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529. 

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16. 

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711. 

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity 

Generating Bureau MID/SSD/80/0026/R. Nottingham, England. 

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation 

Mechanism in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference 

– Guideline on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003. 

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 

22-26 June 2003, San Diego, CA. 
OC Fairburn 55 Trinity Consultants 

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O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

boundary layer. J. Atmos. Sci., 27, 1213-1215. 

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised 

IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to 

IMPROVE Steering Committee, November 2005 

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus 

Algorithms for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User 

Instructions. EPA/600/8-89/041, U.S. EPA, Research Triangle Park, North Carolina. 

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME 

Plume Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting 

& Exhibition, Toronto, Ontario, Canada. 8-13 June 1997. 

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts 

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts. 

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of 

Environmental Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA. 

Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, 

Visibility and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty 

Conference, Guideline on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003. 

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF 

model for offshore and coastal applications. Proceedings of the Tenth International Conference on 

Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), 

Crete, Greece. 17-20 October 2005. 

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model 

with Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution 

Meteorology, Phoenix, Arizona. American Meteorological Society. 11-16 January 1998. 
OC Fairburn 56 Trinity Consultants 

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Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at 

the AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 

7-11 February 2005. 
OC Fairburn Trinity Consultants 

BART Modeling Protocol 

APPENDIX A 

VISTAS BART MODELING PROTOCOL (REVISION 3, JULY 18, 2006)

 
Protocol for the Application of the

CALPUFF Model for Analyses of Best

Available Retrofit Technology (BART)

December 22, 2005

(Revision 3 – 7/18/06)

Visibility Improvement State and Tribal Association

of the Southeast (VISTAS)
Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 1

1.1 Background 1

1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING 5

2.1 Overview of the Regional Haze BART Process 5

2.2 Model Recommendations for the BART Analysis 7

2.3 Performance of a Cap and Trade Program 8

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM 10

3.1 Capabilities and features of CALPUFF 10

3.1.1 Major Features of CALMET 12

3.1.2 Major Features of CALPUFF 14

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 18

3.2 Discussion of CALPUFF Applicability and Limitations 19

3.2.1 Transport and Diffusion 19

3.2.2 Aerosol Constituents 22

3.2.3 Regional Haze 30

4. VISTAS’ COMMON MODELING PROTOCOL 34

4.1 Overview of Common Modeling Approach 34

4.1.1 BART Exemption Analysis 34

4.1.2 BART Control Evaluation 36

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM 36

4.2 Optional Source-Specific Modeling 38

4.3 Initial Procedure for BART Exemption 38

4.3.1 Overview of Initial Approach 38

4.3.2 Discussion of 12-km Initial Exemption Modeling 39

4.3.3 Model Configuration and Settings for Initial Analysis 41

4.4 Finer Grid Modeling Procedures 46

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach 46

4.4.2 Model Configuration and Settings for Finer Grid Modeling 47

4.5 Presentation of Modeling Results 49

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources 54

5. SOURCE-SPECIFIC MODELING PROTOCOL 55
Table of Contents iv

6. QUALITY ASSURANCE 58

6.1 Scope and Purpose of the QA program 58

6.2 QA Procedures for Common Protocol Modeling 59

6.2.1 Quality Control of Input Data 59

6.2.2 Quality Control of Application of CALMET 60

6.2.3 Quality Control of Application of CALPUFF 61

6.2.4 Quality Control of Application of CALPOST and POSTUTIL 62

6.3 Additional QA Issues for Alternative Source-Specific Modeling 63

6.4 Assessment of Uncertainty in Modeling Results 64

7. REFERENCES 65
Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available

Retrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes

common procedures for carrying out air quality modeling to support BART determinations that

are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51

Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common

understanding among the organizations that will be performing BART analyses or reviewing the

BART modeling results in the VISTAS region.

Background

Best Available Retrofit Technology is required for any BART-eligible source that ‘‘emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of

visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “You

can use dispersion modeling to determine that an individual source cannot reasonably be

anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject

to BART.” In the “individual source attribution approach,” a BART-eligible source that is

responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment.

A BART-eligible source that is responsible for a 0.5 dv change or more is considered to

“contribute” to visibility impairment in a Class I area. Any source determined to cause or

contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol

to guide them, their sources, and reviewers in the BART determination and review effort. The

Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat

Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The

VISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon,

AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol

was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier-Czaja and

Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from

the VISTAS federal partners: Federal Land Managers and US EPA. The VISTAS RPO held a

meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with

participants before starting a public comment period. The Protocol underwent formal external

review during the period between September 26, 2005 and October 31, 2005. Numerous

comments were received. All comments were carefully considered and discussed with VISTAS

participants and federal partners. VISTAS gratefully acknowledges the very useful contributions

of those that provided comments. On November 1st, 2005 VISTAS held another meeting with its

participants in Nashville, TN to present and discuss the comments being considered for inclusion

in the Protocol. No formal document will be prepared to address all the comments received on

the Protocol.
Summary S-2

Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide:

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site-specific modeling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the US EPA’s BART

Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading.

Chapter 2 is intended to provide summary background on EPA’s guidance for BART modeling.

The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for

applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the 

specific information that should be included in site-specific protocols. Chapter 6 describes the 

quality assurance requirements for BART analyses in the VISTAS RPO.

Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol are:

I. Process

Follow the BART process steps discussed in Chapter 2:

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in

the engineering analysis
Summary S-3

II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a

single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET

Version 5.7. These versions contain enhancements funded by the Minerals Management Service

(MMS) and VISTAS. They were developed by Earth Tech, Inc. and are maintained on the

CALPUFF website (www.src.com) for public access.

VISTAS is making publicly available 12-km CALMET output files for the entire VISTAS

modeling domain (eastern United States) and intends to also provide CALMET output files for

five 4-km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the

CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS

for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire

VISTAS region, Earth Tech used the No-Obs setting (i.e., did not include additional surface and

upper air observations beyond those incorporated in the MM5 calculations). For finer resolution

subdomains (4 km grid or less), available surface and upper air observations will be used in

addition to MM5 meteorological model outputs. The specific model settings will be provided

with the CALMET files and via the CALPUFF website so that users can review or replicate the

work.

For CALPUFF modeling, source emissions should be defined using the maximum 24-hour actual

emission rate during normal operation for the most recent 3 or 5 years. If maximum 24-hr actual

emissions are not available, continuous emissions data, permit allowable emissions, potential

emissions, and emissions factors from AP-42 source profiles may be used as available.

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL

configurations are highlighted below.

• After running CALPUFF for an individual facility, repartition NO3 in POSTUTIL.

1

• Use ozone data from non-urban monitors as the background ozone input.

• Use the Pasquill-Gifford dispersion method.

2

 

1 The original intent, as expressed in the Final VISTAS BART Modeling Protocol (22 December 2005) was to use

CMAQ-derived background data for SO2, NO3 and NH3 in POSTUTIL. After extensive discussion with the EPA

and FLMs in early 2006, EPA did not approve the recommended approach so background gaseous

concentrations from CMAQ 2002 modeling will not be provided by VISTAS for use in POSTUTIL. Rather the

standard default NH3 concentrations specified on page 14 of the IWAQM Phase 2 report (IWAQM, 1998) will

be used.

2 The Final VISTAS BART Modeling Protocol (Dec. 22, 2005) recommended using turbulence-based AERMOD

dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-

68261. 9 November 2005. Subsequently, EPA Region IV notified the VISTAS states that using turbulence-
Summary S-4

• In CALPOST, use Method 6 with monthly average RH for calculating extinction, as

recommended by the EPA.

• Use EPA default calculations of light extinction under current and natural background

conditions. In addition to the default assumptions, a source may choose to also calculate

visibility using the recently revised IMPROVE algorithm described by Pitchford, et al.,

(2005).

Provide results in tables as illustrated in Chapter 4 that describe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART

For determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier approach. For the initial exemption modeling use CALPUFF with 12-km grid CALMET. For

finer resolution of meteorological fields, use CALPUFF with CALMET of 4-km or smaller grid

size.

VISTAS States are accepting EPA guidance that the threshold value to establish that a source

contributes to visibility impairment is 0.5 deciview.

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If

Q/d for SO2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the

source is subject to BART and no exemption modeling will be performed using VISTAS funds. If

the source agrees with this presumption, then the source can proceed to the BART determination

using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a

source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv.

For sources with Q/d less than or equal to 10, VISTAS intends to fund TRC Environmental

Corp.

3 to assist States with the initial CALPUFF exemption modeling. Each State will prioritize

which sources will be offered modeling by VISTAS. Modeling of these sources will be

conducted in priority order to first accommodate States with nearer term timing constraints in

their SIP development process. To conserve VISTAS resources, modeling will begin with

sources at lower Q/d values and continue with sources with higher Q/d values until a Q/d value

 

based dispersion methods would be considered a non-guideline application of CALPUFF. Thus this Protocol

has been revised to indicate Pasquill-Gifford dispersion coefficients should be used.

3 In April 2006, Earth Tech’s CALPUFF modeling staff became part of TRC Environmental Corporation.

References to Earth Tech and to TRC in this protocol refer to the same technical staff, just at different times.
Summary S-5

that consistently results in a greater than 0.5 dv impact is identified. Chapter 4 addresses the

number of VISTAS sources eligible for BART based on Q/d analysis.

Note that VISTAS does not propose to use Q/d to exempt BART-eligible sources, but only to

prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance

not to use Q/d for exemption purposes.

For the 12-km initial modeling exemption test, compare the highest single 24-hour average value

across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24-hr 

average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the 

highest 24-hr average value is greater than 0.5 dv, then the source may choose to perform finer

grid modeling for exemption purposes or may accept determination that the source is subject to

BART and proceed to establish visibility impacts prior to and after BART controls. If using the

single highest 24-hr average value proves, after initial 12-km grid CALPUFF modeling, to be too

conservative a screening level, VISTAS may allow some exceedances of the threshold value for

exemption purposes, up to no more than the 98thpercentile value.

The 12-km modeling results can be used to focus finer grid modeling for exemption purposes on

only those Class I areas where impacts greater than 0.5 dv were projected in the 12-km modeling.

For finer grid (4 km or less) analyses, use the 98thpercentile impact value for the 24-hr average.

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever

is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre-BART visibility impact and for BART control options

modeling: establish baseline visibility from the pre-BART run; change one control at a time; and

evaluate the change in visibility impact, i.e. the delta-deciview. Note that “no control” may

constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under 

the USEPA BART guideline. If a source accepts to institute the most stringent control, the

engineering analyses are not required.

This common VISTAS Protocol consistently recommends conservative assumptions. Individual

States ultimately have responsibility to determine which, if any, BART controls are

recommended in their State Implementation Plans (SIPs).
Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final

guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70

FR 39104-39172). The regional haze rule includes a requirement for BART for certain large

stationary sources. Sources are BART-eligible if they meet three criteria including potential

emissions of at least 250 tons per year of a visibility-impairing pollutant, were put in place

between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories

in the guidance. A BART engineering evaluation using five statutory factors -- 1) existing

controls; 2) cost; 3) energy and non-air environmental impacts; 4) remaining useful life of the

source; 5) degree of visibility improvement expected from the application of controls -- is

required for any BART-eligible source that can be reasonably expected to cause or contribute to

impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected

under the regional haze rule. (Note that, depending on the five factors, the evaluation may result

in no control.) Air quality modeling is an important tool available to the States to determine

whether a source can be reasonably expected to contribute to visibility impairment in a Class I

area.

Throughout this document the term “BART-eligible emission unit” is defined as any single 

emission unit that meets the criteria described above. A “BART-eligible source” is defined as the

total of all BART-eligible emission units at a single facility. If a source has several emission

units, only those that meet the BART-eligible criteria are included in the definition “BARTeligible source”.

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input.

To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially

subject to BART, the boiler capacities of all electric generating units (EGUs) should be added

together regardless of construction date. In this category, electric generating sources greater than

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for

EGU sources between 250-750 MW. However, units at those sources constructed after the 

BART-eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air

Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program

satisfies the BART requirements for SO2 and NOx. VISTAS states are tentatively accepting this

guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM

emissions are above de minimis values.

As illustrated in Table 1-1, as of December 5, 2005, VISTAS States had identified a total of 274

BART-eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources

with BART-eligible units, 84 sources are utility EGUs and 190 are non-EGU industrial sources.

(Note that these numbers are not final and are subject to slight adjustments and refinements.) No

BART sources are located on Tribal lands.
Introduction and Protocol Objectives 2

Table 1-1. VISTAS BART Eligible Sources (not updated since December 2005)

State Total Number

of Sources

EGU Sources Non-EGU

Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19

Total 273 84 189

1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for

air quality modeling to support BART determinations that are consistent with the EPA guidelines.

