CALMET/CALPUFF 

BART Protocol for 

Class I Federal Area 

Individual Source Attribution 

Visibility Impairment Modeling Analysis 

October 24, 2005 

Colorado Department of Public Health and Environment 

Air Pollution Control Division 

Technical Services Program 

4300 Cherry Creek Drive South 

Denver, Colorado 80246 
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 ii
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

Table of Contents 

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

1.1. VISIBILITY CALCULATIONS.............................................................................................................................6

2. EMISSION ESTIMATES.....................................................................................................................................8

3. CALMET/CALPUFF MODELING METHODOLOGY ..................................................................................9

3.1. CALMET/CALPUFF MODEL SELECTION ...................................................................................................10

3.1.1. CALMET .................................................................................................................................................10

3.1.1.1. CALMET Modeling Domain......................................................................................................................... 11

3.1.1.2. CALMET Performance Evaluation ............................................................................................................... 12

3.1.1.3. Terrain ........................................................................................................................................................... 13

3.1.1.4. Land Use........................................................................................................................................................ 14

3.1.1.5. CALMET ZFACE and ZIMAX Settings....................................................................................................... 15

3.1.1.6. CALMET BIAS Setting ................................................................................................................................ 16

3.1.1.7. CALMET RMIN2 and IXTERP Settings...................................................................................................... 16

3.1.1.8. CALMET Settings: R1, R2, RMAX1, RMAX2, RMAX3 ............................................................................ 16

3.1.1.9. CALMET Surface Stations............................................................................................................................ 16

3.1.1.10. CALMET Upper Air Stations........................................................................................................................ 17

3.1.1.11. CALMET Precipitation Stations.................................................................................................................... 17

3.1.1.12. CALMET Sample Input File ......................................................................................................................... 17

3.1.1.13. CALMET Parameter Summary ..................................................................................................................... 18

3.1.2. CALPUFF ...............................................................................................................................................19

3.1.2.1. Receptor Network and Class I Federal Areas................................................................................................ 19

3.1.2.2. CALPUFF Meteorology ................................................................................................................................ 21

3.1.2.3. CALPUFF Modeling Domain........................................................................................................................ 21

3.1.2.4. CALPUFF Parameter Summary .................................................................................................................... 21

3.1.2.5. Chemical Mechanism .................................................................................................................................... 21

3.1.2.6. Chemical Mechanism – Ammonia Sensitivity Tests ..................................................................................... 24

3.1.2.7. Ammonia Assumptions - Discussion............................................................................................................. 28

3.1.2.8. Ammonia Assumptions.................................................................................................................................. 29

3.1.2.9. Ozone Assumptions....................................................................................................................................... 30

3.1.3. CALPOST Settings and Visibility Post-Processing.................................................................................31

3.1.3.1. Natural Conditions - Determining Hygroscopic And Non-Hygroscopic Values For the Best 20% Visibility 

Days 32

3.1.3.1.1. Natural Background - Objective .................................................................................................................... 32

3.1.3.1.2. Natural Background - Discussion .................................................................................................................. 32

3.1.3.1.3. Natural Background - Method ....................................................................................................................... 33

3.1.3.2. CALPOST and POSTUTIL Parameters......................................................................................................... 35

3.1.3.3. 98th Percentile Methods ................................................................................................................................. 39

4. RESULTS.............................................................................................................................................................41

5. REFERENCES ....................................................................................................................................................44

APPENDIX A – NATURAL BACKGROUND VALUES .........................................................................................45

APPENDIX B – MONTHLY F(RH) VALUES ..........................................................................................................53

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 iii
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 iv
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

1. Introduction 

Federal law requires Best Available Retrofit Technology (BART) 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. Pursuant to federal 

regulations, states have the option of exempting a BART-eligible source from the BART 

requirements based on dispersion modeling demonstrating that the source cannot reasonably be 

anticipated to cause or contribute to visibility impairment in a Class I area. 

Federal regulations implementing the BART requirement afford states some latitude in the 

criteria in determining whether a BART-eligible source is subject to BART. The Division has 

proposed state regulations establishing criteria and procedures for determining which Colorado 

sources will be subject to the BART requirement. The Division’s proposal is scheduled for a 

December 15, 2005 hearing before the Air Quality Control Commission. In advance of the 

hearing, and in preparation for the submittal of a state implementation plan for regional haze, 

the Division will perform air quality modeling with the CALPUFF modeling system to assess 

which BART-eligible sources in Colorado are likely to be subject to BART based on the 

proposed state regulation. 

According to 40 CFR Part 51, Appendix Y (BART guideline), a BART-eligible source is 

considered to “contribute” to visibility impairment in a Class I area if the modeled 98th 

percentile change in deciviews is equal to or greater than the “contribution threshold.” Any 

BART-eligible source determined to cause or contribute to visibility impairment in any Class I 

area is subject to BART. The Division has proposed a state regulation establishing a 

“contribution threshold” of 0.5 deciviews. 

The Division will apply CALPUFF with at least three years of meteorological data to 

determine if the 98th percentile 24-hour change in visibility (delta-deciview) from a BARTeligible source is equal to or greater than a contribution threshold of 0.5 deciviews at any Class 

I area. The initial phase of the BART modeling process is referred to as the “subject-to-BART” 

analysis. The modeling includes SO2, NOx, and direct PM10 emissions from all BARTeligible units at a given facility. 

The Division will use this protocol for the initial subject-to-BART modeling. However, 

additional modeling performed by the Division or source operator may supersede the results. 

Subsequent modeling should use modeling techniques consistent with the recommendations in 

this protocol and the BART guideline. The Division may approve deviations from this protocol 

for a specific source if the changes are acceptable to U.S. EPA and improve model 

performance while retaining consistency with the BART guideline. All modeling will be 

subject to Division review and approval. 

The contribution threshold and other criteria used for this modeling demonstration have not 

been finalized and may change in the final rule adopted by the Commission. Therefore, the 

results of modeling performed with this protocol are not a final agency action. Any source that 

the Division determines is subject to BART will receive a separate notice of the agency’s final 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

determination. Such separate notice will occur after the Commission acts on the proposed 

regulations establishing criteria and procedures for determining which sources will be subject 

to the BART requirement. 

Relevant language from the BART guideline is included, below, to show the modeling 

recommendations in context. Other sections of this protocol explain how the Division proposes 

to implement the recommendations. The BART guidelines set out 40 CFR Part 51, Appendix 

Y, provide in part: 

III. HOW TO IDENTIFY SOURCES “SUBJECT TO BART” 

Once you have compiled your list of BART-eligible sources, you need to determine whether 

(1) to make BART determinations for all of them or (2) to consider exempting some of them 

from BART because they may not reasonably be anticipated to cause or contribute to any 

visibility impairment in a Class I area. If you decide to make BART determinations for all 

the BART-eligible sources on your list, you should work with your regional planning 

organization (RPO) to show that, collectively, they cause or contribute to visibility 

impairment in at least one Class I area. You should then make individual BART 

determinations by applying the five statutory factors discussed in Section IV below. 

On the other hand, you also may choose to perform an initial examination to determine 

whether a particular BART-eligible source or group of sources causes or contributes to 

visibility impairment in nearby Class I areas. If your analysis, or information submitted by 

the source, shows that an individual source or group of sources (or certain pollutants from 

those sources) is not reasonably anticipated to cause or contribute to any visibility 

impairment in a Class I area, then you do not need to make BART determinations for that 

source or group of sources (or for certain pollutants from those sources). In such a case, 

the source is not “subject to BART” and you do not need to apply the five statutory factors 

to make a BART determination. This section of the Guideline discusses several approaches 

that you can use to exempt sources from the BART determination process. 

A. What Steps Do I Follow to Determine Whether A Source or Group of Sources Cause 

or Contribute to Visibility Impairment for Purposes of BART? 

1. How Do I Establish a Threshold? 

One of the first steps in determining whether sources cause or contribute to visibility 

impairment for purposes of BART is to establish a threshold (measured in deciviews)

against which to measure the visibility impact of one or more sources. A single source that 

is responsible for a 1.0 deciview change or more should be considered to “cause” visibility 

impairment; a source that causes less than a 1.0 deciview change may still contribute to 

visibility impairment and thus be subject to BART. 

Because of varying circumstances affecting different Class I areas, the appropriate 

threshold for determining whether a source “contributes to any visibility impairment” for 

the purposes of BART may reasonably differ across States. As a general matter, any 

threshold that you use for determining whether a source “contributes” to visibility 

impairment should not be higher than 0.5 deciviews. 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

In setting a threshold for “contribution,” you should consider the number of emissions 

sources affecting the Class I areas at issue and the magnitude of the individual sources’ 

impacts.5

 In general, a larger number of sources causing impacts in a Class I area may 

warrant a lower contribution threshold. States remain free to use a threshold lower than 

0.5 deciviews if they conclude that the location of a large number of BART eligible sources 

within the State and in proximity to a Class I area justify this approach.6

2. What Pollutants Do I Need to Consider? 

You must look at SO2, NOx, and direct particulate matter (PM) emissions in determining 

whether sources cause or contribute to visibility impairment, including both PM10 and 

PM2.5. Consistent with the approach for identifying your BART-eligible sources, you do 

not need to consider less than de minimis emissions of these pollutants from a source. 

