6-1 6.0 CALPUFF SCREENING APPLICATIONS

The objective of the CALPUFF screening appr oach is to efficiently and conservatively determine whether a source can be exempted from BART controls or whether source-specific modeling

is needed. The screening method described in this chapte r will also help identif y specific Class I areas that might be affected by the source. Should source-specific modeling become necessary, this

information will assist the state or source in tailoring the analysis domain and modeling procedures to focus on the area(s) of greatest likely interest. Due to the nonlinearities in the physical and chemical processes governing visibility impairment and the va riability in wind and disp ersion transport patterns, simple notions about source-receptor relationships (e.g., the closest Class I area will produce the controlling visibility impacts) may not be universally reliable. So the screening analysis may offer some insight here.

CALPUFF screening modeling should be performed consistent with the procedures set forth in this chapter, adapted as necessary to the source or sources of interest. A standard set of default meteorological, air quality and disper sion conditions are assumed that, fo r the most part, are consistent with the IWAQM (1998) and FLAG (2000) recomme ndations. For many sources in the CENRAP region, the screening approach may be sufficient to show whether it is a contributor to visibility problems in a particular Class I area. To support screening applications of CALPUFF, model-ready meteorological data sets have been developed fo r three CENRAP sub-regional domains (Figures 5-1 through 5-4). These data sets, described in Chapter 5, expedite the screening analysis and provide for consistency of application across the CENRAP region.

The CALPUFF screening methodology possesses a higher degree of c onservatism (i.e., systematic tendency to over-predict visibility impacts) compared w ith the source-specific methodology in Chapter 7. For large sources that will clearly exceed the screening thresholds, the screening step may be skipped as the analysis proceeds directly to source -specific modeling.

6.1 Methodology

The screening methodology recommended for s ources in the CENRAP region uses the CALPUFF model with three years of meteorological data and the standard compliment of model algorithms invoked. To ensure that no sources pass the screening test when they should fail, the simple approach, by its nature, must be the most conservative of all the conditions likely to be examined for the source in question. For example, many factors influence the contribution of a source to the Class I area other than distance. The freque ncy of winds transporting the pollutants toward the Class I area may often be important to include for a reliable screening analysis . Also, a more distant Class I area downwind in the predominant wind direc tion from a source may receive a higher visibility impact than a closer Class I area that is infreque ntly downwind of the sour ce. Further, correlations

between winds from certain directions and mete orological conditions may be conducive for higher visibility impacts. Such effects and relationships are addressed in the screening approach. It is designed to ensure conservatism by comparing the maximum visibility impact with the 0.5 dv threshold in making a determinatio n of no contribution to visibility impairment and also by using conservative model settings.

The impact of the source or sources is calculate d from the daily visibility values for each receptor by determining the change in deciviews comp ared against natural visibility conditions. EPA’s “Guidance for Estimating Natural Visibility Cond itions Under the Regional Haze Rule,” EPA-454/B-6-2 03-005 (September 2003) lists recommended natural visibility conditions. To determine whether a source may reasonably be anticipated to cause or cont ribute to visibility impairm ent at Class I area, the impacts predicted by CALPUFF are compared against the pertinent natural vi sibility background and the threshold that has been selected. The steps in the screening approach are outline below. It is described diagrammatically in Figure 1-1 in the introductory chapter.

6.2 Protocol Development and Negotiation

The first step is the development and negotiati on of a modeling protoc ol with the state and FLM. An outline of a typical protocol is presented in Table 6-1, containing the basic elements of such

a document. Portions of these CENRAP BART Mode ling Guidelines may be adopted or referenced directly in a specific screeni ng protocol as appropriate.

6.3 CALMET Model Configuration and Application

As discussed in Chapter 5, the model configurations used to generate the common CALMET meteorological files for visibili ty screening of BART eligible sources followed the IWAQM recommendations (EPA, 1998, Appendi x A), except as noted. For CA LPUFF screening assessments, there is no need to compile CALMET inputs, run the model or process the outpu ts. As discussed in Chapter 7, for source-specific modeling, these activities with CALMET may indeed be appropriate.

