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4.0 ALTERNATIVE MODELING 

 In some situations, the modeling of a BART-eligible source may require more rigorous 

simulation tools in order to adequately estimate the potential that the source adversely impacts a Class 

I receptor or to develop reasonable, cost effective controls on BART sources. Situations where more 

comprehensive fine particulate/visibility modeling might be required might include: 

 > The source’s plume traverses over other strong discrete sources of visibility-reducing 

emissions (e.g., SO2, NOx); 

> Co-mingling of the sources plume with emissions from one or more urban or metropolitan 

areas; 

> Complex meteorological flows entailing significant lateral and/or vertical wind shear or 

stagnation; and 

> Extensive transport distance between source location and the nearest Class I area (e.g., > 

300 km). 

Another significant motivation for some BART sources may be the need to develop more accurate, less 

biased estimates of the source’s impact on the Class I area given the propensity of CALPUFF to overpredict fine particulate aerosol concentrations and attendant visibility effects. In these or other 

situations, alternative full-science modeling systems and associated regional data bases are available 

and allowed by EPA on a case-by-case basis. Though somewhat more costly than routine CALPUFF 

modeling, the potential costs savings associated with more reasoned BART control requirements may 

prompt some states or BART source operators to pursue alternative modeling. 

 This chapter summarizes these tools and provides an assessment of their accuracy and 

reliability in estimating the impacts of BART-eligible sources on visibility in Class I areas. Of course, 

use of a more advanced modeling framework would necessitate a detailed ‘source-specific’ modeling 

protocol and discussions with the state, EPA and FLM. 

4.1 Overview 

 All source-oriented models, including CALPUFF, are derived from the fundamental atmospheric 

species conservation equation (Tesche, 1983). Much of the difference between simple and 

comprehensive models stems from the extent to which the various chemical and physical atmospheric 

processes are treated: rigorously, heuristically, in a highly parameterized fashion, or neglected 

altogether. Model developers must choose what pollutants and processes need to be treated rigorously 

and which ones may be parameterized or neglected altogether when constructing a model to fulfill its 

design objectives. Simple screening tools such as the Gaussian Plume model neglect a host of 

atmospheric processes for the sake of providing solutions on a hand-held calculator or nomograph. As 

noted in Chapter 3, CALPUFF entails a number of simplifications in atmospheric chemistry and 

transport and dispersion in order to yield a system that runs swiftly on a PC with minimal data 

requirements. 

 At the other end of the spectrum, full-science photochemical dispersion models rigorously treat 

the major processes that govern the formation, transport, gas-phase and secondary aerosol chemical 

transformation and removal of atmospheric gas phase and aerosol species and their precursors. These 
 

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state-of-science ‘one-atmosphere’ models include five basic components: a chemical kinetic 

mechanism, an emissions model(s), a meteorological model of pollutant transport, a model for 

pollutant removal at the earth's surface, and a set of numerical algorithms for integrating the governing 

species conservation equations. The scientific rigor in the development, testing, and peer-review of 

one-atmosphere models is well-described in a recent annual report by the National Oceanic and 

Atmospheric Administration (NOAA, 2005) to the EPA. Other in-depth reviews of photochemical and 

meteorological models, chemical kinetic mechanisms, emissions inventorying techniques, deposition 

modules, numerical methods, and data base preparation procedures appear extensively in the literature 

(see, for example: http://www.cmascenter.org/index.html). 

 One-atmosphere photochemical dispersion models employ nested, stationary three-dimensional 

arrays of grid cells within which pollutants are emitted, transported from cell to cell, diffused by 

turbulence, undergo chemical reactions, and are removed from the grid region by precipitation, 

adsorption on the ground, and other means. This Eulerian grid model concept has been usefully 

applied and tested across a large range of domain scales, from microscale to regional and continental 

scales. Current generation ‘one-atmosphere’ models estimate concentrations of VOCs, NO, NO2, 

NOx, O3, fine particulate aerosol including sulfate, nitrate, ammonium, elemental and organic species, 

and product species such as HNO3 and PAN. Model predictions are generated at roughly 6 to 10 

minute intervals and then hourly averaged for each grid cell in the three-dimensional computational 

domain. Current regional models are operated for episodes of several weeks duration up to a year or 

more. Model output is displayed as time series plots at all monitoring stations and as two-dimensional 

‘tile’ plots that show the hourly modeled concentrations for each species across the full domain. 

Comparisons between hourly model estimates of gas phase (e.g., ozone, NOx, VOCs, PAN) and 

particulate (sulfate, nitrate, ammonium, organic and elemental carbon, etc.) species and observations 

are readily made. 

 One-atmosphere models offer numerous advantages for certain BART analyses: 

 > They include detailed characterization of all relevant man-made and natural sources of 

NOx, VOCs, SOx, and primary particulates (e.g., sulfate, nitrate, ammonia, carbon) across 

the entire modeling domain on an hourly basis for the entire year. 

 

 > They include state-of-science treatment of the coupled non-linear atmospheric chemistry of 

gas phase photochemical and secondary aerosol species within comprehensive, tested and 

documented chemical mechanism packages. 

 

 > They include detailed characterization of three-dimensional meteorological processes in 

each grid cell of the modeling domain, account for wind speed and direction shears, 

turbulent mixing, precipitation processes, solar radiation, and pressure variations; 

 > They contain modern procedures for specifically treating the near source plume chemical 

and transport dynamics in the near-field before the plume grows to the size of the finest 

Eulerian grid nest (typically 4-12 km). Elimination of the over-dilution problem in the 

original grid models is accomplished through use of high resolution multi-scale nested 

grids and integrated plume-in-grid (PiG) sub-grid-scale (SGS) modules that simulate the 

full interactive dynamics and chemistry of point source plumes until they are assimilated 

into the grid nest; 
 

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 > As the result of detailed mulsti-scale nesting or plume-in-grid treatment and accounting for 

all manmade and natural source emissions in the entire region, one-atmosphere models are 

well suited to the analysis of single BART sources and especially the combination of 

BART sources in a region, a state, or the entire CENRAP domain; 