The protocol will serve as the basis for establishing a common understanding among the

organizations who will be performing the BART analyses or reviewing the BART modeling

results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers

(FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA

final guidance and comments that were received on VISTAS’ draft protocol4 and provides

additional description of modeling procedures. The original final protocol of 22 December 2005

has been revised since then to clarify items, resolve technical issues, and reflect decisions by the 

EPA and FLMs. This document is the third revision.

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to

comply with the BART modeling requirements of the regional haze rule. A BART-eligible

source will be required to submit a site-specific modeling protocol to the State for review and

approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA

when evaluating the site-specific BART protocols. The site-specific protocol will include the 

 

4 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology

(BART). VISTAS, March 22, 2005 and September 20, 2005.
Introduction and Protocol Objectives 3

source-specific data on source location, stack parameters, and emissions. The methods of the

VISTAS common modeling protocol will be followed in the site-specific protocol unless the

source proposes to the State, and the State approves, alternative methods or assumptions.

Each VISTAS State or Local agency retains responsibility for the specific procedures and

processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA,

and public to determine BART controls for sources in the State. Nothing in the VISTAS process

replaces States’ responsibility to determine BART controls.

The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations:

• Air quality modeling to determine whether a BART-eligible source is “subject to BART”

and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to

BART, to evaluate regional haze benefits of alternative control options and to document

the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s

guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent

chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s

guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal

Register notice. The CALPUFF model is the preferred model recommended by the EPA for

BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The

specific steps to determine whether a BART-eligible source is subject to BART and to evaluate

BART controls are described in Chapter 4. The procedures include initial modeling of BARTeligible sources using CALPUFF run in a conservative mode with regional meteorological

datasets. For sources determined to be subject to BART based on these first modeling analyses,

further finer grid CALPUFF analyses would be performed. The model configuration for the 

common modeling protocol is described in Chapter 4. Details of the source-specific protocol are

described in Chapter 5. A quality assurance plan is outlined in Chapter 6.

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not

develop a protocol for alternative models. This protocol focuses on guidance for the application

of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in

its BART demonstration, the source will need to submit a detailed written justification to the 

State for review and approval. The State will provide the documentation to the EPA and Federal

Land Managers for their review.

Also, this protocol does not address a preferred modeling approach to demonstrate the

effectiveness of an optional emissions cap and trade program. Such a cap and trade program is

not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS
Introduction and Protocol Objectives 4

States are not pursuing a regional trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated in 2006.
Review of EPA’s Guidance 5

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

The final guidance for regional haze BART determinations was published in the Federal Register

on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used

for various stages of the BART analysis process.

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to

provide a comprehensive review of the guidance. Nor does this chapter address specific

recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations

appear in Chapter 4.

2.1 Overview of the Regional Haze BART Process

The process of establishing BART emission limitations consists of four steps:

1) Identify whether a source is “BART-eligible” based on its source category, when it was put in

service, and the magnitude of its emissions of one or more “visibility-impairing” air pollutants. The 

BART guidelines list 26 source categories of stationary sources that are BART-eligible. Sources

must have been put in service between August 7, 1962 and August 7, 1977 in order to be BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx.

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing pollutants for BART eligibility is left for the States to determine on a case-by-case basis.

The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor

pressure should be considered as primary PM2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential

emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not

subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are

met.)

2) Determine whether a BART-eligible source can be excluded from BART controls by

demonstrating that the source cannot be reasonably expected to cause or contribute to visibility

impairment in a Class I area. The preferred approach is an assessment with an air quality model

such as CALPUFF or other appropriate model followed by comparison of the estimated 24-hr 

visibility impacts against a threshold above estimated natural conditions to be determined by the

States.

5 The threshold to determine whether a single source “causes” visibility impairment is set at

 

5 A recent draft settlement agreement with the EPA (to be published in the Federal Register for public comment)

provides that a State has the discretion to decide whether annual average or 20% best natural conditions are to be

used as the reference. This ruling resolves an ambiguity in EPA’s BART guidance, where the BART guideline
Review of EPA’s Guidance 6

1.0 deciview change from natural conditions over a 24-hour averaging period in the final BART

rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may

“contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending

on factors affecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against

the threshold is “driven” by the contribution level, since if a source “causes”, by definition it

“contributes”.

EPA recommends that the 98thpercentile value from the modeling be compared to the contribution

threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not

contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual

period, this implies the 8th highest 24-hr value at a particular Class I area is compared to the

contribution threshold. Over a 3-year modeling period, the 98thpercentile value may be interpreted

as the highest of the three annual 98thpercentile values at a particular Class I area or the 22nd highest

value in the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART-eligible sources within the 

State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be

able to do exactly the opposite, and use regional modeling to conclude that all BART-eligible 

sources in the State do not cumulatively contribute to “measurable” visibility impairment in any

Class I areas. Also, the States have an option to exempt individual sources based on model plant

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area

greater than 50 kilometers or sources with SO2 plus NOx potential emissions of less than 1000

tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM

emissions are not specifically addressed in the model plant analysis, but subsequent discussions

with EPA staff indicate that PM may be considered along with SO2 and NOx, so that a plant could

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting

the “best” alternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of 

options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility,

d) The energy and non air-quality environmental impacts of compliance, and

 

text says “natural conditions” at 70 FR 39162, col. 3, while the preamble to the BART rule says “natural visibility

baseline for the 20% best visibility days” at 70 FR 39125, col. 1.
Review of EPA’s Guidance 7

e) The degree of improvement in visibility that may reasonably be anticipated to result

from the use of such technology.

Note that if a source agrees to apply the most stringent controls available to BART-eligible units,

the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze,

which is due by December 2007.

Instead of applying BART on a source-by-source basis, a State (or a group of States) has the option

of implementing an emissions trading program that is designed to achieve regional haze

improvements that are greater than the visibility improvements that could be expected from BART.

If the geographic distributions of emissions under the two approaches are similar, determining

whether trading is “better than BART” may be possible by simply comparing emissions expected

under the trading program against the emissions that could be expected if BART was applied to

eligible sources. If the geographic distributions of emissions are likely to be different, however, air

quality modeling comparing the expected improvements in visibility from the trading program and

from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA

suggests that regional modeling using a photochemical grid model may be more appropriate than

CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138-39139) that emissions reductions

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis may still be required for PM.

2.2 Model Recommendations for the BART Analysis

To evaluate the visibility impacts of a BART-eligible source at Class I areas beyond 50 km from the 

source, the EPA guidance recommends the use of the CALPUFF model as “the best regulatory

modeling application currently available for predicting a single source’s contribution to visibility

impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA

prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to

submit a written justification and source-specific modeling protocol to its State for review and

approval. As part of the consultation process, the State will provide documentation to EPA and

FLM.

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance

recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and

other methods.” The PLUVUE-II plume visibility model is mentioned as a possible model to

consider instead of CALPUFF for a source within less than 50 km of a Class I area.

The EPA guidance notes that “regional scale photochemical grid models may have merit, but such

models have been designed to assess cumulative impacts, not impacts from individual sources” and
Review of EPA’s Guidance 8

they are “very resource intensive and time consuming relative to CALPUFF”, but States may

consider their use for SIP development in the future as they may be adapted and “demonstrated to

be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be

more appropriate for cumulative modeling options such as in the determination of the aggregate

contribution of all-BART-eligible sources to visibility impairment, but such use should involve

consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling

demonstrations regardless of the distance from the BART-eligible source to the Class I area. EPA’s

role in the development of the protocol is only advisory as the “States better understand the BARTeligible source configurations” and factors affecting their particular Class I areas (70 FR 39126).

In the BART modeling analyses the EPA recommends that the State use the highest 24-hour 

average actual emission rate for the most recent three to five-year period of record. Emissions on

days influenced by periods of start-up, shutdown and malfunction are not to be considered in

determining the appropriate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALPUFF or another appropriate model should be used

to evaluate the improvement in visibility resulting from the application of BART controls.

Visibility improvements may be evaluated on a pollutant-specific basis in the BART determination

(70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA

guidelines state that States are “encouraged to account for the magnitude, frequency, and duration

of the contributions to visibility impairment caused by the source based on the natural variability of

meteorology” (70 FR 39129).

2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to

demonstrate greater “reasonable progress” in reducing haze than would result if BART were

applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading

program that achieves greater progress at reducing emissions will also achieve greater progress at

reducing haze. Such would be the case if the likely geographic distribution of emissions under the

trading program would not be greatly different from the distribution if BART was in place.

If the expected distribution of emissions is different under the two approaches, then “dispersion

modeling” of all sources must be used to determine the difference in visibility at each impacted

Class I area, in order to establish that the optional trading program will result in visibility

improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137-39138).

The BART guidance does not specify the method to be used for this modeling. From a technical

perspective, either applying CALPUFF to every source or using a regional photochemical model

would satisfy the need.
Review of EPA’s Guidance 9

A rulemaking procedure is currently underway to establish final guidance for such alternatives to

BART (70 FR 44154-44175). The rule is expected to be finalized in 2006.
Overview of the CALPUFF Modeling System 10

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities

and limitations. It does not include specific recommendations regarding the use of the model for

BART analysis in the VISTAS region. These specific recommendations can be found in Chapter

4.

3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred

modeling approach for use in the BART analyses. CALPUFF and its meteorological model,

CALMET, are designed to handle the complexities posed by the complex terrain, the large

source-receptor distances, chemical transformation and deposition, and other issues related to

Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a

Guideline Model for source-receptor distances greater than 50 km, and for use on a case-by-case

basis in complex flow situations for shorter distances (68 FR 18440-18482). CALPUFF is

recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG

2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The

final BART guidance recommends CALPUFF as “the best modeling application available for

predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of 

these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its

BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3-1. CALMET is

a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It

produces three-dimensional wind and temperature fields and two-dimensional fields of mixing

heights and other meteorological fields. It contains slope flow effects, terrain channeling, and

kinematic effects of terrain. CALMET includes special algorithms for treating the overwater

boundary layer and coastal interaction effects. CALMET can use meteorological observational

data and/or three-dimensional output from prognostic numerical meteorological models such as

MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine-scale

meteorological fields.

CALPUFF is a non-steady-state Lagrangian puff transport and dispersion model that advects

Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been

designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers

from a source. It includes algorithms for near-field effects such as building downwash, stack tip

downwash and transitional plume rise as well as processes important in the far-field such as

chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to

allow puff splitting in the horizontal and vertical directions, which extends the distance range of

the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition

fluxes evaluated at user-specified receptor locations.
Overview of the CALPUFF Modeling System 11

Figure 3-1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and

allows cumulative source impacts to be assessed, including potential non-linear effects of

ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options

for computing change in extinction and deciviews for visibility assessments. The POSTUTIL

postprocessor includes options for summing contributions of individual sources or groups of 

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid-nitrate

chemical equilibrium module, which allows the cumulative effects of ammonia consumption by

background sources to be assessed in the postprocessor. In addition, the combination of 

CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be 

incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling

components in more detail.
Overview of the CALPUFF Modeling System 12

3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and

micrometeorological modules for overwater and overland boundary layers. When modeling a

large geographical area, as would be necessary for the regional VISTAS domain, the user has the

option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature.

The major features and options of the meteorological model are summarized in Table 3-1. The

techniques used in the CALMET model are briefly described below.

Table 3-1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method

- Overwater Boundary Layer - Profile Method

-- COARE algorithm

-- OCD-based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin-Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3-D)

-- Precipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

(optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and

overwater grid cells.

Overland Boundary Layer Model: Over land surfaces, the energy balance method of Holtslag and

van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface
Overview of the CALPUFF Modeling System 13

friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are

determined from the computed hourly surface heat fluxes and observed temperature soundings

using a modified Carson (1973) method based on Maul (1980). The model also determines

gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model: The aerodynamic and thermal properties of water surfaces

suggest that a different method is best suited for calculating the boundary layer parameters in the

marine environment. A profile technique, using air-sea temperature differences, is used in

CALMET to compute the micro-meteorological parameters in the marine boundary layer. The

version of CALMET being used by VISTAS contains improvements in the overwater boundary

layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response

Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights

(Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the 

model containing these enhancements are described in Scire et al. (2005). An upwind-looking

spatial averaging scheme is optionally applied to the mixing heights and three-dimensional

temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two-step approach to the computation of the wind fields

(Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted for kinematic

effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded

MM5 can be used to define the initial guess field. The second step consists of an objective

analysis procedure to introduce observational data into the Step 1 wind field to produce a final

wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field

defined by the MM5 prognostic meteorological model. Normally, the CALMET computational

domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to

develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms

in CALMET described below apply terrain adjustments to the initial guess field on the fine-scale

CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features

not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate the

effects of the terrain on the wind field. The initial guess field winds are used to compute a terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is

applied iteratively until the three-dimensional divergence is less than a threshold value.

