7-1 7.0 CALPUFF SOURCE-SPECIFIC APPLICATIONS

The BART process provides an option for source -specific modeling to be performed. There are a number of reasons why a more detailed approach than that discussed previously may be desired. For example, sources very close to Class I areas may elect to use finer grid resolution than the 6 km gridded fields provided by CENRAP. Also, it may be desirable to re duce the uncertainties in assumed background ammonia, hydrogen peroxide , and ozone concentrations. Th ese and other refinements in model inputs may help to clarify whether a source should be excluded from BA RT and/or to quantify the expected visibility impact s of alternative BART controls on an affected source.

7.1 Methodology

Source-specific CALPUFF modeling may be helpful in demonstrating more convincingly that a source does not cause or contribute to visibility impairment in any Class I areas, and thus can be exempted from BART controls. If the initial m odeling does not pass the screening threshold for visibility impacts, the next step is to conduct source-specific modeling, perh aps using a finer grid resolution for the meteorological fields, improved trea tment of terrain effects and land use variability, and the inclusion of more refined model parameteri zations such as consider ation of time and spacevarying ammonia and ozone concentration fields. As with the screening met hod, in the source-specific modeling, the visibility threshold is based on the BART guidance of the 98 th percentile change in deciview value.

The BART guidance indicates that the emissions ra tes to be used for ex emption modeling is the highest 24-hr rate during the modeling period. De pending on the availability of source data, the following 24-hr maximum emissions rate information (listed in order of prio rity) should be used with CALPUFF for BART exclusion modeling:

> Emissions for the period 2001-2003 (CEM data); > Emissions for 2002 (CEM data); > Actual 2002 emissions ra tes in state CERR inventory; > Emissions factors from AP-42 source profiles; > Permit allowable emissions, if available; or finally > Potential to emit.

A source-specific modeling analysis will likely be required for all sources located within 50 km of a Class I area. EPA’s BART guidance recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and other methods.” While the PLUVUE-II plume visibility model is mentioned as a possible alterna tive approach to consider in addi tion to CALPUFF within 50 km of a source, the CALPUFF model appears to offer grea ter realism, provided sufficiently small grid resolution (< 4 km) is used. As discussed in Chapte r 3, there are no funda mental reasons why CALPUFF cannot be used for much s horter transport distances than 50 km, as long as the scale of the plume is larger than the scale of the output grid. This requirement must be met in order that the maximum concentrations and the width of the plum e are adequately repres ented (and the sub-grid details of plume structure can be ignored when estim ating effects on light exti nction). Use of a 1 km CALPUFF modeling grid for Class I area receptors should be sufficient for source-receptor distances in the 25-50 km range; a finer grid may be needed fo r transport distances much smaller than 25-30 km.7-2 If source-specific modeling shows that a particular source causes or contributes to visibility impairment at a Class I area, the CALPUFF modeling sy stem can then be used to quantify the visibility benefits from various BART control options. This is accomplished by running CALPUFF with the baseline emissions rates and with emissions after BART controls. It is important that emission reductions be evaluated in the post-processing step rather than by using “negative” emission rates in the CALPUFF model. The chemical scheme requi res that emission rates always be positive.

7.2 Source-Specific Considerations

7.2.1 Protocol Development and Negotiation.

For any source-specific application, a modeling pr otocol that defines source properties and the specific model configuration and application approach is required. An example of typical elements of a source-specific protocol is given in Table 7-1. This protoc ol should be reviewed with the state, EPA, and FLM.

7.2.2 Domain Definition.

While source-specific modeling can be carried out on one or more of the three CENRAP domains (Figures 5-1 through 5-3) with the 6 km MM5/CALMET fields for 2001-2003, it may be more appropriate to develop a specialized modeli ng domain and with finer resolution meteorological fields. Because CENRAP has not developed fine-scale CALMET files, the state or source operator will have to do this. Particularly in rough terrain or in coastal areas , finer horizontal grid spacing may be useful in source-specific simulatio ns to better characterize the flow fields and land use changes. A typical source-specific CALMET me teorological domain might consist of a 300 x 300 array of 1-2 km grid cells covering the domain of interest. This produces a reasonable number of grid points that can easily be accommodated on personal co mputers. Domains with up to 600 + x 600 + grid cells are sometimes necessary (for long tran sport distances), while domains with fewer than 100 x 100 cells generally are not used in a source-specific analys es because they cover insufficient territory.

