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6.0 CALPUFF SCREENING APPLICATIONS 

The objective of the CALPUFF screening approach is to efficiently and conservatively 

determine whether a source can be exempted from BART controls or whether source-specific modeling 

is needed. The screening method described in this chapter will also help identify specific Class I areas 

that might be affected by the source. Should source-specific modeling become necessary, this 

information will assist the state or source in tailoring the analysis domain and modeling procedures to 

focus on the area(s) of greatest likely interest. Due to the nonlinearities in the physical and chemical 

processes governing visibility impairment and the variability in wind and dispersion transport patterns, 

simple notions about source-receptor relationships (e.g., the closest Class I area will produce the 

controlling visibility impacts) may not be universally reliable. So the screening analysis may offer 

some insight here. 

CALPUFF screening modeling should be performed consistent with the procedures set forth in 

this chapter, adapted as necessary to the source or sources of interest. A standard set of default 

meteorological, air quality and dispersion conditions are assumed that, for the most part, are consistent 

with the IWAQM (1998) and FLAG (2000) recommendations. For many sources in the CENRAP 

region, the screening approach may be sufficient to show whether it is a contributor to visibility 

problems in a particular Class I area. To support screening applications of CALPUFF, model-ready 

meteorological data sets have been developed for three CENRAP sub-regional domains (Figures 5-1 

through 5-4). These data sets, described in Chapter 5, expedite the screening analysis and provide for 

consistency of application across the CENRAP region. 

The CALPUFF screening methodology possesses a higher degree of conservatism (i.e., 

systematic tendency to over-predict visibility impacts) compared with the source-specific methodology 

in Chapter 7. For large sources that will clearly exceed the screening thresholds, the screening step 

may be skipped as the analysis proceeds directly to source-specific modeling. 

6.1 Methodology 

The screening methodology recommended for sources in the CENRAP region uses the 

CALPUFF model with three years of meteorological data and the standard compliment of model 

algorithms invoked. To ensure that no sources pass the screening test when they should fail, the 

simple approach, by its nature, must be the most conservative of all the conditions likely to be 

examined for the source in question. For example, many factors influence the contribution of a source 

to the Class I area other than distance. The frequency of winds transporting the pollutants toward the 

Class I area may often be important to include for a reliable screening analysis. Also, a more distant 

Class I area downwind in the predominant wind direction from a source may receive a higher visibility 

impact than a closer Class I area that is infrequently downwind of the source. Further, correlations 

between winds from certain directions and meteorological conditions may be conducive for higher 

visibility impacts. Such effects and relationships are addressed in the screening approach. It is 

designed to ensure conservatism by comparing the maximum visibility impact with the 0.5 dv 

threshold in making a determination of no contribution to visibility impairment and also by using 

conservative model settings. 

The impact of the source or sources is calculated from the daily visibility values for each 

receptor by determining the change in deciviews compared against natural visibility conditions. EPA’s 

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

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03-005 (September 2003) lists recommended natural visibility conditions. To determine whether a 

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

impacts predicted by CALPUFF are compared against the pertinent natural visibility background and 

the threshold that has been selected. The steps in the screening approach are outline below. It is 

described diagrammatically in Figure 1-1 in the introductory chapter. 

6.2 Protocol Development and Negotiation 

The first step is the development and negotiation of a modeling protocol with the state and 

FLM. An outline of a typical protocol is presented in Table 6-1, containing the basic elements of such 

a document. Portions of these CENRAP BART Modeling Guidelines may be adopted or referenced 

directly in a specific screening protocol as appropriate. 

6.3 CALMET Model Configuration and Application 

 

As discussed in Chapter 5, the model configurations used to generate the common CALMET 

meteorological files for visibility screening of BART eligible sources followed the IWAQM 

recommendations (EPA, 1998, Appendix A), except as noted. For CALPUFF screening assessments, 

there is no need to compile CALMET inputs, run the model or process the outputs. As discussed in 

Chapter 7, for source-specific modeling, these activities with CALMET may indeed be appropriate.

6.4 CALPUFF Model Configuration and Application 

6.4.1 Model Codes 

For screening applications, the latest ‘beta’ versions of the CALMET/CALPUFF modeling 

system should be used as given in Table 6-2. Note that these are not the EPA guideline codes but 

rather updated versions containing recent (as of this writing) science improvements and bug fixes. 