 > They can evaluate relative benefits of actual control measures on various man-made 

sources that might influence the fate of BART-eligible plumes, including differences in 

location, source type, and composition; 

 > The results provide complete spatial and temporal mapping of concentration fields (with 

and without the BART-eligible source included) that can be used for direct plume impact 

assessment; there is no need to compute or estimate an artificial ‘natural background 

extinction’ level since the base case model simulation (without BART sources) produces 

this automatically. Furthermore, these background estimates can be rigorously evaluated 

using existing CASTNet, IMPROVE, SEARCH, and related aerosol data sets; 

 > They have been widely applied by state and federal regulatory agencies, industrial 

organizations and academic/consulting organizations worldwide; 

 > They are accompanied by in-depth EPA regulatory guidance documents (for 8-hr ozone, 

PM2.5, and Regional Haze) specifying regarding procedures for justifying model usage in a 

protocol; developing emissions, meteorological, and air quality inputs; evaluating model 

performance; and usage in control strategy and SIP development: 

 > There now exist up-to-date, detailed regional date bases for the entire U.S. (plus northern 

Mexico and southern Canada) to support model applications, including high resolution 

36/12 km data bases over the CENRAP region; 

 While these science advantages over CALPUFF are substantial, they come at a price. Oneatmosphere models are (a) computationally intensive relative to puff models, (b) they have significant 

data requirements, and (c) they require a high level of expertise in their use. However, for some 

BART sources in the CENRAP region, these drawbacks may not dissuade source operators from 

considering their use. When compared to the likely cost of BART controls, the marginal increased 

cost of an alternative modeling study is modest indeed. In fact, recent experience in the RPO modeling 

programs suggests that application of ‘one-atmosphere’ regional models with existing annual regional 

haze data sets can be roughly equivalent to the costs of a typical PSD modeling exercise using 

CALPUFF. The regional models run on inexpensive clusters of Linux PCs. Furthermore, in the U.S. 

today there are over a hundred agency, industry, academic, government laboratory, and consulting 

groups using one-atmosphere models, providing an ample resource base for on-line help, training, 

development assistance, documentation, data bases (e.g., the CMAS clearing house) and so on. 

Several questions need to be addressed in determining whether use of a one-atmosphere model 

is preferred over EPA’s default CALPUFF modeling system: 

> What alternative models are available? 

> How do the models treat the sub-grid-scale interaction of BART plumes with the 

surrounding atmosphere containing emissions from other man-made and biogenic 

emissions sources? 
 

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> What are the steps in using an alternative model? 

 > What are the performance capabilities of alternative models for gas phase and fine 

particulate concentrations and visibility impacts? 

 > What data bases are available in the CENRAP domain to support full-science modeling? 

> What are the computational requirements? 

> What are the schedule implications of using an alternative model? 

 > What is the approval process for an alternative model? 

 > What are the costs of using an alternative model instead of CALPUFF? 

 Each of these questions is addressed in the following section. 

4.2 Available Alternative Models 

Though no atmospheric model is wholly accurate and reliable in estimating visibility and fine 

particulate impacts, two models clearly constitute the state-of-science in modeling fine particulate 

aerosol and visibility: 

> CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling 

system is a ‘one-atmosphere’ photochemical grid model capable of addressing ozone, 

particulate matter (PM), visibility and acid deposition at regional scale for periods up to 

one year (Byun and Ching, 1999). Developed by EPA over a 12 year period, the 

CMAQ modeling system is designed to address air quality as a whole by including 

state-of-the-science capabilities for modeling multiple air quality issues, including 

tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation. As 

with CAMx, CMAQ was designed to have multi-scale capabilities so that separate 

models were not needed for urban and regional scale air quality modeling. The CMAQ 

modeling system contains three types of modeling components: (a) a meteorological 

module for the description of atmospheric states and motions, (b) an emission models 

for man-made and natural emissions that are injected into the atmosphere, and (c) a 

chemistry-transport modeling system for simulation of the chemical transformation and 

fate. 

> CAMx: The Comprehensive Air Quality Model with Extensions (CAMx) modeling 

system is a second state-of-science ‘one-atmosphere’ photochemical grid model capable 

of addressing ozone, particulate matter (PM), visibility and acid deposition at regional 

scale for periods up to one year (ENVIRON, 2004). CAMx is a publicly available 

open-source computer modeling system for the integrated assessment of gaseous and 

particulate air pollution. CAMx is designed to (a) simulate air quality over many 

geographic scales, (b) treat a wide variety of inert and chemically active pollutants 

including ozone, inorganic and organic PM2.5 and PM10 and mercury and toxics, (c) 

provide source-receptor, sensitivity, and process analyses and (d) be computationally 

efficient and easy to use. EPA has approved the use of CAMx for numerous ozone and 
 

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PM SIPs throughout the U.S, and has used both CAMx and CMAQ to evaluate regional 

ozone and fine particulate strategies (e.g., CAIR, CAMR). 

Each of these models have been shown to be significantly more accurate and free from systematic 

bias in estimating visibility reducing air pollutants when compared with CALPUFF (Tesche, 2002; 

Morris et al., 2003, 2005a,b; Tesche et al., 2005). 

A potential compromise between the CMAQ/CAMx comprehensive three-dimensional 

photochemical grid grids with full-science that simulates all sources and the single-source CALPUFF 

Lagrangian model with simplified chemistry is the SCICHEM model (Karamchandani et al., 2000), 

which is also a Lagrangian model designed to treat a single source but uses full-science chemistry: 

> SCICHEM: SCICHEM is the reactive chemistry version of SCIPUFF, which stands 

for Second-Order-Closure (SOC) Integrated Puff, and includes comprehensive 

treatment of gas-phase and aqueous-phase chemistry and aerosol processes. SCIPUFF 

simulates plume transport and dispersion using a second-order closure approach to 

solve the turbulent diffusion equations (Sykes et al., 1988, 1993; Sykes and Henn, 

1995). SCIPUFF represents a plume by a collection of three-dimensional puffs that are 

advected and dispersed according to the local meteorological and micrometeorological 

characteristics. Each puff has a Gaussian representation of the concentrations of emitted 

inert species. The overall plume, however, can have any spatial distribution of these 

concentrations, since it consists of a multitude of puffs that are independently affected 

by the transport and dispersion characteristics of the atmosphere. The turbulent 

diffusion parameterization used in SCIPUFF is based on the second-order turbulence 

closure theories of Donaldson (1973) and Lewellen (1977), providing a direct 

connection between measurable velocity statistics and predicted dispersion rates. The 

closure model has been applied on local scales up to 50 km range (Sykes et al., 1988) 

and also on continental scales up to 3000 km range (Sykes et al., 1993c). Gabruk et al. 