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe,

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both
Overview of the CALPUFF Modeling System 14

advective-gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the

flow down the sides of the valley walls into the floor of the valley, and during the day, upslope 

flows are parameterized. The magnitude of the slope flow depends on the local surface sensible

heat flux and local terrain gradients. The slope flow wind components are added to the wind field

adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude

number at a particular grid point is less than a critical value and the wind has an uphill

component, the wind direction is adjusted to be tangent to the terrain.

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial-guess wind

is the Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An inversedistance squared interpolation scheme is used which weights observational data heavily in the 

vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind

field in regions with no observational data. The resulting wind field is subject to smoothing, an

optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence

minimization to produce a final Step 2 wind field.

The introduction of observational data in the Step 2 calculation is an option. It is also possible to

run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded

data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In NoObs mode, observational data are not used in the Step 2 calculations. The No-Obs mode is

appropriate when the MM5 simulations adequately characterize the regional wind patterns and

when local observations, especially surface observations, reflect local conditions on a scale

smaller than that of the CALMET domain and hence their spatial representativeness may be

limited. Such situations are most likely to occur when the CALMET grid scale is relatively large

i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur

in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff-based formulation and through the use of three-dimensional meteorological data

developed by the CALMET meteorological model, CALPUFF can simulate the effects of timeand space-varying meteorological conditions on pollutant transport from sources in complex

terrain. The major features and options of the CALPUFF model are summarized in Table 3-2 at

the end of this subsection. Some of the technical algorithms are briefly described below.

Complex Terrain: The effects of complex terrain on puff transport are derived from the

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations

are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a

general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical

dispersion derived from the weakly-stratified flow and dispersion module of the Complex Terrain
Overview of the CALPUFF Modeling System 15

Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely

on the receptor elevation (relative to the elevation at the source) and the height of the puff above

the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the

direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain

features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry

et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of

receptors using a dividing streamline height (Hd) to determine which pollutant material is

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET

fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines

is included in CALPUFF, which solve many of the computational difficulties encountered when

applying a puff model to near-field releases. For near-field applications during rapidly-varying

meteorological conditions, an elongated puff (slug) sampling function may be used. An

integrated puff approach may be used during less demanding conditions. Both techniques

reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both

incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the 

choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been

implemented in such a way as to allow the use of wind direction specific building dimensions.

The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as

an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of 

dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of

similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the

use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion

equations based on the CTDM. Options are provided to apply an averaging time correction or

surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF

being used by VISTAS, an option is provided to use the AERMOD turbulence profiles for

determining dispersion rates, which is the most recent approach to dispersion in EPA-approved

regulatory modeling. In addition, turbulence advection is included. For additional details on

these features, see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model

contains both overwater and overland boundary layer algorithms, the effects of water bodies on

plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff

formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion

conditions, including the abrupt changes that occur at the coastline of a major body of water.
Overview of the CALPUFF Modeling System 16

Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry

deposition rates of gases and particulate matter as a function of geophysical parameters,

meteorological conditions, and pollutant species. For particles, source-specific mass distributions

may be provided for use in the resistance model. Of particular interest for BART analyses is the

ability to separately model the deposition of fine particulate matter (< 2.5 μm diameter) from

coarse particulate matter (2.5-10 μm diameter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical

wind shear effects across individual puffs to be simulated. Differential rates of dispersion and

transport among the “new” puffs generated from the original, well-mixed puff can substantially

increase the effective rate of horizontal spread of the material. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size, to account for wind shear

across the puffs.

Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF to compute

the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs.

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

)

employed in the MESOPUFF II model or a set of user-specified, diurnally-varying transformation

rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine

particulate matter (sulfate and nitrate) from emissions of SO2 and NOx and thus allows analyses

of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical

transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia

concentrations are used together with a temperature and relative humidity-dependent equilibrium 

relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis.
Overview of the CALPUFF Modeling System 17

Table 3-2. Major Features of the CALPUFF Dispersion Model

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

- Spatially-variable fields of mixing height, friction velocity, convective velocity scale,

Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions data for point, area, and volume sources

- Temporal or wind-dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time-varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill-Gifford (PG) dispersion coefficients (rural areas)

- McElroy-Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber-Snyder method

- Schulman-Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)
Overview of the CALPUFF Modeling System 18

Table 3-2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD-based method)

- Abrupt change in meteorological conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and

POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing

tabulations that summarize the results of the simulations, identifying, for example, the highest and

second-highest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light extinction

and related measures of visibility (haze index in deciviews), reporting these for a 24-hour 

averaging time.

The CALPOST processor contains several options for evaluating visibility impacts, including the 

method described in the BART guidance, which uses monthly average relative humidity values.

CALPOST contains implementations of the IWAQM-recommended and FLAG-recommended

visibility techniques and additional options to evaluate the impact of natural weather events (fog,

rain and snow) on background visibility and visibility impacts from modeled sources.

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources

from different simulations to be summed, can compute the difference between two sets of
Overview of the CALPUFF Modeling System 19

predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry

module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This

capability allows the potential non-linear effects of ammonia scavenging by sulfate and nitrate

sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes

the full ambient ammonia concentration available to each puff without regard for any scavenging

by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the 

CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or

when nitrate formation is to be attributed to each of several sources that are in a cluster and whose

plumes overlap,

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur

and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the

changes in deposition fluxes resulting from changes in emissions to be quantified. For example

the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity

(ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).

3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport

distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km

should be done cautiously with an awareness of the likely problems involved.”6

IWAQM’s 200-km limitation derives from the observation that, when compared to the data of the

Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF

overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000

km from the source. The apparent reason was insufficient horizontal dispersion of the simulated

plume, presumably because an actual large plume does not remain coherent in the presence of

vertical wind shears that typically occur, especially during the night, and of horizontal wind

shears over the large puffs that arise over long transport distances.

To better represent such situations, an optional puff splitting algorithm has since been added to

CALPUFF to simulate wind shear effects across a well-mixed individual puff by dividing the 

puff horizontally and vertically into two or more pieces. Differential rates of transport among the

new puffs thus generated can increase the horizontal spread of the material in the plume due to

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is

 

6 The IWAQM presentation at EPA’s 6th Modeling Conference provides the background for this recommendation:

“The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration

impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For

larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the

horizontal extent of the dispersion and hence tends to overestimate the surface-level concentration maxima. This

does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such

instances be used cautiously and with some understanding.” (From page D-12 of the IWAQM Phase 2 report.)
Overview of the CALPUFF Modeling System 20

designed to allow large puffs that may grow to be several grid cells or more in size to split into

smaller puffs that can then more accurately respond to variations in the local wind field across the 

original large puff. This will also tend to increase horizontal dispersion of the plume. Since the

creation of additional puffs via puff splitting will increase the computational requirements of the

model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the 

option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km,

or possibly over shorter distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A

of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended

for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported

by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were

designed to be suitable for both short and long distances. In this regard, CALPUFF does contain

algorithms for such near-field effects as plume rise, building downwash, and terrain impingement

and includes routines that deal with the computational difficulties encountered when applying a

puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Quality Models

state, “CALPUFF is appropriate for long range transport (source-receptor distance of 50 to

several hundred kilometers)”, but provisions for using CALPUFF in the near-field in “complex

flow” situations are also included in the regulatory guidance. Complex flow situations may

include complex terrain, coastal areas, situations where plume fumigation is likely, and areas

where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to

changes in valley orientation) are important.

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in

the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently,

additional studies of model performance have been performed at shorter distances, including at a

power plant in New York state in complex terrain (at source-receptor distances of 2 to 8.5 km) 

and a second power plant in Illinois in simple terrain (at source-receptor distances in arcs ranging

from 0.5 km to 50 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation

studies over short-distances include ones by Chang et al. (2001) and Morrison et al. (2003).

These studies demonstrate good model performance over source-receptor distances from a few

hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The

EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill-Gifford

(PG) or turbulence-based dispersion coefficients. CALPUFF has been evaluated and shown to

perform better using turbulence-based dispersion for tall stacks (Strimaitis et al, 1998).

CALPUFF with turbulence-based dispersion has also been evaluated for overwater transport and

coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation

studies conducted by EPA, the use of PG-dispersion, or more specifically the lack of a convective

probability density function (pdf) module, has been demonstrated to result in underprediction of 

peak concentrations.
Overview of the CALPUFF Modeling System 21

In November 2005, EPA approved the AERMOD model, which relies on turbulence-based

dispersion, as a regulatory Guideline Model7. The ISCST3 model and its PG dispersion

coefficients are being phased out as an acceptable regulatory approach. However, EPA Region

IV has indicated that the application of turbulence-based dispersion coefficients in CALPUFF

needs to be further demonstrated before they are approved for BART application. They will

consider accepting the use of turbulence dispersion coefficients on a case-by-case basis for

sources that are close to Class I areas.

For regional haze light extinction calculations, use of a plume-simulating model such as

CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50

km from a source. In this light, the BART guidance strongly recommends using CALPUFF for

source-receptor distances greater than 50 km but also presents CALPUFF as an option that can be 

considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reasons why CALPUFF cannot be

used for even shorter transport distances than 30 km, though, as long as the scale of the plume is

larger than the scale of the output grid so that the maximum concentrations and the width of the

plume are adequately represented and so that the sub-grid details of plume structure can be

ignored when estimating effects on light extinction. The standard 1-km output grid that has been

established for Class I area analyses should serve down to source-receptor distances somewhat

under 30 km; how much closer than 30 km will depend on the topography and meteorology of the 

area and should be evaluated on a case-by-case basis. For extremely short transport distances,

depiction of the concentration distribution will require a grid that is finer than 1 km. (For

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance,

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, if the plume width is small compared to the visual range, the 

atmospheric extinction along a typical sight path of tens of kilometers through the plume will be

inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor

point will not be correct. However, the effect of averaging light extinction estimates for 24 hours,

during which the plume location shifts over various receptor points, is likely to mitigate this

problem to some degree and suggests that using CALPUFF at distances under 30 km will often be

appropriate. For the narrow plumes that result from short transport distances, though, the modeled

peak 24-hr average extinction at a receptor will tend to overstate the effect of the source on

regional haze.

 

7 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.
Overview of the CALPUFF Modeling System 22

The U.S. EPA has suggested that the plume visibility model, PLUVUE-II, could be used in lieu

of CALPUFF for simulating visibility effects at such short distances.

8 PLUVUE-II is a Gaussian

model that simulates the dispersion, chemical conversion, and optical effects of emissions of 

particles, SO2, and NOx from a single source. Its outputs include the discoloration of the sky by

the plume (so called “plume blight”) and the effect of the plume on visibility along user-selected

sight paths that pass through the plume. The impacts of the plume on visibility depend not only

on the plume composition, but also on the sight path chosen and its direction relative to the axis

of the plume and the location of the sun. It isn’t clear how such sight-path dependent results could

be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is

designed to be useful for short transport distances (with features such as the simulation of plume

downwash caused by structures at the source), CALPUFF seems more appropriate than

PLUVUE-II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 or some other size range, because the assumed size distribution of the particles is a user

input. The smaller the particles, the more they disperse like an inert gas. In most cases, the

dispersion of inert PM2.5 particles will be only minutely different from that of an inert gas, but the

behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface.

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine

particles is usually not significant except for ground-level emissions. On the other hand, coarse

particles (those PM10 particles larger than PM2.5) have an average deposition velocity of more

than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks.

CALPUFF includes parametric representations of particle and gas deposition in terms of 

atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the

gravitational settling speed. Gravitational settling, which is of particular importance for the coarse

fraction of PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated

separately in the model. CALPUFF allows for user-specified size categories to be treated as

 

8 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE-II for BART

application
Overview of the CALPUFF Modeling System 23

separate species, which includes calculating size-specific dry deposition velocities for each size

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to

visibility calculations is that of primary sulfate. Although primary sulfate emissions account for

only a small fraction of the total sulfur emissions from such sources, it may be important to

simulate their effect with CALPUFF, especially at shorter distances before significant formation

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF9 simulates the gas phase oxidation

of sulfur dioxide to sulfate by a linear transformation rate that was developed using regression

relationships derived from the analysis of chemical conversion rates produced by a complex

photochemical box model (see Scire et al., 1984, for a description of the development of the

chemical module). As in all empirically-derived models, the relationships are based on easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

oxidation.