(The current guideline CALPUFF code is version 5.7, level 030402). This substitution results from 

EPA phasing out the use of the legacy Pasquill-Gifford (P-G) dispersion parameters with the 

introduction of AERMOD as a new guideline model. CALPUFF employs the AERMOD turbulencebased dispersion coefficients and probability density function (pdf) dispersion methods scheme instead 

of P-G. 

The appropriate model codes may be downloaded from www.src.com or purchased with the 

latest graphical user interface (GUI) from the model developer. The sequence of model processors 

listed in Table 6-2 corresponds to the order in which the programs are typically run. 

6.4.2 Domain Definition 

Three sub-regional modeling domains and associated CALMET meteorological data bases have 

been developed for CENRAP and are available to the states and source operators for performing 

CALPUFF screening analyses for any Class I areas within the domain. The modeling data sets cover 

three contiguous years (2001, 2002, and 2003) and are resolved to a 6 km horizontal resolution grid. 

Details of the modeling domains and the meteorological data bases for 2001, 2002, and 2003 were 

discussed in Chapter 5. 
 

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Receptor Network and Class I Receptors. Experience with the CALPUFF modeling system has shown 

that sizeable emissions sources can have discernible impacts beyond 300 km. Given CALPUFF's 

propensity towards overprediction, one could easily expect CALPUFF to predict that a source causes 

or contributes to visibility impairment at Class I areas greater than 300 km. As discussed in Chapter 8, 

recent model inter comparisons between CALPUFF and full science models such as CAMx and 

CMAQ (Morris et al., 2003) amply demonstrate that CALPUFF substantially over predicts 

concentrations of visibility-reducing fine particulates under various real-world conditions at the larger 

downwind distances when compared to one-atmosphere models. Thus, a dilemma arises in that 

experience with CALPUFF indicates a tendency to overpredict at great downwind distances yet 

sources having a modeled effect beyond, say, 300km should not necessarily be neglected because there 

may be some validity to a particular CALPUFF model simulation of far downwind impacts. 

There are a significant number of BART eligible sources in the CENRAP region with no Class 

I areas within 300 km. Additionally, numerous BART sources are located no closer than 500-600 km 

to their nearest Class I area. The science-based approach to this dilemma is to use a regional model to 

address far downwind impacts. However, for most screening analyses, even out to 500-600km, States 

and source operators will likely find it necessary to employ CALPUFF notwithstanding the 

uncertainties associated with its bias at large transport scales. If CALPUFF is viewed to be a suitable 

model for use over such distances (say 300 – 600 km) States may wish to consider the use of puff 

splitting despite the lack of quantitative measurements demonstrating that puff splitting actually 

reduces CALPUFF model overprediction at large scales. Implementation of puff splitting, coupled 

with fine grid resolution (~ 12 km or less) might be considered as an approach for distant Class I 

receptors, but this strategy will likely lead to computer run times approaching and even exceeding 

those of one-atmosphere models. 

In sum, discrete receptor coordinate data for all Class I federal areas within 600 km of the 

source should be developed, preferably using the National Park Service (NPS) Convert Class I Areas 

(NCC) computer program. Whether all Class I receptors need to be modeled is an issue to be resolved 

in the modeling protocol process in cooperation with the regulatory agencies and land managers. 

Receptor elevations provided by the NPS conversion program should be used. All receptors should be 

included in a single CALPUFF simulation. (http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm).

6.4.3 Model Set-Up 

Following the procedures set forth in the protocol, the agreed to version of CALPUFF is set up 

on one or more of the three CENRAP domains. The screening analysis should use a CALPUFF 

computational domain that includes all Class I areas within 300 km of a source. These Class I areas 

are specified in the CALPUFF control file for analysis. States could decide to require a different value 

for the maximum distance threshold for the CALPUFF domain, depending on the locations of the 

Class I areas in their states and other factors such as meteorological conditions and the magnitudes of 

the emissions from BART-eligible sources. The regional CALMET domain will be unchanged by 

these adjustments. 

At the election of the state or source operator, the screening approach may be designed to 

significantly reduce the CALPUFF simulation time by restricting the computational domain size to 

include only areas where significant impacts are feasible rather than the entire regional domain. 

CALPUFF allows its computational domain to be specified as a subset of the CALMET 

meteorological domain by settings within the CALPUFF input file. The advantage of selecting a 
 

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smaller CALPUFF computational domain is that the model run time is proportional to the number and 

residence time of the puffs on the domain (and other factors such as the number of receptors and the 

internal time step computed by the model). This technique, if used, must be fully described in the 

modeling protocol beforehand. 