As explained in section II, you must use your best judgement to determine whether VOC or 

ammonia emissions are likely to have an impact on visibility in an area. In addition, 

although as explained in Section II, you may use PM10 an indicator for particulate matter 

in determining whether a source is BART eligible, in determining whether a source 

contributes to visibility impairment, you should distinguish between the fine and coarse 

particle components of direct particulate emissions. Although both fine and coarse 

particulate matter contribute to visibility impairment, the long-range transport of fine 

particles is of particular concern in the formation of regional haze. Air quality modeling 

results used in the BART determination will provide a more accurate prediction of a 

source’s impact on visibility if the inputs into the model account for the relative particle 

size of any directly emitted particulate matter (i.e. PM10 vs. PM2.5). 

3. What Kind of Modeling Should I Use to Determine Which Sources and Pollutants 

Need Not Be Subject to BART? 

This section presents several options for determining that certain sources need not be 

subject to BART. These options rely on different modeling and/or emissions analysis 

approaches. They are provided for your guidance. You may also use other reasonable 

approaches for analyzing the visibility impacts of an individual source or group of sources. 

Option 1: Individual Source Attribution Approach (Dispersion Modeling) 

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. Under this option, you can analyze an individual source’s impact on 

visibility as a result of its emissions of SO2, NOx and direct PM emissions. Dispersion 

modeling cannot currently be used to estimate the predicted impacts on visibility from an 

individual source’s emissions of VOC or ammonia. You may use a more qualitative 

 

5 We expect that regional planning organizations will have modeling information that identifies sources affecting 

visibility in individual class I areas.

6 Note that the contribution threshold should be used to determine whether an individual source is reasonably 

anticipated to contribute to visibility impairment. You should not aggregate the visibility effects of multiple sources 

and compare their collective effects against your contribution threshold because this would inappropriately create a 

“contribute to contribution” test.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

assessment to determine on a case-by-case basis which sources of VOC or ammonia 

emissions may be likely to impair visibility and should therefore be subject to BART 

review, as explained in section II.A.3. above. 

You can use CALPUFF7

 or other appropriate model to predict the visibility impacts from a 

single source at a Class I area. CALPUFF is the best regulatory modeling application 

currently available for predicting a single source’s contribution to visibility impairment 

and is currently the only EPA-approved model for use in estimating single source pollutant 

concentrations resulting from the long range transport of primary pollutants.8 It can also 

be used for some other purposes, such as the visibility assessments addressed in today’s 

rule, to account for the chemical transformation of SO2 and NOx. 

There are several steps for making an individual source attribution using a dispersion 

model: 

1. Develop a modeling protocol. 

Some critical items to include in the protocol are the meteorological and terrain data that 

will be used, as well as the source-specific information (stack height, temperature, exit 

velocity, elevation, and emission rates of applicable pollutants) and receptor data from 

appropriate Class I areas. We recommend following EPA’s Interagency Workgroup on Air 

Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling 

Long Range Transport Impacts9 for parameter settings and meteorological data inputs. 

You may use other settings from those in IWAQM, but you should identify these settings 

and explain your selection of these settings. 

One important element of the protocol is in establishing the receptors that will be used in 

the model. The receptors that you use should be located in the nearest Class I area with 

sufficient density to identify the likely visibility effects of the source. For other Class I areas 

in relatively close proximity to a BART-eligible source, you may model a few strategic 

receptors to determine whether effects at those areas may be greater than at the nearest 

Class I area. For example, you might chose to locate receptors at these areas at the closest 

point to the source, at the highest and lowest elevation in the Class I area, at the 

IMPROVE monitor, and at the approximate expected plume release height. If the highest 

modeled effects are observed at the nearest Class I area, you may choose not to analyze the 

other Class I areas any further as additional analyses might be unwarranted. 

You should bear in mind that some receptors within the relevant Class I area may be less 

than 50 km from the source while other receptors within that same Class I area may be 

 

7 The model code and its documentation are available at no cost for download from 

http://www.epa.gov/scram001/tt22.htm#calpuff . 

8 The Guideline on Air Quality Models, 40 CFR part 51, appendix W, addresses the regulatory application of air 

quality models for assessing criteria pollutants under the CAA, and describes further the procedures for using the 

CALPUFF model, as well as for obtaining approval for the use of other, nonguideline models. 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 4 

9 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport Impacts, U.S. Environmental Protection Agency, EPA-454/R-98-019, December 

1998. 
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

greater than 50 km from the same source. As indicated by the Guideline on Air Quality 

Models, 40 CFR part 51, appendix W, this situation may call for the use of two different 

modeling approaches for the same Class I area and source, depending upon the State's 

chosen method for modeling sources less than 50 km. In situations where you are assessing 

visibility impacts for source-receptor distances less than 50 km, you should use expert 

modeling judgment in determining visibility impacts, giving consideration to both 

CALPUFF and other appropriate methods. 

In developing your modeling protocol, you may want to consult with EPA and your 

regional planning organization (RPO). Up-front consultation will ensure that key technical 

issues are addressed before you conduct your modeling. 

2. [Run model in accordance] with the accepted protocol and compare the predicted 

visibility impacts with your threshold for “contribution.”

You should calculate daily visibility values for each receptor as the change in deciviews 

compared against natural visibility conditions. You can use EPA’s “Guidance for 

Estimating Natural Visibility Conditions Under the Regional Haze Rule,” EPA-454/B-03-

005 (September 2003) in making this calculation. To determine whether a source may 

reasonably be anticipated to cause or contribute to visibility impairment at Class I area, 

you then compare the impacts predicted by the model against the threshold that you have 

selected. 

The emissions estimates used in the models are intended to reflect steady-state operating 

conditions during periods of high capacity utilization. We do not generally recommend that 

emissions reflecting periods of start-up, shutdown, and malfunction be used, as such 

emission rates could produce higher than normal effects than would be typical of most 

facilities. We recommend that States use the 24 hour average actual emission rate from the 

highest emitting day of the meteorological period modeled, unless this rate reflects periods 

start-up, shutdown, or malfunction. In addition, the monthly average relative humidity is 

used, rather than the daily average humidity – an approach that effectively lowers the peak 

values in daily model averages. 

For these reasons, if you use the modeling approach we recommend, you should compare 

your “contribution” threshold against the 98th percentile of values. If the 98th percentile 

value from your modeling is less than your contribution threshold, then you may conclude 

that the source does not contribute to visibility impairment and is not subject to BART.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

1.1. Visibility Calculations 

The general theory for performing visibility calculations with the CALPUFF modeling 

system is described in several documents, including: 

 “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report and Recommendations for Modeling Long Range Transport Impacts” 

(IWAQM, 1998) 

 “Federal Land Manager’s Air Quality Related Values Workgroup (FLAG): Phase I 

Report” (FLAG, 2000) 

 “A User's Guide for the CALPUFF Dispersion Model” (Scire, 2000) 

In general, visibility is characterized either by visual range (the greatest distance that a 

large object can be seen) or by the light extinction coefficient, which is a measure of the 

light attenuation per unit distance due to scattering and absorption by gases and particles. 

Visibility is impaired when light is scattered in and out of the line of sight and by light 

absorbed along the line of sight. The light extinction coefficient (bext) considers light 

extinction by scattering (bscat) and light extinction by absorption (babs): 

bext = bscat + babs

The scattering components of extinction can be represented by these components: 

 light scattering due to air molecules = Rayleigh scattering = brayleigh

 light scattering due to particles = bsp

The absorption components of extinction can be represented by these components: 

 light absorption due to gaseous absorption = bag

 light absorption due to particle absorption = bap

Particle scattering, bsp, can be expressed by its components: 

bsp = bSO4 + bNO3 + bOC + bSOIL+ bCoarse

where: 

 bSO4 = scattering coefficient due to sulfates = 3[(NH4)2SO4]f(RH) 

 bNO3 = scattering coefficient due to nitrates = 3[NH4NO3]f(RH) 

 bOC = scattering coefficient due to organic aerosols = 4[OC] 

 bSOIL= scattering coefficient due to fine particles = 1[Soil] 

 bCoarse= scattering coefficient due to coarse particles = 0.6[Coarse Mass] 

Particle absorption from soot is defined as: 

 bap = absorption due to elemental carbon (soot) = 10[EC] 

The concentration values (in brackets) are expressed in micrograms per cubic meter. The 

numeric coefficient at the beginning of each equation is the dry scattering or absorption 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

efficiency in meters-squared per gram. The f(RH) term is the relative humidity adjustment 

factor. 

The total atmospheric extinction can be expressed as: 

bext = bSO4 + bNO3 + bOC + bSOIL+ bCoarse+ bap+ brayleigh

In this equation, the sulfate (SO4) and nitrate (NO3) components are referred to as 

hygroscopic components because the extinction coefficient depends upon relative 

humidity. The other components are non-hygroscopic. 

The variation of the effect of relative humidity on the extinction coefficients for SO4 and 

NO3 can be determined in several ways. According to the BART guideline, monthly f(RH) 

values should be used. 

The CALPUFF modeling techniques in this protocol will provide ground level 

concentrations of visibility impairing pollutants. The concentration estimates from 

CALPUFF are used with the previously shown equations to calculate the extinction 

coefficient. 