6.4 CALPUFF Model Configuration and Application

6.4.1 Model Codes

For screening applications, th e latest ‘beta’ versions of the CALMET/CALPUFF modeling system should be used as given in Table 6-2. Note that these are not the EPA guideline codes but rather updated versions containing recent (as of this wr iting) science improvements and bug fixes. (The current guideline CALPUFF code is versio n 5.7, level 030402). This s ubstitution results from EPA phasing out the use of the le gacy Pasquill-Gifford (P-G) dispersion parameters with the introduction of AERMOD as a new guideline m odel. CALPUFF employs the AERMOD turbulencebased dispersion coefficients and probability density function (pdf) dispersion methods scheme instead

of P-G.

The appropriate model codes may be downloaded from www.src.com or purchased with the latest graphical user interface ( GUI) from the model developer. The sequence of model processors listed in Table 6-2 corresponds to the orde r in which the programs are typically run.

6.4.2 Domain Definition

Three sub-regional modeling domains and associ ated CALMET meteorological data bases have been developed for CENRAP and are available to the states a nd source operators for performing CALPUFF screening analyses for any Class I areas within the domain. The m odeling data sets cover three contiguous years (2001, 2002, and 2003) and are resolved to a 6 km horizontal resolution grid. Details of the modeling domains and the mete orological data bases for 2001, 2002, and 2003 were discussed in Chapter 5.6-3 Receptor Network and Class I Receptors . Experience with the CALPUF F modeling system has shown

that sizeable emissions sources can have disc ernible impacts beyond 300 km. Given CALPUFF's propensity towards overprediction, one could easily expect CALPUFF to predict that a source causes or contributes to visibility impair ment at Class I areas greater than 300 km. As discussed in Chapter 8, recent model inter comparisons between CALPUFF and full science models such as CAMx and CMAQ (Morris et al., 2003) amply demonstrate that CALPUFF substan tially over predicts concentrations of visibility-reduci ng fine particulates under various re al-world conditions at the larger downwind distances when compared to one-atmosphe re models. Thus, a dilemma arises in that experience with CALPUFF indicates a tendency to overpredict at great downwind distances yet sources having a modeled effect beyond, say, 300km s hould not necessarily be neglected because there may be some validity to a pa rticular CALPUFF model simula tion of far downwind impacts.

There are a significant number of BART eligible sources in the CENRAP region with no Class I areas within 300 km. Additionally , numerous BART sources are lo cated no closer than 500-600 km

to their nearest Class I area. The sc ience-based approach to this dilemm a is to use a regional model to address far downwind impacts. However, for most screening analyses, even out to 500-600km, States and source operators will likely find it necessa ry to employ CALPUFF notwithstanding the uncertainties associated with its bias at large trans port scales. If CALPUFF is viewed to be a suitable model for use over such distances (say 300 – 600 km) States may wish to consider the use of puff splitting despite the lack of quantitative measurem ents demonstrating that puff splitting actually reduces CALPUFF model overpredict ion at large scales. Implemen tation of puff splitting, coupled with fine grid resolution (~ 12 km or less) might be c onsidered as an approach for distant Class I receptors, but this strategy will li kely lead to computer run times approaching and even exceeding those of one-atmosphere models.

In sum, discrete receptor coordinate data for all Class I federal areas within 600 km of the source should be developed, preferably using the Na tional Park Service (NPS) Convert Class I Areas (NCC) computer program. Whether a ll Class I receptors need to be modeled is an issue to be resolved in the modeling protocol process in cooperation w ith the regulatory agencies and land managers. Receptor elevations provided by th e NPS conversion program should be used. All receptors should be included in a single CALPUFF simulation. ( http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm).

6.4.3 Model Set-Up

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

on one or more of the three CENRAP domains. The screening analysis should use a CALPUFF computational domain that includes all Class I areas within 300 km of a sour ce. These Class I areas are specified in the CALPUFF control file for analysis . States could decide to require a different value

for the maximum distance threshold for the CA LPUFF domain, depending on the locations of the

Class I areas in their states and other factors such as meteorological conditi ons and the magnitudes of the emissions from BART-eligible sources. The regional CALMET domain will be unchanged by these adjustments.

At the election of the state or source operator, the screening approach may be designed to significantly reduce the CALPUFF simulation time by restricting the comput ational domain size to include only areas where significant impacts are f easible rather than the entire regional domain. CALPUFF allows its computational domain to be specified as a subset of the CALMET meteorological domain by settings within the CALPUFF input file. The advantage of selecting a6-4 smaller CALPUFF computational domain is that th e model run time is propor tional to the number and residence time of the puffs on the domain (and othe r factors such as the number of receptors and the internal time step computed by the model). This technique, if used, must be fully described in the modeling protocol beforehand.