6.4.4 Emissions Input Development 

 

Stack Parameters. Stack parameters required for modeling BART-eligible units include: height of the 

stack opening from ground, inside diameter, flow-rate, exit gas temperature, ground elevation of the 

stack base, and location coordinates of the stack. Because the BART modeling focuses on mesoscale 

transport to Class I areas, other source term parameters (needed to calculate localized impacts) such as 

building heights and widths for calculating downwash may not be needed.

Emission Rates. Emission rates for CALPUFF screening analyses follow EPA’s BART guidance and 

recent staff recommendations. Source terms in the modeling should be based on emissions during 

periods of high capacity utilization during normal operating conditions. Specifically, the 24-hour 

average actual emission rate with normal operations from the highest emitting day of the year should 

be modeled. Excluded from consideration are days where start-up, shutdown or malfunctions occurred 

unless these activities are regular, frequently occurring components of the source’s operation cycle. 

(Note that while potential emissions are used to determine if a source is BART-eligible, 24-hour 

average maximum emissions are used for CALPUFF screening modeling purposes). Pollutants 

considered include SO2, H2SO4, NOx and PM10. Excluded from the modeling are pollutants with 

emissions less than de minimis levels (40 tons per year for SO2 and NOx and 15 tons per year for 

PM10). 

Identification of the maximum 24-hr actual emissions rates should be made for the most recent 

3 or 5 years, according to the following prioritization: 

> Continuous Emissions Monitoring (CEM) data; 

> Facility emissions tests; 

> Emissions factors; 

> Permit limits; or lastly, 

> Potential to emit. 

In cases where a unit is permitted to burn more than one fuel, the fuel resulting in the highest 24-hr 

emission rates should be used for the modeling. Caution is urged when estimating emissions rates in 

cases where abnormal and infrequent fuel usage overstate the potential for visibility improvement. 

Typically BART controls are expected to include the utilization of cleaner fuels (such as those burned 

by a facility in the more recent years). Thus emissions rates should solely be based upon data from the 

most recent years, in order to accurately characterize potential visibility improvements from BART 

controls. 

Emissions Speciation. Definition of the PM speciation profile for the highest 24-hour average actual 

emissions may prove particularly challenging but reliable estimates are necessary given the widely 

varying effects of different types of particulate matter on visibility. For example, the extinction 

coefficient ranges in value from 0.3 to 0.6 m2

/g for coarse particles, to 1.0 to 1.25 m2/g for fine 

inorganic particulate matter, to 1.5 to 4.0 m2

/g for sulfate and nitrate precursors, to 1.8 to 4.7 m2/g for 

organic aerosols, and up to 8-12 m2

/g for elemental carbon (Tombach and McDonald, 2003). Thus, 
 

6-5

generalized, conservative, or arbitrary assignments of particulate emissions to different pollutant 

categories can have a considerable influence on modeled visibility impacts attributable to a single 

facility. Currently, data are quite limited on appropriate speciation of organic/inorganic and 

filterable/condensable emissions by source category. While speciation profiles are available for gasand oil-fired combustion turbines and coal combustion processes, detailed profiles for the full range of 

BART-eligible sources is lacking. 

In practice, except in cases where facilities operate continuous emission monitors on all 

affected equipment, there is likely to be limited information of regarding actual emissions on the 

requisite time resolution (24-hour average), much less speciation profiles for PM species. Thus it is 

particularly important for state/local agencies to work with BART-eligible sources to collect this 

information. Where available, facility-specific particulate matter speciation measurements may be 

used to assign PM10 to its components (e.g., assume primary sulfate is a fraction of SO2 emissions, 

primary sulfate is a fraction of PM10 emissions, all PM10 emissions are PM2.5). Absent this 

information, default speciation profiles may be used as agreed upon in the protocol. 

Condensable Emissions. Condensable emissions are considered primary fine particulate matter. For 

screening assessments all condensable mass should be assigned to the < 0.625 μm category unless the 

source operator has evidence of a different value based on emissions testing or other reliable 

information. This maintains conservatism in the analysis where there may be uncertainty regarding the 

exact size of condensable PM mass. If actual source emissions data are not available, the modeling 

should be based on permit limits. If source-specific size categories are not available, then AP-42 

factors may be used for sources where AP-42 factors are available. For sources where AP-42 factors 

are not available, assumptions for partitioning should be resolved with the reviewing agencies during 

the protocol development process. 