For source-receptor distances < 50km, even fine r grid resolution than 1 km may be needed, particularly if complex terrain or coastal meteor ological effects are likely to be important. This determination should be made on a case-by-case ba sis. There is no si ngle distance at which a particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the source-receptor distances involved, the location of the source relativ e to the terrain features, the physical stack parameters (e.g., a ta ll stack in complex te rrain may be unaffected by the terrain-forced flow), proximity of the source and Class I area to a co astline, and other factors including availability of representative observational data.

7.3 CALMET Configuration for Source-Specific Assessments

The CALMET processor accepts ob servational data in the form of hourly surface observations of winds, temperature, pressure and cloud data, hourly precipitati on observations, and twice-daily upper air sounding data of winds, pre ssure and temperature. In add ition, CALMET allows the use of three-dimensional gridded fields of data from prognostic models such as MM5. CALMET can be run in a mode using observations only (Obs-mode), pr ognostic data only (No-Obs mode) or both sets of data (hybrid mode). For example, one possible a pproach is to consider using the CALMET “hybrid”7-3 mode which incorporates surface observations with progno stic data, but does not include the use of upper air data (option NOOBS = 1).

In preparing the regional MM5/CALMET fields for the CALPUFF screening applications (Chapter 6), CALMET was exercised on a regional 6 km resolution domain (Figure 5-1) in NoObservations mode using the highest resolution MM5 data available for each year modeled. For source-specific modeling of Class I areas lo cated in complex terrain or coastal regions, it may be desirable to develop mete orological fields over fine r resolution grids (i.e.,. 1- 4 km scale). In these cases there may be reasons to invoke the hybrid mode of CALMET option. In determining which method to use, the State or source operator should ev aluate the following factor s in the specific context of the source and the affected Class I area.

Time and space resolution of observational data . Upper air observational da ta are available at roughly 12-hourly intervals and the spac ing between sounding locations is typically several hundred kilometers. Because CALMET performs a time interpolation of wind da ta between the sounding intervals, this linear time-interpolat ed wind data will not be able to retrieve important patterns of wind variation at scales less than 12 hours.

Time and space resolution of 3-D prognostic data

. The MM5 output fields provide hourly “soundings” over a 36 km or 12 km grid at hourly intervals. This hourly time resolution of the prognostic model fields is likely to better characterize the time va riation of wind flows across the domain. However, on scales smaller than the MM5 grid nest (say below 12 km) the MM5 fields may miss potentially important local flow features that might be reflected in local surface measurement networks.

Representativeness of observational data. A key question, then is whethe r one has confidence that the spatial and temporal representativeness of the local measurement stations is sufficient to favor measurements over prognostic modeled fields at th e local scale. While use of measurements is attractive in many situations, too great a reliance m easurements introduces the possibility of spurious wind or thermodynamic variable variations because the CALMET data interpolation methods does not obey the combined laws of momentum and energy cons ervation. This is partic ularly an issue where there are measurement sites of questionable repres entativeness or data quality. Therefore, careful inspection of the actual surface measurement sites to be used in the Obs-mode is needed before the method is used.

Discrepancies between prognostic winds and observations . The CALMET diagnostic wind field module attempts to reduce discrepa ncies between the prognostic model- derived initial guess fields and the observations by giving greater we ight to the observations in the “vicinity’ of the observations and greater weight to the prognostic wi nd fields in portions of the domai n away from the observations. Although the prognostic model simulation reflects observational data through the use of fourdimensional data assimilation, there will sometimes be discrepancies between the observations (which may reflect a very localized flow) and the 36 km or 12 km resolved MM5 winds. Sometimes the differences are due to poor time resolution of the observational data; at other times they may result from poor instrument placement or faulty observations . In such cases, blendi ng of the MM5 data with the observations (i.e., the hybrid mode) can lead to wind structures which may not be realistic.

Model biases . The MM5 model predictions of winds and te mperatures may contain biases relative to the real atmosphere. In partic ular, MM5 sometimes underestimates the frequency of light wind and calm wind conditions and possibly underestimates the strength of the nocturnal surface temperature7-4 inversion. These biases may be mo st important for near surface sour ces when relative coarse grid resolution is used. If the adequacy of the MM5 fiel ds becomes a concern, the st ate or source operator may wish to examine the detailed MM5 model evalua tion reports prepared by the original modelers (see Chapter 5).

