4-1 4.0 ALTERNATIVE MODELING

In some situations, the modeling of a BA RT-eligible source may require more rigorous simulation tools in order to adequately estimate th e 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 st rong discrete sources of visibility-reducing emissions (e.g., SO 2 , NOx);

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

areas;

> Complex meteorological flow s entailing significant lateral a nd/or vertical wind shear or stagnation; and

> Extensive transport distance between source location and the neares t 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 Cla ss I area given the propensity of CALPUFF to overpredict fine particulate aerosol co ncentrations 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 reasone d BART control requirements may prompt some states or BART source ope rators to pursue alternative modeling.

This chapter summarizes these tools and pr ovides 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 discussi ons with the state, EPA and FLM.

4.1 Overview

All source-oriented models, in cluding CALPUFF, are derived fr om the fundamental atmospheric species conservation equation (Tesche, 1983). Much of the difference between simple and comprehensive models stems from the extent to wh ich the various chemical and physical atmospheric processes are treated: ri gorously, heuristica lly, in a highly parameteri zed fashion, or neglected altogether. Model developers must choose what pollu tants 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 sw iftly on a PC with minimal data requirements.

At the other end of the spectru m, full-science photochemical disp ersion models rigorously treat the major processes that govern the formation, tran sport, gas-phase and secondary aerosol chemical transformation and removal of atmospheric gas phase and aerosol species and their precursors. These4-2 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 su rface, and a set of numerical algo rithms 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 re cent annual report by the National Oceanic and Atmospheric Administrati on (NOAA, 2005) to the EPA. Other in -depth reviews of photochemical and meteorological models, chemical kinetic mechanis ms, emissions inventorying techniques, deposition modules, numerical methods, and data base preparation procedures appear extens ively in the literature (see, for example: http://www.cmascenter.org/index.html ).

One-atmosphere photochemical dispersion mode ls employ nested, stati onary three-dimensional arrays of grid cells within which pollutants are emitted, transported from cell to cell, diffused by turbulence, undergo chemical reactions, and are re moved from the grid region by precipitation, adsorption on the ground, and other means. This Eu lerian grid model con cept has been usefully applied and tested across a large range of domain scales, from microscale to regional and continental scales. Current generation ‘one-atmosphere’ mode ls estimate concentrations of VOCs, NO, NO 2 , NOx, O 3 , fine particulate aerosol includi ng sulfate, nitrate, ammonium, elemental and organic species, and product species such as HNO 3 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 monitori ng stations and as two-dimensional ‘tile’ plots that show the hourly modeled concentrations for each species acro ss the full domain. Comparisons between hourly model estimates of gas phase (e.g., ozone, NOx, VOCs, PAN) and particulate (sulfate, nitrate, am monium, organic and elemental car bon, etc.) species and observations are readily made.

One-atmosphere models offer numerous a dvantages for certain BART analyses:

> They include detailed char acterization of all relevant ma n-made and natural sources of NOx, VOCs, SOx, and primary particulates (e.g., sulfate, nitrate, ammonia, carbon) across the entire modeling domain on an hour ly basis for the entire year.

> They include state-of-sci ence 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 specifi cally 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). El imination 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;4-3 > As the result of detailed mulsti-scale nestin g or plume-in-grid trea tment and accounting for

all manmade and natural source emissions in th e 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 BA RT-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 so urce 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 back ground estimates can be rigorously evaluated using existing CASTNet, IMPROVE, SEAR CH, and related aerosol data sets;

> They have been widely applied by stat e and federal regulatory agencies, industrial organizations and academic/consulting organizations worldwide;

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

PM 2.5 , and Regional Haze) specifying regarding proc edures for justifying model usage in a protocol; developing emissions, meteorologi cal, and air quality inputs; evaluating model performance; and usage in cont rol strategy and SIP development:

> There now exist up-to-date, detailed regional da te bases for the entire U.S. (plus northern Mexico and southern Canada) to support mo del applications, including high resolution 36/12 km data bases over the CENRAP region;

While these science advantages over CALPUFF ar e 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 draw backs may not dissuade source operators from considering their use. When compared to the like ly cost of BART controls, the marginal increased cost of an alternative modeling study is modest in deed. In fact, recent experience in the RPO modeling programs suggests that application of ‘one-atmosphe re’ 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 clus ters of Linux PCs. Furt hermore, 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 ba ses (e.g., the CMAS clearing house) and so on.

