# 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 80246CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 iiCALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



# Table of Contents

1. INTRODUCTION

............................................................................................................................... ..................1

1.1. V ISIBILITY C ALCULATIONS .............................................................................................................................6

2. EMISSION ESTIMATES

............................................................................................................................... ......8

3. CALMET/CALPUFF MOD ELING METHODOLOGY ..................................................................................9

3.1. CALMET/CALPUFF M ODEL S ELECTION ...................................................................................................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 N on-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. 98 th 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 iiiCALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 ivCALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



# 1. Introduction

Federal law requires Best Av ailable Retrofit Technology (BAR T) for any BART-eligible source that ‘‘emits any air polluta nt which may reasonably be anti cipated to cause or contribute to any impairment of visibility” in any mandato ry Class I federal area. Pursuant to federal regulations, states have the option of exem pting a BART-eligible source from the BART requirements based on dispersion modeling demonstr ating that the source cannot reasonably be anticipated to cause or cont ribute to visibility impa irment in a Class I area.

Federal regulations implementing the BART requ irement afford states some latitude in the criteria in determining whether a BART-eligible source is subject to BART. The Division has proposed state regulations estab lishing 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 (B ART guideline), a BART-eligible source is considered to “contribute” to visibility impair ment in a Class I area if the modeled 98th percentile change in deciviews is equal to or greater than the “cont ribution threshold.” Any BART-eligible source determined to cause or cont ribute to visibility impairment in any Class I area is subject to BART. The Division ha s proposed a state regul ation establishing a

“contribution threshold” of 0.5 deciviews.

The Division will apply CALPU FF 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 thres hold of 0.5 deciviews at any Class I area. The initial phase of the BART modeling pr ocess 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 th e 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 Divisi on may approve deviations from this protocol for a specific source if the changes are acc eptable to U.S. EPA and improve model performance while retaining c onsistency with the BART gui deline. All modeling will be subject to Division review and approval.

The contribution threshold and other criteria us ed for this modeling demonstration have not been finalized and may change in the final rule adopted by the Comm ission. Therefore, the results of modeling performed with this protocol are not a final agency action. Any source that the Division determines is subj ect to BART will receive a separa te notice of the agency’s final

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 1CALMET/CALPUFF BART Protocol for Class I Fe deral 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 guidelin e 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 S OURCES “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 Cla ss I area. If you decide to ma ke 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 cont ribute to visibility impairment in at least one Class I are a. 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 indi vidual 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 discu sses several approaches that you can use to exempt sources from the BART determ ination process.

A. What Steps Do I Follow to Determine Whether A Source or Group of Sources Cause or Contribute to Visibility Im pairment for Purposes of BART?

1. How Do I Establish a Threshold? One of the first steps in determining whethe r sources cause or contribute to visibility impairment for purposes of BART is to est ablish 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 t hus be subject to BART.

Because of varying circumstances affecting different Class I areas, the appropriate threshold for determinin g 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 de termining whether a source “c ontributes” to visibility impairment should not be hi gher than 0.5 deciviews.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 2CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

In setting a threshold for “c ontribution,” you should consid er 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 particu late matter (PM) emissions in determining

whether sources cause or contribute to visibility impairment, including both PM10 and PM2.5. Consistent with the approach for iden tifying your BART-eligible sources, you do

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

As explained in section II, you must use your best judgement to de termine whether VOC or ammonia emissions are likely to have an im pact on visibility in an area. In addition, although as explained in Secti on II, you may use PM10 an indicator for particulate matter in determining whether a source is BART e ligible, in determining whether a source contributes to visibility im pairment, you should distinguis h between the fine and coarse particle components of direct particulate emissions. Although both fine and coarse particulate matter contribute to visibility im pairment, the long-range transport of fine particles is of particular concern in the form ation 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 emitte d particulate matter (i.e. PM 10 vs. PM 2.5 ).

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 de termining 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 gui dance. You may also use other reasonable approaches for analyzing the visibility impacts of an individu al source or group of sources.

Option 1: Individual Source Attribution Approach (Dispersion Modeling) You can use dispersion modeling to determine that an individual s ource cannot reasonably be anticipated to cause or cont ribute to visibility impairment in a Class I area and thus is not subject to BART. Under this option, you can analyze an individua l source’s impact on visibility as a result of its emissions of SO 2 , NO
x and direct PM emissions. Dispersion modeling cannot currently be us ed 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 October 24, 2005 3CALMET/CALPUFF BART Protocol for Class I Fe deral 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 visibili ty and should therefore be subject to BART review, as explained in section II.A.3. above.