As described in the IWAQM Phase 2 Report, the change in visibility is compared against 

background conditions. The delta-deciview, Δdv, value is calculated from the source’s 

contribution to extinction, bsource, and background extinction, bbackground, as follows:

Δdv = 10 ln((bbackground+ bsource)/ bbackground) 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

2. Emission Estimates 

According to the BART guideline, “The emissions estimates used in the models are intended to 

reflect steady-state operating conditions during periods of high capacity utilization. We do not 

generally recommend that emissions reflecting periods of start-up, shutdown, and malfunction 

be used, as such emission rates could produce higher than normal effects than would be typical 

of most facilities. We recommend that States use the 24 hour average actual emission rate from 

the highest emitting day of the meteorological period modeled, unless this rate reflects periods 

start-up, shutdown, or malfunction.” 

Short-term emission rates (≤24-hours) should be modeled since visibility impacts are 

calculated for a 24-hour averaging period. SO2, NOx, and PM10 (including condensible and 

filterable direct PM101) should be modeled from all BART-eligible units at the facility. The 

Division will initially use allowable emission rates or federally enforceable emission limits. If 

24-hour emissions limits do not exist, limits of a different averaging period may be used. 

Specifically, if limits do not exist, maximum hourly emissions based on emission factors and 

design capacity may be used. 

If the source operator elects to develop emission rates for subject-to-BART modeling, case-bycase procedures should be developed in consultation with the Division. In general, the 

following emission rates are acceptable: 

• Short-term (≤24-hours) allowable emission rates (e.g., emission rates calculated using 

the maximum rated capacity of the source). 

• Federally enforceable short-term limits (≤24-hours). 

• Peak 24-hour actual emission rates (or calculated emission rates) from the most recent 

3 to 5 years of operation that account for “high capacity utilization” during normal 

operating conditions and fuel/material flexibility allowed under the source's permit. In 

situations where a unit is allowed to use more than one fuel, the fuel resulting in the 

highest emission rates should be used for the modeling, even if that fuel has not been 

used in the last 3 to 5 years. 

If short-term rates are not available, emissions rates based on averaging periods longer than 24-

hours are acceptable only in cases where the modeling shows that the source has impacts equal 

to or greater than the contribution threshold. 

 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 8 

1

 Common speciated PM species for CALPUFF include fine particulate matter (PMF), coarse particulate matter 

(PMC), soot or elemental carbon (EC), organic aerosols (SOA), and sulfate (SO4). H2SO4, for example, is a PM10 

species emitted from coal-fired units that is typically modeled as SO4 in CALPUFF. 
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

3. CALMET/CALPUFF Modeling 

Methodology 

For the subject-to-BART modeling, the Division will use the January 2005 

CALMET/CALPUFF parameter settings and input files generated by CH2M HILL for the 

Public Service Company Comanche Unit 3 PSD permit application because it underwent 

extensive review by the Division and by Federal Land Managers as part of the PSD permitting 

process. The Division has modified the CALPUFF input files to include three additional Class 

I areas. It has also been modified as necessary to account for PM10 speciation. An additional 

post-processing step with POSTUTIL has been added to implement ammonia limiting. The 

CALPOST model setup is different from the setup for PSD permit modeling and should be 

consistent with the U.S. EPA’s BART guideline. In addition, the Division has reviewed 

available data to determine appropriate ammonia background values for various parts of 

Colorado. The Division has performed sensitivity tests to understand the response of the model 

to changes in ammonia background concentration levels. Since the current regulatory version 

of CALPOST does not generate 98th percentile results, the Division has modified CALPOST to 

generate a file with a full distribution of daily delta-deciview values for each receptor. In 

addition, the Division developed a FORTRAN processor to generate 98th percentile results. 

The Division will use this protocol for the initial subject-to-BART modeling. However, the 

Division’s initial modeling may be superseded by additional modeling performed by the 

Division or source operator. Subsequent modeling should use modeling techniques consistent 

with the recommendations in this protocol and the BART guideline. All modeling will be 

subject to Division review and approval. The Division may approve deviations from this 

protocol for a specific source if the changes are acceptable to U.S. EPA and improve model 

performance while retaining consistency with the BART guideline. For example, if the source 

operator wants to use 2-kilometer CALMET grid cells instead of 4-kilometer cells and wants to 

include additional meteorological observations in a way that improves the performance of the 

CALMET meteorological fields, the Division would probably approve the analysis. 

This protocol is intended to provide sufficient technical documentation to support the 

application of CALPUFF at distances up to 300 kilometers. While CALPUFF will be used at 

source-to-receptor distances less than 50 kilometers for some receptors, there is a Class I area 

within the 50 to 300 km range from every BART-eligible source in Colorado. Impacts at Class 

I areas greater than 300 km may be used, but it should be recognized that the use of puff 

splitting in CALPUFF would provide more accurate results for Class I areas beyond 300km. 

According to “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report and Recommendations for Modeling Long Range Transport Impacts” (IWAQM Phase 

2 Report): 

In the context of the Phase 2 recommendation, the focus of the visibility analysis is on haze. 

These techniques are applicable in the range of thirty to fifty kilometers and beyond from a 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

October 24, 2005 9 
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

source. At source-receptor distances less than thirty to fifty kilometers, the techniques for 

analyzing visual plumes (sometimes referred to as ‘plume blight’) should be applied. 

For the few cases where BART-eligible source-to-receptors distances are less than 50 

kilometers, both the topography and the meteorological fields are complex and the use of 

CALPUFF appears to be appropriate based on the possibility of recirculation, stagnation, and 

complex flows. The shortest source-to-receptor distance modeled will be about 25 kilometers, 

but it involves an elevation change of about 3000 ft. In addition, in each case, only a portion of 

the Class I area is less than 50 km from the source. If there were issues regarding the 50 km 

distance, PLUVUEII would be an appropriate model to consider for source-to-receptor 

distances less than 50 kilometers. If a PLUVUEII is used, a protocol should be developed. 

3.1. CALMET/CALPUFF Model Selection 

The following model versions will be used: 

 CALPUFF: July 2004 beta version 5.711a, level 040716

 CALMET: July 2004 beta version 5.53a, level 040716

 POSTUTIL: May 2003 version 1.31, level 030528

 CALPOST: July 2003 version 5.51, level 030709

o Modified by Division for this analysis:

 CALPOST_BART98_v3.EXE (version 5.51_CO_v3, level 030709)

 BART98_v3.EXE

The use of CALPUFF is recommended in 40 CFR 51 Appendix Y (BART guideline). The 

primary niche for CALPUFF is as a long-range transport model. It is a multi-layer, non-steadystate puff dispersion model that can simulate the effects of time- and space-varying 

meteorological conditions on pollutant transport, chemical transformations, vertical wind 

shear, and deposition (Scire, 2000). 

3.1.1. CALMET 

The MM5/CALMET meteorological fields have been generated for 1996, 2001, and 2002. 

CALMET is based on the Diagnostic Wind Model (Douglas, S. and R. Kessler, 1988). It 

has been significantly enhanced by Earth Tech, Inc (Scire, 2000). For this particular study, 

the model uses a Lambert Conformal Projection coordinate system to account for the 

Earth's curvature. 

CALMET uses a two-step approach to calculate wind fields. In the first step, an initialguess wind field is adjusted for slope flows and terrain blocking effects, for example, to 

produce a Step 1 wind field. In the second step, an objective analysis is performed to 

introduce observational data into the Step 1 wind field. 

In this application, the initial guess wind fields are based on 36-kilometer MM52

meteorological fields for 1996, 2001, and 2002 (i.e., IPROG=14). The MM5 files were 

provided to the Division by CH2M HILL as part of the Public Service Company (PSCo) 

Comanche Unit 3 PSD permit application. Alpine Geophysics extracted the MM5 data into 

 

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program 

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2

 Fifth-Generation NCAR/Penn State Mesoscale Model. 
CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis 

a CALMET MM5.DAT format for 1996, 2001, and 2002. Both the 1996 and 2001 MM5 

data were generated by the U.S. EPA. The 2002 MM5 data was originally developed for 

the Visibility Improvement State and Tribal Association of the Southeast (VISTAS). While 

the VISTAS data was considered to be acceptable for the PSCo Comanche PSD permit and 

for this analysis based on data availability issues, the Western Regional Air Partnership 

(WRAP) 36km and 12 km 2002 data should be considered as a replacement for the 2002 

VISTAS data if additional CALPUFF modeling is performed beyond this initial effort. In 

addition, the Midwest Regional Planning Organization (MRPO) 36 km 2003 and 2004 

MM5 data should be considered as additional years of data. Finally, if other better 

resolution and more representative meteorological fields become available, they may be 

considered for any future modeling. However, before accepting data from other 

meteorological models, the Division may require submission of a meteorological model 

performance evaluation to demonstrate that the proposed meteorological fields perform 

better than the MM5 fields proposed in this protocol. 

The BART guideline does not specify the exact number of years of mesoscale 

meteorological data for use in CALPUFF, but according to 40 CFR 51 Appendix W, at 

least three years of meteorological data should be used. Five years of meteorological data 

is preferable. At the time of this analysis, five years of agency-approved mesoscale 

meteorological data were not readily available at reasonable grid resolutions for Colorado. 

While the Division has the national 80km 1990 MM4 and 80km 1992 MM5 data sets, use 

of the coarse resolution 1990 and 1992 data sets would not improve the accuracy of the 

modeling results in Colorado. 