Size Classification of Primary PM Emissions. PM emissions should be segregated by size category. 

NPS has developed PM10 emissions speciation for certain source groups and this information can be 

used where appropriate. Modelers using information from AP-42 or other ‘reference’ documents must 

remember that the PM size classification should be applied only to the “filterable” PM mass. 

Furthermore, when modeling PM size classes, an appropriate “mass mean diameter” must be used that 

is within the specified particle size range. Use of a mass mean diameter equal to the top of the range is 

inappropriate since it will overestimate PM deposition and possibly underestimate PM concentrations 

and visibility impacts. 

6.4.5 CALPUFF Model Configuration 

This section discusses the procedures and input assumptions that States or source operators 

should follow in applying the CALPUFF model in a screening mode to assess whether a particular 

source is subject to BART (i.e., BART exemption modeling). 

CALPUFF Model Options. The model options, parameter settings, and ‘switches’ for exercising 

CALPUFF in the BART screening mode are discussed below. Appendix B contains tables that list the 

recommended screening configurations for CENRAP BART modeling. The tables also identify the 

default recommendations from the IWAQM Phase 2 Report (EPA, 1998). 

Visibility Modeling Domain. The CALPUFF domain should be configured to include the source and 

all Class I areas within 300 km. An additional 50 km buffer zone should be established in each 
 

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cardinal direction from the source. For screening applications, CENRAP recommends CALPUFF for 

all source-receptor distances unless otherwise negotiated in the modeling protocol.

Dispersion

Building Downwash. In the unlikely case where a source is immediately upwind of a Class I 

area (say, within 20 km), the IWAQM-recommended algorithms for sources subject to downwash 

should be used. For consistency across all CENRAP regional modeling studies, the CALPUFF 

building downwash algorithms should be used for all source-receptor distances if building data are 

available. 

 Puff Dispersion. The EPA (1998) guidance for plume dispersion modeling should be followed, 

including the use of the Pasquill-Gifford curves. The use of turbulence-based dispersion coefficients 

and probability density function dispersion methods have not been extensively evaluated for use in 

long range transport evaluations. Until such an evaluation has been completed and documented, the 

use of turbulence-based coefficients is outside the current CENRAP guidance and will require a caseby-case determination by the appropriate regulatory reviewing authorities. 

Puff Representation. Use the default integrated puff sampling methodology in CALPUFF. 

Puff Splitting. There is no quantitative evidence that the horizontal and vertical puff-splitting 

algorithms in CALPUFF yield improved accuracy and precision in model estimates of inert or linearly 

reactive pollutants although conceptually the methods have appeal in that they attempt to mimic lateral 

and vertical wind speed and direction shears. Since there is no direct evidence confirming the 

correctness of the puff-splitting algorithms, the decision whether to invoke the algorithms should be 

addressed in the modeling protocol. For screening applications, an added degree of conservatism at 

large down wind distances (> 200 km) may result if puff splitting is not be invoked. For sourcespecific applications (Chapter 7), puff splitting may be used as set forth in the protocol subject to 

concurrence by the cognizant reviewing agency. In sum, until verified with atmospheric data, the puff 

splitting remains an ad hoc procedure whose real impact on model reliability is unknown. In some 

cases, it may be prudent to use full-science regional models to circumvent this limitation of Lagrangian 

models. 

Chemistry

Chemical Mechanism. The MESOPUFF II module should be used for all screening (and 

source-specific) BART applications. For the aqueous phase conversion of SO2 to sulfate (important 

when the plume interacts with clouds and fog), the IWAQM defaults are recommended, i.e., nighttime 

SO2 loss rate (RNITE1) is assumed to be 0.2 percent per hour. The nighttime NOx loss rate (RNITE2) 

and HNO3 formation rate (RNITE3) are both set to 2.0 percent per hour. 

Species Modeled. Species to be modeled in the screening assessment include SO2, SO4, NOx, 

HNO3, NO3 and particulate matter. Absent detailed speciation and size distribution data from the 

source, PM should be modeled in two (2) size categories, fine (0.0-2.5 μm) and coarse (2.5-10.0 μm), 

consistent with the IMPROVE reconstructed mass equation. Particulate matter emissions by size 

category should be combined wherever possible into the appropriate species for the visibility analysis. 