Several questions need to be addressed in de termining whether use of a one-atmosphere model is preferred over EPA’s defa ult CALPUFF modeling system:

> What alternative models are available?

> How do the models treat the sub-grid-sca le interaction of BART plumes with the surrounding atmosphere containing emissi ons from other man-made and biogenic

emissions sources?4-4 > 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 pro cess for an alternative model?

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

Each of these questions is a ddressed 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 m odeling 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 de position at regional scale for periods up to one year (Byun and Ching, 1999). Devel oped by EPA over a 12 year period, the CMAQ modeling system is designed to addr ess air quality as a whole by including state-of-the-science capabilities for mode ling 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 re gional scale air quality modeling. The CMAQ modeling system contains th ree types of modeling compone nts: (a) a meteorological module for the description of atmospheric st ates and motions, (b) an emission models

for man-made and natural emissions that ar e injected into the atmosphere, and (c) a chemistry-transport modeling system for simulation of the chemical transformation and

fate.

> CAMx: The Comprehensive Air Quality Mode l with Extensions (CAMx) modeling system is a second state-of-science ‘one-a tmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid de position 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 PM 2.5 and PM 10 and mercury and toxics, (c) provide source-receptor, sensitivity, and proc ess analyses and (d) be computationally efficient and easy to use. EPA has approve d the use of CAMx for numerous ozone and4-5 PM SIPs throughout the U.S, and has used both CAMx and CMAQ to evaluate regional ozone and fine particulate st rategies (e.g., CAIR, CAMR).

Each of these models have been shown to be signi ficantly more accurate an d free from systematic bias in estimating visibility re ducing air pollutants when compar ed with CALPUFF (Tesche, 2002;

Morris et al., 2003, 2005a,b; Te sche et al., 2005).

A potential compromise between the CMAQ/ CAMx comprehensive three-dimensional photochemical grid grids with full-science that si mulates 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 trea t a single source but uses full-science chemistry:

> SCICHEM: SCICHEM is the reactive chemistry version of SCIPUFF, which stands for Second-Order-Closure (SOC) Integrat ed Puff, and includes comprehensive treatment of gas-phase and aqueous-phase chemistry and aerosol processes. SCIPUFF simulates plume transport and dispersion us ing a second-order closure approach to

solve the turbulent diffusi on equations (Sykes et al., 1988, 1993; Sykes and Henn, 1995). SCIPUFF represents a plume by a collect ion of three-dimensional puffs that are advected and dispersed according to the lo cal 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 characteri stics 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), pr oviding a direct connection between measurable velocity sta tistics 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 scal es 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 simi larity theory to relate the dispersion coefficients to micrometeorological variables (e.g., as us ed 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 significan tly, thereby leading to an excessive computational burden. A puff merging algorit hm 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 ri se and initial dispersion are simulated by solving the conservation equations for ma ss, 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 SO 2 to sulfate. A4-6 pathway is also included for the hete rogeneous production of nitrate from N 2 O 5 hydrolysis in the presence of cloud or fog droplets. For PM 2.5 calculations, SCICHEM includes modules to simulate the partitio ning 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 ``ben chmarks'' for controls on sources subject to BART. For example, regional sc ale 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 i ndividual pollutant species that co mbine to reduce vi sibility. 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 im pact 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 Intersta te Rule (CAIR) and of the most-stringent-case

BART for EGUs.

What EPA has not addressed, bot h 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 el sewhere 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 organiza tions 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-pha se and aerosol species concentrations in isolated, major point source pl umes 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 EP A to improve the character ization 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 modeli ng domains, chemical processes and pollutant concentration levels from point source emissions are greatly impacted. Consequently, the PinG approach models the meteorological, photochemical, a nd aerosol processes at the appropriate spatial dimension by allowing for the realistic, gradual hori zontal/vertical growth of each subgrid scale plume. The PinG treatment has been fully integrated into the CMAQ chemical transport model (CTM)4-7 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 wa s to provide a realistic treatment of the dynamic and chemical/aerosol processes impacting pollutant c oncentrations in major point source plumes. They formulated the CMAQ/PinG model with an imbedde d Lagrangian plume tec hnique to simulate the relevant atmospheric dispersion processes governing vertical and ho rizontal plume expansion. This more realistic treatment of plume growth allows photochemistry and ae rosol 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 AE 3 aerosol algorithm in CMAQv4.4 was incorporated and successfully tested in the PinG module. Th e current 2005 public rele ase (CMAQ4.5) contains