3.1.1.1. CALMET Modeling Domain 

The modeling domain is shown in Figure 1. It is based on a Lambert Conformal 

Conic projection. As determined by CH2M HILL, the Standard Parallels within the 

domain are 1/6th and 5/6th of the north-to-south extent instead of the 30-degree and 

60-degree lines that are listed as defaults in CALMET. This was done to minimize 

distortion. See Figure 7 for specific parameter settings. 

The domain includes all Class I areas in Colorado with the exception of Mesa Verde 

NP. Mesa Verde was excluded because it is more than 300 km from all of the BARTeligible sources in Colorado and because the BART-eligible sources in Colorado 

would have higher impacts at other Class I areas. That is, preliminary modeling 

indicates that impacts at Mesa Verde will not be the controlling 98th percentile values 

for this analysis. The domain does not include Class I areas in any nearby states 

because the 98th percentile impacts from Colorado’s BART-eligible sources are 

expected to be highest at Class I areas in Colorado. This assumption is based on 

source-to-receptor distances and professional judgment regarding prevailing air 

pollutant transport regimes. The CALMET domain includes almost the entire state of 

Colorado. It is about 480 km x 480 km in the longitudinal and meridonal directions, 

respectively, with 4-kilometer CALMET grid cells. 

Any modeling beyond this initial analysis should consider a larger domain that 

extends south of Albuquerque, New Mexico and west of the Canyonlands NP Class I 

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area in Utah so that all Class I areas within 300 kilometers of every BART-eligible 

source in Colorado are included in the domain. 

If a source operator elects to perform additional subject-to-BART modeling beyond 

the Division’s initial modeling using a different CALMET/CALPUFF setup, the 

Division may approve a smaller modeling domain on a case-by-case basis. For 

example, if the Division’s initial modeling shows that a source has impacts above the 

contribution threshold at only two Class I federal areas, the Division may approve a 

smaller modeling domain if the reduction in size is necessary to implement 2 km 

CALMET grid spacing. 

Figure 1. CALMET/CALPUFF modeling domain. 

3.1.1.2. CALMET Performance Evaluation 

The meteorological fields developed by the MM5/CALMET modeling system were 

evaluated by CH2M HILL for Xcel Energy as part of the PSCo Comanche Unit 3 

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PSD permit. Specifically, “CH2M HILL examined vector plots of selected periods 

within the CALMET output for validation of the wind fields with the CalDESK 

(Environmodeling Ltda.) program (CH2M HILL, 2005).” The Division replicated the 

CALMET modeling and performed additional review of the meteorological fields 

with the Lakes Environmental CALPUFF View software package. In general, the 

meteorological fields were found to be reasonable given the 36km MM5 resolution, 

although model performance could be improved with better resolution 

MM5/CALMET fields and the inclusion of more observations in CALMET. 

If the meteorological fields described in this protocol are not used and new CALMET 

fields are generated, the meteorological fields should be evaluated by a 

meteorologist. 

3.1.1.3. Terrain 

Gridded terrain elevations for the modeling domain are derived from 3 arc-second 

digital elevation models (DEMs) produced by the United States Geological Survey 

(USGS). The files cover 1-degree by 1-degree blocks of latitude and longitude. 

USGS 1:250,000 scale DEMs were used. The elevations are in meters relative to 

mean sea level and have a resolution of about 90 meters, shown in Figure 2. 

Figure 2. CALMET Terrain. 

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3.1.1.4. Land Use 

The land use data is based on the Composite Theme Grid format (CTG) using Level I 

USGS land use categories were “mapped into the 14 primary CALMET land use 

categories (CH2M HILL, 2005),” shown in Figure 3. The land use categories are 

described in Figure 4. 

Figure 3. CALMET land use categories. 

Figure 4. Land use categories table from CALMET User's Guide. 

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3.1.1.5. CALMET ZFACE and ZIMAX Settings 

Eleven vertical layers have been used with vertical cell face (ZFACE) heights at: 0, 

20, 100, 200, 350, 500, 750, 1000, 2000, 3000, 4000, and 5000 meters. 

ZIMAX was set to 4500 meters based on analyses of soundings for summer ozone 

events. The analysis suggests mixing heights in Denver are often well above the 

CALMET default value of 3000 meters during the summer. For example, on some 

summer days, ozone levels are elevated all the way to 6000 meters MSL or beyond 

during some meteorological regimes, including some regimes associated with high 

ozone episodes. A sounding from the evening of July, 1 2002 (see Figure 5), which is 

a day the 8-hour ozone standard was exceeded at Rocky Mountain National Park, 

suggests the mixing height was probably around 6000 meters MSL. The mixing 

height estimate is based on the relative uniformity of the water vapor mixing ratio 

below 6000 meters, the temperature profile, the inverted "V" in the sounding, and 

data from a NOAA ozonesonde from Boulder that shows relatively constant ozone 

levels with height. Although low mixing heights can occur during the summer, 

maximum summertime daytime mixing heights in the Denver area often range from 

about 12,000 feet (3700 m) to 20,000 feet (6000 m) MSL. Since the CALMET 

ZIMAX setting is above ground level (AGL), not above mean sea level (MSL), the 

maximum summer daytime mixing height range over the plains would be about 

15000 feet (4500 m) AGL. Thus, a ZIMAX setting of 4500 m is used. 

Figure 5. Example Denver summertime sounding. 

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3.1.1.6. CALMET BIAS Setting 

The BIAS settings for each vertical cell determine the relative weight given to the 

vertically extrapolated surface meteorological observations and upper air soundings. 

The initial guess field is computed with an inverse distance weighting of the surface 

and upper air data. It can be modified by the layer-dependent bias factor (BIAS). The 

values for BIAS can range from –1.0 to 1.0. For example, if BIAS is set to +0.25, the 

weight of the surface wind observation is reduced by 25%. If BIAS is set to –0.25, 

the weight of the upper air wind observation is reduced by 25%. If BIAS is set to 

zero, there is no change in the weighting from the normal inverse distance squared 

weighting. As recommended by the NPS, the default values of 0.0 have been used for 

all 11 vertical layers in this analysis. 

3.1.1.7. CALMET RMIN2 and IXTERP Settings 

Vertical extrapolation of data from a surface station is skipped if the surface station is 

close to the upper air station. The variable RMIN2 sets the distance between an upper 

air station and a surface station that must exceeded in order for the extrapolation to 

take place. RMIN2 has been set to the default value of 4, as recommended by the 

NPS. The default value of –4 for IEXTRP is used. By setting IEXTRP to –4 (as 

opposed to +4), layer 1 data at upper air stations is ignored. When IEXTRP=±4, the 

van Ulden and Holtslag wind extrapolation method is used. The method uses 

similarity theory and observed data to extend the influence of the surface wind speed 

and direction aloft. 

3.1.1.8. CALMET Settings: R1, R2, RMAX1, RMAX2, RMAX3 

An inverse-distance method is used to determine the influence of observations in the 

Step 1 wind field. R1 controls weighting of the surface layer and R2 controls 

weighting of the layers aloft. For example, R1 is the distance from an observational 

station at which the observation and first guess field are equally weighted. In 

addition, RMAX1, RMAX2, and RMAX3 determine the radius of influence over 

land in the surface layer, over land in layers aloft, and over water, respectively. That 

is, an observation is excluded if the distance from the observational site to a given 

grid point exceeds the maximum radius of influence. As recommended by the NPS, 

R1 and RMAX1 have been set to 30 km so that the initial guess field does not 

overwhelm the surface observations. R2 is set to 50 km and RMAX2 is set to 100 

km. RMAX3 is not much of a factor in Colorado given the lack of large water 

bodies. RMAX3 is set to 500 km. 

3.1.1.9. CALMET Surface Stations 

Eleven surface stations shown in Figure 6 were used, including Alamosa (ALS), 

Colorado Springs (CYS), Denver (DEN), Eagle (EGE), Limon (LIC), Pueblo (PUB), 

Trinidad (TAD), Cheyenne (CYS), Laramie (LAR), Rocky Mountain NP (ROM), 

and Gothic (GTH). Any future modeling analyses should consider additional surface 

stations. 

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Figure 6. Surface meteorological stations. 

3.1.1.10. CALMET Upper Air Stations 

Two upper air stations were included: Grand Junction and Denver. 

3.1.1.11. CALMET Precipitation Stations 

CH2M HILL obtained precipitation data from the National Climatic Data Center 

(NCDC). All available data in fixed-length, TD-3240 format were ordered for the 

modeling domain. CH2M HILL processed the data with the PXTRACT and 

PMERGE processors. Stations with incomplete or poor quality data for a given year 

were excluded. The number of stations used for each year is as follows (CH2M 

HILL, 2005): 

 1996 - 84 stations 

 2001 - 82 stations 

 2002 - 86 stations 

3.1.1.12. CALMET Sample Input File 

Figure 7 summarizes some of the key CALMET parameters. 

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3.1.1.13. CALMET Parameter Summary 

Figure 7 summarizes some of the key CALMET settings. 

Figure 7. CALMET parameter summary. 

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3.1.2. CALPUFF 

The default technical options in CALPUFF should be used, unless specified otherwise in 

this protocol. If non-default options or values are used, the reason should be explained and 

justified in the modeling report. 

