7-1

7.0 CALPUFF SOURCE-SPECIFIC APPLICATIONS 

The BART process provides an option for source-specific modeling to be performed. There are 

a number of reasons why a more detailed approach than that discussed previously may be desired. For 

example, sources very close to Class I areas may elect to use finer grid resolution than the 6 km 

gridded fields provided by CENRAP. Also, it may be desirable to reduce the uncertainties in assumed 

background ammonia, hydrogen peroxide, and ozone concentrations. These and other refinements in 

model inputs may help to clarify whether a source should be excluded from BART and/or to quantify 

the expected visibility impacts of alternative BART controls on an affected source. 

7.1 Methodology 

Source-specific CALPUFF modeling may be helpful in demonstrating more convincingly that a 

source does not cause or contribute to visibility impairment in any Class I areas, and thus can be 

exempted from BART controls. If the initial modeling does not pass the screening threshold for 

visibility impacts, the next step is to conduct source-specific modeling, perhaps using a finer grid 

resolution for the meteorological fields, improved treatment of terrain effects and land use variability, 

and the inclusion of more refined model parameterizations such as consideration of time and spacevarying ammonia and ozone concentration fields. As with the screening method, in the source-specific 

modeling, the visibility threshold is based on the BART guidance of the 98th percentile change in 

deciview value. 

The BART guidance indicates that the emissions rates to be used for exemption modeling is the 

highest 24-hr rate during the modeling period. Depending on the availability of source data, the 

following 24-hr maximum emissions rate information (listed in order of priority) should be used with 

CALPUFF for BART exclusion modeling: 

> Emissions for the period 2001-2003 (CEM data); 

> Emissions for 2002 (CEM data); 

> Actual 2002 emissions rates in state CERR inventory; 

> Emissions factors from AP-42 source profiles; 

> Permit allowable emissions, if available; or finally 

> Potential to emit. 

A source-specific modeling analysis will likely be required for all sources located within 50 km 

of a Class I area. EPA’s BART guidance recommends that expert modeling judgment be used, “giving 

consideration to both CALPUFF and other methods.” While the PLUVUE-II plume visibility model 

is mentioned as a possible alternative approach to consider in addition to CALPUFF within 50 km of a 

source, the CALPUFF model appears to offer greater realism, provided sufficiently small grid 

resolution (< 4 km) is used. As discussed in Chapter 3, there are no fundamental reasons why 

CALPUFF cannot be used for much shorter transport distances than 50 km, as long as the scale of the 

plume is larger than the scale of the output grid. This requirement must be met in order that the 

maximum concentrations and the width of the plume are adequately represented (and the sub-grid 

details of plume structure can be ignored when estimating effects on light extinction). Use of a 1 km 

CALPUFF modeling grid for Class I area receptors should be sufficient for source-receptor distances 

in the 25-50 km range; a finer grid may be needed for transport distances much smaller than 25-30 km. 
 

7-2

If source-specific modeling shows that a particular source causes or contributes to visibility 

impairment at a Class I area, the CALPUFF modeling system can then be used to quantify the visibility 

benefits from various BART control options. This is accomplished by running CALPUFF with the 

baseline emissions rates and with emissions after BART controls. It is important that emission 

reductions be evaluated in the post-processing step rather than by using “negative” emission rates in 

the CALPUFF model. The chemical scheme requires that emission rates always be positive. 

7.2 Source-Specific Considerations 

7.2.1 Protocol Development and Negotiation. 

For any source-specific application, a modeling protocol that defines source properties and the 

specific model configuration and application approach is required. An example of typical elements of a 

source-specific protocol is given in Table 7-1. This protocol should be reviewed with the state, EPA, 

and FLM. 

7.2.2 Domain Definition. 

While source-specific modeling can be carried out on one or more of the three CENRAP 

domains (Figures 5-1 through 5-3) with the 6 km MM5/CALMET fields for 2001-2003, it may be 

more appropriate to develop a specialized modeling domain and with finer resolution meteorological 

fields. Because CENRAP has not developed fine-scale CALMET files, the state or source operator 

will have to do this. Particularly in rough terrain or in coastal areas, finer horizontal grid spacing may 

be useful in source-specific simulations to better characterize the flow fields and land use changes. A 

typical source-specific CALMET meteorological domain might consist of a 300 x 300 array of 1-2 km 

grid cells covering the domain of interest. This produces a reasonable number of grid points that can 

easily be accommodated on personal computers. Domains with up to 600+

 x 600+ grid cells are 

sometimes necessary (for long transport distances), while domains with fewer than 100 x 100 cells 

generally are not used in a source-specific analyses because they cover insufficient territory. 