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

and PM 2.5 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 plum e 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 potentia l 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 in to EPA’s CMAQ model and evaluated with monitoring program data is desc ribed by Vijayaraghavan, et al., ( 2004). CMAQ-APT-PM consists of the EPA CMAQ model with an embedded reactive plum e model, the Second-order Closure Integrated puff model (SCIPUFF) with CHEMistry (SCICH EM). 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 disper sed 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 dist ribution of these concentra tions, 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 inho mogeneous velocity field. The effects of buoyancy on plume rise and initial di spersion are simulated by solving the conservation equations for mass, heat,

and momentum. The formulation of nonlinear chem ical kinetics within the puff framework is described by Karamchandani et al. (2000). Chemical species concentrati ons 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 a ny chemical kinetic mechanism to be treated. The4-8 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 anot her 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 Modeli ng and Analysis System (CMAS) center. CMAQMADRID-APT utilizes the same emissions, meteorol ogical and air quality files as the other CMAQ

versions discussed above. It also ha s 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: IR ON 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 av erage output concentration fields for postprocessing and analysis.

Implementation of PM chemistry into the IRON Pi G 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 (t o determine the evoluti on 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 pl ume chemistry and physics takes advantage of the ‘flexi-nesting’ capability of CA Mx. In this method, a series of nested grids ar e defined with successively smaller horizontal grid spacing, provid e 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 define d that directly overcomes the pl ume over-dilution problem of older Eulerian models while retaining manageable com putational 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 th e modeler to ensure that the fine grid mesh is

used to best effect.4-9 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 alternativ e model with representatives from the state, EPA, and FLM. Since decisions on accep tability of an altern ative model is a joint responsibility, it is prudent to engage the EPA an d 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 pr otocol (see Chapter 7), additi onal technical details would be required in the description of th e model configuration, model-set up procedures, data bases to be employed, model evaluation and sensitivity te sts, and procedures for application.

Once the alternative modeling protocol is approv ed, the state or source operator (or consultant) would implement the procedures set forth in the do cument. Typically, these w ould include: selection of appropriate emissions, meteorological, and air quality models; selection of m odeling 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; mode l performance evaluation and sensitivity testing; modeling of BART source(s) with appropriate sub-grid-scale pl ume technology; calculation of background visibility; comparison of BART source plume increment re lative to background visibility; uncertainty analysis; reporting; and m odeling 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 fi ne particulate aerosols

than CALPUFF. While this like lihood is easily seen in compara tive 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 particulat e 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, ther e simply are no detailed atmospheric plume data bases that record the chemical evolution of point sour ce 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 meas ured.) If such data bases existed, it would be a simple matter to compare alternative modeling platfo rms. 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 chemi cal transformation of point source emissions. Thus, one cannot evaluate CALPUFF pred ictions against downwind second ary 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.4-10 Several evaluations of CALPUFF that have been carried out using inert atmospheric tracers (see Section 3.6). These studies are useful in elucidat ing the model’s ability to simulate the transport and dispersion of non-reactive materi als. In contrast, there is a rich and evolving performance evaluation history of regional on e-atmosphere models, stimulate d in large measure by the CMAQ and/or CAMx modeling being performed by WR AP, 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, a nd 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 an alyses. For the CENRAP modeling (Morris et al., 2005d), these evaluations, for NO, NO 2 , ozone, sulfate, nitrate, ammonium, ammonia, EC, OC, secondary organic aerosol, PM 2.5 , extinction coefficient, and vari ous 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 B ugle plot as a func tion of the species concentration. This helps characterize di fferences 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. Pe rformance 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 o ccurs because the model mis-locates the high concentrations by just a few grid cells from the obs erved high concentrations.

> Stacked bar time-series plots for each site : These plots show c ontributions 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 & wo rst 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 m odel 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 clea n to polluted conditions.

Three recent studies summarize the current pe rformance capabilities of the CMAQ and CAMx regional models for aerosol species and visibility . Morris et al., (2005d) summa rize the performance of CAMx and CMAQ over the 36 km CENRAP domain fo r the full calendar ye ar 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 2002.4-11 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) a nd criteria (the level of accuracy that is considered to be acceptable for re gulatory applications) that vary as a function of concentration and extinction. Spec ifically, 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. Additiona lly, 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 recommendati ons are based upon an anal ysis of numerous PM and visibility modeli ng studies performed throughout the country.