6.4.4 Emissions Input Development

Stack Parameters. Stack parameters required for modeling BA RT-eligible units include: height of the stack opening from ground, inside diameter, flow-rate , exit gas temperature, ground elevation of the stack base, and location coordinates of the stack. Because the BART modeling focuses on mesoscale transport to Class I areas, other sour ce term parameters (needed to calculate localized impacts) such as building heights and widths for calcu lating downwash may not be needed.

Emission Rates. Emission rates for CALPUFF screening analyses follow EPA’s BART guidance and recent staff recommendations. Source terms in the modeling should be based on emissions during periods of high capacity utilization during norma l operating conditions. Specifically, the 24-hour average actual emission rate with normal operations from the highest emitting day of the year should be modeled. Excluded from consideration are days where start-up, shutdown or malfunctions occurred unless these activities are regular, frequently occu rring components of the source’s operation cycle. (Note that while potential emissions are used to determine if a source is BART-eligible, 24-hour average maximum emissions are used for CALPUF F screening modeling purposes). Pollutants

considered include SO 2 , H 2 SO 4 , NO

x and PM 10 . Excluded from the modeling are pollutants with

emissions less than de minimis levels (40 tons per year for SO 2 and NO

x and 15 tons per year for

PM 10 ).

Identification of the maximum 24-hr actual emissions rates should be made for the most recent 3 or 5 years, according to the following prioritization:

> Continuous Emissions Monitoring (CEM) data; > Facility emissions tests; > Emissions factors; > Permit limits; or lastly,

> Potential to emit.

In cases where a unit is permitted to burn more than one fuel, the fuel resulting in the highest 24-hr emission rates should be used for the modeling. Cau tion is urged when estimating emissions rates in cases where abnormal and infrequent fuel usage ove rstate the potential for visibility improvement. Typically BART controls are expected to include the utilization of cleaner fuels (such as those burned by a facility in the more recent year s). Thus emissions rates should so lely be based upon data from the most recent years, in order to accurately charac terize potential visibility improvements from BART

controls.

Emissions Speciation. Definition of the PM speciation profile for the highest 24-hour average actual emissions may prove particularly challenging but reliable estimates are necessary given the widely varying effects of different type s of particulate matter on visibili ty. For example, the extinction coefficient ranges in value from 0.3 to 0.6 m 2 /g for coarse particles, to 1.0 to 1.25 m 2 /g for fine inorganic particulate matter, to 1.5 to 4.0 m 2 /g for sulfate and nitrate precursors, to 1.8 to 4.7 m 2 /g for organic aerosols, and up to 8-12 m 2 /g for elemental carbon (Tombach and McDonald, 2003). Thus,6-5 generalized, conservative, or arbi trary assignments of particulate emissions to different pollutant categories can have a considerable influence on m odeled visibility impacts attributable to a single facility. Currently, data are qu ite limited on appropriate specia tion of organic/inorganic andfilterable/condensable emissions by source category. While speciation profiles are available for gasand oil-fired combustion turbines and coal combustion processes, detailed profiles for the full range of BART-eligible sources is lacking.

In practice, except in cases where facilitie s operate continuous emission monitors on all affected equipment, there is likely to be limite d information of regarding actual emissions on the requisite time resolution ( 24-hour average), much less speciation pr ofiles for PM species . Thus it is particularly important for state/local agencies to work with BART-eligible sources to collect this information. Where available, facility-specific pa rticulate matter speciation measurements may be used to assign PM 10 to its components (e.g., assume pr imary sulfate is a fraction of SO 2 emissions, primary sulfate is a fraction of PM 10 emissions, all PM 10 emissions are PM 2.5 ). Absent this information, default speciation profiles may be used as agreed upon in the protocol.

Condensable Emissions. Condensable emissions are considered primary fine partic ulate matter. For screening assessments all condensable mass should be assigned to the < 0.625 μm category unless the source operator has evidence of a different value based on emissions testi ng or other reliable

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

exact size of condensable PM mass. If actual sour ce emissions data are not available, the modeling should be based on permit limits. If source-specifi c size categories are not available, then AP-42 factors may be used for sources wh ere AP-42 factors are available. For sources where AP-42 factors are not available, assumptions for partitioning should be resolved with the reviewing agencies during the protocol development process.