You can use CALPUFF 7 or other appropriate model to pred ict 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 sour ce’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 th e visibility assessments addressed in today’s rule, to account for the ch emical transformation of SO 2 and NO

x .

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

model: 1. Develop a modeling protocol.

Some critical items to include in the protocol are the mete orological and terrain data that will be used, as well as the source-specific information (stack height, temperature, exit velocity, elevation, and emission rates of applic able 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 Impacts 9 for parameter settings and me teorological data inputs. You may use other settings fr om those in IWAQM, but you sh ould 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 eff ects of the source. Fo r other Class I areas in relatively close proximity to a BART-elig ible 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 lo cate 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 expect ed plume release height. If the highest modeled effects are observed at the nearest Cl ass I area, you may choose not to analyze the other Class I areas any further as add itional 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 recepto rs 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 Ag ency, EPA-454/R- 98-019, December

1998.CALMET/CALPUFF BART Protocol for Class I Fe deral 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 situati on may call for the use of two different modeling approaches for the same Class I ar ea 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 sour ce-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, y ou 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 accep ted protocol and compare the predicted visibility impacts with your threshold for “contribution.”

You should calculate daily visibility values fo r each receptor as the change in deciviews compared against natural visibility c onditions. 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 calcula tion. To determine whether a source may reasonably be anticipated to caus e 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 norma l effects than would be typical of most facilities. We recommend that States use the 24 hour average actual em ission 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 98 th percentile value from your modeling is less than your contribution threshold, then you may conclude

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

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 5CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



### 1.1. Visibility Calculations
The general theory for performing visibil ity calculations with the CALPUFF modeling system is described in se veral documents, including:

 “Interagency Workgroup on Air Qualit y Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeli ng Long Range Transport Impacts” (IWAQM, 1998)

 “Federal Land Manager’s Air Quality Re lated 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 gr eatest distance that a large object can be seen) or by the light extinction coefficien t, which is a measure of the light attenuation per unit distan ce due to scattering and abso rption by gases and particles.

Visibility is impaired when light is scattere d in and out of the line of sight and by light absorbed along the line of sight. The light extinction coefficient (b ext ) considers light extinction by scattering (b scat ) and light extinction by absorption (b abs ):

b ext = b scat + b abs

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

 light scattering due to air mol ecules = Rayleigh scattering = b rayleigh

 light scattering due to particles = b sp

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

 light absorption due to gaseous absorption = b ag
 light absorption due to particle absorption = b ap

Particle scattering, b sp , can be expressed by its components:

b sp = b SO4 + b NO3 + b OC + b SOIL + b Coarse

where:

 b SO4 = scattering coefficient due to sulfates = 3[(NH 4 ) 2 SO 4 ]f(RH)

 b NO3 = scattering coefficient due to nitrates = 3[NH 4 NO 3 ]f(RH)

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

 b SOIL = scattering coefficient due to fine particles = 1[Soil]

 b Coarse = scattering coefficient due to co arse particles = 0.6[Coarse Mass]

Particle absorption from soot is defined as:

 b ap = absorption due to elem ental carbon (soot) = 10[EC]

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

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

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 6CALMET/CALPUFF BART Protocol for Class I Fe deral 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:

b ext = b SO4 + b NO3 + b OC + b SOIL + b Coarse + b ap + b rayleigh

In this equation, the sulfate (SO4) and nitrate (NO3) components are referred to as hygroscopic components because the extin ction coefficient depends upon relative humidity. The other com ponents 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 th is protocol will provide ground level concentrations of visibility impairing po llutants. The concentration estimates from CALPUFF are used with the previously s hown equations to calc ulate the extinction

coefficient.

As described in the IWAQM Phase 2 Report, the change in visibility is compared against background conditions. The delta-deciview, Δ dv, value is calculated from the source’s contribution to extinction, b

source , and background extinction, b background , as follows:

Δ dv = 10 ln ((b background + b

source )/ b background )

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 7CALMET/CALPUFF BART Protocol for Class I Fe deral 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, sh utdown, and malfunction be used, as such emission rates could produce higher than normal effect s than would be typical of most facilities. We recommend that States use the 24 hour aver age actual emission rate from the highest emitting day of the meteorological pe riod modeled, unless this rate reflects periods start-up, shutdown, or malfunction .”