In particular, Boylan and Russell (2005) tabulated the mean fract ional 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 frac tional error for each component of

PM as well as PM 10 and PM 2.5 . Overall model performance for PM 2.5 is fairly good with about 50% of the points meeting the goals and a large majority m eeting the criteria. Sulfat e performance is better than most components of PM 2.5 that were examined, with a majority of the points meeting the goal and only two points falling outside the criteria. This was attrib uted to good estimates of SO 2 emissions, sulfate chemistry being less complex than other co mponents 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 th e 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 performa nce falls between that of sulfate and nitrate. Overall, the performance is fairly good because th e majority of the ammonium is associated with sulfate which performs well. Less than 40% of the organic performan ce 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 urba n 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 out side 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) perfor med an identical analysis for components of light extinction. They report similar performance patte rns 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 impr oved over that of their PM mass performance. In fact, only a handful of soils and co arse mass points fall outside the cr iteria lines. Using the identified metrics, sulfate and EC components of PM 2.5 and light extinction are generally the most accurately simulated, while nitrate and organic carbon performan ce are poor. Poor soil performance is of concern

for PM 2.5 , but not visibility. Poor coarse ma ss performance does not impact total PM 2.5 and has minimal impact on total light exti nction. Detailed model evaluati on 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/4-12 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 curre nt one-atmosphere models such as CMAQ and CAMx reveals that for some species (sulfate, PM 2.5 ) the models do quite well while for certain others the models exhibit bias and large un certainty (e.g., nitrate concentratio ns, 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 con tinuous years (2001, 2002, and 2003). This performance record clearly surpasses – in rigor, scope, and statistics – any evaluati on 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 inform ation would be quite helpful in supporting discussions with EPA Re gional 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 a nd 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 gr id scales are available over the CENRAP states and may be obtaine d from one or more RPOs. Deta ils 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 tech nical 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, st aff, 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 ha rdware requirements are still well within the range of modern scientific computing set-ups. Give n the availability of m odel-ready meteorological, emissions, and air quality data bases from the various RPOs, the only significant computational cost in alternative modeling is in runni ng 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 n eed to develop new meteorology. Unlike CALPUFF, there is no need to develop estim ates of background emissions , or air quality; these are already compiled in the existi ng RPO data bases. The only da ta input development need, common

to CALPUFF too, is the characteriza tion 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 c onfiguration for applying CMAQ in multi-processor mode might include 3 dual Athlon MP 2800+ computers (6 total proces sors), 1 to 2.5 Gybte RAM for each node, a GigE switch connection, and 1-3 Tbyte ex ternal hard disk storage for model input and output files. With four such clus ters, 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-proce ssor Athlon MP machines would be employed with the ‘master" node4-13 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 rath er 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 proj ects do not seem to vary significantly between CMAQ and CAMx platforms.

Finally, EPA provides a website for the comm unity to share different experiences in acquiring, configuring, and appl ying different hardware soluti ons 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 tran scends the skill level normally associated with operating EPA guideline models. Particular ar eas of modeling expertis e involve meteorology, atmospheric chemistry, emissions inventory developm ent, 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 agen cies, regional EPA offices, governmental laboratories,

and academic institutions. There is al so a fairly large consultant commu nity 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 av ailable 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 BA RT application. Of course, so me studies can be performed more quickly and others may ta ke 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 regi onal 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, th ere are some important decisions to be ratified (calculati on 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. S o, depending on the number of years modeled and the number of BART control strategies examined, com puter simulations alone may necessitate two months

or more of calendar time. 1 Third, unlike CALPUFF model applica tions, the alternativ e model protocol development and annual visibility simulations can not realistically be carried out in parallel. Negotiation of the protocol must first be comple ted 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.4-14 CAMx simulations is actually implemented. Based on th ese factors, it is reasonable to expect that an alternative BART modeling applica tion 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 mu st follow the procedures set forth in the Guideline on Air Quality Models for obtain ing 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 alternativ e model is a Regional Office responsibility. If it can be demonstrated that in a sp ecific 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 St ate or the source show th at 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 performa nce evaluation has been conducte d using measured air quality data and the results of that evaluation indicate the alternative model performs better for the application than a comparab le model in appendix A; and