For source-receptor distances < 50km, even finer grid resolution than 1 km may be needed, 

particularly if complex terrain or coastal meteorological effects are likely to be important. This 

determination should be made on a case-by-case basis. There is no single distance at which a 

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the 

source-receptor distances involved, the location of the source relative to the terrain features, the 

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrain-forced 

flow), proximity of the source and Class I area to a coastline, and other factors including availability of 

representative observational data. 

7.3 CALMET Configuration for Source-Specific Assessments

 The CALMET processor accepts observational data in the form of hourly surface observations 

of winds, temperature, pressure and cloud data, hourly precipitation observations, and twice-daily 

upper air sounding data of winds, pressure and temperature. In addition, CALMET allows the use of 

three-dimensional gridded fields of data from prognostic models such as MM5. CALMET can be run 

in a mode using observations only (Obs-mode), prognostic data only (No-Obs mode) or both sets of 

data (hybrid mode). For example, one possible approach is to consider using the CALMET “hybrid” 
 

7-3

mode which incorporates surface observations with prognostic data, but does not include the use of 

upper air data (option NOOBS = 1). 

 In preparing the regional MM5/CALMET fields for the CALPUFF screening applications 

(Chapter 6), CALMET was exercised on a regional 6 km resolution domain (Figure 5-1) in NoObservations mode using the highest resolution MM5 data available for each year modeled. For 

source-specific modeling of Class I areas located in complex terrain or coastal regions, it may be 

desirable to develop meteorological fields over finer resolution grids (i.e.,. 1-4 km scale). In these 

cases there may be reasons to invoke the hybrid mode of CALMET option. In determining which 

method to use, the State or source operator should evaluate the following factors in the specific context 

of the source and the affected Class I area. 

Time and space resolution of observational data. Upper air observational data are available at roughly 

12-hourly intervals and the spacing between sounding locations is typically several hundred 

kilometers. Because CALMET performs a time interpolation of wind data between the sounding 

intervals, this linear time-interpolated wind data will not be able to retrieve important patterns of wind 

variation at scales less than 12 hours. 

Time and space resolution of 3-D prognostic data. The MM5 output fields provide hourly “soundings” 

over a 36 km or 12 km grid at hourly intervals. This hourly time resolution of the prognostic model 

fields is likely to better characterize the time variation of wind flows across the domain. However, on 

scales smaller than the MM5 grid nest (say below 12 km) the MM5 fields may miss potentially 

important local flow features that might be reflected in local surface measurement networks. 

Representativeness of observational data. A key question, then is whether one has confidence that the 

spatial and temporal representativeness of the local measurement stations is sufficient to favor 

measurements over prognostic modeled fields at the local scale. While use of measurements is 

attractive in many situations, too great a reliance measurements introduces the possibility of spurious 

wind or thermodynamic variable variations because the CALMET data interpolation methods does not 

obey the combined laws of momentum and energy conservation. This is particularly an issue where 

there are measurement sites of questionable representativeness or data quality. Therefore, careful 

inspection of the actual surface measurement sites to be used in the Obs-mode is needed before the 

method is used. 

Discrepancies between prognostic winds and observations. The CALMET diagnostic wind field 

module attempts to reduce discrepancies between the prognostic model-derived initial guess fields and 

the observations by giving greater weight to the observations in the “vicinity’ of the observations and 

greater weight to the prognostic wind fields in portions of the domain away from the observations. 

Although the prognostic model simulation reflects observational data through the use of fourdimensional data assimilation, there will sometimes be discrepancies between the observations (which 

may reflect a very localized flow) and the 36 km or 12 km resolved MM5 winds. Sometimes the 

differences are due to poor time resolution of the observational data; at other times they may result 

from poor instrument placement or faulty observations. In such cases, blending of the MM5 data with 

the observations (i.e., the hybrid mode) can lead to wind structures which may not be realistic. 

Model biases. The MM5 model predictions of winds and temperatures may contain biases relative to 

the real atmosphere. In particular, MM5 sometimes underestimates the frequency of light wind and 

calm wind conditions and possibly underestimates the strength of the nocturnal surface temperature 
 

7-4

inversion. These biases may be most important for near surface sources when relative coarse grid 

resolution is used. If the adequacy of the MM5 fields becomes a concern, the state or source operator 

may wish to examine the detailed MM5 model evaluation reports prepared by the original modelers 

(see Chapter 5). 