(1999) have shown that the second-order closure algorithm provides better model 

performance than both empirical algorithms such as the Pasquill-Gifford-Turner (PGT) 

method or first-order closure algorithms that use similarity theory to relate the 

dispersion coefficients to micrometeorological variables (e.g., as used in CALPUFF). 

SCICHEM can simulate the effect of wind shear since individual puffs will evolve 

according to their respective locations in an inhomogeneous velocity field. As puffs 

grow larger, they may encompass a volume that cannot be considered homogenous in 

terms of the meteorological variables. A puff splitting algorithm accounts for such 

conditions by dividing puffs that have become too large into a number of smaller puffs. 

Conversely, puffs may overlap significantly, thereby leading to an excessive 

computational burden. A puff merging algorithm allows individual puffs that are 

affected by the same (or very similar) micro-scale meteorology to combine into a single 

puff. Also, the effects of buoyancy on plume rise and initial dispersion are simulated by 

solving the conservation equations for mass, heat, and momentum. The gas-phase 

chemical reactions within the puffs are simulated using a general framework that allows 

any chemical kinetic mechanism (e.g., CB-IV, SAPRC) to be treated. SCICHEM 

includes an aqueous-phase chemistry module based on Walcek and Taylor (1986). The 

module includes the major pathways for aqueous-phase conversion of SO2 to sulfate. A 
 

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pathway is also included for the heterogeneous production of nitrate from N2O5

hydrolysis in the presence of cloud or fog droplets. For PM2.5 calculations, SCICHEM 

includes modules to simulate the partitioning of species among the gas, aqueous, and 

solid phases. Particle size distribution is simulated using a sectional representation with 

2 size sections. 

In the 1 August 2005 Federal Register notice (70 FR, 44154), EPA invited comments on the 

use of CMAQ or CAMx in estimating the BART ``benchmarks'' for controls on sources subject to 

BART. For example, regional scale models can be used to inform BART determinations at many 

sources simultaneously through the use of source apportionment techniques which track multiple 

single source contributions of individual pollutant species that combine to reduce visibility. EPA 

notes that this “benchmark” modeling is different from the traditional use of regional scale models 

like CMAQ and/or CAMx to assess the cumulative impact on visibility after controls on all “subject 

to BART” sources have been made. Indeed, this usage is consistent with EPA’s application of 

CMAQ and CAMx in support of the Clean Air Interstate Rule (CAIR) and of the most-stringent-case 

BART for EGUs. 

 What EPA has not addressed, both in the BART Rule and the BART Modeling Guidelines, is 

the capability of full-science models such as CMAQ and CAMx to reliably model the photochemical 

and aerosol processes in individual plumes. EPA’s characterization of regional models in the BART 

Rule and the Guidelines on Air Quality Models makes no mention of the substantial body of model 

research and development performed at EPA and elsewhere over the past 10-20 years in extending grid 

model to treat sub-grid-scale reactive point source plumes. Below, we summarize highlights of the 

model development activities at EPA and other organizations that have resulted in full-science models 

that are indeed capable of simulating the fine details of BART plumes within the context of a regionalscale domain. 

4.3 Treatment of Sub-Grid-Scale Plumes 

The Plume-in-Grid (PiG) technique constitutes an attempt to account rigorously for the subgrid-scale treatment of the dynamic and chemical processes governing gas-phase and aerosol species 

concentrations in isolated, major point source plumes within the grid-based (Eulerian) modeling 

system. Since the original PiG model formulation and atmospheric testing in the 1970s sponsored by 

the Electric Power Research Institute (Seigneur et al., 1981; Tesche et al., 1981), there have been 

sustained research efforts within and outside of EPA to improve the characterization of reactive point 

source plumes within a variety of host photochemical grid models. 

Indeed, development and verification of plume-in-grid (PinG) treatment has been an integral 

component of EPA’s CMAQ modeling system. EPA’s PinG approach was specifically developed to 

simulate the relevant processes governing pollutant concentrations in subgrid scale plumes released 

from major point sources within the CMAQ grid modeling domain. Since excessive dilution occurs 

when high NOx or SOx point source emissions are instantly emitted into large volumes of Eulerian 

grid cells defined for typical regional modeling domains, chemical processes and pollutant 

concentration levels from point source emissions are greatly impacted. Consequently, the PinG 

approach models the meteorological, photochemical, and aerosol processes at the appropriate spatial 

dimension by allowing for the realistic, gradual horizontal/vertical growth of each subgrid scale plume. 

The PinG treatment has been fully integrated into the CMAQ chemical transport model (CTM) 
 

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4.3.1 CMAQ PinG 

Godowitch and Young (2005) describe the current status of EPA’s program to implement and 

verify the plume-in-grid (PinG) technique in the CMAQ chemical transport model. As described by 

Gillani and Godowitch (1999) EPA’s original goal was to provide a realistic treatment of the dynamic 

and chemical/aerosol processes impacting pollutant concentrations in major point source plumes. They 

formulated the CMAQ/PinG model with an imbedded Lagrangian plume technique to simulate the 

relevant atmospheric dispersion processes governing vertical and horizontal plume expansion. This 

more realistic treatment of plume growth allows photochemistry and aerosol formation to evolve at the 

proper spatial and temporal scales. (Godowitch and Young, 2005). The current PinG module in CMAQ 

utilizes the same chemical mechanisms (i.e., CB-IV, SAPRC-99) and aerosol modules used by the 

parent chemical transport model. The updated AE3 aerosol algorithm in CMAQv4.4 was incorporated 

and successfully tested in the PinG module. The current 2005 public release (CMAQ4.5) contains 

EPA’s most recent PinG module that has significantly improved capability to simulate aerosol species 

and PM2.5 along with gas-phase photochemistry in the subgrid plumes. 