Size Classification of Primary PM Emissions. PM emissions should be se gregated by size category. NPS has developed PM 10 emissions speciation for certain sour ce groups and this information can be used where appropriate. Modelers using information from AP-42 or other ‘reference’ documents must

remember that the PM size classification should be applied only to the “filterable” PM mass. Furthermore, when modeling PM size classes, an a ppropriate “mass mean diamet er” must be used that

is within the specified particle size range. Use of a mass mean diameter equal to the top of the range is inappropriate since it will overestimate PM depositio n and possibly underestimate PM concentrations and visibility impacts.

6.4.5 CALPUFF Model Configuration

This section discusses the proced ures and input assumptions that States or source operators should follow in applying the CALPUFF model in a sc reening mode to assess whether a particular source is subject to BART (i .e., BART exemption modeling).

CALPUFF Model Options. The model options, parameter setti ngs, and ‘switches’ for exercising CALPUFF in the BART screening mode are discussed below. Appendix B contains tables that list the recommended screening configurations for CENRAP BART modeling. The tables also identify the default recommendations from the IWAQ M Phase 2 Report (EPA, 1998).

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

all Class I areas within 300 km. An additional 50 km buffer zone should be established in each6-6 cardinal direction from the source. For screenin g applications, CENRAP recommends CALPUFF for all source-receptor distances unless otherw ise negotiated in the modeling protocol.

Dispersion

Building Downwash . In the unlikely case where a source is immediately upwind of a Class I area (say, within 20 km), the IWAQM-recommended algorithms for sources subject to downwash should be used. For consistency across all CENRAP regional modeling studies, the CALPUFF building downwash algorithms should be used for all source-receptor distances if building data are

available.

Puff Dispersion . The EPA (1998) guidance for plume di spersion modeling should be followed, including the use of the Pasquill-G ifford curves. The use of turbul ence-based dispersion coefficients and probability density function dispersion methods ha ve not been extensively evaluated for use in long range transport evaluations. Until such an evaluation has been completed and documented, the

use of turbulence-based coefficien ts is outside the current CENRAP guidance and will require a caseby-case determination by the appropriate regulatory review ing authorities.

Puff Representation. Use the default integrated pu ff sampling methodology in CALPUFF.

Puff Splitting. There is no quantitative evidence that the horizontal and ve rtical puff-splitting algorithms in CALPUFF yield improved accuracy and pr ecision in model estimates of inert or linearly reactive pollutants although conceptually the methods ha ve appeal in that they attempt to mimic lateral and vertical wind speed and direc tion shears. Since there is no direct evidence confirming the correctness of the puff-splitting algorithms, the d ecision whether to invoke th e algorithms should be addressed in the modeling protocol. For screening a pplications, an added degree of conservatism at large down wind dist ances (> 200 km) may result if puff splitting is not be invoked. For sourcespecific applications (Chapter 7), puff splitting may be used as set forth in the protocol subject to concurrence by the cognizant reviewing agency. In sum, until verified with atmospheric data, the puff splitting remains an ad hoc procedure whose real impact on mode l reliability is unknown. In some cases, it may be prudent to use full-science regional models to circumvent this limitation of Lagrangian

models.

Chemistry

Chemical Mechanism. The MESOPUFF II module should be used for all screening (and source-specific) BART applications. For the aqueous phase conversion of SO 2 to sulfate (important when the plume interacts with clouds and fog), the IWAQM defaults are recommended, i.e., nighttime

SO 2 loss rate (RNITE1) is assumed to be 0.2 percen t per hour. The nighttim e NOx loss rate (RNITE2)

and HNO 3 formation rate (RNITE3) are bot h set to 2.0 percent per hour.

Species Modeled. Species to be modeled in the screening assessment include SO 2 , SO 4 , NO

x , HNO 3 , NO 3 and particulate matter. Absent detailed sp eciation and size distribution data from the source, PM should be modeled in two (2) size ca tegories, fine (0.0-2.5 μm) and coarse (2.5-10.0 μm),

consistent with the IMPROVE reconstructed ma ss equation. Particulate matter emissions by size category should be combined wherever possible into th e appropriate species for the visibility analysis. These species include (a) elemental carbon (EC), (b) fi ne PM or “soil” (< 2.5 μm in diameter), (c) coarse PM (between 2.5-10 μm in diameter) and (d) organics, re ferred to as secondary organic6-7 aerosols in the CALPOST postprocessor. If source-sp ecific emissions factors are not available, AP-42

factors can be used to estimate th e PM speciation for those source sectors for which AP-42 emissions factors have been developed. Otherwise assumptions will need to be proposed by the source operator and approved by the state, EPA and FLM.