Level of effort vs. benefit. The preparation of observational datasets for CALMET on large regional 

domains involves a considerable effort due to the large number of surface, precipitation and upper air 

stations involved. The question is whether the effort associated with the preparation of this dataset is 

worthwhile given that the regional winds over the three CENRAP sub-domains (Figure 5-1) may be 

better characterized by the three-dimensional MM5 model dataset than time- and space-interpolated of 

observational data, especially above the surface where sounding data is fairly sparse spatially and 

poorly-resolved in time. For source-specific modeling, the decision whether to exercise CALMET in 

the hybrid mode is left to the state or source operator who are in the best position to assess the relative 

strengths and drawbacks of the two modes of operation in the specific context of the source they are 

modeling. Given the orography of the CENRAP domain, the regional wind patterns are likely to be 

adequately characterized at the 6 km scale via the use of MM5/CALMET with No-obs. However, at 

the finer grid scales that may be considered in source specific modeling (1-4 km), introduction of 

particularly well sited monitors – especially in key impact areas – may be appropriate. 

 Care must be taken to ensure that the introduction of the local observations into the CALMET 

processing does not extend the influence of the observational data beyond its true representativeness 

and result in internally inconsistent flow features. On smaller domains with higher CALMET grid 

resolution, more control over the region of influence of the meteorological observations can be 

achieved and it may be easier for the diagnostic model to allow the local flow observations to have 

appropriate influence in the vicinity of the observation, yet still allow terrain-adjusted flow to dominate 

away from the observations. Given that the sub-regional domains will be used especially in complex 

flow situations (complex terrain and coastal regions), the relatively coarse-scale MM5 simulations are 

less likely to be adequate, and the introduction observational data may indeed yield improvements in 

the resulting meteorological fields. 

 The decision on how the MM5/CALMET processing should be performed for source-specific 

modeling requires consultation with the State, regional EPA office and the FLM overseeing the Class I 

area of concern. A decision to allow ingestion of meteorological observations into the CALMET 

processing at the finer grid scales (i.e., below 6 km) should be contingent upon commitment to a 

thorough analysis of the instrument location, quality assurance record, and spatial/temporal 

representativeness of the each monitoring station to be used in the CALMET interpolation. For the 

reasons discussed above, absent this assessment of the measurement data representativeness, ingestion 

of observational data may in fact degrade the integrity of the resultant CALMET meteorological fields 

and the modeler/decision-maker may not be aware of it. 

 

7.4 CALPUFF Configuration for Source-Specific Assessments 

 Domain-specific CALPUFF input parameters must be defined for each source-specific 

modeling analysis. In addition to finer grid spacing in the CALMET and CALPUFF models and the 

possible introduction of observational data in the CALMET simulations, several other modeling 

enhancements can serve to increase the realism of the source-specific modeling. These include use of 

higher resolution terrain digital elevation model data (~3 arcsec USGS data) in defining the gridded 
 

7-5

terrain fields, and the use of time-varying ammonia and ozone concentrations rather than constant 

values. 

7.4.1 Background Conditions 

Background Ozone Concentrations. Ozone concentrations required as input to source-specific 

CALPUFF should be derived from regional model simulations obtained from CENRAP or the other 

RPOs in preference to the use of AIRS/AQS ozone measurements at discrete monitors (see discussion 

in section 7.4.3 below). 

Background Ammonia Concentrations. NH3 concentrations required as input to source-specific 

CALPUFF modeling should be derived from regional model simulations obtained from CENRAP/s 

CAMx or CMAQ modeling or the modeling by other RPOs in preference to the use of CASTNet or 

other network ammonia measurements at discrete monitors (see discussion in section 7.4.3 below). 

In addition to representing a better-science approach, this has the advantage of making the BART 

analyses more consistent with the CENRAP’s reasonable progress modeling for regional haze. 

 7.4.2 Ammonia Limiting Method (ALM) 

CALPUFF calculates linear oxidation of sulfur dioxide (SO2) to sulfate (SO4) and nitric oxides 

(NOx) to nitric acid (HNO3) and nitrate (NO3). The equilibrium between nitric acid and nitrate that is a 

nonlinear function of temperature and relative humidity and the equilibrium constant can vary 

substantially over a typical diurnal cycle, as the temperature and relative humidity change. In addition, 

the availability of ammonia (to form ammonium nitrate) affects ambient concentrations of nitrate. 