During a simulation in which point sources are treated as PinG, CMAQ provides boundary 

conditions at the plume edges in the PinG model. For example, in Figure 4-1, the five individual 

plumes embedded in the regional grid all receive gaseous and aerosol boundary condition updates at 

each integration time step (~6 min). In the PinG model, a continuous plume is simulated by hourly 

emissions released into a new plume section. The PinG model resolves the detailed horizontal internal 

structure of each plume section by an attached set of plume cells. Figure 4-2 shows the modeled cross 

section of a high NOx/ low SO2 industrial plume in the middle Tennessee area. Once a plume section 

reaches the grid size, its subgrid PinG simulation ceases and a feedback of plume material into the 

CTM grid is performed. The net impact of a potential source is determined by subtracting a base case 

simulation (i.e., a NoPinG run) from a full CMAQ-PinG simulation (see Figure 4-3 for example). A 

full description of the capabilities of EPA’s CMAQ-PinG model and its technical formulation were 

described in EPA’s original science document (Gillani and Godowitch, 1999) and more recently by 

Godowitch (2001, 2004) and NOAA (2005). 

4.3.2 CMAQ-APT-PM 

Another PinG formulation incorporated into EPA’s CMAQ model and evaluated with 

monitoring program data is described by Vijayaraghavan, et al., (2004). CMAQ-APT-PM consists of 

the EPA CMAQ model with an embedded reactive plume model, the Second-order Closure Integrated 

puff model (SCIPUFF) with CHEMistry (SCICHEM). SCICHEM uses a second-order closure 

approach to solve the turbulent diffusion equations. The plume is represented by a myriad of threedimensional puffs that are advected and dispersed according to the local micrometeorological 

characteristics. Each puff has a Gaussian representation of the concentrations of emitted inert species. 

The overall plume, however, can have any spatial distribution of these concentrations, since it consists 

of a multitude of puffs that are independently affected by the transport and dispersion characteristics of 

the atmosphere. SCICHEM can simulate the effect of wind shear since individual puffs will evolve 

according to their respective locations in an inhomogeneous velocity field. The effects of buoyancy on 

plume rise and initial dispersion are simulated by solving the conservation equations for mass, heat, 

and momentum. The formulation of nonlinear chemical kinetics within the puff framework is 

described by Karamchandani et al. (2000). Chemical species concentrations in the puffs are treated as 

perturbations from the background concentrations. The chemical reactions within the puffs are 

simulated using a general framework that allows any chemical kinetic mechanism to be treated. The 
 

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full CMAQ PM aerosol and aqueous-phase chemistry is included in the PinG formulation of CMAQAPT-PM (Karamchandani et al., 2005). 

 4.3.3 CMAQ-MADRID-APT 

 Karamchandani, et al., (2005) describe another implementation of plume-in-grid technology 

into the CMAQ framework, but this modeling system used an alternative state-of-science gas-phase 

and aerosol chemical mechanism. CMAQ-MADRID (CMAQ with the Model of Aerosol Dynamics, 

Reaction, Ionization and Dissolution, MADRID) is a version of CMAQ that is publicly available and 

currently distributed by EPA’s Community Modeling and Analysis System (CMAS) center. CMAQMADRID-APT utilizes the same emissions, meteorological and air quality files as the other CMAQ 

versions discussed above. It also has been tested with atmospheric data. 

 4.3.4 CAMx PM IRON PiG

 Implementation of plume-in-grid algorithms in CAMx has been underway since the mid 1990s. 

Emery and Yarwood (2005) and Yarwood et al., (2005) describe the science formulation, 

implementation and testing of two versions: IRON PiG and PM PiG. The CAMx IRON (Incremental 

Reactions for Organics and NOx) Plume-in-Grid model features: 

> New puff structure and dynamics to better account for deforming shears; 

> Second-order closure puff spread calculation following SCIPUFF equations; 

> Puff initialization and transport performed on the time step of finest grid; 

> Full photochemistry using CB-IV or SAPRC99; 

> Revised puff dumping approach for IRON PiG based on comparison to cell area; and 

> "Virtual dumping" of puff mass into average output concentration fields for postprocessing and analysis. 

Implementation of PM chemistry into the IRON PiG closely paralleled the approach for grid 

chemistry. The PM chemistry modules are used in each puff following the same incremental 

chemistry approach that IRON PiG uses for the gas-phase ozone chemistry; i.e., by separate 

integrations for background and puff + background (to determine the evolution of puff incremental 

concentrations.) The PM PiG updates have been incorporated into CAMxv4.20_PMPiG. Details on 

the PiG implementations in CAMx are available at www.camx.com) 

 4.3.5 CAMx Multi-Scale Flexi-nesting

A final procedure for treating sub-grid-scale plume chemistry and physics takes advantage of 

the ‘flexi-nesting’ capability of CAMx. In this method, a series of nested grids are defined with 

successively smaller horizontal grid spacing, provide a cascading multi-scale representation from local 

plume scale (say a few hundred meters) to regional scale (12 or 36 km). Through careful assessment 

of the meteorological conditions influencing the BART source and Class I receptor(s), a multi-scale 

nested grid system can be defined that directly overcomes the plume over-dilution problem of older 

Eulerian models while retaining manageable computational times over the annual period. CENRAP’s 

existing MM5/CALMET meteorological fields for 2001-2003 can be used to define the sector(s) of 

greatest concern for the BART plume, thereby allowing the modeler to ensure that the fine grid mesh is 

used to best effect. 
 

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4.4 Steps in an Alternative Modeling Study 

 The motivation to consider an alternative model to CALPUFF for the BART exemption and/or 

BART determination analysis would typically come from a State or source operator. The first step in 

the process would be to discuss the rationale for use of an alternative model with representatives from 

the state, EPA, and FLM. Since decisions on acceptability of an alternative model is a joint 

responsibility, it is prudent to engage the EPA and FLM early in this process. Section 4.8 describes the 

specific steps in the approval process. 