The surrogate factors included in the parameterized chemistry during the daytime hours include

solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For 

example, gas phase SO2 oxidation is a function of OH radical concentrations. Ozone

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 conversion rate in CALPUFF is based on this correlation relationship. The

philosophy is that OH radical measurements are not available and cannot easily be computed

within a model like CALPUFF, but ozone is commonly measured throughout the country, so the 

use of the well-known surrogate variable (ozone) is more useful in the empirical relationship than

factors that are unknown or have a high degree of uncertainty. The same logic applies to the

other variables in the relationship. They are surrogates for factors that the regression analysis has

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 is represented by an additive 

term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF

represents the chemical conversion as a linear process because it requires linear independence

between puffs, although as explained below, non-linear behavior in nitrate formation can be

modeled.

 

9 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3,

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately).

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be

more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional

CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM- and FLM-recommended

MESOPUFF-II chemistry is most appropriate.
Overview of the CALPUFF Modeling System 24

The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing

the gas phase sulfate formation but that it does not adequately account for the aqueous phase

oxidation of SO2. Actual aqueous phase oxidation in clouds or fog can proceed at rates much

greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in

such situations. However, aqueous phase oxidation depends on liquid water content, not relative 

humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much

below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative

humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is

high but the cloud water that enables aqueous conversion is not present. Therefore, the direction

of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1)

overestimation of sulfate formation when NOx concentrations in the plume are high and in

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2;

and (2) lack of direct consideration of the effect of temperature on the conversion rates, which

may cause the model to overstate sulfate formation on cold days (below 10C or 50°F) (Morris et

al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated

for indirectly by the use of the solar radiation surrogate variable in the empirical conversion

equations.

Whether these potential errors are important will depend on the setting. For example, Figure 3-2

shows a comparison of predicted and observed 24-hour sulfate concentrations, due to a large

number of SO2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et

al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether

CALPUFF predictions would compare as well with measurements in the Southeast remains to be

seen.

CALPUFF does not identify the chemical form of the sulfate compound that results from its

reactions, which will generally be some form of ammoniated sulfate whose degree of

neutralization will depend on the availability of ammonia in the atmosphere. This consideration,

which has been found to be relevant for calculating light extinction in the VISTAS region, is not

addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulfate formation calculations are

derived from ambient measurements, although concentrations simulated by regional models can

be used.
Overview of the CALPUFF Modeling System 25

Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

concentration, ambient ozone concentration, and atmospheric stability class during the day. The

conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature- and

humidity-dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken

into account when estimating the ammonium nitrate particle concentration, an equilibrium that

depends on the ambient concentration of ammonia. The user supplies the value of the ambient

concentration of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that

ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium

nitrate.

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1)

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Bridger IM PROVE[SO4].Units:ug/m**3 of SO4

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4
Overview of the CALPUFF Modeling System 26

transformation of NOx to nitric acid in the plume; and (2) when ambient temperature is below 10

C, and thus the transformation rate is much slower and the nitrate concentration may be lower

than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may

overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate

concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when

temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations

tend to be the least. CALPUFF may overstate the impacts of NOx emissions at those times,

especially in the colder northern states. This potential overestimate of nitrate was not evident,

however, in an evaluation of CALPUFF-modeled nitrate against actual observational data in the

Wyoming study, as shown in Figure 3-3a (Scire et al., 2001),

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the 

background concentration of ammonia available to each puff, and that amount is scavenged by

the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of 

ammonium nitrate that is formed in total if, in reality, the combined scavenging by the

overlapping puffs at a location would deplete the available ammonia enough that the combined

nitrate formation was limited by the availability of ammonia. This effect of such ammonia

limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one

modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly

0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting

and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia

limiting was considered (Escoffier-Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system

has included the optional POSTUTIL postprocessing program, which repartitions the ammonia

and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia-limiting

effects on the development of nitrate. This allows non-linearity associated with ammonia limiting

effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate

concentrations from all sources (modeled sources plus inflow boundary conditions) and estimates

the amount of ammonia available for total nitrate formation after the preferential scavenging of 

ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is

added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the

new source and the change in background nitrate as a result of the incremental depletion of

ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of

ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and

spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage 

of the increased accuracy provided by POSTUTIL. The processor requires estimated

concentrations of ammonia throughout the modeling domain and period. Such estimates can be

inferred from CASTNet measurements, which are integrated over a week, from 24-hr SEARCH

measurements, or from the output of a regional photochemical model such as CMAQ or CAMx.

The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large,
Overview of the CALPUFF Modeling System 27

so defining the ammonia concentration throughout the Southeast would require extensive

interpolation or extrapolation from the measured values. The quality of the SEARCH

measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS

domain. Modeled concentrations have the advantage of being resolved in space and time, but

their accuracy should be evaluated by comparison with measurements wherever possible.

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to

determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation

associated with temperature and relative humidity effects. For example, results of the Wyoming

study (see Figure 3-3a) show that POSTUTIL adjustments produced daily average nitrate

concentrations well within the factor of two lines and with very little mean bias. On the other

hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced

consistent overpredictions of nitrate by factors of 2-3 and 3-4, respectively, as shown in Figure 3-

3b (Scire et al., 2003).

Figure 3-3a. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al.,

2001)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Bridger IM PROVE[NO3].Units:ug/m**3 of NO3

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3
Overview of the CALPUFF Modeling System 28

Figure 3-3b. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant 

ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench,

2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90

to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g.,

that from the burning of vegetation and deriving from VOC emissions from vegetation) and only

5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky

Mountains National Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of

the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle-caused

light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary

organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to

have a large impact (around 1%) on current visibility. (Man-caused burning of vegetation can

have significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current

fossil fuel burning would contribute less than 2% to visibility in an atmosphere that represents

natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART

[NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE

Observations

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

CALPUFF Summed Repartition (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

0.4 0.6 0.8 1 1.2

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

CALPUFF Summed Repartition (Seasonal NH3)

CALPUFF Separated Repartition (NH3=0.5ppb)

CALPUFF Separated Repartition (NH3=1.0ppb)

Factor 1

Factor 2 

or 3

Factor 2

Factor 3

Factor 4
Overview of the CALPUFF Modeling System 29

sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact

from a particularly large VOC source cannot be dismissed out of hand.

CALPUFF has only rudimentary capabilities for addressing formation of visibility-impairing

organic particles from some forms of volatile organic carbon (VOC). The capabilities that do

exist include the following.

First, PM10 emissions (such as from power plants) are often divided into filterable and

condensable components, with the condensable mass being 100-200% of the filterable mass. For

purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically

treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction varies

with process and process equipment, and can range from 20 to 100% of the condensable mass.

These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable

material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions

was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This

simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that

is currently considered best suited for biogenic organics. It relies on the conventional wisdom that

only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and

Seinfeld, 1989). For example, according to this rule, isoprene (C5H8) does not make SOA but

terpenes do, making pine trees more important biogenic contributors to SOA than oak trees.

10

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA

module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101

estimated 24-hr SOA concentrations were within 2% of the measured values (Scire et al., 2001).

This performance seems promising, although the developers view the SOA module as needing

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are

characterized as condensable PM10 and with biogenic VOCs, although the soundness of

concentration estimates by these approaches when modeling a plume from a single source is

largely untested.

11 The CALPUFF simulation of VOC emissions from sources whose VOC

emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used

for the simplified biogenic SOA module may be extended to anthropogenic VOCs when

speciated VOC emissions information is available. If only those VOCs with more than six carbon

atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC

emissions. For example, the fugitive emissions of butane and ethane during petroleum processing

 

10 Recent research suggests that isoprene may be a SOA precursor, however.

11 Note that neither of these VOC-related simulation approaches is described in the current (Version 5) CALPUFF

User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.
Overview of the CALPUFF Modeling System 30

are not important, while aromatic emissions (such as of toluene and xylene) are considered by the

SOA module’s mechanism. Development, testing, and evaluation would be needed before one

could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART-eligible sources,

means other than CALPUFF will be needed. A technical approach using a regional

photochemical model to evaluate visibility impacts of VOC emissions is presented in Section

4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction

of PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component concentrations on

light extinction is carried out in the CALPOST postprocessor. The formula used is the usual

IMPROVE/EPA formula, which is applied to determine a change in light extinction due to

changes in component concentrations. Using the notation of CALPOST, the formula is the

following:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

+ 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh

scattering term (bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for

tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula

for estimating light extinction (i.e., Equation 3-1) and that of IMPROVE and EPA. First, the OC

in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation),

which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is

synonymous with LAC in the IMPROVE formula. CALPOST now offers the option of using the 

old IMPROVE f(RH) curve, whose values are documented in the December 2000 FLAG report,

or the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze guidance

documents). Also, CALPOST sets the maximum RH at 98% by default (although the user can

change it), while the EPA’s guidance now caps it at 95%.

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext is in Mm-1. The impact of a source is determined

by comparing HI for estimated natural background conditions with the impact of the source and

without the impact of the source.
Overview of the CALPUFF Modeling System 31

CALPOST Methods

CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest

and provides various methods for estimating the background extinction against which the

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (called Methods 1-7). These are

usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition,

there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio

of 24-hour average extinctions, or as the average of 24-hour ratios). A brief summary of the

techniques is provided below. Method 2 is the current default, recommended by both IWAQM

(EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART

guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current

actual levels of extinction and thus are not consistent with the “natural conditions” the BART

proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs

of estimated (e.g., natural conditions) background extinction or component concentrations, and

thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient

for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species,

and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH)

curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98%

or a user-selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled

sulfate and nitrate.

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 

9.09 Mm-1 that results from EPA’s default natural conditions concentrations, together with an

assumption that for natural conditions, say, 0.9 Mm–1 (or 10%) of this amount results from

hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%.

In Method 2, user-specified, speciated monthly concentration values are used to describe the

background. When applied to natural conditions, for which EPA’s default natural conditions

concentrations are annual averages, the same component concentrations would have to be used

throughout the year (unless potential refinements to those default values resulted in

concentrations that vary during the year). Hourly background extinction is then calculated using

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one
Overview of the CALPUFF Modeling System 32

than that in Method 1) or, optionally, the EPA regional haze f(RH) curve.

12 Again the RH is

capped at either 98% (default) or a user-selected value (most commonly at 95%).

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the

selected maximum) is dropped from the analysis. When 24-hr extinction is computed, no fewer

than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as

“missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly

climatologically representative values in EPA (2003a, b)) are used in place of hourly values for

calculating both the extinction impact of the source emissions and the background conditions

extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates

calculated using the monthly-average relative humidity in f(RH), are compared against the

monthly default natural background concentrations. Thus the monthly-averaged relative humidity

is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and

the background extinction with Method 6.

Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant

Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review

case in Montana, that “natural conditions” should reflect the visibility impairment caused by

significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI,

2003).13 Under Method 7, during hours when visibility is obscured by meteorological conditions,

the actual measured visibility is used to represent natural conditions instead of the value that is

calculated from EPA’s default natural conditions concentrations under Method 2. A recent

modification developed in response to FLM comments on Method 7, in which the daily average

natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibility

Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations

are evaluating whether refinements are warranted to the methods recommended in EPA’s

guidance to calculate default estimates of natural background visibility. In particular, IMPROVE

has recently approved an alternative to the formula (Eq. 3-1) it uses to estimate extinction from

particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity

 

12 Note that the hourly-varying natural background extinction in this method is not consistent with that prescribed

by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH)

that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

13 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural

conditions is unknown.
Overview of the CALPUFF Modeling System 33

- Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq.

3-1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3-1) for 

sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated

by IMPROVE for all Class I areas.

For the purposes of calculating current, future, and natural background visibility at VISTAS Class

I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality

modeling results using both the current EPA recommended assumptions and the newly revised

aerosol extinction formula. If a BART-eligible source chooses to consider its projected impacts

using the newly revised formula as well as the current formula, then modifications would need to

be made to CALPOST to carry out calculations with the new algorithm.
VISTAS’ Common Modeling Protocol 34

4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two

purposes:

1) Evaluating whether a BART-eligible source is exempt from BART controls because it

is not reasonably expected to cause or contribute to impairment of visibility in Class I 

areas, and

2) Quantifying the visibility benefits of BART control options.

For purpose 1), States must determine whether a source emits any air pollutant (SO2, NOx, PM,

and in certain cases VOC and NH3) that “may reasonably be anticipated to cause or contribute to

any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART-eligible sources in State are subject to BART.

B) Demonstrate that all BART-eligible sources in the State together do not cause or

contribute to any visibility impairment

C) Determine if the impact from each individual BART-eligible source is greater than a 

threshold value.