Short-term emission rates ( ≤ 24-hours) should be modeled si nce visibility impacts are calculated for a 24-hour averaging period. SO 2 , NOx, and PM10 (including condensible and

filterable direct PM10 1 ) should be modeled from all BART-e ligible units at the facility. The Division will initially use allowable emission rate s 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 emi ssion rates for subject-to-BART modeling, case-bycase procedures should be developed in cons ultation with the Divi sion. 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 calculate d 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 flexibil ity 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 avai lable, emissions rates based on averaging periods longer than 24- hours are acceptable only in cases where the mode ling shows that the source has impacts equal to or greater than th e 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), orga nic aerosols (SOA), an d sulfate (SO4). H 2 SO 4 , 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 Fe deral 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 i nput 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 nece ssary 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 guidelin e. In addition, the Division has reviewed available data to determine appropriate am monia 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 98 th percentile results, the Divisi on 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 98 th percentile results.

The Division will use this protocol for the initial subject-to-BART modeling. However, the Division’s initial modeling may be supersed ed by additional modeling performed by the Division or source operator. Subsequent mode ling 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 cons istency with the BART guideline. For example, if the source operator wants to use 2-kilometer CALMET grid cel ls instead of 4-kilometer cells and wants to include additional meteorological observations in a way that imp roves 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 di stances up to 300 kilometers. Wh ile CALPUFF will be used at source-to-receptor distances less than 50 kilomete rs 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 pr ovide more accurate results for Class I areas beyond 300km.

According to “ Interagency Workgroup on Air Qualit y Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeli ng Long Range Transport Impacts ” (IWAQM Phase 2 Report): In the context of the Phase 2 recommendation, the fo cus of the visibility analysis is on haze. These techniques are applicable in the range of thirty to fifty kilo meters and beyond from a

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 9CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

source. At source-receptor distanc es 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 meteor ological fields are complex and the use of CALPUFF appears to be appropria te 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 sour ce. If there were issu es regarding the 50 km distance, PLUVUEII would be an appropriate m odel to consider for source-to-receptor

distances less than 50 kilometers. If a PLUVUE II 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 (v ersion 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 tran sport 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 transpor t, 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 Mode l (Douglas, S. and R. Kessler, 1988). It has been significantly enhanced by Earth Tec h, Inc (Scire, 2000). For this particular study, the model uses a Lambert Conformal Projecti on coordinate system to account for the

Earth's curvature.

CALMET uses a two-step approach to calculate wind fields. In the fi rst step, an initialguess wind field is adjusted for slope flows and terrain blocki ng effects, for example, to produce a Step 1 wind field. In the second ste p, an objective analysis is performed to

introduce observationa l data into the Step 1 wind field.

In this application, the initial guess wind fields are based on 36-kilometer MM5 2
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 Pub lic Service Company (PSCo) Comanche Unit 3 PSD permit application. Alpi ne Geophysics extracted the MM5 data into

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 10 2 Fifth-Generation NCAR/Pen n State Mesoscale Model.CALMET/CALPUFF BART Protocol for Class I Fe deral 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 accep table 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 representati ve meteorological fields become available, they may be considered for any future modeling. However, before accepting data from other meteorological models, the Division may requi re 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 th e 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 analys is, five years of agency-approved mesoscale meteorological data were not readily availabl e 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 se ts 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 HI LL, the Standard Pa rallels 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 list ed 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 Co lorado with the excep tion 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 Cla ss I areas. That is, preliminary modeling indicates that impacts at Mesa Ve rde will not be the controlling 98 th percentile values for this analysis. The domain does not incl ude Class I areas in any nearby states

because the 98 th 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 professi onal judgment regarding prevailing air pollutant transport regimes. The CALMET do main 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 w ithin 300 kilometers of every BART-eligible

source in Colorado are in cluded 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 examin ed vector plots of selected periods within the CALMET output for validation of the wind fields with the CalDESK (Environmodeling Ltda.) program (CH2M HI LL, 2005).” The Division replicated the CALMET modeling and performed additional re view of the meteorological fields

with the Lakes Environmental CALPUFF View software package. In general, the meteorological fields were found to be reasonable given th e 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 meteorologi cal 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 el evations 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 w ith 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 an alyses of soundings for summer ozone events. The analysis suggests mixing he ights in Denver are often well above the