3.1.2.1. Receptor Network and Class I Federal Areas 

The modeling domain should contain all Class I federal areas in Colorado within 300 

kilometers of the BART-eligible source. Class I areas outside Colorado within 300 

kilometers should be included if an expanded domain is used. The setup 

recommended by the Division includes eleven Class I federal areas in Colorado: 

 Flat Tops Wilderness Area 

 Rawah Wilderness Area 

 Mt Zirkel Wilderness Area 

 Weminuche Wilderness Area 

 Rocky Mountain National Park 

 Maroon Bells-Snowmass Wilderness Area 

 La Garita Wilderness Area 

 Great Sand Dunes National Park 

 West Elk Wilderness Area 

 Eagles Nest Wilderness Area 

 Black Canyon of the Gunnison National Park 

The discrete receptors for eight of the Class I federal areas were generated by the 

National Park Service (NPS) for CH2M HILL using the NPS Convert Class I Areas

(NCC) computer program. For the remaining three areas not included in the CH2M 

HILL modeling, receptors were generated by the Division with the NCC program. 

Receptor elevations provided by the NPS conversion program have been used. The 

receptors for each Class I area are shown in Figure 8 

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Figure 8. Class I federal area receptors. 

All receptors should be included in a single CALPUFF simulation. To calculate the 

visibility impacts in CALPOST for each Class I area, the NCRECP parameter can 

be used. It specifies the receptor range to be processed in CALPOST. The range of 

receptors in the Division’s recommended setup is shown in Figure 9. 

Figure 9. Receptor numbers for specific Class I areas. 

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3.1.2.2. CALPUFF Meteorology 

Refer to the CALMET section of the report for details. 

3.1.2.3. CALPUFF Modeling Domain 

The CALPUFF modeling domain is identical to the CALMET modeling domain. 

3.1.2.4. CALPUFF Parameter Summary 

Figure 10 summarizes some of the key CALPUFF settings. 

Figure 10. CALPUFF parameter summary. 

3.1.2.5. Chemical Mechanism 

The MESOPUFF II pseudo-first-order chemical reaction mechanism (MCHEM=1) is 

used for the conversion of SO2 to sulfate (SO4) and NOx to nitrate (NO3). Refer to 

the CALPUFF User’s Guide for a description of the mechanism (Scire, 2000). 

In the MESOPUFF II mechanism, the ammonia background concentration affects the 

equilibrium between nitric acid, ammonia, and ammonium nitrate. The equilibrium 

constant for the reaction is a non-linear function of temperature and relative humidity 

(Scire, 2000). Unlike sulfate, the calculated nitrate concentration is limited by the 

amount of available ammonia, which is preferentially scavenged by sulfate (Scire, 

2000). In particular, the amount of ammonia available for the nitric acid, ammonium 

nitrate, and ammonia reactions is determined by subtracting sulfate from total 

ammonia. 

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While the chemical mechanism simulates both the gas phase and aqueous phase 

conversion of SO2 to sulfate, the aqueous phase method, which is important when the 

plume interacts with clouds and fog, can significantly underestimate sulfate 

formation. In this report, as recommended by the IWAQM Phase 2 report, the 

“nighttime SO2 loss rate (RNITE1)” is set to 0.2 percent per hour. The “nighttime 

NOx loss rate (RNITE2)” is set to 2.0 percent per hour and the “nighttime HNO3

formation rate (RNITE3)” is set to 2.0 percent per hour. 

According to the 1996 “Mt. Zirkel Wilderness Area Reasonable Attribution Study of 

Visibility Impairment. Volume II: Results of Data Analysis and Modeling - Final 

Report,” 

The CALPUFF chemical module is formulated around linear transformation rates for 

SO2 to sulfate and NOX to total nitrate. There are two options for specifying these 

transformation rates: 

Option 1: An internal calculation of rates based on local values for several controlling 

variables (e.g., solar radiation, background ozone, relative humidity, and plume NOX) 

as used in MESOPUFF-II. The parametric transformation rate relationships employed 

were derived from box model calculations using the mechanism of Atkinson et al. 

(1982). 

Option 2: A user-specified input file of diurnally varying but spatially uniform 

conversion rates. 

Morris et al. (1987) reviewed the MESOPUFF-II mechanism as part of the U.S. EPA 

Rocky Mountain Acid Deposition Model Assessment study. They found that it provided 

physically plausible responses to many of the controlling environmental parameters. 

However, the mechanism had no temperature dependence, which is an important factor 

in the Rocky Mountain region where there are wide variations in temperature. 

Furthermore, the MESOPUFF-II transformation scheme was based on box model 

simulations for conditions more representative of the Eastern U.S. than of the Rocky 

Mountains. 

The largest deficiency in the MESOPUFF-II chemical transformation algorithm is the 

lack of explicit treatment for in-cloud (aqueous-phase) enhanced oxidation of SO2 to 

sulfate. The MESOPUFF-II chemical transformation algorithm includes a surrogate 

reaction rate to account for aqueous-phase oxidation of SO2 to sulfate as follows: 

 Kaq = 3 × 10-8 × RH4 (%/hr) (B.2-1) 

Thus, at 100% relative humidity (RH), the MESOPUFF-II aqueous-phase surrogate 

SO2 oxidation rate will be 3% per hour. Measurements in generating station plumes 

suggest spatially- and temporally-integrated SO2 oxidation rates due to oxidants in 

clouds to be 10 times this value. 

Another issue is the amount of ammonia available for nitrate chemistry. According to 

a paper by EarthTech (Escoffier-Czaja and Scire, 2002), 

“In the CALPUFF model, total nitrate (TNO3 = HNO3 + NO3) is partitioned into 

each species according to the equilibrium relationship between HNO3 and NO3. 

This equilibrium varies as a function of time and space, in response to both the 

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ambient temperature and relative humidity. In addition, the formation of nitrate is 

subject to the availability of NH3 to form ammonium nitrate (NH4NO3), the 

assumed form of nitrate in the model. In CALPUFF, a continuous plume is 

simulated as a series of puffs, or discrete plume elements. The total concentration 

at any point in the model is the sum of the contribution of all nearby puffs from 

each source. Because CALPUFF allows the full amount of the specified 

background concentration of ammonia to be available to each puff for forming 

nitrate, the same ammonia may be used multiple times in forming nitrate, resulting 

in an overestimate of nitrate formation. In order to properly account for ammonia 

consumption, a program called POSTUTIL was introduced into the CALPUFF 

modeling system in 1999. POSTUTIL allows total nitrate to be repartitioned in a 

post-processing step to account for the total amount of sulfate scavenging ammonia 

from all sources (both project and background sources) and the total amount of 

TNO3 competing for the remaining ammonia. In POSTUTIL, ammonia availability 

is computed based on receptor concentrations of total sulfate and TNO3, not on a 

puff-by-puff basis.” 

Ammonia-limiting methods will be used repartitioning nitric acid and nitrate on a 

receptor-by-receptor and hour-by-hour basis to account for over prediction due to 

overlapping puffs in CALPUFF. Specifically, the use of the MNIRATE=1 option in 

POSTUTIL is acceptable. At this time, other ammonia-limiting methods, including 

iterative techniques that use observational data to resolve backward the 

thermodynamic equilibrium equation between NO3/HNO3 for each hour to minimize 

available ammonia, are not acceptable. Generally, for regulatory CALPUFF 

modeling in Colorado, techniques that assume the atmosphere is always ammonia 

poor are not acceptable, particularly in eastern Colorado. 

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3.1.2.6. Chemical Mechanism – Ammonia Sensitivity Tests 

To better understand the response of the modeling system to background ammonia 

when a single point source with significant emissions of SO2 and NOx is modeled, 

the Division performed sensitivity tests for a source in northeast Colorado and a 

source in northwest Colorado using the 2002 MM5/CALMET meteorology. In the 

test case, SO2, NOx, and filterable PM10 emissions were modeled. The ammonia 

background value was varied from 0 to 100 ppb. In the northeast Colorado test case, 

the SO2 emission rate is about 3 times higher than the NOx emission rate. In the 

northwest Colorado test case, the modeled NOx emission rate is about 4.4 times 

higher than the SO2 rate. 

In both cases, when the background ammonia concentration is zero, the model 

produces no nitrate, as expected; however, it produces sulfate. 

For the northeast Colorado sensitivity test (see Figure 11), where the modeled SO2

emission rate is significantly higher than the NOx emission rate, the change in 

visibility (delta-deciview) is not very sensitive to the background ammonia 

concentration across the range from 1.0 ppb to 100.0 ppb because of the high SO2

emission rates relative to NOx and the way sulfate is produced in the MESOPUFF II 

chemical mechanism. Visibility impacts drop significantly when the ammonia 

background is less than 1.0 ppb, but even at 0.0 ppb of ammonia, sulfate impacts 

remain relative high. 

Figure 11. Sensitivity of CALPUFF visibility impacts (delta-deciview) to 

ammonia backgrounds from 0 ppb to 100 ppb from a source with high SO2

emissions relative to NOx. 

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For the northeast Colorado case, on days with the highest visibility impacts, the 

relative contribution of nitrate and sulfate vary (see Figure 12 and Figure 13), but 

most of the modeled visibility impairment is due to sulfate. When comparing these 

figures, be aware the relative rank for some days is different. For example, day 85 is 

the 2nd worst day for the 0.1 ppb ammonia case, but it’s the 3rd worst day for the 100 

ppb case. On the day with the highest impact (day 84), the contribution from sulfate 

is 98.8% for the 0.1 ppb ammonia case and 72.7% for the 100 ppb ammonia case. 