VISTAS States intend to follow Option C (determine if the visibility impact from individual

sources exceeds a contribution threshold) for SO2 and NOx emissions. The methods for Option C

are described in Section 4.1.1. In early 2006, VISTAS pursued Option B (demonstrate that all

BART eligible sources in a State do not impact visibility) for VOC, NH3 and PM emissions. The

approach and results for Option B are described in Section 4.1.3. As a result of this exercise, the

VISTAS States have determined that the Option C exemption analyses should also include PM

emissions and, for sources with large NH3 emissions, NH3. The States determined that

anthropogenic VOC emissions do not cause or contribute to visibility impairment at VISTAS

Class I areas and that VOC emissions do not need to be considered in BART analyses.

4.1.1 BART Exemption Analysis

As illustrated in Figure 4-1, three steps will evaluate whether a BART-eligible source of SO2,

NOx, or PM is subject to BART:

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d

for SO2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to

BART. If the source agrees with this presumption, then no exemption modeling is required

and the source can proceed to the BART determination using CALPUFF to evaluate impacts

of control options and can perform the engineering analyses. If a source disagrees, the source
VISTAS’ Common Modeling Protocol 35

may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5

dv.

Figure 4-1. Flow chart showing the components of the VISTAS common modeling protocol.

Assessment should be made for each Class I Area. (If a source agrees to install the most stringent 

controls then the modeling steps indicated above and engineering analyses and visibility impact 

modeling would not be required.)

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12-

km regional VISTAS domain can be used to answer questions whether (a) a particular source

may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were

to be undertaken, which Class I areas should be included. Assumptions for the initial

modeling assessment are conservative so that a source that contributes to visibility impairment

is not exempted in error. If a source is shown not to contribute to visibility impairment using

the initial modeling assessment, the source would not be subject to BART and would be

exempted from further BART analyses. If a source is shown to contribute to visibility

impairment using the initial modeling assessment, the source has the option to undertake finer

grid CALPUFF modeling to evaluate further whether it is subject to BART.

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the

definitive test as to whether a source is subject to BART.
VISTAS’ Common Modeling Protocol 36

For large sources that will clearly exceed the initial screening thresholds, this step can be skipped

and the analysis may proceed directly to the finer grid modeling analysis, which is described in

Section 4.4.

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process

involves evaluating the visibility benefits of different BART control measures. These benefits

will be determined by making additional CALPUFF simulations using the same CALMET and

CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exception is

that the source and emissions data used in the CALPUFF control evaluation simulations will

reflect the BART control measures being evaluated. Using the same model configuration will

produce an “apples-to-apples” comparison, where differences in impacts are due to the

effectiveness of the controls rather than model configuration differences. For example, a control

scenario evaluation that uses more conservative assumptions than the base case simulation may

produce results showing no or little improvement in visibility impacts. That control scenario run

with the same model configuration as the base case may show significant visibility improvement.

Therefore, in order to not obscure the response to predicted visibility improvements by

differences in the modeling approach, the same model configuration should be used in the BART

control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current

emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing

steps and procedures are the same as in the BART eligibility simulation. Side-by-side

comparison of the visibility impacts will be tabulated to quantify the effectiveness of each control

scenario relative to the base case.

The modeling evaluation is a unit-by-unit evaluation and can be conducted on a pollutant specific

basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to

decide which control technology, if any, is appropriate. Finally, if a source decides to use the 

most stringent control technology available, the BART control analysis, including modeling, is

not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM

Volatile Organic Compounds

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its

capability to simulate secondary organic aerosol formation from VOC emissions is not adequately

tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be

calculated from PM10.) 

VISTAS has performed a weight of evidence analysis to demonstrate, using the CMAQ regional

air quality model, that the combined VOC emissions from all point sources (BART-eligible and

non-BART) in each State do not contribute to visibility impairment. Emissions sensitivity
VISTAS’ Common Modeling Protocol 37

simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid

and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no

response of organic carbon levels and light extinction at Class I areas to changing VOC emissions

from all anthropogenic sources in the VISTAS 12-km modeling domain (eastern US). Georgia

Tech repeated the sensitivity analyses using the VISTAS 12-km domain and CMAQ v 4.4 with a

refined SOA module for summer (Jun 1-Jul 10) and winter (Nov 19-Dec 19) periods in 2002.

VOC emissions from all anthropogenic point sources in every VISTAS State were reduced by

100% (i.e., eliminated). The maximum 24-hr impact of all VOC emissions from all point sources

throughout the VISTAS domain was thus determined to be less than 0.5 dv (compared to annual

average natural background) at every Class I area in the VISTAS domain and in adjacent States.

It follows that the impact of any one BART-eligible source would be much less than 0.5 dv.

Based on these analyses, the VISTAS States have concluded that VOC emissions from BART

sources do not cause or contribute to visibility impairment and do not need to be included in

BART analyses.

Ammonia

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS also contracted with Georgia Tech to calculate NH3 emissions sensitivities

using CMAQ v 4.4 with a refined SOA module and the same Jun-Jul and Nov-Dec periods in

2002 that were used for the VOC sensitivity evaluation. The NH3 emissions from all point

sources (BART-eligible and not-BART) in every State were reduced by 100% for these analyses.

This sensitivity evaluation showed that the collective impact of all VISTAS region point NH3

emissions is greater than 0.5 dv (compared to annual average natural background) at several Class

I areas. When the NH3 emissions were scaled to represent 100% reduction from only the BARTeligible sources in each State, then the maximum impact of those sources was under 0.5 dv at

most, but not all Class I areas. The high values appear to result primarily from emissions from 13

large NH3 sources. In the absence of those 13 facilities, the scaled NH3 emissions peak impacts at

Class I areas were 0.3 dv or less. Based on these analyses, the VISTAS States recommended that,

except for these 13 facilities, NH3 emissions not be included in BART modeling. States will

provide instructions to those 13 sources as to how to evaluate contributions of their NH3

emissions to visibility impairment. For documentation purposes, in summer 2006 VISTAS is

repeating the NH3 emissions sensitivity calculations, using CMAQ v4.5 with Base F emissions

and reducing 100% of NH3 emissions from only the BART-eligible sources in the VISTAS states.

Primary Particulate Matter

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to

which primary PM from BART-eligible sources contributes to impairment at Class I areas in the 

southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU sources in the VISTAS states, two CMAQ sensitivity runs for the first and third quarters of 

2002 were carried out by VISTAS’ CMAQ modeling team of ENVIRON, UCR, and Alpine
VISTAS’ Common Modeling Protocol 38

Geophysics In one run, all primary PM from EGUs was removed while in the other run all

primary PM from non-EGU sources was removed. All other CMAQ modeling components were

held constant. At almost all Class I areas in the VISTAS region, primary PM emissions

contribute to regional haze, with the collective impact of all EGU and non-EGU point primary

PM emissions being greater than 0.5 dv compared to annual average natural background. In fact,

the impacts of EGU PM emissions alone or of non-EGU PM emissions alone were each mostly

greater than 0.5 dv. Although the impacts of BART sources alone would be smaller, the VISTAS

States have concluded that all BART-eligible sources need to consider the impacts of their PM

emissions.

4.2 Optional Source-Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts

in a more detailed way than the standard VISTAS common protocol. A source may propose

source-specific modeling procedures to address special issues to the State for State review. For

example, sources very close to Class I areas may be better treated by a finer grid resolution that

the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some

situations, higher resolution MM5 or other prognostic meteorological datasets may be available

than the standard 12-km or 36-km MM5 datasets provided by VISTAS. Because it is not possible 

to anticipate all of the situations where there would be a benefit to conducting more detailed

source-specific analyses, the option to pursue this option is left as an open issue, to be resolved

and justified based on specific factors relevant for the source in question.

A source-specific modeling protocol is required for each source. This document should describe 

the data sources and model configuration, and provide rationale for any changes in the model

approach from the common protocol. This source-specific protocol must be provided for review

and approval by the State. The State will share the protocol with EPA and the Federal Land

Managers for their review. Discussion of approaches to source-specific modeling and an outline 

of the typical contents of the source-specific protocol are presented in Chapter 5. Discussions

with the regulatory authorities should be conducted prior to development of a source-specific

protocol to ensure all of the relevant issues are included in the protocol.

4.3 Initial Procedure for BART Exemption

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4-1, is a simple procedure

to evaluate whether a source can be exempted from BART controls using a consistent set of 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR

39104-39172) and other EPA and FLAG model guidance, will allow relatively simple initial

simulations. The regional initial domain is designed to allow any Class I areas within the

VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF

modeling options. The second important question that this first screening step will answer is, if
VISTAS’ Common Modeling Protocol 39

initial modeling indicates a source may impact visibility significantly, what Class I areas should

be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a

source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the 

closest Class I area will produce the controlling visibility impacts) are not likely to be universally

reliable.

4.3.2 Discussion of 12-km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre-computed regional CALMET meteorological files will

be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with

a consistent meteorological database and consistent CALPUFF modeling options.

The following three years of MM5 meteorological data have been assembled by VISTAS for use

in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning

Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has

produced annual CALMET meteorological files at 12-km grid resolution for the domain shown in

Figure 4-2. The CALMET modeling output files in the form of CALPUFF-ready threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed CALMET meteorological fields for the years 2001-2003 on the 12-km CALMET

domain in Figure 4-2 and simulates with CALPUFF any BART-eligible source to be screened.

The CALMET simulations will be developed using the highest resolution MM5 data available for

each year (i.e., 36-km MM5 data for 2003, 12-km MM5 data for 2001 and 2002).

The development of the regional CALMET meteorological fields from MM5 data will be 

conducted in No-Observations (“No-Obs”) mode. The MM5 data already reflect assimilation of

observational data and are likely to adequately characterize regional wind patterns that are 

consistent with the 12-km grid scale. Blending of MM5 data with local observations (which are

mainly at the surface) could lead to wind structures that may not be realistic under some 

conditions and may result in poorer characterization of the regional winds. Thus, the effort

required to prepare observational data sets for CALMET for the large regional domain involves

considerable effort that may not provide corresponding improvement of the wind field.
VISTAS’ Common Modeling Protocol 40

Figure 4-2. VISTAS Regional 12-km Resolution CALMET Modeling Domain (color area with 

terrain contours). The locations of the 36-km resolution MM5 grid points are shown on the plot.

For 2003, the 36-km MM5 data will be used as CALMET’s initial guess field and then the

CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data are 

used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of

the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid

scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will

compute mixing heights, turbulence parameters and other meteorological parameters that are

required by CALPUFF.
VISTAS’ Common Modeling Protocol 41

Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source

“contributes” to visibility impairment is 0.5 dv change from natural conditions14 (although States

may set a lower threshold). The 98thpercentile (8th highest annual) 24-hr average predicted

impact at the Class I area, as calculated using CALPOST Method 6 (monthly average relative

humidity values), is to be compared to this contribution threshold value. For this comparison, the

predicted impact at the Class I area on any day is taken to be the highest 24-hr average impact at

any receptor in the Class I area on that day. (Note that the receptor where the highest impact

occurs can change from day to day.) According to clarification of the BART guidance received

from EPA, for a three-year simulation the modeling values to be compared with the threshold are 

the greatest of the three annual 8th highest values or the 22nd highest value over all three years

combined, whichever is greater.

For the purposes of the initial analysis, however, the highest value over the three-year period (not

the 98thpercentile value) is to be compared to the contribution threshold. This ensures a 

significant measure of conservatism in the initial approach. VISTAS will evaluate the initial

CALPUFF results to determine if using the single highest value provides too conservative a 

screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the 

contribution threshold that would be allowed and still qualify to exempt a source.

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were

developed by Earth Tech, Inc. and they are maintained on the CALPUFF website (www.src.com)

for public access. This version includes CALMET, CALPUFF, CALPOST, CALSUM, and

POSTUTIL as well as CALVIEW.

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within

300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis.

States could decide to require a different value for the maximum distance threshold for the

CALPUFF domain, depending on the locations of the Class I areas in their states and other

factors such as meteorological conditions and the magnitudes of the emissions from BARTeligible sources. The regional CALMET domain will be unchanged by these adjustments.

Also, the initial approach is designed to significantly reduce the CALPUFF simulation time by

restricting the CALPUFF computational domain size to include only areas where significant

impacts are feasible rather than the entire regional domain. CALPUFF allows its computational

domain to be specified as a subset of the CALMET meteorological domain by settings within the

 

14 As described in Footnote 5 on page 6, States have the option of defining natural conditions as either the annual

average default conditions or the average of the 20% best natural condition days.
VISTAS’ Common Modeling Protocol 42

CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in

the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and

residence time of the puffs on the domain (and other factors such as the number of receptors and

the internal time step computed by the model). A CALPUFF domain covering an area 300 km

from a source in all directions would involve only 50 x 50 12-km grid cells, which will require

modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4-2 will be provided to

VISTAS by Earth Tech. These files will be in CALPUFF-ready format, and as such, no

CALMET user inputs will be required. An option in CALMET allows finer grid CALMET input

files to be calculated from the 12-km CALMET files.

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the

initial analyses are listed below.