CALMET default value of 3000 meters duri ng 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, incl uding 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 exceed ed at Rocky Mountain National Park, suggests the mixing height was probabl y 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 mixi ng 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 f eet (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 de termine the relative weight given to the vertically extrapolated surface meteorol ogical observations and upper air soundings. The initial guess field is computed with an inverse distance wei ghting of the surface and upper air data. It can be modified by th e 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 de fault 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 ski pped if the surf ace station is close to the upper air stati on. The variable RMIN2 sets the distance between an upper

air station and a surface stati on that must exceeded in order for the extrapolation to take place. RMIN2 has been set to the de fault 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 ai r stations is ignored. When IEXTRP= ± 4, the van Ulden and Holtslag wind extrapolat ion method is used. The method uses similarity theory and observed data to ex tend 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 dete rmine the influence of observations in the Step 1 wind field. R1 controls weighti ng 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 firs t 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 distan ce 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 st ations shown in Figure 6 were used, including Alamosa (ALS), Colorado Springs (CYS), Denver (DEN), Eagl e (EGE), Limon (LIC), Pueblo (PUB), Trinidad (TAD), Cheyenne (CYS), Laramie (LAR), Rocky Mountain NP (ROM), and Gothic (GTH). Any future modeling an alyses should consider additional surface

stations.

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Figure 6. Surface meteorological stations.

3.1.1.10. CALMET U pper Air Stations Two upper air stations were include d: Grand Junction and Denver.

3.1.1.11. CALMET Preci pitation Stations CH2M HILL obtained precipitation data fr om the National Climatic Data Center (NCDC). All available data in fixed-le ngth, TD-3240 format were ordered for the modeling domain. CH2M HILL processe d the data with the PXTRACT and PMERGE processors. Stations with incomple te or poor quality data for a given year

were excluded. The number of stations us ed 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 s hould be used, unless specified otherwise in this protocol. If non-default options or values are used, the reason s hould 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 Cla ss 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 elev en 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 Cl ass 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 remaini ng 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 si ngle 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 r ecommended 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 identi cal 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 chemi cal reaction mechanism (MCHEM=1) is

used for the conversion of SO 2 to sulfate (SO4) and NOx to nitrate (NO3). Refer to

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

In the MESOPUFF II mechanism, the amm onia background concentration affects the equilibrium between nitric acid, ammonia, and ammonium nitrate. The equilibrium

constant for the reaction is a non-linear func tion of temperature and relative humidity (Scire, 2000). Unlike sulfate, the calculated nitrate concentration is limited by the amount of available ammonia, which is pr eferentially sc avenged 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 SO 2 to sulfate, the aqueous phase me thod, which is important when the plume interacts with clouds and fog, can significantly unde restimate sulfate formation. In this report, as recomm ended by the IWAQM Phase 2 report, the “nighttime SO 2 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 HNO 3 formation rate (RNITE3)” is set to 2.0 percent per hour.

According to the 1996 “Mt. Zirkel Wilder ness 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

SO 2 to sulfate and NO X

to total nitrate. There are two options for specifying these transformation rates:

Option 1: An internal calculation of ra tes based on local values for several controlling variables (e.g., solar radiation, background ozone, relative humidity, and plume NO X ) as used in MESOPUFF-II. The parametric tr ansformation 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 Assessm ent study. They found that it provided physically plausible responses to many of th e controlling environmental parameters. However, the mechanism had no temperature de pendence, which is an important factor in the Rocky Mountain region where there are wide variations in temperature. Furthermore, the MESOPUFF-II transform ation scheme was based on box model simulations for conditions more representative of the East ern U.S. than of the Rocky

Mountains.

The largest deficiency in the MESOPUFF-II chemical transforma tion algorithm is the lack of explicit treatment for in-cloud (aqueous-phase) enhanced oxidation of SO 2 to sulfate. The MESOPUFF-II chemical trans formation algorithm includes a surrogate reaction rate to account for aqueous-phase oxidation of SO2 to sulfate as follows:

K aq = 3 × 10 -8 × RH 4 (%/hr) (B.2-1)

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

SO 2 oxidation rate will be 3% per hour. Me asurements in generating station plumes suggest spatially- and temporally-integrated SO 2 oxidation rates due to oxidants in

clouds to be 10 times this value.