Level of effort vs. benefit . The preparation of observational da tasets for CALMET on large regional domains involves a considerable effort due to the large number of surface, precipitation and upper air stations involved. The question is whether the effort associated with the preparation of this dataset is worthwhile given that the regiona l winds over the three CENRAP su b-domains (Figure 5-1) may be better characterized by the three-dimensional MM5 m odel dataset than time- a nd space-interpolated of observational data, especially above the surface where sounding data is fairly sparse spatially and poorly-resolved in time. For source-specific mode ling, the decision whether to exercise CALMET in the hybrid mode is left to the stat e or source operator who are in the best position to a ssess the relative strengths and drawbacks of the two modes of operation in the specific context of the source they are modeling . Given the orography of the CENRAP domain, the regional wind patter ns are likely to be adequately characterized at the 6 km scale via th e use of MM5/CALMET with No-obs. However, at the finer grid scales that may be considered in source specific modeling (1-4 km), introduction of particularly well sited monitors – especially in key impact areas – may be appropriate.

Care must be taken to ensure that the intr oduction of the local obser vations into the CALMET processing does not extend the influence of the obser vational data beyond its true representativeness and result in internally inconsistent flow featur es. On smaller domains with higher CALMET grid resolution, more control over the region of influe nce of the meteorological observations can be achieved and it may be easier for the diagnostic mode l to allow the local flow observations to have appropriate influence in the vicinity of the observati on, yet still allow terrain-ad justed flow to dominate away from the observations. Given that the sub-re gional domains will be used especially in complex flow situations (complex terrain and coastal regions), the relatively coarse-scale MM5 simulations are less likely to be adequate, and th e introduction observational data ma y indeed yield improvements in the resulting meteorological fields.

The decision on how the MM5/CALMET processi ng should be performed for source-specific modeling requires consultation with the State, regional EPA office and the FLM overseeing the Class I area of concern. A decision to allow ingestion of meteorologica l observations into the CALMET processing at the finer grid scal es (i.e., below 6 km) should be contingent upon commitment to a thorough analysis of the instrument location, qua lity assurance record, and spatial/temporal representativeness of the each monitoring station to be used in the CALMET interpolation. For the reasons discussed above, absent this assessment of the measurement data representativeness, ingestion of observational data may in fact degrade the integrity of the resu ltant CALMET meteor ological fields and the modeler/decision-maker may not be aware of it.

7.4 CALPUFF Configuration for Source-Specific Assessments

Domain-specific CALPUFF input parameters must be defined for each source-specific modeling analysis. In a ddition to finer grid spacing in th e CALMET and CALPUFF models and the possible introduction of observational data in the CALMET simulations, several other modeling enhancements can serve to increase the realism of the source-specific modeling. These include use of higher resolution terrain digital el evation model data (~3 arcsec USGS data) in defining the gridded7-5 terrain fields, and the use of time-varying ammonia and ozone concentrations rather than constant values.

7.4.1 Background Conditions

Background Ozone Concentrations. Ozone concentrations required as input to source-specific CALPUFF should be derived from regional model simulations obtaine d from CENRAP or the other RPOs in preference to the use of AIRS/AQS ozone measurements at discrete monitors (see discussion in section 7.4.3 below).

Background Ammonia Concentrations. NH 3 concentrations required as input to source-specific CALPUFF modeling should be deri ved from regional model simulations obtained from CENRAP/s CAMx or CMAQ modeling or the modeling by other RP Os in preference to the use of CASTNet or other network ammonia measurements at discrete m onitors (see discussion in section 7.4.3 below). In addition to representing a better-science appro ach, this has the advant age of making the BART analyses more consistent with the CENRAP’s reasonable progress modeling for regional haze.