Background Ozone Concentrations

. Ozone concentration data for 2001-2003 from ambient
AIRS/AQS monitors located within the particular domain being modeled should be used to develop background

estimates. Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average ozone background values should be computed from daytime average ozone concentrations (6 am to 6 pm average).

Background Ammonia Concentrations. In CALPUFF screening a pplications, the background ammonia concentration is assumed to be temporal ly and spatially invariant. Background ammonia estimates should be developed from CENRAP’s mo st recent CMAQ or CAMx simulation for the 2002 base year. Because CMAQ/CAMx modeled and obser ved monthly averaged ammonia concentrations exhibit wide spatial variability, we recomme nd obtaining separate mont hly-averaged ammonia concentrations from CMAQ or CAMx for the CENR AP north, central and south modeling domains, respectively. These would then be used as input to CALPUFF. The CENRAP 2002 monthly domain average ammonia for each CENRAP subdomain should also be used for the corresponding months in

2001 and 2003.

6.4.6 Post Processing

POSTUTIL Parameters. User-selected options, parameter sett ings, and ‘switches’ for exercising POSTUTIL in a screening mode are presented in Appe ndix C. This appendix contains tables that list the recommended screening and defa ult configurations for CENRAP BART modeling. The ammonialimiting method (ALM) in CALPUFF (Escoffier-Czaja a nd Scire, 2002, 2005) repar titions nitric acid and nitrate on a receptor-by-receptor and hour-by-hour basis to account for the models systematic over-prediction due to overlapping puffs. For screeni ng applications, the user should set the parameter MNIRATE=1 in POSTUTIL to implement this appr oximate correction in its simplest form. The background ammonia concentration obtained from CAMx or CMAQ re gional simulations on the three CENRAP subdomains should be used for consistency.

CALPOST Parameters. Appendix D contains summarizes the CALPOST post-processor options, parameters, and switches. Tables are presented containing recommended and default configurations for CENRAP BART modeling. Wh ile all receptors should be included in a single CALPUFF simulation, one may calculate the visibility impact s in CALPOST for each Class I area separately using the NDRECP parameter. It specifies the recepto r range to be processed in CALPOST. Given the importance of the CALPOST processo r to the entire BART visibility estimation, process, we focus specifically on the parameter settings and input a ssumptions for CALPOST in the following section.

6.5 Visibility Assessment

The recommended procedure for quantifying visibility impacts was described in Chapter 3. The key point is that the light extinction coefficient (b ext ) can be calculated from the IMPROVE Equation as:

b ext = 3 f(RH) [(NH 4 ) 2 SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + (6-1)6-8 + 0.6[Coarse Mass] + 10[EC] + b Ray

The monthly site-specific f(RH) values are obtaine d for each mandatory Federal Class I Area from Table A-3 in the EPA (2003) guidance document. Then, the haze index (HI), in deciviews, is

calculated in terms of the extinction coefficient via:

HI = 10 ln (b ext /10) (6-2)

The change in visibility (measured in terms of ‘d elta-deciviews’) is then compared against background conditions. The delta-deciview, ∆ dv, value is calculated from the source’s contribution to extinction,

b

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

∆ dv = 10 ln ({b background + b

source }/ b background ) (6-3)

If the ∆ dv value is greater than the 0.5 dv threshold, the source is said to contribute to visibility impairment and is thus ‘subject to BART ’ controls. If not, it is BART-exempt.

6.5.1 Visibility Impacts from BART-Eligible Sources

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

I areas. These areas are listed in Table 6-4. For each area, the National Park Service has developed a receptor file containing specific coordinates and elevation data. ( http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm ).