Another issue is the amount of ammonia av ailable for nitrate chemistry. According to a paper by EarthTech (Escoffier-Czaja and Scire, 2002),

“In the CALPUFF model, total nitrate (T NO3 = HNO3 + NO3) is partitioned into each species according to the equilibri um 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 forma tion 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 discre te 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 acc ount for the total amount of sulfate scavenging ammonia from all sources (both project and ba ckground sources) and the total amount of TNO3 competing for the remaining ammoni a. In POSTUTIL, ammonia availability is computed based on receptor concentra tions of total sulfa te and TNO3, not on a puff-by-puff basis.”

Ammonia-limiting methods will be used re partitioning 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. Specifical ly, the use of the MNIRATE=1 option in POSTUTIL is acceptable. At this time , other ammonia-limiting methods, including iterative techniques that use observati onal data to resolve backward the thermodynamic equilibrium equation between NO3/HNO 3 for each hour to minimize available ammonia, are not acceptable. Generally, for regulatory CALPUFF modeling in Colorado, techniques that assu me the atmosphere is always ammonia poor are not acceptable, particul arly in eastern Colorado.

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3.1.2.6. Chemical Mechanism – Ammonia Sensitivity Tests

To better understand the re sponse of the modeling system to background ammonia when a single point source with significant emissions of SO 2 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, SO 2 , NOx, and filterable PM10 emissi ons were modeled. The ammonia background value was varied from 0 to 100 ppb. In the northeast Colorado test case,

the SO 2 emission rate is about 3 times highe r than the NOx emission rate. In the northwest Colorado test case, the modele d NOx emission rate is about 4.4 times higher than the SO 2 rate.

In both cases, when the background amm onia 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 SO 2 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 SO 2 emission rates relative to NOx and the way sulfate is produced in the MESOPUFF II chemical mechanism. Visibility impact s 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 impact s (delta-deciview) to ammonia backgrounds from 0 ppb to 100 ppb from a source with high SO 2 emissions relative to NOx.

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

relative contribution of nitr ate 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 2 nd 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 a nd 72.7% for the 100 ppb ammonia case.

For the 8 th high delta-deciview value, the contri bution 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 SO 2 emission rate, the change in visibility (delta-deciview) is not sensitive to the background a mmonia 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 ammoni a level is less than 1.0 ppb.

Figure 14. Sensitivity of CALPUFF visibility impact s (delta-deciview) to ammonia backgrounds from 0 ppb to 100 ppb from a source with high NOx

emissions relative to SO 2 .

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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. Ho wever, 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 extrapolat ing the results of these tests to other CALPUFF applications.

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Since the MESOPUFF II chemical mechanis m 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 am monia rich. Thus, setting a domain-wide background is problematic. As discussed in the previous section, when modeling a single large source with high SO 2 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 particul ate nitrate is dependent on the ambient concentration of ammonia, which preferentially reacts with sulfate. The ambient ammonia concentration is an input to the model. Accura te specification of this parameter is critical to the accu rate estimation of particulate nitrate concentrations. Based on a review of ava ilable data, Langford et al. (1992) suggest that typical (within a factor of 2) bac kground values of ammonia are: 10 ppb for grasslands, 0.5 ppb for forest, and 1 ppb for ar id lands at 20 C. Langford et al. (1992) provide strong evidence that backg round levels of ammonia show strong dependence with ambient temper ature (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 leve ls provided above, unless specific data are available for the m odeling domain that would disc redit the values cited. It should be noted, however, that in areas wh ere 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 ne utralized by available ammonium and is present in the form of a mmonium sulfate.... The Northern Front Range is ammonia rich. There was sufficient ammonia, on mo st days during winter, to completely

neutralize available nitr ic acid (NFRAQS, 1998).”

For northeast Colorado, a background ammonia concentration of 30.4 μg/m 3 (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 met hod at the Brighton and Welby sites and with the filter-pack method (i.e., impregnated cellul ose-fiber filters behind Teflon-membrane filters) at the other s ites. Average ammonia concentrations

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were 30.4 ± 53.4 μg/m3 at the core site s 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 we re 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 are a, site-to-site variations were more pronounced than seasonal or diurnal variat ions. 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 hi gher 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. Twen ty-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 re asonable, 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 concentr ations for ammonia were assumed

to be 0.5 μ g/m 3 and 30 ppb

v for ozone, consistent with me asured values at the Hayden

VOR site .” An ammonia co ncentration of 0.5 μ g/m 3 is about 0.7 ppb.