7.4.2 Ammonia Limiting Method (ALM)

CALPUFF calculates linear oxida tion of sulfur dioxide (SO 2 ) to sulfate (SO 4 ) and nitric oxides (NO x ) to nitric acid (HNO 3 ) and nitrate (NO 3 ). The equilibrium between nitr ic acid and nitrate that is a nonlinear function of temperature and relative humi dity and the equilibrium constant can vary substantially over a typical diurnal cycle, as the temperature and rela tive humidity change. In addition, the availability of ammonia (to form ammonium nitr ate) affects ambient concen trations of nitrate. Because sulfate preferentially scavenges ammoni a, the amount of ammonia remaining for the formation of nitrate may be limited. Thus, amm onia-limiting effects can be important under certain conditions in assessing the impacts of nitrate aer osols on visibility (B lanchard and Hidy; 2003; Escoffier-Czaja and Scire, 2002).

In the screening approach (Chapter 6), the ALM was recommended using a constant background ammonia concentration relevant to th e dominant land use in the Class I area being modeled was assumed (i.e., 0.5 ppb for forest, 1.0 ppb for arid lands, 10 ppb for grasslands.) For source-specific analyses , time-varying, user-supplied NH 3 concentrations may be imposed as boundary conditions to each modeled puff, while cumula tive ammonia consumption is modeled in the POSTUTIL processor for conditions at the final endpoi nt of the puff (i.e., the Class 1 receptor). In addition, the boundary concentration module within CALPUFF can be invoked to allow for time- and space-varying background concentrations. Use of the ALM method in conjunction with background source boundary conditions may improve the modeled NO 3 predictions somewhat in source-specific analyses.

In CALPUFF, a nominal background ammonia con centration is used in conjunction with the HNO 3 /NO 3 equilibrium relationship to estimate the amount of nitrate formed. However, the model’s Lagrangian puff formulation causes an overestimation of the amount of ammonia available to form nitrate because the comp etition for ambient ammonia by indivi dual puffs and by background sources is neglected. ALM was developed to tr eat this limitation. The post-proces sing method repartitions nitric acid and nitrate on a receptor-by-receptor and hour- by-hour basis and approximates the influence of other background sources of sulfate and nitrate in computation of incrementa l nitrate formation. Details of the ALM, incorporated into POSTUTIL are described by Escoffier-Czaja and Scire (2005).7-6 Here we provide a summary of their description of the method as it might be used in CENRAP sourcespecific modeling.

With the ALM procedure, the co ntribution of an individual s ource or facility to nitrate formation is determined via a four-step procedure:

Step 1. The total background ammonia (ambient fr ee ammonia + ammonia associated with sulfate/nitrate aerosols) is estimated either th rough (a) use of observational data at existing monitoring networks such as CASTNet, (b) from the output of one-atmosphere models such as CMAQ or CAMx. For example, assume weekly average sulfate, nitrate and nitric acid concentrations at CASTNet stations can be used to estimate the ammonia in the domain. An iterative scheme is used split total nitrate (TNO 3 = HNO 3 + NO 3 ) into HNO 3 and NO 3 , leading to the following relationship,

[NH 3 ] total = [NO 3 ] + 2 [SO 4 ] + NH 3 (g)

where [NO 3 ] is the observed nitrate concentration (ppb), [SO 4 ] is the observed sulfate concentration (ppb), and NH 3 (g) is the estimated free ammonia concentration (ppb) derived by applying the chemical equilibrium relationshi p. Using a 5-year monthly average of NO 3 and TNO 3 ( = NO 3 + HNO 3 ) and the hourly temperature and re lative humidity measured at a CASTnet site, an iterative scheme is used to estimate the free component of the NH 3 and the estimation of background sulfate and nitrate.

Step 2. The project sources are modeled in the normal way by CALPUFF to predict TNO 3 from those sources.

Step 3. The ‘boundary condition’ CALPUFF m odel is used to simulate pollutant concentrations (SO 2 , SO 4 , NO x , HNO 3 and NO 3 ) from distant sources (outside the modeling domain) at the receptors of interest. The bac kground sulfate concentrations are used with the monthly averaged total NH 3 in Step 1 to compute availabl e ammonia (i.e., after consumption by sulfate). The available ammonia will vary hourly and spatially even though the total ammonia may be constant, because the su lfate concentrations are varying.

Step 4. POSTUTIL is then used to develop the total concentrations of sulfat e, nitrate and nitric acid from all sources (project source, boundary conditions and background in-domain sources if any), and to re-partition the nitrate to refl ect the total concentrati ons of ammonia-scavenging pollutants from all sources. In this step, the ammonia consumed by background sources of sulfate and nitrate as well as due to puff overlap from modeled sources is all accounted for in the re-application of the e quilibrium relationship.