CALMET Modeling Configuration (12-km initial exemption modeling)

The CALMET model configuration for the regional CALMET simulations will be defined by

Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow

the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted

below.

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET

- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid

cells as the 12-km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320,

640, 1200, 2000, 3000, 4000.

- CALMET mode: No-Observations mode including option to read overwater data directly

from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain

blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but

no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2,

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode.
VISTAS’ Common Modeling Protocol 43

- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and an appropriate value

determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature

allows the temperature field over large bodies of water such as the Atlantic Ocean and the

Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the

regional simulations based on sensitivity tests. The purpose of the testing is to optimize the 

variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM-GTOPO30 30-arcsec terrain data, Composite 

Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET

datasets can be found on the CALPUFF data page of the official CALPUFF site

(www.src.com).

CALPUFF Modeling Configuration (Initial exemption modeling)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the 

recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below:

- CALPUFF domain configured to include the source and all Class I areas within 300km of 

the source plus 50km buffer zone in each direction. CALPUFF is recommended for all

source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module

- Background concentrations of SO4 and TNO3 (HNO3 + NO3) from CMAQ 2001-2003

annual runs

- Species modeled: SO2, SO4, NOx, HNO3, NO3 and particulate matter in size categories of

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic

diameters. As noted below, the particulate matter emissions by size category will be

combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC),

fine PM or “soil” (< 2.5 μm in diameter), coarse PM (between 2.5-10 μm in diameter) and

organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual

emission rate with normal operations from the highest emitting day of the meteorological

period modeled (excluding days where start-up, shutdown or malfunctions occurred

sometime during the day.) Note that potential emissions are used to determine if a source is

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOx and PM10.
VISTAS’ Common Modeling Protocol 44

Condensable emissions are considered as primary fine particulate matter and allocated

equally to the two submicrometer-particle size classes. If actual source emissions data are

not available, the modeling should be based on permit limits. If source-specific size

categories are not available, then AP-42 factors may be used for sources where AP-42

factors are available. For sources where AP-42 factors are not available, alternative 

approaches to speciation are given below.

Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10). De minimis levels

are plant wide for each visibility-impairing pollutant, so individual units may be modeled

even if they have emissions below de minimis if the plant total is greater than de minimis.

- Particulate emissions speciation: Break down, as appropriate, filterable and condensable

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. The

process is illustrated in Figure 4-3. If source-specific speciated emissions factors are not

available, AP-42 factors or speciation information developed by the National Park Service

(http://www2.nature.nps.gov/air/permits/ect/index.cfm) can be used to estimate the PM

speciation for many source sectors.

Otherwise, assumptions will need to be proposed by the source, and reviewed and approved

by the State. Possible acceptable alternative approaches to estimating speciation include the 

following:

 Speciation profiles developed by the SMOKE emissions model for use in

VISTAS’ CMAQ regional air quality modeling (available at http://www.vistassesarm.org/BART/calpuff.asp).

 The approach described in a memo available at http://www.vistassesarm.org/BART/calpuff.asp, which provides reasonably conservative estimates

in situations where data are incomplete.

- Class I receptors: Use FLM Class I receptor list with receptor elevations provided

(available from the NPS).

- CALPUFF model options: Use IWAQM (EPA, 1998) default guidance, including

Pasquill-Gifford dispersion coefficients.

- Ozone dataset – use observed ozone data for 2001-2003 from CASTNet and AIRS stations.

Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average

ozone (backup) background values are to be computed based on daytime average ozone

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations

computed by month).
VISTAS’ Common Modeling Protocol 45

Figure 4-3. Speciation of PM-10 Emissions. (PMC is coarse particulate matter -- 2.5 to 10 μm

diameter.)

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) value for 

ammonia.

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unless source is within 50-km of a Class I

area and the State instructs the source to specifically consider building downwash.

CALPOST and POSTUTIL Configuration (Initial exemption modeling)

- Use Visibility Method 6 in CALPOST

- Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic

emissions), soil, coarse PM

- Natural background light extinction: Several options are acceptable at the discretion of the

State: (1) A single annual average natural background extinction for each Class I area, as

presented in Appendix B of EPA’s natural conditions guidance (EPA, 2003b); (2) A single 

value that represents the average haze index on the 20% best natural conditions days, again

as presented in the same Appendix B; or (3) A monthly average natural background as

PM-10

Filterable Condensable

EC PM-2.5 PMC PM

(including

H2SO4)

As Prim. SO4)

Organic PM

(SOA)

EC Soil PMC SO4

f(RH)

Non-SO4

Inorganic

OC
VISTAS’ Common Modeling Protocol 46

calculated by CALPOST under Method 6, based on annual average default natural

conditions component concentrations and monthly average f(RH) values for the centroid of

the Class I area, from Table A-3 in the natural conditions guidance document,.

A special procedure is needed for options 1 and 2, since CALPOST requires input of natural

background concentrations of PM components while the backgrounds for options 1 and 2

are expressed in EPA’s guidance document as extinction coefficients or haze indices (in

deciviews). In order to produce the appropriate natural background in CALPOST for these

options, use Equation 3-2 to calculate the extinction coefficient that corresponds to EPA’s

haze index value for the Class I area (if necessary), subtract the Rayleigh scattering value of 

10 Mm-1, and enter a soil concentration (in μg/m3) into CALPOST that is numerically equal

to this result. (Since the extinction efficiency of soil is 1 m

2

/g, Equation 3-1 shows that this

process produces a background extinction that equals the EPA’s value.) Leave the 

concentrations of all other species blank, since the number that is entered represents

extinction by all components, and set all values of f(RH) in CALPOST to unity since the 

EPA’s extinction and haze index values already account for particle growth due to humidity.

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, the new

IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in

addition to the default IMPROVE algorithm. (Calculations would need to be performed

outside CALPOST or CALPOST would need to be modified to accommodate the new

algorithm.)

- Nitrate repartitioning in POSTUTIL: Do not use for the initial modeling.

The initial run results will be based on the highest change in light extinction (deciviews) from

natural conditions over the three-year modeling period for each Class I area considered.

Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid

CALPUFF modeling analysis.

4.4 Finer Grid Modeling Procedures

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are two potential applications for finer grid CALPUFF modeling:

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate

that a source does not cause or contribute to visibility impairment in any Class I areas, and thus

can be excluded from BART controls. As shown in Figure 4-1, if the initial regional modeling

results are not below the threshold for visibility impacts, the next step is to conduct modeling

using a finer grid resolution for the meteorological fields and the treatment of terrain effects and

land use variability. In the finer grid modeling the predicted visibility impairment that is

compared to the threshold is based on the BART guidance of the 98thpercentile change in

deciviews value rather than the more conservative highest value used in the initial analysis.
VISTAS’ Common Modeling Protocol 47

The BART guidance indicates that the emissions rate to be used for such modeling is the highest

24-hr rate during the modeling period. Depending on the availability of source data, the following

emissions information (listed in order of priority) should be used with CALPUFF for BART

exclusion modeling:

- 24 hr maximum value emissions for the period 2001-2003 (Continuous Emission Monitor,

CEM data)

- 24 hr maximum value from continuous emissions monitoring data 

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP-42 source profiles

Quantify Benefits of BART. The second application of refined modeling is to quantify the

visibility benefits from the BART control options. This is accomplished by running CALPUFF

with the baseline emissions rates and again with emissions after BART controls. It is important

that emission reductions be evaluated in the postprocessing step rather than by using “negative”

emission rates in the CALPUFF model. The chemical scheme requires that emission rates always

be positive.

For any of these applications, a source-specific modeling protocol that defines source properties

and the specific model configuration is required. As discussed in Section 5, the source specific

protocol should include source-specific emissions data and can refer to this document for all

methods and assumptions that follow this common protocol.

4.4.2 Model Configuration and Settings for Finer Grid Modeling

Grid resolution substantially better than 12-km is needed for a finer grid CALPUFF assessment of

visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus,

the CALMET fine grid resolution in the subregional modeling domains used for finer grid

modeling will depend on the terrain, land use (especially coastal boundaries), location of the 

source, distance of the source from Class I areas, and total size of the subregional modeling

domain.

VISTAS States have 2001-2003 CALMET files for five 4-km sub-regional domains as illustrated

in Figure 4-4. The subdomains are designed to address all BART eligible sources within each

VISTAS states and all Class I areas within 300 km of the BART-eligible sources. For application

for a single source, a smaller domain of roughly 200-300 km by 200-300 km is recommended.

Requests to obtain the 4-km CALMET files should be made to the State BART representatives.
VISTAS’ Common Modeling Protocol 48

In some instances, as part of the source-specific protocol, a source may propose to the State to use

an even finer grid simulation to properly characterize the flow fields and land use changes that

affect dispersion. An application for source-receptor distances within about 50 km may require a 

grid resolution less than 1 km if complex terrain effects are likely to be important. This

determination should be made on a case-by-case basis. There is not a single distance at which a

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the

source-receptor distances involved, the location of the source relative to the terrain features, the

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrainforced flow), proximity of the source and Class I area to a coastline, and other factors including

availability of representative observational data.

Figure 4-4. The five subregional domains for 4-km CALMET modeling.

The finer grid CALMET simulations were run in hybrid mode, using both MM5 data to define

the initial guess fields and meteorological observational data in the Step 2 calculations.
VISTAS’ Common Modeling Protocol 49

Overwater (buoy) data will be provided in addition to the hourly surface meteorological

observations, precipitation observations and twice-daily upper air sounding data.

A domain-specific set of modeling parameters will be defined for each subregional domain. The

proper selection of the CALMET diagnostic wind field parameters that are used to blend

observations with the Step 1 wind field depends on factors such as the locations of the

meteorological stations relative to terrain and coastal features (which affects the

representativeness of the observational data), the terrain length scale, and the quality (resolution) 

of the MM5 data used to define the initial guess field and its ability to properly resolve wind

flows on the fine-scale CALMET domain. The definition of the proper CALMET parameters is

done as part of sensitivity testing where model performance is evaluated against available

observations and expected terrain effects, such as channeling of flows within a valley.

In addition to the better grid resolution and the introduction of observational data in the finer grid

simulations, several other modeling refinements can enhance the accuracy of the finer grid

modeling. These include use of the higher resolution terrain DEM data (~3 arc sec USGS data)

in defining the gridded terrain fields and application of the ammonia limiting method in the

POSTUTIL post-processor. Otherwise, the source configuration, emissions, pollutant speciation,

Class I receptors, ozone datasets and CALPUFF model options will be the same as in the initial

runs. Similarly, CALPOST will be used in the same manner as for the initial analyses. However,

POSTUTIL can be used to repartition nitrate in the finer grid modeling, using background

ammonia concentrations according to the IWAQM Phase 2 report (IWAQM, 1998).

For the finer grid BART exclusion analysis, the test for evaluating whether a source is

contributing to visibility impairment is based on the 98thpercentile modeled value (rather than the

highest predicted value used for the initial evaluation), which is consistent with EPA’s BART

guidance.

4.5 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. A sample of the

summary table produced by CALPOST is shown in Table 4-1. For evaluating compliance with

the VISTAS screening threshold, the highest change in extinction value, located at the bottom of

the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the

sample shown in Table 4-1, the summary at the bottom shows that the highest visibility impact is

1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Therefore this source

would not pass the initial analysis, and finer grid modeling would be required.

In addition to the highest change in deciview value on each day over all the receptors in a

particular Class I area, the CALPOST summary table in Table 4-1 contains the coordinates of the

receptor, receptor type (D indicates discrete receptors), the total haze level (background + source,

in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term

applied to hygroscopic aerosols (f(RH)), and the contribution of each species to light extinction
VISTAS’ Common Modeling Protocol 50

Table 4-1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF

2001 2 0 3 20.540 79.782 D 5.397 5.358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94

2001 4 0 1 24.723 77.951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.08

.

.

.

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.12 47.09 1.67 0.64 0.00 2.48

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00

--- Number of days with Delta-Deciview => 0.50: 9

--- Number of days with Delta-Deciview => 1.00: 2

--- Largest Delta-Deciview = 1.219
VISTAS’ Common Modeling Protocol 51

(in percent of the total source contribution) for SO4, NO3, organics, elemental carbon, coarse and

fine particulate matter.

For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on

the delta dv column. Table 4-2 shows an example of the ranked visibility impacts (change in dv)

for each of three years at six different Class I areas. The 98thpercentile (8th highest value) in the

sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example

shown in this table, the source passes the finer grid analysis because the highest 98thpercentile 

visibility impact is below the contribution threshold of 0.5 dv.

The Results section of the CALPUFF modeling report should contain the following information:

1. Map of source location and Class I areas within 300 km of the source

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source, as illustrated in Table 4-3.