For the 8th high delta-deciview value, the contribution from sulfate is 86.3% for the 

0.1 ppb case and 67.9% for the 100 ppb case. 

Figure 12. Contribution of sulfate and nitrate to the modeled change in 

deciviews, assuming a background ammonia of 0.1 ppb in CALPUFF. 

Figure 13. Contribution of sulfate and nitrate to the modeled change in 

deciviews, assuming a background ammonia of 100 ppb. 

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For the northwest Colorado sensitivity test (see Figure 14), where the modeled NOx 

emission rate is significantly higher than the SO2 emission rate, the change in 

visibility (delta-deciview) is not sensitive to the background ammonia concentration 

across the range from 10 ppb to 100 ppb. While there is a moderate drop in impacts 

when ammonia is dropped from 10 ppb to 1.0 ppb, the model is very sensitive to 

ammonia when the background ammonia level is less than 1.0 ppb. 

Figure 14. Sensitivity of CALPUFF visibility impacts (delta-deciview) to 

ammonia backgrounds from 0 ppb to 100 ppb from a source with high NOx 

emissions relative to SO2. 

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For the northwest Colorado test case, according to CALPUFF as implemented here, 

impairment is primarily due to nitrate (see Figure 15 and Figure 16), but the 

contribution due to nitrate varies significantly depending on the assumed ammonia 

background level. For the 100 ppb background case, the nitrate contribution is 

greater than 90% for the top 20 days. However, for the 0.1 ppb case, the nitrate 

contribution varies from 43% to 81% for the top 20 days. 

Figure 15. Contribution of sulfate and nitrate to the modeled change in 

deciviews, assuming a background ammonia of 0.1 ppb in CALPUFF. 

Figure 16. Contribution of sulfate and nitrate to the modeled change in 

deciviews, assuming a background ammonia of 100 ppb in CALPUFF. 

Caution should be used when extrapolating the results of these tests to other 

CALPUFF applications. 

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Since the MESOPUFF II chemical mechanism used in this analysis depends on 

several parameters, including ozone and ammonia background concentrations, the 

methods for determining the background ozone and ammonia concentration fields are 

discussed in more detail in the next two sections. 

3.1.2.7. Ammonia Assumptions - Discussion 

In CALPUFF, as used in this application, the background ammonia concentration is 

temporally and spatially uniform. It is likely that some portions of the modeling 

domain are ammonia poor and some are ammonia rich. Thus, setting a domain-wide 

background is problematic. As discussed in the previous section, when modeling a 

single large source with high SO2 emission rates relative to NOx, the assumed 

background ammonia concentration is not a critical parameter for determining 

visibility impacts. 

According to the IWAQM Phase 2 Report, 

A further complication is that the formation of particulate nitrate is dependent on 

the ambient concentration of ammonia, which preferentially reacts with sulfate. The 

ambient ammonia concentration is an input to the model. Accurate specification of 

this parameter is critical to the accurate estimation of particulate nitrate 

concentrations. Based on a review of available data, Langford et al. (1992) suggest 

that typical (within a factor of 2) background values of ammonia are: 10 ppb for 

grasslands, 0.5 ppb for forest, and 1 ppb for arid lands at 20 C. Langford et al. 

(1992) provide strong evidence that background levels of ammonia show strong 

dependence with ambient temperature (variations of a factor of 3 or 4) and a strong 

dependence on the soil pH. However, given all the uncertainties in ammonia data, 

IWAQM recommends use of the background levels provided above, unless specific 

data are available for the modeling domain that would discredit the values cited. It 

should be noted, however, that in areas where there are high ambient levels of 

sulfate, values such as 10 ppb might overestimate the formation of particulate 

nitrate from a given source, for these polluted conditions. Furthermore, areas in the 

vicinity of strong point sources of ammonia, such as feedlots or other agricultural 

areas, may experience locally high levels of background ammonia. 

The Northern Front Range is assumed to be ammonia rich. “Sulfate along the 

Northern Front Range is completely neutralized by available ammonium and is 

present in the form of ammonium sulfate.... The Northern Front Range is ammonia 

rich. There was sufficient ammonia, on most days during winter, to completely 

neutralize available nitric acid (NFRAQS, 1998).” 

For northeast Colorado, a background ammonia concentration of 30.4 μg/m3 (about 

44 ppb) or less appears to be reasonable based on measurements for this modeling 

study. According to monitoring conducted for NFRAQS, 

• "With respect to gaseous measurements, only ammonia was acquired at all nine 

sites with the denuder difference method at the Brighton and Welby sites and 

with the filter-pack method (i.e., impregnated cellulose-fiber filters behind 

Teflon-membrane filters) at the other sites. Average ammonia concentrations 

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were 30.4 ± 53.4 μg/m3 at the core sites and 10.3 ±12.6 μg/m3 at the satellite 

sites. The large standard deviation is mainly due to elevated ammonia 

concentrations found at the Evans site. Maximum 24-hour ammonia 

concentrations were 187.0 ± 5.4 μg/m3 at the Evans core site on 01/17/97 and 

66.7 ± 3.5 μg/m3 at the Masters site on 01/20/97. Figure 6.3-5 shows that 

during the mid-January episode, 24-hour ammonia concentrations varied by 

orders of magnitude at the nine NFRAQS sites."

• "For the 6- and 12-hour samples, Figure 6.4-3[not included in this report]

ammonia concentrations were rather consistent throughout the day, with 

apparent site -to-site and season-to-season variation. Average ammonia 

concentrations at the Brighton site were double those at the Welby site during 

Winter 97. Summertime ammonia concentrations were ~1 to 2 μg/m3 higher 

than the wintertime at the Welby site. Since ammonia concentrations closely 

reflect the vicinity of the sampling area, site-to-site variations were more 

pronounced than seasonal or diurnal variations. This is evidenced by the graph 

in Figure 6.4-4[not included in this report], which shows ammonia 

concentrations were factors of 10 to 20 higher at the Evans site than at most of 

the other sites during Winter 97. Elevated concentrations exceeded 50 μg/m3 on 

20% of the days at the Evans site. Twenty-four hour ammonia concentrations at 

the Masters and Longmont sites were also factors of 5 to 10 higher than at the 

other sites." 

For other areas like northwest Colorado, an annual background ammonia concentration 

of about 1 ppb or less is probably more reasonable, based on ammonia measurements 

from the Mt. Zirkel Visibility Study.

In the Aerosol Evolution Model (AEM) simulations done for the Mt Zirkel Study for 

a specific period, “base case background air concentrations for ammonia were assumed 

to be 0.5 μg/m3 and 30 ppbv for ozone, consistent with measured values at the Hayden 

VOR site.” An ammonia concentration of 0.5μg/m3 is about 0.7 ppb. 

In the CALPUFF modeling section of the Mt Zirkel Study report, 

“The CALPUFF default value for background ammonia concentrations of 10 ppb 

was also considered far too high as a representative area-average. Measurements 

from the Buffalo Pass and Gilpin Creek sites were used to adjust ammonia 

concentration to episode and site-mean values.” 

Based on a review of CALUFF files used for the Mt. Zirkel Study, for the August 

simulations, the assumed ammonia background (BCKNH3) was 1.6 ppb; for the 

October simulation, the assumed background was 0.5 ppb; and for the September 

simulation, the assumed background was 0.8 ppb. 

3.1.2.8. Ammonia Assumptions 

Based on information in the previous section, for sources located in northeast 

Colorado and along the South Platte River, a domain-wide ammonia background 

value of 44 ppb is used. For sources located in northwest Colorado, a background 

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ammonia concentration of 1.0 ppb is used. For sources located in southeastern 

Colorado and for source located along the Arkansas River, a background value of 10 

ppb is used. 

3.1.2.9. Ozone Assumptions 

According to the IWAQM Phase 2 Report, 

CALPUFF provides two options for providing the ozone background data: (1) a 

single, typical background value appropriate for the modeling region, or (2) hourly 

ozone data from one or more ozone monitoring stations. The second and preferred 

option requires the creation of the OZONE.DAT file containing the necessary data. 

For the Demonstration Assessment, the domain was large (700 km by 1000 km) 

such that the second option was necessary. The IWAQM does not anticipate such 

large domains as being the typical application. Rather, it is anticipated that the 

more typical application will involve domains of order 400 km by 400 km or 

smaller. But even for smaller domains, the ability to provide at least monthly 

background values of ozone is deemed desirable. The problem in developing time 

(and perhaps spatial) varying background ozone values is having access to 

representative background ozone data. Ozone data are available from EPA’s 

Aerometric Information Retrieval System (AIRS); however, AIRS data must be used 

with caution. Many ozone sites are located in urban and suburban centers and are 

not representative of oxidant levels experienced by plumes undergoing long range 

transport.

In this study, “CH2M HILL obtained hourly ozone data from the following stations 

located within the modeling domain for some or all of the years 1996, 2001, and 

2002: 

• Gothic (Gunnison County, Colorado) 

• Rocky Mountain National Park 

Additional, hourly data for 1996, 2001, and 2002 were provided to CH2M HILL by 

the APCD for the following stations along the Front Range: 

• Greeley 

• Highlands Ranch 

• Colorado Springs 

Data recovery for the years 2001 and 2002 for the Greeley station was very low, and 

therefore data from the nearby Fort Collins station were used instead. Any data 

missing from the hourly records were replaced with a domain-wide default 

concentration of 60 parts per billion (ppb), as determined by the APCD/NPS (CH2M 

HILL, 2005).” 