Because sulfate preferentially scavenges ammonia, the amount of ammonia remaining for the 

formation of nitrate may be limited. Thus, ammonia-limiting effects can be important under certain 

conditions in assessing the impacts of nitrate aerosols on visibility (Blanchard and Hidy; 2003; 

Escoffier-Czaja and Scire, 2002). 

In the screening approach (Chapter 6), the ALM was recommended using a constant 

background ammonia concentration relevant to the dominant land use in the Class I area being 

modeled was assumed (i.e., 0.5 ppb for forest, 1.0 ppb for arid lands, 10 ppb for grasslands.) For 

source-specific analyses, time-varying, user-supplied NH3 concentrations may be imposed as boundary 

conditions to each modeled puff, while cumulative ammonia consumption is modeled in the 

POSTUTIL processor for conditions at the final endpoint of the puff (i.e., the Class 1 receptor). In 

addition, the boundary concentration module within CALPUFF can be invoked to allow for time- and 

space-varying background concentrations. Use of the ALM method in conjunction with background 

source boundary conditions may improve the modeled NO3 predictions somewhat in source-specific 

analyses. 

In CALPUFF, a nominal background ammonia concentration is used in conjunction with the 

HNO3/NO3 equilibrium relationship to estimate the amount of nitrate formed. However, the model’s 

Lagrangian puff formulation causes an overestimation of the amount of ammonia available to form 

nitrate because the competition for ambient ammonia by individual puffs and by background sources is 

neglected. ALM was developed to treat this limitation. The post-processing method repartitions nitric 

acid and nitrate on a receptor-by-receptor and hour-by-hour basis and approximates the influence of 

other background sources of sulfate and nitrate in computation of incremental nitrate formation. 

Details of the ALM, incorporated into POSTUTIL are described by Escoffier-Czaja and Scire (2005). 
 

7-6

Here we provide a summary of their description of the method as it might be used in CENRAP sourcespecific modeling. 

With the ALM procedure, the contribution of an individual source or facility to nitrate 

formation is determined via a four-step procedure: 

Step 1. The total background ammonia (ambient free ammonia + ammonia associated with 

sulfate/nitrate aerosols) is estimated either through (a) use of observational data at existing 

monitoring networks such as CASTNet, (b) from the output of one-atmosphere models such as 

CMAQ or CAMx. For example, assume weekly average sulfate, nitrate and nitric acid 

concentrations at CASTNet stations can be used to estimate the ammonia in the domain. An 

iterative scheme is used split total nitrate (TNO3 = HNO3 + NO3) into HNO3 and NO3, leading 

to the following relationship, 

 [NH3]total = [NO3] + 2 [SO4] + NH3(g) 

where [NO3] is the observed nitrate concentration (ppb), [SO4] is the observed sulfate 

concentration (ppb), and NH3(g) is the estimated free ammonia concentration (ppb) derived by 

applying the chemical equilibrium relationship. Using a 5-year monthly average of NO3 and 

TNO3 ( = NO3 + HNO3) and the hourly temperature and relative humidity measured at a 

CASTnet site, an iterative scheme is used to estimate the free component of the NH3 and the 

estimation of background sulfate and nitrate. 

Step 2. The project sources are modeled in the normal way by CALPUFF to predict TNO3

from those sources. 

Step 3. The ‘boundary condition’ CALPUFF model is used to simulate pollutant 

concentrations (SO2, SO4, NOx, HNO3 and NO3) from distant sources (outside the modeling 

domain) at the receptors of interest. The background sulfate concentrations are used with the 

monthly averaged total NH3 in Step 1 to compute available ammonia (i.e., after consumption 

by sulfate). The available ammonia will vary hourly and spatially even though the total 

ammonia may be constant, because the sulfate concentrations are varying. 

Step 4. POSTUTIL is then used to develop the total concentrations of sulfate, nitrate and nitric 

acid from all sources (project source, boundary conditions and background in-domain sources 

if any), and to re-partition the nitrate to reflect the total concentrations of ammonia-scavenging 

pollutants from all sources. In this step, the ammonia consumed by background sources of 

sulfate and nitrate as well as due to puff overlap from modeled sources is all accounted for in 

the re-application of the equilibrium relationship. 