These species include (a) elemental carbon (EC), (b) fine PM or “soil” (< 2.5 μm in diameter), (c) 

coarse PM (between 2.5-10 μm in diameter) and (d) organics, referred to as secondary organic 
 

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aerosols in the CALPOST postprocessor. If source-specific emissions factors are not available, AP-42 

factors can be used to estimate the PM speciation for those source sectors for which AP-42 emissions 

factors have been developed. Otherwise assumptions will need to be proposed by the source operator 

and approved by the state, EPA and FLM. 

Background Ozone Concentrations. Ozone concentration data for 2001-2003 from ambient 

AIRS/AQS monitors located within the particular domain being modeled should be used to develop background 

estimates. Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone background values should be computed from daytime average ozone concentrations (6 am to 6 

pm average). 

Background Ammonia Concentrations. In CALPUFF screening applications, the background 

ammonia concentration is assumed to be temporally and spatially invariant. Background ammonia 

estimates should be developed from CENRAP’s most recent CMAQ or CAMx simulation for the 2002 

base year. Because CMAQ/CAMx modeled and observed monthly averaged ammonia concentrations 

exhibit wide spatial variability, we recommend obtaining separate monthly-averaged ammonia 

concentrations from CMAQ or CAMx for the CENRAP north, central and south modeling domains, 

respectively. These would then be used as input to CALPUFF. The CENRAP 2002 monthly domain 

average ammonia for each CENRAP subdomain should also be used for the corresponding months in 

2001 and 2003. 

6.4.6 Post Processing 

 

POSTUTIL Parameters. User-selected options, parameter settings, and ‘switches’ for exercising 

POSTUTIL in a screening mode are presented in Appendix C. This appendix contains tables that list 

the recommended screening and default configurations for CENRAP BART modeling. The ammonialimiting method (ALM) in CALPUFF (Escoffier-Czaja and Scire, 2002, 2005) repartitions nitric acid 

and nitrate on a receptor-by-receptor and hour-by-hour basis to account for the models systematic 

over-prediction due to overlapping puffs. For screening applications, the user should set the parameter 

MNIRATE=1 in POSTUTIL to implement this approximate correction in its simplest form. The 

background ammonia concentration obtained from CAMx or CMAQ regional simulations on the three 

CENRAP subdomains should be used for consistency. 

CALPOST Parameters. Appendix D contains summarizes the CALPOST post-processor options, 

parameters, and switches. Tables are presented containing recommended and default configurations 

for CENRAP BART modeling. While all receptors should be included in a single CALPUFF 

simulation, one may calculate the visibility impacts in CALPOST for each Class I area separately 

using the NDRECP parameter. It specifies the receptor range to be processed in CALPOST. Given the 

importance of the CALPOST processor to the entire BART visibility estimation, process, we focus 

specifically on the parameter settings and input assumptions for CALPOST in the following section. 

6.5 Visibility Assessment 

The recommended procedure for quantifying visibility impacts was described in Chapter 3. The key 

point is that the light extinction coefficient (bext) can be calculated from the IMPROVE Equation as: 

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] + (6-1) 
 

6-8

 + 0.6[Coarse Mass] + 10[EC] + bRay

The monthly site-specific f(RH) values are obtained for each mandatory Federal Class I Area from 

Table A-3 in the EPA (2003) guidance document. Then, the haze index (HI), in deciviews, is 

calculated in terms of the extinction coefficient via: 

 

HI = 10 ln (bext/10) (6-2) 

The change in visibility (measured in terms of ‘delta-deciviews’) is then compared against background 

conditions. The delta-deciview, ∆dv, value is calculated from the source’s contribution to extinction, 

bsource, and background extinction, bbackground, as follows:

 

∆dv = 10 ln ({bbackground

+ bsource}/ bbackground) (6-3) 

If the ∆dv value is greater than the 0.5 dv threshold, the source is said to contribute to visibility 

impairment and is thus ‘subject to BART’ controls. If not, it is BART-exempt. 

 

6.5.1 Visibility Impacts from BART-Eligible Sources 

Class I Receptors. Within the CENRAP domain, there are twenty three (23) Federally mandated Class 

I areas. These areas are listed in Table 6-4. For each area, the National Park Service has developed a 

receptor file containing specific coordinates and elevation data. 

(http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm). 