Visibility Impact Method

. In the screening approach, CALP OST should be run using Method 6 (MVISBK=6) for calculating extinction. That is, monthly f(RH) adjustment factors are applied directly to the background and m odeled sulfate and nitrate concen trations, as recommended in the BART guidelines. Note that the RHMAX parameter (the maximum relative humidity factor used in the particle growth equation) is no t used when Method 6 is selecte d. Similarly, the relative humidity adjustment factor (f(RH)) cu rves in CALPOST (e.g., IWAQM gr owth curve and the 1996 IMPROVE curve) are not used when MV ISBK is equal to 6.

Monthly average Class I area-specific relative humidity values should be employed in the extinction analysis (EPA, 2003, Table A-3). Species to be considered include SO 4 , NO 3 , EC, SOA (i.e., condensable organic emissions), soil, and co arse PM. With Method 6, background extinction coefficients are computed from EPA (2003) monthly estimates of concentrati ons of ammonium sulfate (BKSO4), ammonium nitrate (BKNO3), coarse part iculates (BKPMC), orga nic carbon (BKOC), soil (BKSOIL), and elemental carbon (BKEC). Values for these coefficients are listed in Table 6-3 and Appendix B. In screening analyses, the extinction due to Rayleigh scattering (i.e., the scattering of light by natural particles much smaller than th e wavelength of the light) should be set to 10 Mm -1
(BEXTRAY = 10.0) for all Class I areas.

Natural Background Light Extinction

. EPA’s BART guidance recommends that visibility impacts should evaluated against ‘natural’ background conditions . EPA (2003) describes the calculation of the annual average background extinction (in 1/Mm) for a Class I area using the area's annual f(RH) and average natural concentrations based on the area 's geographic location. Annual average background extinction values (in 1/Mm) are converted to annua l average Haze Index (HI) values (in deciview or6-9 dv). Then, the average HI value for the 20% best vi sibility days (Best Days (dv)) is estimated from 10th percentile of the annual aver age HI value for a Class I area assuming a normal distribution. Thus, no average natural concentrations ar e provided for determining extinction for the 20% best visibility days. EPA maintains that the above definition of natura l visibility baseline as the 20% best visibility days is likely to be reasonably conservative and c onsistent with the Regional Haze Rule goal of natural

conditions.

There are major technical issues with this appro ach: (a) the same concentrations assumed at all Class I areas in the East or West, (b) the same c oncentrations assumed to occur every month of the year, and (c) fine sea salt and associated water is not included. Also, in the calculation of 20% best visibility days, the same frequency distribution is assumed for every Cl ass I area in the East or in the West. In other words, ‘one size fits all’ (T ombach, 2004). But this really is not the case.

The background extinction computation with Method 6 in CALPOST involves user-supplied monthly concentrations of SO 4 , NO 3 , PM coarse, organic carbon, soil , and elemental carbon species. In practice, concentrations for only 2 species, SO 4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the CALPOST input file to represent hygroscopic an d non-hygroscopic portions of background extinction, respectively. Furthermore, the species concentrations are held constant over the annual cycle (i.e., no daily, monthly, or seasonal variatio n). Finally, the EPA natural back ground default values are defined separately for the eastern and western U.S. result in natural background extinction values that vary spatially and temporally only in response to the spa tial distribution and mo nthly variation of climatologically-representative relative humidity values (EPA, 2003, Table A-3). Thus, the default

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

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

extinction should be used for current and natu ral background conditions. Table 6-3 provides the species concentrations representing natural backgr ound conditions for the western and eastern Class I areas. As shown in Figure 6-1, EPA’s east-west division cuts through th e middle of the CENRAP domain. Accordingly, in the modeling protocols, the states and source operators will need to determine whether receptors in a par ticular Class I area fall within the East or West domains as this determines the appropriate default background ligh t extinction values to use from Table 6-3.

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

whether a source “contributes” to vi sibility impairment is 0.5 dv cha nge from natural conditions. States may set a lower threshold. BART determinations are based upon the 98 th percentile of the predicted 24 hour averaged deciview impact deduced from the CALPOST postprocessor. More specifically, to determine if a source may be ex empted from BART, the highest modeled deltadeciview value for each modeling day for each modeled receptor should be determined. Depending on the yearly distribution of the results, the most cons ervative (i.e., highest) 98 % impact may come from

the maximum 8 th highest value for each of the three years or the 22 nd highest value for all years combined (if three years of data and 365 values fo r each year are calculated). States and source operators should use both methods in order to identi fy the higher value. The peak impact is then compared to the 0.5 dv contribution th reshold value. If the maximum 8 th highest value for each of the three years (or the 22 nd highest value for all years combined) exceeds the “contribu tion” threshold of