In a separate run of POSTUTIL, the contribution of any individual source or group of sources 

to the total nitrate can be determined. The flow chart diagram in Figure 7-1 shows the POSTUTIL 

process to attribute a fraction of total NO3 to any individual source (Step 3 in Figure 7-1). Once the 

total nitrate from all sources is determined with ammonia limiting effects evaluated (i.e., Steps 1 and 2 

in Figure 7-1), a separate POSTUTIL run computes the TNO3 from the project source alone, and 

attributes the nitrate formed from that particular source as a fraction of the total NO3 formed based on 

that source’s contribution to total TNO3. 
 

7-7

 The distribution of CASTNet and IMPROVE monitoring sites in the central United States is 

shown in Figure 5-5. Most Class I areas in this region include either a CASTNet and/or an IMPROVE 

monitoring site and many others are in the vicinity. CALPUFF’s ALM procedure described above 

uses the CASTNet monitoring network to produce boundary conditions of sulfate, nitrate and 

precursors and to derive empirical estimates of ammonia concentrations. However, CASTNet 

measurements have inherent errors and biases that tend to overestimate HNO3 and underestimate NO3

(some NO3 will volatilize to HNO3 within the sample after collection and before laboratory analysis). 

Thus, applying CASTNet data to estimate background NH3 would tend to underestimate the ambient 

NH3 and would therefore underestimate visibility impacts. 

One-atmosphere regional modeling results are an alternative to the use of CASTNet or other 

monitoring network observations. CENRAP’s gridded, hourly CAMx or CMAQ data bases for 2002 

may be used in the CALPUFF ALM methodology in two ways: 

> Boundary Conditions and Background Ammonia. Regional model concentrations of 

SO2, SO4, NO3, HNO3 and NOx can be used to define the boundary conditions for the 

CALPUFF boundary module run. Regional model ammonia concentration fields over 

the Class I receptors of interest can also be used directly as input to the ALM method in 

CALPUFF. Monthly averaged, BCs and NH3 concentrations can easily be derived 

from CENRAP’s 2002 annual CMAQ or CAMx modeling files. 

> Sulfate, Nitrate and Ammonia at Class I Receptors. One may extract SO4, NO3 and 

NH3 concentrations at the Class I receptors from regional model simulations on an 

hourly or longer time-average at the Class I receptors as direct input into the CALPUFF 

ALM procedure. Here, the CALPUFF boundary condition module is replaced by the 

direct use of CMAQ-predicted concentrations. 

The value in using CMAQ or CAMx modeling results include the availability of hourly 

concentrations of all relevant gas phase and fine particulate aerosol species from all sources in the U.S. 

The models have been evaluated against virtually all of the individual monitoring stations identified in 

Figure 5-5 and these results are easily obtained from the UCR/CENRAP website: 

(http://pah.cert.ucr.edu/aqm/cenrap/index.shtml). 

Considering the various sources of uncertainty (e.g., inadequate aerosol chemistry in CALPUFF, 

uncertainties in regional ammonia emissions inventories; inconsistency in measurement protocols 

among the monitoring networks in Figure 5-5, and uncertainties in regional models), use of CMAQ or 

CAMx modeling data bases is an attractive option over reliance on one or more CASTNet sites for 

some source-specific analyses. 

 For source-specific modeling, we recommend using regional model ozone, nitrate, sulfate, and 

ammonia concentration fields available over the central U.S. for 2001, 2002, 2003 from the CENRAP, 

VISTAS, and MRPO modeling studies instead of ammonia concentrations based on CASTNet, 

IMPROVE, and STN measurements and ozone measurements based on the AIRS/AQS data. 

Straightforward processing of the modeled CMAQ or CAMx nitrate and sulfate fields can be used to 

generate the inputs needed for CALPUFF boundary conditions. Gridded model output fields should be 

used to generate daily average ozone and NH3 concentrations (averaged for each calendar month) 

along the trajectory path from source to Class I area. This approach offers an improved 

characterization of the atmospheric environment through which BART plumes traverse compared to 
 

7-8

point measurements that likely do not coincide with the plume path and certainly do not characterize 

the full chemical history of the plume. Gridded CMAQ or CAMx output files are available from 

CENRAP and the other RPOs. 