 Consistent with the approval process and to guide the actual modeling study, a detailed sourcespecific modeling protocol must be prepared. While this protocol would generally follow the content 

of the source-specific CALPUFF protocol (see Chapter 7), additional technical details would be 

required in the description of the model configuration, model-set up procedures, data bases to be 

employed, model evaluation and sensitivity tests, and procedures for application. 

 Once the alternative modeling protocol is approved, the state or source operator (or consultant) 

would implement the procedures set forth in the document. Typically, these would include: selection of 

appropriate emissions, meteorological, and air quality models; selection of modeling year(s) to use; 

selection and acquisition of pertinent regional modeling data bases (e.g., from CENRAP, VISTAS, 

MRPO, or WRAP); base case model simulation; model performance evaluation and sensitivity testing; 

modeling of BART source(s) with appropriate sub-grid-scale plume technology; calculation of 

background visibility; comparison of BART source plume increment relative to background visibility; 

uncertainty analysis; reporting; and modeling data archival and distribution. 

4.5 Accuracy of Regional PM and Visibility Models 

A major motivation for using full-science models for BART analysis is the expectation that 

they provide more accurate and less biased predictions of visibility reducing fine particulate aerosols 

than CALPUFF. While this likelihood is easily seen in comparative evaluations of CALPUFF and 

one-atmosphere model science formulations (e.g., Morris et al., 2003), a comparison of model 

performance against actual gas-phase, fine particulate aerosol, and visibility measurements is also 

instructive. Indeed, as noted in Section 4.8 below, one of the factors influencing EPA and FLM 

assessment of an alternative model is whether it performs better for the application than CALPUFF 

and better satisfies the BART Rule regulatory requirements. 

 Unfortunately, it is not possible to carry out a clean, head-to-head comparative model 

evaluation between CALPUFF and full-science models for their ability to simulate fine particulate 

species and visibility. First, there simply are no detailed atmospheric plume data bases that record the 

chemical evolution of point source plumes at numerous downwind locations (i.e., from point of 

emission to downwind distances of 200-400 km where the concentrations of sulfates, nitrates, EC, OC, 

secondary organic aerosols (SOA), and extinction coefficient are measured.) If such data bases 

existed, it would be a simple matter to compare alternative modeling platforms. Second, CALPUFF 

has no reliable means for accounting for the effects of all other manmade and natural emissions 

sources in the region that participate in the chemical transformation of point source emissions. Thus, 

one cannot evaluate CALPUFF predictions against downwind secondary aerosol measurements or 

visibility. Measurements reflect the interaction of a variety of upwind sources; CALPUFF outputs 

principally the fate of emissions from the point source(s) under consideration. 

 
 

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Several evaluations of CALPUFF that have been carried out using inert atmospheric tracers 

(see Section 3.6). These studies are useful in elucidating the model’s ability to simulate the transport 

and dispersion of non-reactive materials. In contrast, there is a rich and evolving performance 

evaluation history of regional one-atmosphere models, stimulated in large measure by the CMAQ 

and/or CAMx modeling being performed by WRAP, CENRAP, MRPO, VISTAS and MANE-VU for 

regional haze. These regional modeling studies have examined one or both models over grid domains 

of 36 km and 12 km for the years 2001, 2002, and 2003 (depending upon the RPO). Detailed 

performance evaluations of the models have been carried out by various groups, focusing on several 

performance attributes of relevance to BART analyses. For the CENRAP modeling (Morris et al., 

2005d), these evaluations, for NO, NO2, ozone, sulfate, nitrate, ammonium, ammonia, EC, OC, 

secondary organic aerosol, PM2.5, extinction coefficient, and various other species are reported as: 

 > Daily scatter and time-series plots at each individual site.

 > Monthly average model performance metrics. Bar plots for 12 months showing bias 

and error averaged over all sites in the CENRAP region, providing a quick 

summary of the variability in seasonal performance. 

 > Bugle plots grouped both by species and by monitoring network. Monthly average 

error and bias terms are plotted in a Bugle plot as a function of the species 

concentration. This helps characterize differences in model performance at clean 

versus polluted conditions. These plots also include curves showing model 

benchmarks and performance goals. Benchmark curves indicate the skill of a wide 

array of previous PM modeling studies. Performance goals are arbitrarily defined 

based on ozone performance goals. 

 > Animated spatial plots with data overlaid on the model predictions: These animated, 

daily average plots show the IMPROVE and AQS data superimposed over the 

model simulated data. These are useful for showing spatial distributions and 

instances where poor model performance occurs because the model mis-locates the 

high concentrations by just a few grid cells from the observed high concentrations. 

 > Stacked bar time-series plots for each site: These plots show contributions of each 

PM component to visibility impairment for the daily average for each day with 

ambient data in one plot, and for each of the 365 modeled days in a second plot. 

 > Stacked bar plots for the best & worst 20% day average at each site using (1) 

matched in space & time; (2) relaxed in time; and (3) relaxed in both space and 

time: These comparisons show whether the model is capable of simulating the full 

range of clean to polluted conditions seen in the ambient data at the individual sites. 

It increases confidence in the model as a "reasonable version of reality" if it can 

simulate the range of clean to polluted conditions. 

Three recent studies summarize the current performance capabilities of the CMAQ and CAMx 

regional models for aerosol species and visibility. Morris et al., (2005d) summarize the performance of 

CAMx and CMAQ over the 36 km CENRAP domain for the full calendar year 2002. For VISTAS, 

Morris et al., (2005a,b) and Tesche et al., (2005a) summarize detailed performance evaluations of 

CAMx and CMAQ for the VISTAS region, also for 2002. 
 

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Boylan and Russell (2005) proposed PM model performance goals (the level of accuracy that is 

considered to be close to the best a model can be expected to achieve) and criteria (the level of 

accuracy that is considered to be acceptable for regulatory applications) that vary as a function of 

concentration and extinction. Specifically, it has been proposed that a model performance goal has 

been met when both the mean fractional error (MFE) and the mean fractional bias (MFB) are less than 

or equal to +50% and ±30%, respectively. Additionally, the model performance criteria has been met 

when both the MFE ≤ +75% and MFB ≤ ±60%. Less abundant species would have less stringent 

performance goals and criteria. These recommendations are based upon an analysis of numerous PM 

and visibility modeling studies performed throughout the country. 