0.5 deciviews the source is declar ed ‘subject to BART’, potential ly triggering a source-specific CALPUFF modeling analysis (Chapter 7). Otherwise, the source is BART-excluded. In a cooperative agreement with EPA Regions VI a nd VII and federal land managers, CENRAP guidance deviates from6-10 use of the 98 th percentile. The CALMET datasets as describe d in this protocol were processed with the ‘NO-OBs’ options, (i.e., surface obser vations were not used in the CA LMET wind field interpolation). Aware that exercising CALMET with ‘NO-OBs’ may lead, in some applications, to potentially less conservatism in the CALPUFF visibility results comp ared with the use of CA LMET with observations, CENRAP has agreed to EPA’s reco mmendation that the maximum visibili ty impact rather than the 98 th
percentile value should be used for screening analyses using the CENRAP developed CALMET

datasets.

To conserve computational and analysis reso urces, the CALPUFF screening modeling may be performed sequentially for cale ndar years 2001, 2002, and 2003. If a BART source is found to be subject to BART following the first or second annua l evaluation, the additiona l year(s) need not be processed. Evaluation of all three years will be required to exclud e a BART eligible source from the BART determination process.

Since the current regulatory vers ion of CALPOST does not generate 98 percentile results, states or source operators may wish to us e a modified version of CALPOST th at generates a file with a full distribution of daily delta-deciview values for each receptor. The Colorado Department of Public Health and Environment (CDPHE , 2005) has developed a FORTRA N processor to generate 98 th
percentile results and it is available upon request.

6.5.2 Change in Visibility Due to BART Controls

According to the BART guidelines, a single so urce responsible for a 1.0 dv change or more should be considered to “cause” visibility impa irment and a source that causes less than a 1.0 dv change may still contribute to visibility impairment and thus be subject to BART. For sources determined to be subject to BART, additional mode ling is needed to assist in identifying various control options and selecting the “best” alternative. The BART guidelines iden tify five factors to be considered when determining what BART should be for a specific source. These include: (a) any pollution control equipment in use at the source (whi ch affects the availability of options and their impacts), (b) the costs of compliance with control opti ons, (c) the remaining useful life of the facility, (d) the energy and non air-quality environmental impacts of compliance, and (e) the degree of improvement in visibility that may reasonably be antic ipated to result from the use of such technology.

For the visibility analysis (the fifth factor in determining what BART shoul d be), it is up to the state to determine how to weigh the impacts. The 1.0 ∆ dv threshold might be one of the parameters that is used although it is not required. The 98% value at this step in the analysis is also not required. For example, visibility improvement of 1.0 ∆ dv may be predicted on 4 days out of the year, and the state may determine that is significant rather than judging it to be insignificant because it does not occur on 8 or more days.

6.6 Presentation of Modeling Results

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

the maximum 8 th highest value for each of the three years or the 22 nd highest value for all years must be compared to the 0.5 deciviews threshold. At a minimum, tabular presen tation of the following results should be provided:

> Number of receptors within each Class I area with impacts > 0.5 dv;6-11 > Number of days at all receptors with in each Class I area with impacts > 0.5 dv;

> Number of Class I areas with impacts > 0.5 dv; and > At each receptor, the magnit ude of the change in extinction.

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

6.7 Reporting of CALPUFF Screening Assessment Modeling Results

The report accompanying the scr eening CALPUFF modeling should provide a clear description of the modeling procedures followed and the results of the analysis. Any departures from the approved modeling protocol should be discusse d and justified. The report should also include a discussion of the uncertainty in the modeling result s and the likelihood that the screening process was effective in its determination. Any needs for s ource-specific or alternative mode ling should be identified. Accompanying the modeling report should be an el ectronic archive (CDs, DVDs, or removable USB2/IEEE 1394 hard drives as ap propriate) that includes the full set of CALPUFF inputs and model output fields and well as any pre- or post-processor codes used to generate the results. The CENRAP 6 km regional CALPUFF-ready meteorological fields do not need to be included in the archive. The modeling data archive should be su fficiently complete as to allow an independent modeler to fully corroborate the CALPUFF screening results.6-12 Table 6-1. Contents of a Typical Screening BART Modeling Protocol.