Visibility Impact Method. In the screening approach, CALPOST should be run using Method 6 

(MVISBK=6) for calculating extinction. That is, monthly f(RH) adjustment factors are applied 

directly to the background and modeled sulfate and nitrate concentrations, as recommended in the 

BART guidelines. Note that the RHMAX parameter (the maximum relative humidity factor used in the 

particle growth equation) is not used when Method 6 is selected. Similarly, the relative humidity 

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

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

 

Monthly average Class I area-specific relative humidity values should be employed in the 

extinction analysis (EPA, 2003, Table A-3). Species to be considered include SO4, NO3, EC, SOA (i.e., 

condensable organic emissions), soil, and coarse PM. With Method 6, background extinction 

coefficients are computed from EPA (2003) monthly estimates of concentrations of ammonium sulfate 

(BKSO4), ammonium nitrate (BKNO3), coarse particulates (BKPMC), organic carbon (BKOC), soil 

(BKSOIL), and elemental carbon (BKEC). Values for these coefficients are listed in Table 6-3 and 

Appendix B. In screening analyses, 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) for all Class I areas. 

Natural Background Light Extinction. EPA’s BART guidance recommends that visibility impacts 

should evaluated against ‘natural’ background conditions. EPA (2003) describes the calculation of the 

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

average natural concentrations based on the area's geographic location. Annual average background 

extinction values (in 1/Mm) are converted to annual average Haze Index (HI) values (in deciview or 
 

6-9

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 a normal distribution. Thus, 

no average natural concentrations are provided for determining extinction for the 20% best visibility 

days. EPA maintains that the above definition of natural visibility baseline as the 20% best visibility 

days is likely to be reasonably conservative and consistent with the Regional Haze Rule goal of natural 

conditions. 

There are major technical issues with this approach: (a) the same concentrations assumed at all 

Class I areas in the East or West, (b) the same concentrations assumed to occur every month of the 

year, and (c) fine sea salt and associated water is not included. Also, in the calculation of 20% best 

visibility days, the same frequency distribution is assumed for every Class I area in the East or in the 

West. In other words, ‘one size fits all’ (Tombach, 2004). But this really is not the case. 

The background extinction computation with Method 6 in CALPOST involves user-supplied 

monthly concentrations of SO4, NO3, PM coarse, organic carbon, soil, and elemental carbon species. 

In practice, concentrations for only 2 species, SO4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the 

CALPOST input file to represent hygroscopic and non-hygroscopic portions of background extinction, 

respectively. Furthermore, the species concentrations are held constant over the annual cycle (i.e., no 

daily, monthly, or seasonal variation). Finally, the EPA natural background default values are defined 

separately for the eastern and western U.S. result in natural background extinction values that vary 

spatially and temporally only in response to the spatial distribution and monthly variation of 

climatologically-representative relative humidity values (EPA, 2003, Table A-3). Thus, the default 

definition of natural conditions does not take into account meteorologically caused visibility 

impairment. 

For CALPUFF screening analyses, these EPA (2003) default procedures for calculation of light 

extinction should be used for current and natural background conditions. Table 6-3 provides the 

species concentrations representing natural background conditions for the western and eastern Class I 

areas. As shown in Figure 6-1, EPA’s east-west division cuts through the middle of the CENRAP 

domain. Accordingly, in the modeling protocols, the states and source operators will need to 

determine whether receptors in a particular Class I area fall within the East or West domains as this 

determines the appropriate default background light extinction values to use from Table 6-3. 

Impact Threshold. The EPA BART guidance recommends that the threshold value for defining 

whether a source “contributes” to visibility impairment is 0.5 dv change from natural conditions. 

States may set a lower threshold. BART determinations are based upon the 98th percentile of the 

predicted 24 hour averaged deciview impact deduced from the CALPOST postprocessor. More 

specifically, to determine if a source may be exempted from BART, the highest modeled deltadeciview value for each modeling day for each modeled receptor should be determined. Depending on 

the yearly distribution of the results, the most conservative (i.e., highest) 98 % impact may come from 

the maximum 8th highest value for each of the three years or the 22nd highest value for all years 

combined (if three years of data and 365 values for each year are calculated). States and source 

operators should use both methods in order to identify the higher value. The peak impact is then 

compared to the 0.5 dv contribution threshold value. If the maximum 8th highest value for each of the 

three years (or the 22nd highest value for all years combined) exceeds the “contribution” threshold of 

0.5 deciviews the source is declared ‘subject to BART’, potentially triggering a source-specific 

CALPUFF modeling analysis (Chapter 7). Otherwise, the source is BART-excluded. In a cooperative 

agreement with EPA Regions VI and VII and federal land managers, CENRAP guidance deviates from 
 

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use of the 98th percentile. The CALMET datasets as described in this protocol were processed with the 

‘NO-OBs’ options, (i.e., surface observations were not used in the CALMET wind field interpolation). 