In particular, Boylan and Russell (2005) tabulated the mean fractional bias and mean fractional 

error for several recent one-atmosphere modeling studies: SAMI, WRAP, VISTAS, Midwest RPO, and 

EPRI. The one-atmosphere models evaluated included CAMx, CMAQ, CMAQ-MADRID, and URM. 

Figure 4-9 presents so-called ‘bugle plots’ of fractional bias and fractional error for each component of 

PM as well as PM10 and PM2.5. Overall model performance for PM2.5 is fairly good with about 50% of 

the points meeting the goals and a large majority meeting the criteria. Sulfate performance is better 

than most components of PM2.5 that were examined, with a majority of the points meeting the goal and 

only two points falling outside the criteria. This was attributed to good estimates of SO2 emissions, 

sulfate chemistry being less complex than other components of PM species, and the high spatial 

homogeneity of sulfate. On the contrary, nitrate performance is poorer with approximately 40% of the 

points falling outside the criteria and only values smaller 0.8 μg m-3 meeting the goal. As pointed out 

by recent EPA researchers (Yu et al., 2005) nitrate performance with all atmospheric models continues 

to be a significant challenge. Ammonium performance falls between that of sulfate and nitrate. 

Overall, the performance is fairly good because the majority of the ammonium is associated with 

sulfate which performs well. Less than 40% of the organic performance assessments meet the goal and 

a fair number fall outside the criteria. The large errors are mainly due to simulated organic levels 

being lower than observations, especially at the urban STN sites. Elemental carbon concentrations are 

typically below 1.0 μg m-3 and the performance is within the performance goals for all assessments 

except one, which is just barely outside the goal. Approximately 40% if the soil points meet the goal, 

while another 45% are outside the criteria. Most of the soil error is caused by large overestimations by 

the model. Finally, coarse mass performance is poor with only 2 points meeting the goal and a 

majority falling outside the criteria. 

Boylan and Russell (2005) performed an identical analysis for components of light extinction. 

They report similar performance patterns to those of PM for sulfate, nitrate, ammonium, organics, and 

elemental carbon. These similarities are due to the fact that each of these components receive 

multipliers that are on the same order of magnitude as the scale change when converting from mass to 

light extinction. However, since soils and coarse mass do not get large multipliers due to their minimal 

impact on visibility, their performance is much improved over that of their PM mass performance. In 

fact, only a handful of soils and coarse mass points fall outside the criteria lines. Using the identified 

metrics, sulfate and EC components of PM2.5 and light extinction are generally the most accurately 

simulated, while nitrate and organic carbon performance are poor. Poor soil performance is of concern 

for PM2.5, but not visibility. Poor coarse mass performance does not impact total PM2.5 and has 

minimal impact on total light extinction. Detailed model evaluation results for the CENRAP, MPPO, 

VISTAS, and WRAP modeling studies may be found at: 

 CENRAP: http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml

 MRPO: http://www.ladco.org/
 

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 VISTAS: http://pah.cert.ucr.edu/vistas/vistas2/results.shtml

 WRAP: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

In summary, the performance status of current one-atmosphere models such as CMAQ and 

CAMx reveals that for some species (sulfate, PM2.5) the models do quite well while for certain others 

the models exhibit bias and large uncertainty (e.g., nitrate concentrations, secondary organic aerosols). 

The models have been tested over a full range of atmospheric conditions covering the CENRAP region 

and have been evaluated for up to three continuous years (2001, 2002, and 2003). This performance 

record clearly surpasses – in rigor, scope, and statistics – any evaluation that has been published to date 

for CALPUFF. The current status of one-atmosphere models in simulating fine particulate aerosols 

and visibility impairment is thus well established and this information would be quite helpful in 

supporting discussions with EPA Regional Offices and the FLM on the merits of using full-science 

visibility models as alternatives to CALPUFF.

4.6 Data Base Requirements 

The data sets needed to support the evaluation and application of alternative regional models 

may be grouped into three general categories: emissions, meteorology, and air quality. Fully 

developed CAMx and CMAQ modeling data sets at 36/12 km grid scales are available over the 

CENRAP states and may be obtained from one or more RPOs. Details are described in Chapter 5.

 

4.7 Resource Requirements 

 

The current availability of RPO-developed regional PM modeling data bases and publiclyavailable modeling tools and technical support venues now make it feasible to perform a BART 

visibility impact study in a matter of a few months at a cost that is not too much larger than a 

traditional PSD modeling exercise. A summary of current computer, staff, and schedule requirements 

associated with a BART exemption or BART determination study is given below.

 4.7.1 Computers 

 While the computational costs to run CMAQ or CAMx for a typical BART application far 

exceed those of a guideline CALPUFF analysis, the hardware requirements are still well within the 

range of modern scientific computing set-ups. Given the availability of model-ready meteorological, 

emissions, and air quality data bases from the various RPOs, the only significant computational cost in 

alternative modeling is in running the base case simulation to establish background conditions, and the 

BART impact cases to assess determine if the source is ‘subject to BART’ and if so, what appropriate 

controls might be. As with the CALPUFF applications, there is no need to develop new meteorology. 

Unlike CALPUFF, there is no need to develop estimates of background emissions, or air quality; these 

are already compiled in the existing RPO data bases. The only data input development need, common 

to CALPUFF too, is the characterization of the point source’s emissions. 

 CMAQ and CAMx run efficiently on modern commodity PCs under the Linux operating 

system. For example, a typical Linux cluster configuration for applying CMAQ in multi-processor 

mode might include 3 dual Athlon MP 2800+ computers (6 total processors), 1 to 2.5 Gybte RAM for 

each node, a GigE switch connection, and 1-3 Tbyte external hard disk storage for model input and 

output files. With four such clusters, an annual CMAQ simulation can be performed by quarter with 15 

days spin-up. Four quarters are run in parallel, saving project calendar time. In this example, four (4) 

sub-clusters of three dual-processor Athlon MP machines would be employed with the ‘master" node 
 

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of each sub-cluster maintaining one 250 Gbyte drive for meteorological data per quarter and one 250 

Gbyte drive for output for quarter. Experience shows that using more than 3 nodes (i.e., 6 processors) 

per quarter does not yield substantial run-time speed up. 