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3.1.3. CALPOST Settings and Visibility Post-Processing 

The CALPUFF results will be post-processed with a modified version of CALPOST 

(version 5.51_CO_v3, level: 030709), POSTUTIL (version 1.31, level 030528), and 

BART98_v3. The CALPOST modifications were performed by the Division and do not 

affect any of the calculations in CALPOST for the deciview values used in this report; 

however, some simple calculations were done within CALPOST in order to output deltadeciview values (instead of percent change values) for the individual species that contribute 

to the overall delta-deciview value, but these values are not used for the subject-to-BART 

modeling. Otherwise, the CALPOST code modification consists of a “write” statement and 

supporting code. It outputs all daily delta-deciview values for every receptor to a file called 

“deciview24.dat.” The 98th percentile values are computed from “deciview24.dat” by a 

separate FORTRAN processor (BART98_v3) written by the Division specifically for this 

analysis. The Division’s processors are available upon request. 

For the initial modeling analysis, all PM10 may be assumed to have a scattering efficiency 

of 1.0 since the contribution of direct PM10 emissions is expected to be relatively small 

compared to visibility impairment caused by SO2 and NOx emissions. However, if 

modeled impacts are below the contribution threshold, condensible and filterable PM10 

emissions should be quantified and speciated. Alternatively, a sensitivity test could be 

performed to determine if speciation would change the outcome of the subject-to-BART 

demonstration. For example, if all PM10 is modeled as PMF in CALPOST, the scattering 

efficiency for PMF could be changed from 1.0 to 10.0 to simulate a worst-case speciation 

scenario. If this type of sensitivity test or another analysis suggests that PM10 speciation 

could change the outcome of the analysis, then speciation should be performed. If speciated 

PM10 emissions are modeled, the following species should be considered: fine particulates 

(PMF), coarse particulates (PMC), elemental carbon (EC), organic carbon (SOA), and 

sulfate (SO4). 

To calculate background light extinction, MVISBK should be set to 6. That is, monthly RH 

adjustment factors are applied directly to the background and modeled sulfate and nitrate 

concentrations, as recommended by the BART guideline. The RHMAX parameter, which 

is the maximum relative humidity factor used in the particle growth equation for visibility 

processing, is not used when method 6 is selected. Similarly, the relative humidity 

adjustment factor (f(RH)) curves in CALPOST (e.g., IWAQM growth curve and the 1996 

IMPROVE curve) are not used when MVISBK is equal to 6. 

The natural background is based on the 20 percent best visibility days, as recommended by 

the BART guideline preamble: 

Finally, these BART guidelines use the natural visibility baseline for the 20 percent 

best visibility days for comparison to the "cause or contribute" applicability 

thresholds. We believe this estimated baseline is likely to be reasonably 

conservative and consistent with the goal of natural conditions (70 FR 39125). 

The method for estimating natural background is presented in section 3.1.3.1. Specifically, 

for hygroscopic components, BKSO4 in CALPOST should be set to 0.0893 for all months. 

For non-hygroscopic components, BKSOIL should be set to 1.620 for all months. The 

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BKSO4 and BKSOIL values have been computed specifically for the Colorado Class I 

areas in the modeling domain. 

The extinction due to Rayleigh scattering (i.e., the scattering of light by natural particles 

much smaller than the wavelength of the light) should be set to 10 Mm-1 (BEXTRAY = 

10.0). 

3.1.3.1. Natural Conditions - Determining Hygroscopic And NonHygroscopic Values For the Best 20% Visibility Days 

3.1.3.1.1. Natural Background - Objective 

 The spreadsheet shown in Figure 17 was created to determine the hygroscopic 

(3[BKSO4]) and non-hygroscopic (equivalent to [BKSOIL]) portions of natural 

background for the best 20% visibility days (Best Days) at all Class I areas in 

Colorado's BART modeling. These concentrations, [BKSO4] and [BKSOIL], are 

used in CALPOST with monthly relative humidity adjustment factors (f(RH)) to 

determine monthly natural background visibility that would, on average, represent 

the average natural background visibility for the best 20% days in EPA's “Guidance 

for Estimating Natural Visibility Conditions Under the Regional Haze Program” 

(EPA, 2003). 

3.1.3.1.2. Natural Background - Discussion 

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze 

Program” (EPA, 2003), section 2.4, describes the calculation of the annual average 

background extinction (in 1/Mm) for a Class I area using the area's annual f(RH) 

and average natural concentrations based on the area's geographic location (east 

versus west). Annual average background extinction values (in 1/Mm) are 

converted to annual average Haze Index (HI) values (in deciview or dv). Then, the 

average HI value for the 20% best visibility days (Best Days (dv)) is estimated from 

10th percentile of the annual average HI value for a Class I area assuming normal 

distribution. Thus, no average natural concentrations are provided for determining 

extinction for the 20% best visibility days. 

 

For background extinction computation methods 2, 3, and 6 in CALPOST, 

background extinction is calculated with user-supplied monthly concentrations of 

SO4, NO3, PM coarse, organic carbon, soil, and elemental carbon species. In 

practice, concentrations for only 2 species, SO4 ([BKSO4]) and soil ([BKSOIL]), 

are supplied in the CALPOST input file to represent hygroscopic and nonhygroscopic portions of background extinction, respectively. 

To determine background extinction for the BART analysis with CALPOST, 

average natural concentrations that represent average natural background visibility 

for the best 20% days need to be determined. 

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3.1.3.1.3. Natural Background - Method 

Following EPA's approach of using regional average natural concentrations and the 

concept of using simplified inputs in CALPOST, the same hygroscopic (3[BKSO4]

best20) and non-hygroscopic ([BKSOIL]best20) values would be used in CALPOST for 

all Class I areas in Colorado's BART modeling. 

The spreadsheet calculates an average background (dv) based on monthly 

background extinction (1/Mm) for each Class I area in Colorado's BART modeling 

using the following equations: 

1. Monthly background extinction in 1/Mm (bextmonth) = 3[BKSO4]best20f(RH) 

+ [BKSOIL] best20 + Rayleigh 

2. Annual average background extinction in 1/Mm (bextannual_ave) = (bextJan + 

bextFeb + ... + bextDec)/12 

3. Calculated Best Days in dv = 10ln(bextannual_ave/10) 

EPA guidance provides f(RH) values based on the centroid of the Class I area (see 

Appendix B – Monthly f(RH) Values) and a Best Days (dv) value for each of the 

Class I areas (see Appendix A – Natural Background Values). 

The hygroscopic (3[BKSO4]) and non-hygroscopic ([BKSOIL]) values determined 

yielded the lowest sum of the absolute differences between the published Best Days 

(dv) and calculated Best Days (dv) for all Class I areas in the analysis: 

∑ ( ) ( ) =

11

1n

n DaysBest calculated - DaysBest published n Minimize

 where: n = number of Class I areas in analysis 

The "hygro (3[BKSO4])” and "non-hygro ([BKSOIL])" values of 0.268 and 1.620 

were calculated in Microsoft Excel using the "solver add-in" tool for optimization 

and equation solving (Figure 17). As can be seen from the “difference” values in 

Figure 17, the annual 20% best visibility days background concentrations for each 

Class I area calculated with this method are within 0.01 deciviews or less of the 

annual 20% best visibility days background values recommended by EPA. For 

CALPOST, the hygroscopic component of extinction is divided by 3 (the extinction 

coefficient of sulfate and nitrate) and input as BKSO4 (i.e., BKSO4 = 0.268/3 = 

0.0893). The non-hygroscopic component is used directly (i.e., BKSOIL = 1.620). 

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Figure 17. Spreadsheet showing the "hygro (3[BKSO4])" (0.268)and "nonhygro ([BKSOIL])" (1.620) values calculated in Microsoft Excel using the 

"solver add-in" tool for optimization and equation solving. 

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3.1.3.2. CALPOST and POSTUTIL Parameters 

Two post-processing examples are provided. In example #1, fine particulate 

emissions are speciated into PMF, PMC, EC, SOA, and SO4 and explicitly included 

as species in CALPUFF. Emission rates for each species are included in CALPUFF. 

Figure 18 summarizes some of the key CALPOST settings. The monthly f(RH) 

values (RHFAC), which are different for each Class I area, are from Appendix B – 

Monthly f(RH) Values. 

Figure 18. CALPOST - key parameters (example #1 setup). 

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In example #1, POSTUTIL is used to compute the partition for the total 

concentration fields with MNITRATE=1 and the appropriate ammonia background 

concentration. The ammonia background concentration, BCKNH3, in POSTUTIL is 

the same as the background value presented in section 3.1.2.8. In POSTUIL, the 

input species include SO2, SO4, NOX, HNO3, NO3, SOA, PMF, PMC, and EC and 

the output species include SO4, HNO3, NO3, SOA, PMF, PMC, and EC. Key 

POSTUTIL parameters are shown in Figure 19. 

Figure 19. POSTUTIL - key parameters for cases with nitrate partitioning and 

speciated PM10 concentrations (example #1 setup). 