Aware that exercising CALMET with ‘NO-OBs’ may lead, in some applications, to potentially less 

conservatism in the CALPUFF visibility results compared with the use of CALMET with observations, 

CENRAP has agreed to EPA’s recommendation that the maximum visibility impact rather than the 98th

percentile value should be used for screening analyses using the CENRAP developed CALMET 

datasets. 

To conserve computational and analysis resources, the CALPUFF screening modeling may be 

performed sequentially for calendar years 2001, 2002, and 2003. If a BART source is found to be 

subject to BART following the first or second annual evaluation, the additional year(s) need not be 

processed. Evaluation of all three years will be required to exclude a BART eligible source from the 

BART determination process. 

Since the current regulatory version of CALPOST does not generate 98 percentile results, states 

or source operators may wish to use a modified version of CALPOST that generates a file with a full 

distribution of daily delta-deciview values for each receptor. The Colorado Department of Public 

Health and Environment (CDPHE, 2005) has developed a FORTRAN processor to generate 98th

percentile results and it is available upon request. 

6.5.2 Change in Visibility Due to BART Controls 

According to the BART guidelines, a single source responsible for a 1.0 dv change or more 

should be considered to “cause” visibility impairment and a source that causes less than a 1.0 dv 

change may still contribute to visibility impairment and thus be subject to BART. For sources 

determined to be subject to BART, additional modeling is needed to assist in identifying various 

control options and selecting the “best” alternative. The BART guidelines identify five factors to be 

considered when determining what BART should be for a specific source. These include: (a) any 

pollution control equipment in use at the source (which affects the availability of options and their 

impacts), (b) the costs of compliance with control options, (c) the remaining useful life of the facility, 

(d) the energy and non air-quality environmental impacts of compliance, and (e) the degree of 

improvement in visibility that may reasonably be anticipated to result from the use of such technology. 

For the visibility analysis (the fifth factor in determining what BART should be), it is up to the 

state to determine how to weigh the impacts. The 1.0 ∆dv threshold might be one of the parameters 

that is used although it is not required. The 98% value at this step in the analysis is also not required. 

For example, visibility improvement of 1.0 ∆dv may be predicted on 4 days out of the year, and the 

state may determine that is significant rather than judging it to be insignificant because it does not 

occur on 8 or more days. 

6.6 Presentation of Modeling Results 

 The CALPOST processor computes the daily maximum change in deciviews. As noted above, 

the maximum 8th highest value for each of the three years or the 22nd highest value for all years must 

be compared to the 0.5 deciviews threshold. At a minimum, tabular presentation of the following 

results should be provided: 

 > Number of receptors within each Class I area with impacts > 0.5 dv; 
 

6-11

 > Number of days at all receptors within each Class I area with impacts > 0.5 dv; 

 > Number of Class I areas with impacts > 0.5 dv; and 

 > At each receptor, the magnitude of the change in extinction. 

A variety of other tabular and graphical summaries may be desirable as well. 

6.7 Reporting of CALPUFF Screening Assessment Modeling Results 

The report accompanying the screening CALPUFF modeling should provide a clear description 

of the modeling procedures followed and the results of the analysis. Any departures from the approved 

modeling protocol should be discussed and justified. The report should also include a discussion of the 

uncertainty in the modeling results and the likelihood that the screening process was effective in its 

determination. Any needs for source-specific or alternative modeling should be identified. 

Accompanying the modeling report should be an electronic archive (CDs, DVDs, or removable 

USB2/IEEE 1394 hard drives as appropriate) that includes the full set of CALPUFF inputs and model 

output fields and well as any pre- or post-processor codes used to generate the results. The CENRAP 6 

km regional CALPUFF-ready meteorological fields do not need to be included in the archive. The 

modeling data archive should be sufficiently complete as to allow an independent modeler to fully 

corroborate the CALPUFF screening results. 
 

6-12

Table 6-1. Contents of a Typical Screening BART Modeling Protocol. 