 CAMx4.2 employs a shared memory scheme rather than a parallel processing scheme, so 

model simulations are performed on one stand-alone system. Multiple jobs can be run in parallel 

however. The overall computer run times of a projects do not seem to vary significantly between 

CMAQ and CAMx platforms. 

 Finally, EPA provides a website for the community to share different experiences in 

acquiring, configuring, and applying different hardware solutions for CMAQ and other oneatmosphere tools and data bases. Go to: http://www.cmascenter.org/html/hardware.html. 

4.7.2 Staff Expertise 

Setting up and exercising the CMAQ or CAMx modeling system requires a high level of 

atmospheric modeling expertise and typically transcends the skill level normally associated with 

operating EPA guideline models. Particular areas of modeling expertise involve meteorology, 

atmospheric chemistry, emissions inventory development, regional air quality modeling, data analysis, 

and computational science. However, there are over 100 groups in the U.S. today that possess this 

level of expertise; many reside within state agencies, regional EPA offices, governmental laboratories, 

and academic institutions. There is also a fairly large consultant community with the requisite level of 

experience. Accordingly, for the limited set of states or source operators for whom alternative regional 

modeling may be attractive, there should be no shortage of skilled practitioners available to assist with 

the modeling analyses. 

 4.7.3 Schedule 

Typically a 3 to 4 month time period is required to design, negotiate, implement, and complete 

CALPUFF modeling for a nominal BART application. Of course, some studies can be performed 

more quickly and others may take longer than 4 months. 

 An alternative modeling study will require more time than a CALPUFF application for three 

reasons. First, because the use of advanced regional models may be somewhat new to some CENRAP 

state or federal agencies, the length of time to negotiate a consensus CMAQ or CAMx BART 

Modeling Protocol will take longer. Part of this stems from the requirement to justify the need and 

appropriateness of alternative models to CALPUFF. In this connection, there are some important 

decisions to be ratified (calculation of ‘natural visibility background’, number of years to be modeled, 

appropriate sub-grid-scale plume technology to be used, etc) and this will require discussion and 

negotiation with state and federal agencies. Second, the full-science models require a couple of weeks 

or more to simulate a full calendar year. So, depending on the number of years modeled and the 

number of BART control strategies examined, computer simulations alone may necessitate two months 

or more of calendar time.1

 Third, unlike CALPUFF model applications, the alternative model protocol 

development and annual visibility simulations can not realistically be carried out in parallel. 

Negotiation of the protocol must first be completed before the commitment to run annual CMAQ or 

 

1

 Most modeling centers operate several clusters of high-speed Linux machines; by configuring the clusters and model 

simulation jobs thoughtfully, multiple annual CAMx or CMAQ simulations can be carried out in parallel, thus 

condensing elapsed time for the model applications.
 

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CAMx simulations is actually implemented. Based on these factors, it is reasonable to expect that an 

alternative BART modeling application would require on the order of 4 to 6 months to complete. 

4.8 Approval Process for an Alternative Model 

 The decision to use a full-science alternative model would typically be made by the State or the 

particular source. To justify the use of an alternative model, one must follow the procedures set forth 

in the Guideline on Air Quality Models for obtaining approval for the use of a non guideline models. 

EPA encourages selection of the best techniques for each individual air quality analysis, but requires 

that the selection be done in a consistent manner. 

Determination of acceptability of an alternative model is a Regional Office responsibility. If it 

can be demonstrated that in a specific application the alternative model is more appropriate than 

CALPUFF, the model may be used subject to certain requirements. This demonstration before the 

Regional Administrator normally requires that the State or the source show that either CALPUFF is 

not appropriate for the specific application or the alternative model is available and is applicable. 

There are three separate conditions under which an alternative model will normally be approved for 

use: 

> If a demonstration can be made that the model produces concentration estimates 

equivalent to the estimates obtained using a preferred model; 

> If a statistical performance evaluation has been conducted using measured air quality 

data and the results of that evaluation indicate the alternative model performs better for 

the application than a comparable model in appendix A; and 

> If there is no preferred model for the specific application but a refined model is needed 

to satisfy regulatory requirements. 

Any one of these three separate conditions may warrant use of an alternative model. 

The procedures and techniques for determining the acceptability of a model for an individual 

case based on superior performance is contained in the document entitled “Interim Procedures for 

Evaluating Air Quality Models” should be followed. Preparation and implementation of an evaluation 

protocol which is acceptable to both control agencies and regulated industry is an important element in 

such an evaluation. If it can be shown that CALPUFF is not applicable to the BART source-specific 

modeling problem at hand, an alternative refined model may be used provided that: 

> The model can be demonstrated to be applicable to the problem on a theoretical basis; 

> The data bases which are necessary to perform the analysis are available and adequate; 

and 

> Performance evaluations of the model in similar circumstances have shown that the 

model is not biased toward underestimates. 

In short, the guidance requires that the alternative model be evaluated from both a theoretical and a 

performance perspective. 
 

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EPA has prepared a document entitled “Interim Procedures for Evaluating Air Quality Models” 

to assist in developing a consistent approach when justifying the use of other than the preferred 

modeling techniques. Another EPA document “Protocol for Determining the Best Performing 

Model”provides a general framework for objective decision-making on the acceptability of an 

alternative model for a given regulatory application. These documents contain procedures for 

conducting both the technical evaluation of the model and the field test or performance evaluation. 

Ultimately, the Regional Administrator has the authority to determine whether an alternative 

model is acceptable for a source-specific BART analysis. However, recognizing the need for 

assistance and guidance in the selection process so that fairness and consistency in modeling decisions 

is fostered among the various Regional Offices and the States, EPA’s Model Clearinghouse serves as 

the final authority on modeling issues. 