7.4.3 Computing Light Extinction 

The methodology for calculating light extinction outlined in the BART guidance constitutes the 

default approach presently, but ongoing research suggests that improved methods are or soon will be 

available and recommended for use by the IMPROVE committee, EPA, and other knowledgeable 

groups. Thus, any changes to the default method (e.g., deviation from the use of the EPA monthly 

f(RH) CALPOST Method 6) will have to be justified in the source-specific modeling protocol and 

evaluated on a case-by-case basis. 

Currently, the IMPROVE Steering Committee, EPA, the Electric Power Research Institute, the 

Federal Land Managers, the Regional Planning Organizations and other scientists are discussing 

whether refinements are warranted to the method recommended in EPA’s guidance for calculating 

light extinction (i.e., the IMPROVE equation). Among the topics being considered are: 

 > Increase OC to OC Mass multiplier from 1.4 to 2; 

> Use formula to provide for varying sulfate and nitrate extinction efficiencies depending 

on concentration; 

> Accounting for anthropogenic transboundary pollution’ 

> Corrections to the statistical assumptions used to estimate 20% best and 20% worst 

days; and 

> Use actual IMPROVE derived relative humidity data rather than EPA’s 

“climatological” values 

Other refinements under consideration, which also apply to the CALPOST extinction formula 

(Equation 3-1), include the following: 

> Adding a sea salt term to the formula, which would include a growth factor due to 

relative humidity; 

> Increasing the factor used to calculate the mass of particulate organic matter from 

organic carbon measurements; 

> Slightly modifying the relative humidity growth formula, f(RH), for sulfates and 

nitrates; and 

> Revising some of the extinction efficiencies (the numerical constants in Equation 3-1), 

including making those for sulfates, nitrates, and organic carbon vary with 

concentration 

Presumably any changes that are made by the IMPROVE Steering Committee would also be reflected 

in the EPA’s approach. If this is the case, then CENRAP source-specific modeling protocols should 

adopt the emergent recommendations unless the source can justify, and the state approves, a different 

approach. Thus, for example, in the source-specific modeling, consideration of sea salt in the natural 

background, elevation effects on Rayleigh scattering (see below) and natural weather effects such as 
 

7-9

fog, rain and snow might be appropriate. Specific configuration settings for these and other model 

features should be proposed in the source-specific modeling protocol and negotiated with the state, 

regional EPA office and the FLM. 

7.4.4 Estimating Rayleigh Scattering. 

The Rayleigh scattering coefficient for clear air has a default value 10 Mm-1 (see Equation 3-1). 

This default value is appropriate for an elevation of 1600 m (about 5000 ft). Since the correct value at 

sea level is about 12 Mm-1, the default value could never be attained at low altitude sites and therefore 

the relative impact (in dv) of a source on haze would be overstated by using the default Rayleigh value. 

Publicly available codes are available for making the elevation-dependent adjustments to the high 

elevation default value of 10 Mm-1. For the source-specific analyses, an effort should be made to be 

consistent with the approach taken by the CENRAP and the other RPOs in defining the Rayleigh 

scattering coefficient for their regional analyses. 

7.4.5 Natural Baseline Conditions. 

The visibility impacts of BART-eligible sources must be evaluated against natural conditions. 

Default values of natural conditions have been specified in EPA guidance documents. These values are 

held as constant throughout the East, which result in natural background extinction that varies spatially 

and temporally only in response to the spatial distribution and monthly variation of climatologicallyrepresentative relative humidity values. (This default definition of natural conditions does not take 

into account meteorologically caused visibility impairment such as fog and rain.) 

The EPA allows refinements to the default natural conditions values, with adequate scientific 

justification. Three changes seem to have wide support: (a) increasing the multiplier of the measured 

organic carbon concentration from 1.4 to a larger value, probably in the range of 1.8 to 2.1; (b) 

changing the estimates of natural conditions extinction on the 20% clearest and haziest days to better 

reflect the statistical distribution of extinction; and (c) including sea salt in the IMPROVE/EPA 

formula, as discussed in the preceding section. The organic carbon multiplier adjustment applies to all 

BART-related calculations. Since most of the extinction under natural conditions is attributed to 

organic carbon, the first change will substantially increase the natural background level of extinction 

from the default level. This, in turn, will reduce the modeled percentage or deciview impacts of most 

sources, which may affect their BART status. 