1.0 INTRODUCTION 1.1 Objectives

1.2 Location of Source and Relevant Class I Areas 1.3 Source Impact Evaluation Criteria 1.4 Modeling Study Participants

1.5 Protocol Review Process

1.6 Schedule

2.0 SOURCE DESCRIPTION 2.1 Unit-specific Source Data 2.2 Nearby Sources Affecting Same Class I Areas

3.0 MODEL INPUT DATA 3.1 Modeling Domain

3.2 Terrain and Land Use

3.3 Emissions Data Base

3.3.1 Stack Parameters

3.3.2 Emissions Rates

3.3.3 Condensable Emissions 3.3.4 Speciation and Size Distributions

3.4 Meteorological Data Base

3.4.1 CENRAP CALMET Data Sets

3.4.2 Observational Data

3.5 Air Quality Data Base

3.5.1 Ozone Concentrations

3.5.2 Ammonia Concentrations

3.5.3 Concentrations of Other Pollutants 3.5.5 CENRAP Regional Modeling Data Sets

3.6 Natural Conditions at Class I Areas

4.0 CALPUFF MODELING METHODOLOGY

4.1 Model Selection 4.2 Domain Configuration and Receptors 4.3 CALPUFF Configuration 4.4 Light Extinction and Haze Impact Calculations 4.5 Modeling Results 4.6 Uncertainty Analysis

5.0 EXEMPTION MODELING

6.0 BART CONTROL MODELING RESULTS

7.0 REPORTING

REFERENCES

APPENDIX A: CALMET Inputs APPENDIX B: CALPUFF Inputs APPENDIX C: POSTUTIL Inputs APPENDIX D: CALPOST Inputs6-13 Table 6-2. CALMET/CALPUFF Model Code s for Screening Applications.

P ROCESSOR V ERSION L EVEL

TERREL 3.311 030709

CTGCOMP 2.42 030709

CTGPROC 2.42 030709

MAKEGEO 2.22 030709

CALMM5 a 2.4 050413

CALMET 5.53a 040716

CALPUFF 5.711a 040716

POSTUTIL 1.4 040818

CALPOST 5.51 030709

a: The versions of CALMM5 (version 2.4, leve l 050413) and of CALMET (version 5.53a, level 040716) are not compatible as published. Alpine Geophysics developed software

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

.

Table 6-3. Default Natural Back ground Concentrations (μg/m 3 ) for Eastern and Western U.S. Class I areas. (Source: EPA, 2003, Table 2-1)

Average Natural

Concentration West ( μ g/m 3 ) Average Natural

Concentration

East ( μ g/m 3 ) Error

Factor Dry Extinction Efficiency (m 2 /g)

Ammonium sulfate b 0.12 0.23 2 3

Ammonium nitrate 0.10 0.10 2 3

Organic carbon mass c 0.47 1.40 2 4

Elemental carbon 0.02 0.02 2-3 10

Soil 0.50 0.50 11⁄2 - 2 1

Coarse Mass 3.0 3.0 11⁄2 - 2 0.6

a: After Trijonis (1990) b: Values adjusted to represent chemical species in current IMPROVE light extinction algorithm; Trijonis estimates were 0.1 μg/m 3 and 0.2 μg/m 3 of ammonium bisulfate. c: Values adjusted to represent chemical species in current IMPROVE light extinction algorithm; Trijonis estimates were 0.5 μg/m 3 and 1.5 μg/m 3 of organic compounds.6-14 Table 6-4. Federal Class I Areas in the CENRAP Domain.

Federal Class I Areas in the CENRAP Domain

Class I Area ST Name

Big Bend National Park TX BIBE
Boundary Waters Canoe Area MN BOWA1

Breton LA BRET1
Caney Creek AR CACR1
Guadalupe Mountains Nat'l Park TX GUMO1

Hercules Glades MO HEGL1

Mingo MO MING1
Upper Buffalo Wilderness AR UPBU1
Voyageurs National Park MN VOYA2

Wichita Mountains OK WIMO16-15 Figure 6-1. Division of the U.S. for the Pu rpose of Assigning Default Natural Background Concentrations (μg/m 3 ) for Eastern and Western U.S. Class I areas. Depicted are

the Estima tes of the De fault 10% Natural Ha z e Index Va lues (in dv). (Source: EPA, 2003).