3. A discussion of the number of Class I areas with visibility impairment from the source on

98thpercentile days in each year greater than 0.5 dv (total visibility impairment minus

impairment on 20% best days for natural background visibility equals delta-dv, the

visibility impact attributed to the source).

4. For the Class I area with the maximum impact, discussion of the number of days below

the 98thpercentile that the impact of the source exceeds 0.5 dv, the number of receptors in

the Class I area where the impact exceeds 0.5 dv, and the maximum impact.

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report

same results as provided for 12-km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format.

For each control option and for each Class I area where the impact of the source exceeded

0.5 dv, report the change in pollutant emissions and the change in visibility impact from

the source as a result of the control option. The effectiveness of candidate control options

are to be compared to each other, not to a specific target improvement.

States will provide further guidance on graphic presentation of results to simplify

evaluation of effectiveness of control measures. For example, a temporal plot of the 

change in deciviews between the controlled and uncontrolled cases could be developed for

the receptor with the maximum modeled impact in each Class I area.

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF,

CALPOST and POSTUTIL runs should be archived and provided to the State.
VISTAS’ Common Modeling Protocol 52

Table 4-2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

Great Smoky NP

0.99

0.88

0.62

0.59

0.55

0.52

0.48

0.47

0.95

0.63

0.51

0.50

0.46

0.42

0.37

0.36

1.20

0.90

0.73

0.72

0.59

0.47

0.45

0.42

Linville Gorge

0.67

0.45

0.43

0.33

0.29

0.27

0.25

0.23

0.81

0.69

0.65

0.50

0.45

0.33

0.31

0.29

0.76

0.47

0.37

0.35

0.31

0.30

0.28

0.28

Shining Rock

0.66

0.43

0.41

0.35

0.26

0.24

0.23

0.22

0.73

0.69

0.63

0.52

0.46

0.34

0.29

0.26

0.75

0.45

0.36

0.34

0.28

0.27

0.26

0.25

Cohutta

0.26

0.23

0.22

0.21

0.20

0.19

0.18

0.16

0.54

0.47

0.43

0.37

0.37

0.31

0.31

0.30

0.61

0.42

0.30

0.29

0.28

0.28

0.25

0.25

Joyce Kilmer-Slickrock

0.34

0.33

0.31

0.26

0.24

0.20

0.18

0.17

0.52

0.43

0.32

0.31

0.30

0.28

0.24

0.24

0.27

0.24

0.23

0.20

0.14

0.13

0.11

0.10

Mammoth Cave NP

0.56

0.44

0.38

0.29

0.25

0.24

0.22

0.21

0.57

0.56

0.53

0.35

0.33

0.33

0.30

0.29

0.50

0.37

0.36

0.35

0.31

0.24

0.21

0.19
VISTAS’ Common Modeling Protocol 53

Table 4-3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12-km Modeling

Domain to Determine if a BART Eligible Source is Subject to BART.

Class I area Distance

(km)

from

source to

Class I

area

boundary

# of days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2001

# of days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2002

# of days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2003

# of days

1 and

# of receptors

with impact

> 1.0 dv in

Class I area

for 3-yr

period

Max. 24-hr

impact over

3-yr period

Dolly Sods, WV

Shenandoah, VA

James River

Face, VA

Mammoth Cave,

KY

Sipsey, AL

Great Smoky

Mtns, TN

Cohutta, GA

Shining Rock,

NC

Linville Gorge,

NC

Swanquarter, NC

Cape Romain,

SC

Okefenokee, GA

Saint Marks, FL

Chassahowitzka,

FL

Everglades, FL

Brigantine, NJ

Breton Island,

LA

Caney Creek,

AR

Upper Buffalo,

AR

Mingo, MO

Hercules Glade,

MO

1

Days below the 98th percentile of days in each year or the three-year modeling period, as appropriate
VISTAS’ Common Modeling Protocol 54

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources

VISTAS will provide updates and supporting information concerning the Common Modeling

Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly

available the following data bases developed by Earth Tech:

• VISTAS version of the CALPUFF modeling system, maintained on the CALPUFF website.

Version 5.754 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in

December 2005. The last update in this VISTAS version is a CALMET update that addresses

over water dispersion, which was developed for the Minerals Management Service (MMS) in

fall 2005. This VISTAS version of CALPUFF will not be updated further unless errors are

found in the code, except that a new one-step POSTUTIL procedure will be incorporated.

BART-eligible sources in the VISTAS states will be able to use this VISTAS version

throughout the BART modeling exercise.

• 12-km CALMET output files for 2001, 2002, and 2003 produced as described in previous

sections. Further detail on model configuration and settings will be provided with the output

files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into

CALMET to be used to generate finer grid CALMET files without having to go back to the

original MM5 output files

• Five 4-km CALMET subdomains for 2001, 2002, and 2003, produced as described in

previous sections. Further detail on model configuration and settings will be provided with

the output files and will be made available on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF

modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia concentrations used to run CALPUFF.

o All other set up files used in VISTAS 12-km CALPUFF run

Samples of these data files and examples of their application with CALPUFF for BART

screening analyses can be found on the CALPUFF web site at

(http://www.src.com/verio/download/sample_files.htm).
Source-Specific Modeling Protocol 55

5. SOURCE-SPECIFIC MODELING PROTOCOL

Sources are required to submit a source-specific protocol to the State for review and approval

prior to source-specific modeling. States will provide the documentation to EPA and FLM for

their review. An outline of the typical contents of the site-specific protocol is provided in Table

5-1.

If a source-specific modeling approach is proposed that differs from the common approach in

Chapter 4, a more-detailed modeling protocol than that required under the common procedures is

required. This protocol must explain the data sources, model configuration, and rationale for

changes in the model approach from the common protocol and must be approved by the State.

Unit-specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO2, H2SO4, NOx and PM10).

Additional building dimension information (building width, length, height and corner locations)

is needed for short stacks that are less than Good Engineering Practice (GEP) height. This

information is used in providing effective structure dimensions for building downwash

calculations. (The requirement to conduct building downwash modeling may be waived by

individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET

and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal

Conic (LCC) coordinate system will be used. The required parameters to define an LCC

coordinate include two matching parallels, latitude/longitude of the projection origin, coordinate 

datum, and false Easting and Northing (if used) of the projection origin. Subregional and sourcespecific domains may be using either an LCC or UTM projection.

The CALPUFF Graphical User Interface (GUI) system provides software (called COORDS) to

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In

addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial

photographs to be used as base maps to confirm source locations. Links to sources of suitable

base maps can be found on the CALPUFF data site (www.src.com) in the section on “Aerial

Photos”.
Source-Specific Modeling Protocol 56

Table 5-1. Sample Table of Contents of a Source-Specific Fine-Scale Modeling Protocol.

1. INTRODUCTION

1.1 Objectives

1.2 Location of Source vs. Relevant Class I Areas

1.3 Source Impact Evaluation Criteria

2. SOURCE DESCRIPTION

2.1 Unit-specific Source Data

2.2 Boundary Conditions

3. GEOPHYSICAL AND METEOROLOGICAL DATA

3.1 Modeling Domain and Terrain

3.2 Land Use

3.3 Meteorological Data Base

3.3.1 MM5 Simulations

3.3.2 Measurements and Observations

3.4 Air Quality Data Base

3.4.1 Ozone Concentrations – Measured or Modeled

3.4.2 Ammonia Concentrations – Measured or Modeled

3.4.3 Concentrations of Other Pollutants – Measured or Modeled

3.5 Natural Conditions at Class I Areas

4. AIR QUALITY MODELING METHODOLOGY

4.1 Plume Model Selection

4.1.1 Major Relevant Features of CALMET

4.2.2 Major Relevant Features of CALPUFF

4.2 Modeling Domain Configuration

4.3 CALMET Meteorological Modeling

4.4 CALPUFF Computational Domain and Receptors

4.5 CALPUFF Modeling Option Selections

4.6 Light Extinction and Haze Impact Calculations

4.7 Modeling Products

5. REVIEW PROCESS

6.1 CALMET Fields

6.2 CALPUFF, CALPOST, and POSTUTIL Results

6. REFERENCES

APPENDICES

A.1 VISTAS BART MODELING PROTOCOL

A.2 ... other appendices as needed
Source-Specific Modeling Protocol 57

An example of the data that need to be reported is provided in Table 5-2. More detail on the 

stack data, emissions species, and particulate size fractions to be reported will be made available

on the CALPUFF website, www.src.com, Check with your State for the more detailed format of

Table 5-2 that is to be used.

Discussions with the regulatory authorities should be conducted prior to development of a

protocol to ensure all of the relevant issues are included in the protocol.

Table 5-2. Example of Source Documentation for BART Eligible Source.

Unit name 

and/or 

description

Start-up dates SO2 potential

emissions (tpy)

NOx potential

emissions (tpy)

Total PM

potential

emissions (tpy) 

Emissions source

name

...

Total emissions

Potential BARTeligible 

emissions
Quality Assurance 58

6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining

whether a BART-eligible source can be reasonable expected to cause or contribute to visibility

impairment in a Class I area, and therefore whether this source should be subject to BART

controls, and if so, to determine the relative benefits of various BART controls. The purpose of

the quality assurance (QA) program is to establish procedures for ensuring that products produced

by the application of the modeling techniques for BART studies satisfy the regulatory objectives

of the BART program.

The scope of the QA program affects different users differently. Common features of most

applications will be the setup and execution of the CALPUFF air quality model and processing of

modeling results to determine if a source contributes to visibility impairment at a Class I area. In

many cases, users will be provided meteorological datasets that have been developed with

VISTAS funding under a suitable QA program for use in the BART modeling. Other users will

be involved in site-specific or source-specific analyses that will use additional datasets and

potentially different modeling options and/or tools. More extensive quality assurance will be

required in these latter types of applications. It is the responsibility of the modeler to ensure that

an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system contains built-in features to facilitate quality assurance of the 

modeling results. These include the automatic production of “QA” files for various datasets,

including geophysical fields, sources and receptors, and imbedded tracking of model options and

switches within the output files from the major modeling units of the modeling system. The

Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system

allows these QA files to be displayed graphically.

In addition, a detailed software management system is in place to track version and level numbers

associated each program and utility within the CALPUFF modeling system. This information is

carried forward in all of the output files to create an audit trail of software versions and major

model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be

differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall

responsibility for the process and may also execute some or all of the modeling. VISTAS is

contributing general guidance via this protocol and is preparing meteorological fields and

performing modeling under the guidance of the States. The sources that are BART-eligible need

to provide process information and emissions data for use in the analyses. In addition, those

sources that are involved in BART assessments will need to be actively involved in control
Quality Assurance 59

technology decisions and assessments. Finally, some of the modeling steps may be carried out by

contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure that it is providing correct information

to others and to evaluate the quality of any analyses it is performing, whether with data of its own

or from others. This chapter provides general guidance and information on those aspects of

quality assurance that are specific to the CALPUFF modeling effort, irrespective of which

organization is carrying out the effort. The focus is on the common protocol efforts described in

Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique

aspects of the source-specific modeling described in Chapter 5.

6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in

conducting regional scale screening modeling to determine the whether a particular source or

group of sources is subject to BART controls. In the initial application, the regional CALPUFFready meteorological data files will be provided by VISTAS. The amount of effort for end-users

performing QA of these pre-defined meteorological fields will be reduced from what is required

in developing source-specific meteorological fields, as described below. Also, VISTAS is

planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready

format. The development of these CALMET datasets will be subject to a QA program as part of

their development, so the necessary quality assurance activity of end-users is again reduced from

what would be required in the development of the dataset. It is not expected that the quality

assurance steps in the development will be repeated in each application. The VISTAS-provided

regional and subregional meteorological fields will include a test case simulation for

demonstrating that expected modeling results are obtained on the user’s computer platform. This

test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection

Agency for application to BART analyses, a considerable amount of expertise and modeling

judgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise,

a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET

modeling (EPA, 1998; pp. 9-10,). Current methods for performing refined chemistry calculation

also require an understanding of the chemical and meteorological processing affecting

ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training

to assist States in obtaining the necessary expertise with the latest CALPUFF modeling tools and

techniques. An appropriate level of knowledge of the model formulation, technical approach and

assumptions is essential for successful BART modeling.

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is

required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options

and switches. Most of the modeling option choices are specified or recommended by regulatory
Quality Assurance 60

guidance and default values (see references in Section 4.3.3). However, remodeling of the

boundary conditions is not required for VISTAS-provided finer grid domains so the expertise 

level is not as high as it would be for development of the boundary conditions files from scratch.

To the extent that modeling applications are using pre-defined CALMET files and CALPUFF

templates, the quality assurance will be straightforward. More detailed steps are needed for the

setup of modeling files for source-specific applications of subregional domains finer than 4 km.