> If there is no preferred m odel 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 th e acceptability of a m odel 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 bot h control agencies and regulated industry is an important element in

such an evaluation. If it can be shown that CALP UFF is not applicable to the BART source-specific modeling problem at hand, an al ternative refined model may be used provided that:

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

> The data bases which are necessary to perf orm 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 evalua ted from both a theoretical and a performance perspective.4-15 EPA has prepared a document entitled “Interim Procedures for Evaluating Air Quality Models” to assist in developing a consiste nt 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 object ive decision-making on the acceptability of an alternative model for a given re gulatory application. These doc uments contain procedures for conducting both the technical evaluati on of the model and the field te st or performance evaluation.

Ultimately, the Regional Administrator has the au thority to determine whether an alternative model is acceptable for a source-specific BART an alysis. However, recognizing the need for assistance and guidance in the select ion process so that fairness and consistency in modeling decisions is fostered among the various Regional Offices a nd 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 modelin g study for a source can vary depending upon

several factors includin g: (a) availability of meteor ological and air quality data sets, (b) availability of emissions source characterization (especially particul ate 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 su ccessfully negotiate the modeling protocol with the state, EPA and FLM, and various other factors. Computer cost s are minimal since the CALPUFF system runs on personal computers equipped with ine xpensive 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 source 2 is in the range of $20,000 to $50,000 depending upon the nature of the problem, the expertise and cost structure of th e organization performing the modeling, the modeling experience of the state or source operator, and the extent of interaction re quired 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 applicab le regional data sets. Key cost elements in an alternativ e modeling study include: (a) protocol development and negotiation with the reviewing agen cies, (b) obtaining and setting up one or more RPO data bases on the host computer network 3 , (c) running the base case regiona l model simulation to establish ‘background visibility conditions, (d) running the regi onal 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 modeli ng is whether the state, EPA, and/or FLM will require that more than one calendar year be mode led 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 d ouble. However, even if the reviewing agencies require a full three years of CMAQ or CAMx modeling, there are still several opportunities for cost reducti on. Opportunities include:

2 This exercise would includes analyses to determine whet her the source is ‘subject to BART’, followed by several CALPUFF model runs to estimate the visibility impacts of different levels of BA RT 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.4-16 > If three full years of alternative modeling is performed to confirm that a source is subject to BART, then the BART control mode ling can likely focus ju st 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 gr id nesting SGS technologies in the regional model only during those episodic periods wh ere 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 alternativ e models can very likely reduce the overall program cost. The greatest variable in estimating the resources requ ired 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.4-17 Figure 4-1. Plume Trajectory Paths and Growth Rates of Sub-Grid-Sca le 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).4-18 Figure 4-2. Reactive Plume Species Concentratio ns in the Cross Section from a High NOx/Low

SO2 Emissions Source at 5 Ho urs After Release. Ozone (O 3 ) is the solid red line. Units: SO4 (μg/m 3 ), OH (ppt), SO 2 and HNO 3 (ppb). (Source: Godowitch, 2004).

Figure 4-3. Aerosol Sulfate (SO 4 ) Difference Field Determined by Subtracting the CMAQNoPinG Concentrations from the CMAQ-P inG Concentrations at 2 pm EDT on 7 July 1995. (Source: Godowitch, 2004).4-19 Figure 4-4. Change in the Co ntributions of Fourteen (14) EGUs to Local Sulfate (SO 4 ) Concentrations When the CMAQ-APT-P M 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 (NO 3 ) Concentrations When the CMAQ-APT-P M Plume-in-Grid Approach is Used. (Source: Karamchandani et al., 2005).4-20 Figure 4-6. CAMx PM PiG Simulation of Ozone Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the Ozone Increments Asso ciated with Sub-Grid-Scale Treatment of Reactive Plumes. (Sou rce: Yarwood et al., 2005).

Figure 4-7. CAMx PM PiG Simulation of SO 2 and Sulfate Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the SO 2 and SO 4 Increments Associated with Sub-GridScale Treatment of Reactive Plumes . (Source: Yarwood et al., 2005).4-21 Figure 4-8. CAMx PM PiG Simulation of NO 3 and NH 4 Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the NO 3 and NH 4 Increments Associated with SubGrid-Scale Treatment of Reactive Pl umes. (Source: Yarwood et al., 2005).4-22 (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).