4.9 Cost of Alternative Modeling 

The cost of a CALPUFF dispersion modeling study for a source can vary depending upon 

several factors including: (a) availability of meteorological and air quality data sets, (b) availability of 

emissions source characterization (especially particulate matter speciation and size fractionization), (c) 

the number of Class I areas to be examined, (d) the distance(s) from source to Class I area(s), (e) the 

effort required to prepare and successfully negotiate the modeling protocol with the state, EPA and 

FLM, and various other factors. Computer costs are minimal since the CALPUFF system runs on 

personal computers equipped with inexpensive external hard disk drive storage. (Current storage costs 

run less than $1 per Gigabyte). Experience shows that the cost of a typical CALPUFF modeling 

exercise for a BART source2

 is in the range of $20,000 to $50,000 depending upon the nature of the 

problem, the expertise and cost structure of the organization performing the modeling, the modeling 

experience of the state or source operator, and the extent of interaction required with the reviewing 

agencies. 

 An alternative modeling study using CAMx or CMAQ costs more than a CALPUFF BART 

application but the differential is much less today given the ready availability of applicable regional 

data sets. Key cost elements in an alternative modeling study include: (a) protocol development and 

negotiation with the reviewing agencies, (b) obtaining and setting up one or more RPO data bases on 

the host computer network3

, (c) running the base case regional model simulation to establish 

‘background visibility conditions, (d) running the regional model with the BART source included, and 

(e) post processing and reporting the results. Assuming that the alternative modeling was performed 

for one calendar year (e.g., 2001, 2002, or 2003), the cost range is $50,000 to $75,000. 

 The greatest cost variable in alternative modeling is whether the state, EPA, and/or FLM will 

require that more than one calendar year be modeled to establish whether a source is ‘subject to 

BART’ and/or the level of BART emissions controls needed. If a full three year CMAQ or CAMx 

modeling analysis is required, the aforementioned cost range could double. However, even if the 

reviewing agencies require a full three years of CMAQ or CAMx modeling, there are still several 

opportunities for cost reduction. Opportunities include: 

 

2 This exercise would includes analyses to determine whether the source is ‘subject to BART’, followed by several 

CALPUFF model runs to estimate the visibility impacts of different levels of BART emissions controls. 3

 The computational platform needed to efficiently run the full-science models is a Linux- or Unix-based cluster with 

multiple nodes and several TBytes of external disk storage. 
 

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> If three full years of alternative modeling is performed to confirm that a source is 

subject to BART, then the BART control modeling can likely focus just on that year for 

which the maximum visibility impacts were calculated. 

> Exercise the CAMx or CMAQ models in an episodic mode only, simulating only those 

infrequent time periods when peak 24-hr average visibility impacts are greatest. 

 > Invoke the plume-in-grid or multi-scale grid nesting SGS technologies in the regional 

model only during those episodic periods where peak 24-hr average impacts were 

estimated in the initial modeling; 

> Exercise the CAMx or CMAQ models in an episodic mode only, simulating only those 

infrequent time periods when peak 24-hr average visibility impacts are greatest. 

Thoughtful application of alternative models can very likely reduce the overall program cost. The 

greatest variable in estimating the resources required to employ CAMx or CMAQ in BART modeling 

is the level of effort required to work with the state, EPA, and FLM to reach a consensus on how the 

model(s) should be applied. Since this is likely to be ‘new territory’ for some agency staff, it may 

require somewhat more effort to reach consensus. 
 

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Figure 4-1. Plume Trajectory Paths and Growth Rates of Sub-Grid-Scale Reactive Plumes 

within the CMAQ-PinG Model Simulation of the July 1995 SOS Field Program in 

Nashville, TN. Plumes Released at 1500 UTC on 7 July 1995 on the Modeling 

Domain with the 36 km Grid. (Source: Godowitch, 2004). 
 

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Figure 4-2. Reactive Plume Species Concentrations in the Cross Section from a High NOx/Low 

SO2 Emissions Source at 5 Hours After Release. Ozone (O3) is the solid red line. 

Units: SO4 (μg/m3), OH (ppt), SO2 and HNO3 (ppb). (Source: Godowitch, 2004). 

Figure 4-3. Aerosol Sulfate (SO4) Difference Field Determined by Subtracting the CMAQNoPinG Concentrations from the CMAQ-PinG Concentrations at 2 pm EDT on 7 

July 1995. (Source: Godowitch, 2004). 
 

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Figure 4-4. Change in the Contributions of Fourteen (14) EGUs to Local Sulfate (SO4) 

Concentrations When the CMAQ-APT-PM Plume-in-Grid Approach is Used. 

(Source: Karamchandani et al., 2005). 

Figure 4-5. Change in the Contributions of Fourteen (14) EGUs to Local Nitrate (NO3) 

Concentrations When the CMAQ-APT-PM Plume-in-Grid Approach is Used. 

(Source: Karamchandani et al., 2005). 
 

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Figure 4-6. CAMx PM PiG Simulation of Ozone Over the Upper Midwest on 14 June 2002: Plot 

on Right Reveals the Ozone Increments Associated with Sub-Grid-Scale Treatment 

of Reactive Plumes. (Source: Yarwood et al., 2005). 

Figure 4-7. CAMx PM PiG Simulation of SO2 and Sulfate Over the Upper Midwest on 14 June 

2002: Plot on Right Reveals the SO2 and SO4 Increments Associated with Sub-GridScale Treatment of Reactive Plumes. (Source: Yarwood et al., 2005). 
 

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Figure 4-8. CAMx PM PiG Simulation of NO3 and NH4 Over the Upper Midwest on 14 June 

2002: Plot on Right Reveals the NO3 and NH4 Increments Associated with SubGrid-Scale Treatment of Reactive Plumes. (Source: Yarwood et al., 2005). 
 

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(a) Mean Fractional Bias in 24-hr Average Model Predictions 

(b) Mean Fractional Error in 24-hr Average Model Predictions 

Figure 4-9. Mean Fractional Bias and Error in Fine Particulate Species Predictions from the 

CAMx, CMAQ, URM, and CMAQ-MADRID Models over Several Regional PM and 

Visibility Model Evaluation Studies. (Source: Boylan and Russell, 2005).