The basic procedures that will apply to all CALPUFF model applications will include a 

confirmation of the source data, including units, verification of the correct source and receptor

locations, including datum and projection, confirmation of the switch selections relative to

modeling guidance, checks of the program switches and file names for the various processing

steps, and confirmation of the use of the proper version and level of each model program. It is a

common and recommended procedure for an independent modeler not involved in the setup of

the modeling files to independently confirm the model switches and data entry in the actual

model input files and to conduct an independent run of the worst case event as a confirmation

check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be

created that contains all of the data and program files needed to reproduce the model results

presented in a report. The model list files from each step are included on the project CD. This

information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF-ready CALMET meteorological files, a number of large

datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready

for use with the CALPUFF model. The QA steps associated with the development of the

VISTAS common datasets will be provided separately as part of the modeling documentation. It

is not expected that the QA steps conducted in the development of the meteorological datasets

will be repeated in each application, although tests to confirm that the dataset is suitable for the 

application for which it is being used should be performed as part of the QA. This is discussed in

more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12-km Lambert Conformal

Conic (LCC) grid system. The subregional and source-specific domains may be defined in either

LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection,

two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false

Easting and Northing (if used) of the projection origin must also be defined. For any domains in

UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum

must be defined. The appropriate projection and map factors are provided as part of the

definition of the VISTAS regional grid system. For a source-specific domain, the grid parameters

will be provided as part of the source-specific protocol.
Quality Assurance 61

Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch

settings. Except as modified in Chapter 4 of this protocol or in a source-specific protocol, the 

IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model

obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP.

Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window,

it is essential that the control file be reviewed as part of the CALMET QA analysis. The

CALMET GUI retains all of the input descriptive information that is part of the standard

CALPUFF.INP file structure. This includes the default value for each variable, a text description

of the variable, the meaning of each variable option, the units of the variable and interrelationships among variables indicating if/when the variable is used. Some third-party

commercial GUIs strip out this descriptive information, which makes the QA step more difficult,

although it is essential for perform nonetheless using the variable names as references for the

variables in the file.

Part of the CALPUFF modeling system’s built-in QA capabilities is a variable tracking system

that retains the control file inputs for CALMET and CALPUFF in the output files create by the

models. This information includes the Version and Level numbers of the processor codes and

main model codes used in the simulations as well as the control files from the main models

(CALMET and CALPUFF). The information from the preprocessing steps and the CALMET

and CALPUFF model simulations is all carried forward and saved in the

CALPUFF/postprocessor output files so that the final concentration/flux files contain a history of

the model options and switch settings. This allows a user or reviewing agency to confirm the

switch settings provided in a control file with that actually used in the model simulations. An

optional switch in the CALPOST processor creates a complete listing of the QA data. This step

requires access to the output CALPUFF concentration and/or flux files, which are normally

practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF

modeling. Also, many errors are found in source coordinates and related projection/datum 

parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of

pre-defined receptors as provided by the National Park Service (NPS) receptor dataset is

recommended in the VISTAS common protocol. However, although the latitude and longitude of

each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates

have been computed for computational domain selected. In particular, the datum of the NPS

conversion software is not specified, so it is recommended that coordinates be checked using the

CALPUFF GUI’s COORDS software or another comparable coordinate translation software

package that recognizes various datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of
Quality Assurance 62

this protocol or in a source-specific protocol, the IWAQM guidance should be used in setting up

the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII

“control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the

CALPUFF GUI retains all of the input descriptive information that is part of the standard

CALPUFF.INP file structure. Some third-party commercial GUIs strip out this descriptive

information, which makes the QA step more difficult, although it is essential for perform

nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality Control of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility

statistics for evaluating compliance with the BART screening and finer grid modeling thresholds.

The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard

EPA BART guidance), entry of Class I area-specific data for computing background extinction

(either average or best 20% natural conditions, as prescribed by the State) and monthly relative 

humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that allow

subsets of a receptor network modeling in CALPUFF to be selected for processing in a given

CALPOST run. This is how receptors within a single Class I area are selected for processing

from a CALPUFF output file that may contain receptors from several Class I areas. CALPOST

contains options for creating plot files that will help in the confirmation that the proper receptor

subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model

simulation over all receptors included in CALPOST analysis. Receptors will normally be

selected in each CALPOST run so that each CALPOST run represents the impacts at a single 

Class I area. The table includes the data shown in the example in Table 4-1. For a screening

assessment, the peak value of the change in extinction is shown at the bottom of the visibility

table (see Table 4-1). For a finer grid simulation, the 98thpercentile value (8th highest day) is

used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily

change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I-specific relative humidity factors for Method 6

- Background light extinction values
Quality Assurance 63

- Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate,

organics, (as SOA), coarse and fine particulate matter and elemental carbon.

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area

specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatible with CALVIEW that allow confirmation

of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF

simulations into a total concentration file. In addition, it contains options to scale the 

concentrations from different modeled species (e.g., different particle sizes) into speciesdependent size distributions for the particulate matter. For example, PM is often simulated with

unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions

of each size category based on the species being considered (e.g., elemental carbon, coarse

particulate matter, etc.) are combined to form the species concentrations for input into

CALPOST. This process, although simple, requires a careful review of the weighting factors for

each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the 

effects of ammonia limiting conditions.

If source-specific modeling is performed using different sources of data or different techniques,

the source-specific modeling protocol should provide justification for deviations from the

VISTAS common protocol, and a QA plan specific for the application provided to address the 

quality assurance of the data used.

6.3 Additional QA Issues for Alternative Source-Specific Modeling

The level of QA required for application of source-specific protocols will be substantially higher 

than for the use of datasets that have already been subject to a QA procedure. For example,

source-specific protocols may include the use of on-site meteorological datasets, the use of higher

resolution prognostic meteorological (e.g., MM5) datasets, alternative visibility calculations,

different extinction coefficients, or other changes to the common protocol. In addition to

providing a source-specific modeling protocol describing and justifying the changes to the 

modeling approach from the VISTAS common protocol, the site-specific applications should

include the development of a QA plan to properly evaluate the data used in the site-specific 

modeling.

The critical CALMET input parameters depend on the mode in which the model is run

(observations mode, hybrid mode or no-observations mode), and the location and spatial
Quality Assurance 64

representativeness of any observational data. In a site specific protocol involving the 

development of a meteorological dataset, the elements of the QA process include preparation of

wind rose (using observed, MM5 and CALMET-derived data), including examination of the data 

as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses,

and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other 

features of the flow expected to occur within the domain. For example, 2-D vector plots

produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good

for assessing the performance of the CALMET model configuration and switches in reproducing

terrain effects because these conditions are likely to maximize the terrain impacts in the model.

Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea

breeze circulations that may be important for certain applications. Customization of the QA

process for the individual site-specific domain based on the availability of data and the physical

processes expected to be important at that location should be conducted as part of the site-specific

QA plan development.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted,

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate

concentrations at measurement sites within the site-specific modeling domain should be

evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than

derived ammonia based on aerosol measurements) will require an evaluation of the model

performance relative to the techniques in the VISTAS common protocol.

In any site-specific protocol a site-specific QA plan should be prepared.

6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific

modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer

should interpret results in light of these limitations. It is expected that the performance of the

model will be better in predicting changes in visibility impacts due to BART controls than in

predicting absolute visibility values. This is because uncertainties in meteorological conditions

transport and dispersion are expected to be less important in evaluating a change in impact, since

a comparable effect will be included in both the base and sensitivity simulations.
References 65

7. REFERENCES

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex

Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest Laboratory,

Richland, Washington.

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer.

Boundary-Layer Meteorol., 56, 261-274.

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed layer

and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E.

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle:

the RUC, Mon. Wea. Rev., 132, 495-518.

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer.

Quart. J. Roy. Meteor. Soc., 99, 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and

VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the

Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources

Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network.

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes,

Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze Rule;

EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle

Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling

Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.
References 66

Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation

and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on the Applications

of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003:

Bulk parameterization of air-sea fluxes: Updates and verification for

the COARE algorithm. Journal of Climate, 16, 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of 

PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory.

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report.

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR

MM5, NCAR TN-398-STR, NCAR Technical Note.

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary Organic

Aerosols. Atmos. Environ., 23, 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes

from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E.

Gryning and F.A. Schiermeier. Plenum Press, New York, NY.

IWAQM, 1998: Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report

and Recommendations for Modeling Long Range Transport Impacts. EPA-454/R-98-019.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4,

10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating

Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism

in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on

Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 22-

26 June 2003, San Diego, CA.
References 67

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary

boundary layer. J. Atmos. Sci., 27, 1213-1215.

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering

Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J.

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms

for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8-

89/041, U.S. EPA, Research Triangle Park, North Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME Plume

Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390.

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park,

NC.

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting &

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental

Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, Visibility

and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline

on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for

offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation

within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20

October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model with

Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology,

Phoenix, Arizona. American Meteorological Society. 11-16 January 1998.
References 68

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11

February 2005.
OC Fairburn Trinity Consultants 

BART Modeling Protocol 

APPENDIX B 

OZONE STATIONS

TABLE B-1. OZONE STATION LIST

 

LLC East LLC North LLC East LLC North LLC East LLC North

Station ID (km) (km) Station ID (km) (km) Station ID (km) (km)

GAS153 1170.523 -672.718 370870036 1252.261 -390.930 010731010 967.541 -657.326

COW137 1229.455 -453.598 370990005 1239.198 -400.268 010732006 946.229 -676.954

PNF126 1334.943 -319.947 371730002 1222.110 -412.801 010735002 954.539 -641.378

GRS420 1174.357 -397.763 371990003 1320.281 -363.587 010735003 967.091 -664.909

SPD111 1171.303 -304.752 450010001 1336.320 -519.232 010736002 946.448 -656.360

SND152 1010.988 -569.551 450070003 1318.968 -471.653 010790002 885.300 -577.908

CDZ171 811.577 -314.207 450730001 1251.077 -478.941 010830004 922.968 -535.615

ESP127 1008.639 -374.834 450770002 1289.086 -486.370 010890014 949.944 -531.983

130210012 1256.072 -702.464 450830009 1352.469 -442.236 011011002 1009.450 -779.843

130570001 1139.448 -549.352 470010101 1144.101 -365.035 011030011 916.271 -554.965

130590002 1254.834 -576.988 470090101 1174.258 -397.969 011130002 1118.129 -759.409

130670003 1139.250 -583.559 470090102 1189.001 -398.987 011170004 944.975 -685.678

130770002 1135.678 -652.314 470630003 1235.119 -313.310 011190002 827.746 -804.263

130850001 1183.893 -536.675 470650028 1074.235 -473.707 011250010 888.892 -717.148

130890002 1173.273 -615.391 470651011 1071.665 -466.918 011270003 913.955 -653.259

130893001 1177.813 -596.819 470890002 1196.997 -340.676 210470006 856.587 -295.362

130970004 1127.615 -615.450 470930021 1182.962 -346.068 210830003 753.467 -307.101

131130001 1164.895 -642.517 470931020 1174.336 -354.619 212130004 925.904 -310.227

131210055 1166.574 -612.634 470931030 1168.822 -368.942 212210013 795.825 -302.764

131350002 1189.463 -582.201 471210104 1089.655 -447.963 280030004 764.221 -526.995

131510002 1189.010 -641.453 471410004 1036.010 -352.860 280750003 778.066 -808.887

132130003 1125.896 -499.303 471550101 1203.013 -386.462 280810005 755.740 -599.366

132150008 1130.430 -751.782 471550102 1215.162 -399.615 470370026 928.096 -372.034

132151003 1139.510 -748.543 471632002 1293.574 -277.991 470850020 809.431 -395.717

132230003 1100.492 -598.405 471632003 1287.596 -274.355 470990002 864.448 -494.075

132450091 1385.536 -611.294 010270001 1039.991 -678.453 471251010 873.625 -325.374

132470001 1195.378 -623.660 010331002 852.167 -534.899 471490101 935.347 -417.676

210130002 1177.018 -288.501 010510001 1019.435 -768.434 471650007 923.428 -356.195

370210030 1296.426 -394.063 010550011 1008.439 -612.793 471650101 929.325 -338.158

370750001 1193.453 -436.901 010731003 934.603 -668.004 471870106 884.508 -398.994

370870004 1263.765 -398.624 010731005 928.160 -686.034 471890103 959.089 -378.464

370870035 1281.317 -409.988 010731009 912.513 -694.165
OC Fairburn Trinity Consultants 

BART Modeling Protocol 

FIGURE B-1. OZONE STATION LOCATIONS IN THE BART APPLICABILITY ANALYSIS

800 900 1000 1100 1200 1300 1400

Lambert Conformal Coordinate East (km)

-800

-700

-600

-500

-400

-300

Lambert Conformal Coordinate North (

km)

OC Fairburn

Ozone Monitoring

Station Class I Area