The statistical adjustment applies for the calculation of conditions on the 20% clearest and 

haziest days. The clearest days adjustment is relevant if the baseline for evaluating source impacts in 

the BART exclusion modeling is based on the 20% clearest days. It would increase the deciview 

impact of a source’s emissions. The haziest days adjustment is of relevance for the assessment of the 

benefits of the application of BART, for which the 20% haziest days are the baseline. It slightly 

increases the default haziness on those days and therefore reduces the absolute deciview impact of a 

source’s emissions. Finally, the sea salt adjustment applies to the description of natural conditions, and 

has effect mainly near the seacoast. 

Other refinements to natural conditions estimates may be appropriate at specific locations or at 

specific times of the year. Also, any refinements to the light extinction estimation formula of Equation 

3-1 would be reflected in natural conditions extinction estimates. 
 

7-10

7.4.6 Visibility Impact Assessment. 

As recommended in the final BART guidance, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the screening evaluation). States or sources can accomplish this in one 

of two ways. First, a coding change can be made to the CALPOST postprocessor to allow the 98th

percentile change in extinction to be computed. This may be accomplished by using a specific program 

that post-processes the CALPOST output (CDPHE, 2005). Alternatively, because the CALPOST 

processor produces visibility impacts in deciviews, these impacts can be externally processed in a 

spreadsheet to calculate the changes in deciviews. Table 7-2 shows a hypothetical example of the 

ranked visibility impacts (change in dv) for each of three years at six different Class I areas. The 98th

percentile (8th highest value) in the sorted table would be compared to the contribution threshold (e.g., 

0.5 dv). In the example shown in Table 7-2, the source passes the refined analysis because the highest 

98th percentile visibility impact is below the contribution threshold of 0.5 dv. The actual procedure 

selected by the State or source should be set forth clearly in the protocol. Any CALPUFF code 

changes should be documented and copies of the source code and input/output files provided to the 

reviewing agency for corroboration. 

7.5 Assessment of Modeling Results 

The report accompanying the source-specific 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 specifically discussed and justified. The report should also 

include a discussion of uncertainty in the modeling results. There are many sources of uncertainty in 

modeling due to factors such as errors and approximations used in the calculation of emission rates, 

speciation of the emissions, particle size distributions, meteorological data inputs including the MM5 

data sets used for determining the wind fields, the representativeness of meteorological observations 

used in the analysis, uncertainties in the meteorological and dispersion model itself due to its 

parameterizations of transport, dispersion and chemical transformation, and in the methods used to 

compute light extinction from particulate matter concentrations predictions. In addition, the use of 

monthly average relative humidity in the hygroscopic aerosol growth equations results in error. 

Furthermore, grid resolution affects the ability of the model to resolve terrain features and for wind and 

other meteorological fields to respond to geophysical features such as terrain, land-sea boundaries, 

surface characteristics such as roughness, albedo and other parameters. 

As appropriate, the main sources of uncertainty in the BART modeling should be addressed in 

the final report. Good discussions of uncertainty in air quality modeling are readily available, e.g., 

Russell and Dennis (2000) and the citations in the reference section of this document. The uncertainty 

analysis is not intended to be exhaustive but rather shed light on important areas of model uncertainty 

and the likely range of applicability of the CALPUFF (or alternative model) results. 

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

USB/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. While it is not 

necessary to archive the MM5 data files used to generate the CALMET fields, the CALPUFF-ready 

meteorological fields should be included in the archive. The modeling data archive should be 

sufficiently complete as to allow an independent group to corroborate the modeling results developed 

in the source-specific analysis. 
 

7-11

Table 7-1. Contents of a Typical Source-Specific 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 Refined Meteorological Modeling Base 

 3.4.1 MM5 Simulations 

 3.4.2 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 CALMET MODELING METHODOLOGY 