In the CALPUFF modeling section of the Mt Zirkel Study report, “The CALPUFF default value for backgr ound ammonia concentrations of 10 ppb was also considered far too high as a re presentative area-average. Measurements from the Buffalo Pass and Gilpin Cre ek sites were used to adjust ammonia concentration to episod e and site-mean values.”

Based on a review of CALUFF files used for the Mt. Zirkel Study, for the August simulations, the assumed ammonia b ackground (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 s ection, for sources lo cated in northeast Colorado and along the South Platte Ri ver, a domain-wide ammonia background value of 44 ppb is used. For sources lo cated 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 prov iding the ozone background data: (1) a single, typical background value appropria te for the modeling region, or (2) hourly ozone data from one or more ozone mon itoring stations. The second and preferred option requires the creation of the OZON E.DAT file containing the necessary data. For the Demonstration Assessment, th e domain was large (700 km by 1000 km) such that the second option was necessa ry. The IWAQM does not anticipate such large domains as being the typical applica tion. Rather, it is anticipated that the more typical application will involve dom ains of order 400 km by 400 km or smaller. But even for smaller domains, th e ability to provide at least monthly background values of ozone is deemed desirable. The problem in developing time (and perhaps spatial) varying backgr ound ozone values is having access to representative background ozone data. Oz one 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 expe rienced by plumes undergoing long range

transport.

In this study, “ CH2M HILL obtained hourly ozone dat a from the following stations located within the modeling domain fo r 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 Co llins 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) .”

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 30CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

3.1.3. CALPOST Settings and Visibility Post-Processing The CALPUFF results will be post-processe d with a modified version of CALPOST (version 5.51_CO_v3, level: 030709), POSTUT IL (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 fo r 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 va lues) for the individual species that contribute to the overall delta-deciview value, but thes e values are not used for the subject-to-BART modeling. Otherwise, the CALPOS T code modification consists of a “write” statement and supporting code. It outputs all da ily delta-deciview values for ev ery receptor to a file called

“deciview24.dat.” The 98 th percentile values are com puted from “deciview24.dat” by a separate FORTRAN processor (BART98_v3) writ ten by the Division spec ifically for this analysis. The Division’s proce ssors 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 emis sions is expected to be relatively small compared to visibility impairment caused by SO 2 and NOx emissions. However, if modeled impacts are below the contribution threshold, condensible and filterable PM10 emissions should be quantified and speciated. Alternatively, a sensit ivity 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), elem ental 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 CA LPOST (e.g., IWAQM growth curve and the 1996 IMPROVE curve) are not used wh en MVISBK is equal to 6.

The natural background is based on the 20 percen t 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 estimate d baseline is likely to be reasonably

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

The method for estimating natural ba ckground is presented in section 3.1.3.1 . Specifically, for hygroscopic components, BKSO4 in CALPOS T should be set to 0.0893 for all months. For non-hygroscopic components, BKSOIL should be set to 1.620 for all months. The

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 31CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

BKSO4 and BKSOIL values have been com puted 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 (equivale nt to [BKSOIL]) portions of natural background for the best 20% vi sibility days (Best Days) at all Class I areas in Colorado's BART modeling. These concen trations, [BKSO4] and [BKSOIL], are used in CALPOST with monthly relative humidity adjustment factors (f(RH)) to determine monthly natural background visibi lity 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 Cla ss I area using the area's annual f(RH) and average natural concentrations base d on the area's geographic location (east versus west). Annual average back ground 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 con centrations are provided for determining extinction for the 20% best visibility days.

For background extinction computati on methods 2, 3, and 6 in CALPOST, background extinction is calcula ted with user-supplied m onthly concentrations of SO4, NO3, PM coarse, organic carbon, soil, and elemental carbon species. In practice, concentrations for only 2 speci es, SO4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the CALPOST input file to represent hygroscopic and nonhygroscopic portions of backgr ound extinction, respectively.