1.0 INTRODUCTION 

1.1 Objectives 

1.2 Location of Source and Relevant Class I Areas 

1.3 Source Impact Evaluation Criteria 

1.4 Modeling Study Participants 

1.5 Protocol Review Process 

1.6 Schedule 

2.0 SOURCE DESCRIPTION 

 2.1 Unit-specific Source Data 

 2.2 Nearby Sources Affecting Same Class I Areas 

3.0 MODEL INPUT DATA 

3.1 Modeling Domain 

3.2 Terrain and Land Use 

3.3 Emissions Data Base 

 3.3.1 Stack Parameters 

 3.3.2 Emissions Rates 

 3.3.3 Condensable Emissions 

 3.3.4 Speciation and Size Distributions 

3.4 Meteorological Data Base 

 3.4.1 CENRAP CALMET Data Sets 

 3.4.2 Observational Data 

3.5 Air Quality Data Base 

 3.5.1 Ozone Concentrations 

 3.5.2 Ammonia Concentrations 

 3.5.3 Concentrations of Other Pollutants 

 3.5.5 CENRAP Regional Modeling Data Sets 

3.6 Natural Conditions at Class I Areas 

4.0 CALPUFF MODELING METHODOLOGY 

4.1 Model Selection 

 4.2 Domain Configuration and Receptors 

4.3 CALPUFF Configuration 

4.4 Light Extinction and Haze Impact Calculations 

4.5 Modeling Results 

4.6 Uncertainty Analysis 

5.0 EXEMPTION MODELING 

6.0 BART CONTROL MODELING RESULTS 

 7.0 REPORTING 

 REFERENCES 

APPENDIX A: CALMET Inputs 

APPENDIX B: CALPUFF Inputs 

APPENDIX C: POSTUTIL Inputs 

APPENDIX D: CALPOST Inputs
 

6-13

Table 6-2. CALMET/CALPUFF Model Codes for Screening Applications. 

PROCESSOR VERSION LEVEL

TERREL 3.311 030709 

CTGCOMP 2.42 030709 

CTGPROC 2.42 030709 

MAKEGEO 2.22 030709 

CALMM5a 2.4 050413 

CALMET 5.53a 040716 

CALPUFF 5.711a 040716 

POSTUTIL 1.4 040818 

CALPOST 5.51 030709 

a: The versions of CALMM5 (version 2.4, level 050413) and of CALMET (version 5.53a, level 

040716) are not compatible as published. Alpine Geophysics developed software 

modifications to reconcile the two. The code is available at: www.alpinegeophysics.com. 

Table 6-3. Default Natural Background Concentrations (μg/m3) for Eastern and Western U.S. 

Class I areas. (Source: EPA, 2003, Table 2-1) 

Average Natural 

Concentration West 

(μg/m3)

Average Natural 

Concentration 

East (μg/m3)

Error 

Factor

Dry Extinction 

Efficiency (m2/g)

Ammonium sulfateb 0.12 0.23 2 3 

Ammonium nitrate 0.10 0.10 2 3 

Organic carbon massc 0.47 1.40 2 4 

Elemental carbon 0.02 0.02 2-3 10 

Soil 0.50 0.50 11⁄2 - 2 1 

Coarse Mass 3.0 3.0 11⁄2 - 2 0.6 

a: After Trijonis (1990) 

b: Values adjusted to represent chemical species in current IMPROVE light extinction 

algorithm; Trijonis estimates were 0.1 μg/m3

 and 0.2 μg/m3 of ammonium bisulfate. 

c: Values adjusted to represent chemical species in current IMPROVE light extinction 

algorithm; Trijonis estimates were 0.5 μg/m3

 and 1.5 μg/m3 of organic compounds. 
 

6-14

Table 6-4. Federal Class I Areas in the CENRAP Domain. 

Federal Class I Areas in the CENRAP Domain 

Class I Area ST Name 

Big Bend National Park TX BIBE 

Boundary Waters Canoe Area MN BOWA1 

Breton LA BRET1 

Caney Creek AR CACR1 

Guadalupe Mountains Nat'l Park TX GUMO1 

Hercules Glades MO HEGL1 

Mingo MO MING1 

Upper Buffalo Wilderness AR UPBU1 

Voyageurs National Park MN VOYA2 

Wichita Mountains OK WIMO1 
 

6-15

Figure 6-1. Division of the U.S. for the Purpose of Assigning Default Natural Background 

Concentrations (μg/m3) for Eastern and Western U.S. Class I areas. Depicted are 

the Estimates of the Default 10% Natural Haze Index Values (in dv). (Source: EPA,

2003).