4.1 Domain Configuration 

4.2 MM5 Extraction Procedures 

4.3 CALMET Configuration 

 4.3.1 Terrain 

 4.3.2 Land Use 

 4.3.3 Vertical Layer Structure 

 4.3.4 Diagnostic Model Settings 

 4.3.5 BIAS, RMIN2, IXTERP settings 

 4.3.6 R1, R2, RMAX1, RMAX2, RMAX3 settings 

 4.3.7 Surface Stations 

4.3.8 Upper Air Stations 
 

7-12

4.3.9 Precipitation Stations 

4.4 Evaluation of CALMET Datasets 

5.0 CALPUFF MODELING METHODOLOGY 

5.1 Model Selection 

 5.2 Domain Configuration and Receptors 

5.3 CALPUFF Configuration 

5.3.1 CALPUFF model options 

 5.3.2 Visibility Modeling Domain 

 5.3.3 Dispersion 

- Building downwash 

- Puff representation 

- Puff Splitting 

 5.3.4 Chemistry 

5.3.5 Chemical Mechanism 

5.3.6 Speciation 

 5.3.7 Background Ozone Concentrations 

5.3.8 Background NH3 Concentrations 

5.3.9 Background H2O2 Concentrations 

5.4 Post Processing Methodology 

5.4.1 POSTUTIL Parameters 

5.4.2 CALPOST Parameters 

5.5 Light Extinction and Haze Impact Calculations 

 5.5.1 Natural Background Conditions 

5.5.2 Visibility Impact Methodology 

5.5.3 Ammonia Limiting Method 

 

5.6 Modeling Results 

5.7 Uncertainty Analysis 

 6.0 EXEMPTION MODELING (if performed) 

7.0 BART CONTROL MODELING RESULTS 

 8.0 REPORTING 

REFERENCES 

APPENDIX A: CALMET Inputs 

APPENDIX B: CALPUFF Inputs 

APPENDIX C: POSTUTIL Inputs 

APPENDIX D: CALPOST Inputs
 

7-13

Table 7-2. Example of Hypothetical Visibility Impact Rankings at Five CENRAP Class I 

Areas. 

Class I Area 2001 2002 2003 

DeltaDeciview 

Ranks 1-8 

DeltaDeciview 

Ranks 1-8 

DeltaDeciview 

Ranks 1-8 

Caney Creek 

0.99 

0.88 

0.62 

0.59 

0.55 

0.52 

0.48 

0.47 

0.95 

0.63 

0.51 

0.50 

0.46 

0.42 

0.37 

0.36 

1.20 

0.90 

0.73 

0.72 

0.59 

0.47 

0.45 

0.42 

Upper Buffalo 

0.67 

0.45 

0.43 

0.33 

0.29 

0.27 

0.25 

0.23 

0.81 

0.69 

0.65 

0.50 

0.45 

0.33 

0.31 

0.29 

0.76 

0.47 

0.37 

0.35 

0.31 

0.30 

0.28 

0.28 

Boundary Waters 

0.66 

0.43 

0.41 

0.35 

0.26 

0.24 

0.23 

0.22 

0.73 

0.69 

0.63 

0.52 

0.46 

0.34 

0.29 

0.26 

0.75 

0.45 

0.36 

0.34 

0.28 

0.27 

0.26 

0.25 

Voyageurs 

0.26 

0.23 

0.22 

0.21 

0.20 

0.19 

0.18 

0.16 

0.54 

0.47 

0.43 

0.37 

0.37 

0.31 

0.31 

0.30 

0.61 

0.42 

0.30 

0.29 

0.28 

0.28 

0.25 

0.25 

Hercules Glade 

0.34 

0.33 

0.31 

0.26 

0.24 

0.20 

0.18 

0.17 

0.52 

0.43 

0.32 

0.31 

0.30 

0.28 

0.24 

0.24 

0.27 

0.24 

0.23 

0.20 

0.14 

0.13 

0.11 

0.10 
 

7-14

Figure 7-1. POSTUTIL Processing of the Ammonia Limiting Method (ALM), NO3/HNO3

Repartitioning for an Individual Source, and Estimating Ammonia Consumption 

from Modeled Sources and Boundary Conditions. (Source: Escoffier-Czaja and 

Scire. 2005.) 

CALPUFF new source(s): 

CALPUFF.CON 

NO3_1; HNO3_1, 

TNO3_1=NO3_1+HNO3_1 

CALPUFF Incoming Boundary: 

BOUNDARY.CON 

NO3_2; HNO3_2; 

TNO3_2=NO3_2+HNO3_2 

POSTUTIL Step 1 

TNO3_1+TNO3_2 are repartitioned in 

new NO3_total/HNO3_total using 

NH3 available, hourly Temperature 

and Relative Humidity 

POSTUTIL Step 2 

NO3_total and HNO3_total are renamed 

NO3ALL and HNO3ALL 

POSTUTIL Step 3 

NO3ALL/(NO3ALL+HNO3ALL)= 

NO3src/(NO3_1 +HNO3_1) 

NO3src; HNO3src are the new output 

TNO3src=NO3src+HNO3src=TNO3_

1