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In example #2, PM10 is included as a species in CALPUFF and ammonia limiting is 

performed with POSTUTIL. The example #2 CALPOST setup is the same as shown 

in example #1 (see Figure 18) except LVPMC=F, since there are is no coarse PM, 

and SPECPMF=SOIL because the PM10 emissions from CALPUFF are reallocated 

to the species SOIL and EC in the first of two POSTUTIL runs. The first POSTUTIL 

setup for example #2 (see Figure 20) is intended to provide a post-processing 

opportunity to divide the PM10 concentrations into SOIL and EC components; 

however, in the setup example shown in Figure 20, all of the PM10 is allocated to 

SOIL and none is allocated to EC. 

Figure 20. POSTUTIL setup for simulations where PM10 is divided into SOIL 

and EC species (example #2 setup). 

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In the second POSTUTIL setup for example #2, POSTUTIL is used to compute the 

partition for the total concentration fields with MNITRATE=1 and the appropriate 

ammonia background concentration. The ammonia background concentration, 

BCKNH3, in POSTUTIL is the same as the background value presented in section 

3.1.2.8. In this POSTUIL setup, the input species include SO4, NO3, HNO3, EC, 

SOIL, and SOA and the output species include SO4, NO3, HNO3, EC, SOIL, and 

SOA. Key POSTUTIL parameters are shown in Figure 19. 

Figure 21. POSTUTIL setup for simulations where ammonia limiting is 

performed using the output file generated from the POSTUTIL setup in Figure 

20 (example #2 setup). 

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3.1.3.3. 98th Percentile Methods 

According the BART guideline: 

...you should compare your “contribution” threshold against the 98th percentile of 

values. If the 98th percentile value from your modeling is less than your contribution 

threshold, then you may conclude that the source does not contribute to visibility 

impairment and is not subject to BART. (70 FR 39162) 

The BART guideline does not contain a specific method for calculating the “98th

percentile value” and CALPOST version 5.51 does not generate a 98th percentile 

delta-deciview value. Consequently, the Division developed a FORTRAN program 

(BART98_v3) to compute 98th percentile results. The program implements several 

methods because, at the time the code was written, U.S. EPA had not yet specified 

an explicit method for determining the 98th percentile value. 

The U.S.EPA recommends using the 98th percentile value from the distribution of 

values containing the highest modeled delta-deciview value for each day of the 

simulation from all modeled receptors at a given Class I area. The 98th percentile 

delta-deciview value should be determined in several ways: 

 The 8th highest value for each year modeled 

 The 3-year average of the annual 8th high values 

 The 22nd highest value for the 3-year modeling period 

The highest value from all of the above methods should be compared to the 

contribution threshold. The contribution threshold has an implied level of precision 

equal to the level of precision reported from CALPOST. Specifically, the 98th

percentile results should be reported to three decimal places. 

The Division’s processor BART98_v3 calculates the 98th percentile value with the 

method recommended by U.S. EPA. The Division refers to the method as the “dayspecific method” or “method 1.” The first step in the method is to find the highest 

modeled delta-deciview value for each day of the simulation from all modeled 

receptors for the selected time period. While this set of delta-deciview values is 

generated by CALPOST in an unranked format, the Division’s processor 

BART98_v3 outputs all daily delta-deciview values for each receptor from 

CALPOST and finds the highest impact for each day. Next, the processor ranks the 

daily delta-deciview maxima in descending order for the number of days processed 

in CALPOST. Then, the processor determines the 98th percentile value from the 

distribution of ranked modeled daily maximum values, irrespective of receptor 

location. For example, for a 365-day simulation, the 98th percentile value would be 

the 8th highest modeled delta-deciview value from the list of ranked delta-deciview 

values. That is, the top 7 days are ignored, even though the values being ignored 

may be at different receptors. Similarly, for a 3-year period, the 98th percentile 

would be the 22nd highest modeled delta-deciview value. 

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The processor BART98_v3 also generates 98th percentile values using the “receptorspecific method” or “method 2.” This method, which calculates 98th percentile 

values on a receptor-by-receptor basis, is not used for the subject-to-BART 

modeling in Colorado. 

In order to make the processor more general and to handle missing data, the “8th

high” (for one year) and “22nd high” (for 3 years) values recommended by U.S. EPA 

are not hardwired into the processor; rather, the processor contains an algorithm that 

calculates the appropriate “nth high” value from the distribution of data. The 8th high 

and 22nd high values recommended by U.S. EPA are consistent with the values that 

would be generated from the equations in 40 CFR 50 Appendix N - “Interpretation 

of the National Ambient Air Quality Standards for PM2.5” – for determining 98th

percentile values for PM2.5 monitoring. Thus, the Appendix N method is used in the 

processor. For the exact algorithm, see Appendix N, the BART98_v3 source code, 

or the BART98_v3 “readme” file. 

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4. Results 

The CALPUFF modeling results will include eleven of the twelve Class I federal areas in 

Colorado. Mesa Verde was excluded because it is more than 300 km from all of the BARTeligible sources in Colorado. In addition, the BART-eligible sources in Colorado would have 

higher impacts at other Class I areas. That is, impacts at Mesa Verde would not be the 

controlling 98th percentile values for this analysis. 

The results for source-to-receptor distances beyond 300 kilometers may be used, but they may 

overestimate impacts because puff splitting has not been used. The model setup used here 

should provide reasonable estimates for source-to-receptor distances up to 300 kilometers. The 

modeling report should include a figure such as Figure 22 that shows the 50km and 300 km 

radius circles around the BART-eligible source. 

Figure 22. Example figure showing Class I areas within 50 and 300 kilometers of the 

BART-eligible source. 

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The results section of the report should include a table like Table 1 and a figure like Figure 23

that show the 98th percentile daily delta-deciview values for each Class I area in the modeling 

domain. 

The 98th percentile delta-deciview value should be determined several ways: 

 The 8th highest value for each year modeled 

 The 3-year average of the annual 8th high values 

 The 22nd highest value for the 3-year modeling period 

The highest value from the three methods above should be compared to the contribution 

threshold. The contribution threshold has an implied level of precision equal to the level of 

precision reported from CALPOST. Specifically, the 98th percentile results should be reported 

to three decimal places. 

 

Table 1. Example table showing maximum 98th percentile value, 98th percentile values 

calculated with several methods, and the number of days the impact is equal to or greater than 

0.5 deciviews for the entire period modeled. 

0.000

BART-eligible source name: 

1996 2001 2002

3-year 

Average

Flat Tops WA

Rawah WA

Mt Zirkel WA

Weminuche WA

Rocky Mountain NP

Maroon Bells-Snowmass WA

La Garita WA

Great Sand Dunes NP

West Elk WA

Eagles Nest WA

Black Canyon of the Gunnison NP

CALPUFF Individual Source Attribution Analysis Maximum 98th Percentile Value =

8th High Delta-Deciview Value

22nd High DeltaDeciview Value 

from 3-year 

Class I federal area Modeling Period

98th Percentile Daily Change in Visibility from BART-Eligible 

Source Compared Against Natural Background Conditions

Number of 

Days Impact 

>0.5dv (1996, 

2001, 2002)

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Figure 23. Example graph comparing 98th percentile daily change in visibility values (deltadeciviews). The highest value should be compared to the contribution threshold of 0.5 

deciviews. 

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5. References 

CH2M HILL. “Application for Permit to Construct Unit 3, Comanche Generating Station, 

Pueblo, Colorado.” Submitted by Public Service Company of Colorado, Prepared by CH2M 

HILL, January 2005. 

Escoffier-Czaja, Christelle and J. Scire, 2002. “The Effects of Ammonia Limitation on Nitrate 

Aerosol Formation and Visibility Impacts in Class I Areas.” Earth Tech, Inc., Extended 

abstract. 12th Joint Conference on the Applications of Air Pollution Meteorology with the Air 

and Waste Management Association, Norfolk, VA, Amer. Meteor. Soc, J5.13. 

Douglas, S. and R. Kessler, 1988. User's Guide to the Diagnostic Wind Model (Version 1.0). 

Systems Applications, Inc., San Rafael, CA. 

“Federal Land Manager’s Air Quality Related Values Workgroup (FLAG): Phase I Report,” 

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service, December 2000. 

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule.” EPA454/B-03-005. September 2003. 

“Guidance for Tracking Progress Under the Regional Haze Rule.” U.S. EPA, EPA-454/B-03-

004. September 2003. 

“Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and 

Recommendations for Modeling Long Range Transport Impacts.” EPA-454/R-98-019, 

December 1998. 

“Mt. Zirkel Wilderness Area Reasonable Attribution Study of Visibility Impairment. Volume 

II: Results of Data Analysis and Modeling - Final Report.” Prepared by John G. Watson, et al, 

Prepared for Colorado Department of Public Health and Environment, 1996. 

“Northern Front Range Air Quality Study Final Report.” Prepared for Colorado State 

University, Prepared by John G. Watson, Eric Fujita, Judith C. Chow Barbara Zielinska 

(Desert Research Institute), L. Willard Richards (Sonoma Technology, Inc.), William Neff 

(NOAA), David Dietrich (Air Resource Specialists, Inc.), 1998.

Scire J.S., D.G. Strimaitis, R.J. Yamartino. “A User's Guide for the CALPUFF Dispersion 

Model.” Earth Tech, Concord, MA, January 2000. 

Scire J.S., F. Robe, F.E.. Fernau, R.J. Yamartino. “A User's Guide for the CALMET 

Meteorological Model.” Earth Tech, Concord, MA, January 2000. 

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Appendix A – Natural Background Values 

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Appendix B – Monthly f(RH) Values 

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