To determine background extinction for the BART analysis with CALPOST, average natural concentrations that repr esent average natural background visibility for the best 20% days need to be determined.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 32CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

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 CA LPOST, 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 aver age background (dv) based on monthly background extinction (1/Mm) for each Cla ss I area in Colorado's BART modeling using the following equations:

1. Monthly background extinction in 1/Mm (bext month ) = 3[BKSO4] best20 f(RH) + [BKSOIL] best20 + Rayleigh 2. Annual average background extinction in 1/Mm (bext annual_ave ) = (bext Jan + bext Feb + ... + bext Dec )/12

3. Calculated Best Da ys in dv = 10ln(bext annual_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-hygrosco pic ([BKSOIL]) values determined yielded the lowest sum of the absolute di fferences between the published Best Days (dv) and calculated Best Days (dv) for all Class I areas in the analysis:



## ( ) ( ) 

## ∑

= 11

1n n n DaysBest calculated - DaysBest published Minimize

where: n = number of Cl ass 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 decivi ews or less of the annual 20% best visibility days back ground values recommended by EPA. For CALPOST, the hygroscopic component of ex tinction is divided by 3 (the extinction coefficient of sulfate and nitrate) a nd input as BKSO4 (i.e., BKSO4 = 0.268/3 = 0.0893). The non-hygroscopic component is us ed directly (i.e., BKSOIL = 1.620).

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 33CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

Figure 17. Spreadsheet showing the "hygro (3[BKSO4])" (0.268)and "nonhygro ([BKSOIL])" (1.620) values calc ulated in Microsoft Excel using the "solver add-in" tool for optimi zation and equation solving.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 34CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

3.1.3.2. CALPOST and POSTUTIL Parameters Two post-processing examples are provi ded. In example #1, fine particulate emissions are speciated into PMF, PMC, EC, SOA, and SO4 and explicitly included as species in CALPUFF. Emission rates fo r each species are included in CALPUFF. Figure 18 summarizes some of the key CALP OST 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 parame ters (example #1 setup).

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 35CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

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 c oncentration, 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, HN O3, 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 pa rameters for cases with nitrate partitioning and speciated PM10 concentrat ions (example #1 setup).

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 36CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

In example #2, PM10 is included as a speci es 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 emi ssions 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 concentra tions 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 simulati ons where PM10 is divided into SOIL and EC species (example #2 setup).

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 37CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

In the second POSTUTIL setup for exampl e #2, POSTUTIL is used to compute the partition for the total concentration fiel ds with MNITRATE=1 and the appropriate ammonia background concentration. The ammonia background concentration, BCKNH3, in POSTUTIL is the same as th e 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 simu lations where ammonia limiting is performed using the output file generated from the POSTUTIL setup in Figure

20 (example #2 setup).

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 38CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

3.1.3.3. 98 th Percentile Methods According the BART guideline:

... you should compare your “contribution” th reshold against the 98t h percentile of values. If the 98 th percentile value from your modeli ng is less than your contribution threshold, then you may conclude that the source does not contri bute to visibility impairment and is not subject to BART. (70 FR 39162)

The BART guideline does not contain a specific method for calculating the “98 th
percentile value” and CALPOST vers ion 5.51 does not generate a 98 th percentile delta-deciview value. Consequently, the Division developed a FORTRAN program (BART98_v3) to compute 98 th percentile results. The program implements several methods because, at the time the code wa s written, U.S. EPA had not yet specified an explicit method for determining the 98 th percentile value.

The U.S.EPA recommends using the 98 th percentile value from the distribution of values containing the highest modeled de lta-deciview value for each day of the simulation from all modeled receptors at a given Class I area. The 98 th 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 8 th high values

 The 22 nd highest value for the 3-year modeling period The highest value from all of the abov e methods should be compared to the

contribution threshold. The contribution thre shold has an implied level of precision equal to the level of precision reporte d from CALPOST. Specifically, the 98 th
percentile results should be re ported to three decimal places.

The Division’s processor BART98_v3 calculates the 98 th percentile value with the method recommended by U.S. EPA. The Divisi on refers to the method as the “dayspecific method” or “method 1.” The first st ep 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 unranke d format, the Division’s processor BART98_v3 outputs all daily delta-decivi ew 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 98 th percentile value from the

distribution of ranked mode led daily maximum values, irrespective of receptor location. For example, for a 365-day simulation, the 98 th percentile value would be

the 8 th highest modeled delta-deciview value from the list of ra nked delta-deciview values. That is, the top 7 days are igno red, even though the values being ignored may be at different receptors. Similarly, for a 3-year period, the 98 th percentile

would be the 22 nd highest modeled delta-deciview value.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 39CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

The processor BART98_v3 also generates 98 th percentile values using the “receptorspecific method” or “method 2.” This method, which calculates 98 th 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 “8 th
high” (for one year) and “22 nd 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 “n th high” value from the distribution of data. The 8 th high

and 22 nd 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 St andards for PM2.5” – for determining 98 th
percentile values for PM2.5 monitoring. Thus , the Appendix N method is used in the processor. For the exact algorithm, s ee Appendix N, the BART98_v3 source code, or the BART98_v3 “readme” file.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 40CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



# 4. Results

The CALPUFF modeling results will include eleven of the twel ve 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 98 th percentile values for this analysis.

The results for source-to-receptor distances be yond 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-t o-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 a reas within 50 and 300 kilometers of the BART-eligible source.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 41CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

The results section of the repor t should include a table like Table 1 and a figure like Figure 23

that show the 98 th percentile daily delta-deciview values for each Class I area in the modeling

domain.

The 98 th percentile delta-deciview value s hould be determined several ways:

 The 8th highest value for each year modeled

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

 The 22 nd highest value for the 3-year modeling period The highest value from the three methods a bove should be compared to the contribution threshold. The contribution threshold has an imp lied level of precision equal to the level of precision reported from CALP OST. Specifically, the 98 th percentile results should be reported to three decimal places.

Table 1. Example table showing maximum 98 th percentile value, 98 th 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 To p s W A

Rawah W A

Mt Zirkel W A

Weminuche W A

Rock y Mountain NP
Maroon Bells-Snowmass W A
La Garita W A
Great Sand Dunes NP

West Elk W A

Ea g les Nest W A
Black Can y on 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

Modelin g Period Class I federal area 98th Percentile Daily Change in Visibility from BART-Eligible Source Compared Against Natural Background Conditions

Number of Days Impact >0.5dv (1996, 2001, 2002)

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 42CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis

Figure 23. Example graph comparing 98 th percentile daily change in visibility values (deltadeciviews). The highest value should be compared to the contribution threshold of 0.5

deciviews.

Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 43CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



# 5. References

CH2M HILL. “Application for Permit to Construct Unit 3, Comanche Generating Station, Pueblo, Colorado.” Submitted by Public Servi ce Company of Colorado, Prepared by CH2M HILL, January 2005.

Escoffier-Czaja, Christelle and J. Scire, 2002. “The Effects of Ammoni a Limitation on Nitrate Aerosol Formation and Visibility Impacts in Class I Areas.” Earth Tech, Inc., Extended

abstract. 12th Joint Conference on the A pplications of Air Polluti on 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 Relate d 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 Re gional Haze Rule.” U.S. EPA, EPA-454/B-03- 004. September 2003.

“Interagency Workgroup on Air Quality M odeling (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 Attrib ution Study of Visibility Impairment. Volume II: Results of Data Analysis and Modeling - Final Report.” Prep ared by John G. Watson, et al, Prepared for Colorado Department of Public Health and Environment, 1996.

“Northern Front Range Air Qu ality Study Final Report.” Pr epared for Colorado State University, Prepared by John G. Watson, Eric Fujita, Judith C. C how Barbara Zielinska (Desert Research Institute), L. Willard Richards (Sonoma Technology, Inc.), William Neff (NOAA), David Dietrich (Air Reso urce 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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Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 52CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis



# Appendix B – Monthly f(RH) Values

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Colorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services Program October 24, 2005 54CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source AttributionVisibility Impairment Modeling AnalysisColorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services ProgramOctober 24, 2005 55CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source AttributionVisibility Impairment Modeling AnalysisColorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services ProgramOctober 24, 2005 56CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source AttributionVisibility Impairment Modeling AnalysisColorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services ProgramOctober 24, 2005 57CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source AttributionVisibility Impairment Modeling AnalysisColorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services ProgramOctober 24, 2005 58CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source AttributionVisibility Impairment Modeling AnalysisColorado Department of Public Health & Environment / Air Pollution Control Division / Technical Services ProgramOctober 24, 2005 59