In a separate run of POSTUTIL, the contributi on of any individual source or group of sources to the total nitrate can be determined. The flow chart diagram in Figure 7-1 shows the POSTUTIL process to attribute a fraction of total NO 3 to any individual source (Step 3 in Figure 7-1). Once the total nitrate from all sources is determined with ammonia limiting e ffects evaluated (i.e., Steps 1 and 2 in Figure 7-1), a separate POSTUTIL run computes the TNO 3 from the project source alone, and attributes the nitrate formed from that pa rticular source as a fraction of the total NO 3 formed based on that source’s contribution to total TNO 3

.7-7 The distribution of CASTNet a nd IMPROVE monitoring sites in the central United States is shown in Figure 5-5. Most Class I areas in this region include eith er a CASTNet and/or an IMPROVE monitoring site and many others are in the vicinity. CALPUFF’ s ALM procedure described above uses the CASTNet monitoring network to produ ce boundary conditions of sulfate, nitrate and precursors and to derive empirical estimates of ammonia concentrations. However, CASTNet measurements have inherent errors a nd biases that tend to overestimate HNO 3 and underestimate NO 3 (some NO 3 will volatilize to HNO 3 within the sample after collection and before laboratory analysis). Thus, applying CASTNet data to estimate background NH 3 would tend to underestimate the ambient NH 3 and would therefore underest imate visibility impacts.

One-atmosphere regional modeling results are an alternative to the use of CASTNet or other monitoring network observations. CENRAP’s gri dded, hourly CAMx or CMAQ data bases for 2002 may be used in the CALPUFF ALM methodology in two ways:

> Boundary Conditions and Background Ammonia . Regional model concentrations of SO 2 , SO 4 , NO 3 , HNO 3 and NO x can be used to define the boundary conditions for the CALPUFF boundary module run. Regional model ammonia concentration fields over the Class I receptors of interest can also be used directly as inputto the ALM method in CALPUFF. Monthly averaged, BCs and NH 3 concentrations can easily be derived from CENRAP’s 2002 annual CMAQ or CAMx modeling files.

> Sulfate, Nitrate and Ammoni a at Class I Receptors . One may extract SO 4 , NO 3 and NH 3 concentrations at the Class I recepto rs from regional model simulations on an hourly or longer time-average at the Class I r eceptors as direct i nput into the CALPUFF ALM procedure. Here, the CALPUFF boundary condition module is replaced by the direct use of CMAQ-predicted concentrations.

The value in using CMAQ or CAMx modeling re sults include the avai lability of hourly concentrations of all relevant gas pha se and fine particulate aerosol species from all sources in the U.S. The models have been evaluated against virtually all of the individual monitoring stations identified in Figure 5-5 and these results are easily obtained from the UCR/CENRAP website:

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

Considering the various sources of uncertainty (e.g., inadequate aerosol chemistry in CALPUFF, uncertainties in regional ammonia emissions invent ories; inconsistency in measurement protocols among the monitoring networks in Figure 5-5, and uncertainties in regional models), use of CMAQ or CAMx modeling data bases is an attractive opti on over reliance on one or more CASTNet sites for some source-specific analyses.

For source-specific modeling, we recommend usi ng regional model ozone, nitrate, sulfate, and ammonia concentration fields available over the central U.S. for 2001, 2002, 2003 from the CENRAP, VISTAS, and MRPO modeling studies instead of ammonia concentrations based on CASTNet, IMPROVE, and STN measurements and ozone m easurements based on the AIRS/AQS data. Straightforward processing of the modeled CMAQ or CA Mx nitrate and sulfate fi elds can be used to generate the inputs needed for CALPUFF boundary c onditions. Gridded model out put fields should be used to generate daily average ozone and NH 3 concentrations (averaged for each calendar month) along the trajectory path from source to Class I area. This approach offers an improved characterization of the atmospheric environment th rough which BART plumes traverse compared to7-8 point measurements that likely do not coincide with the plume path and certainly do not characterize the full chemical history of the plume. Gridde d CMAQ or CAMx output files are available from CENRAP and the other RPOs.

7.4.3 Computing Light Extinction

The methodology for calculating light extinction ou tlined in the BART guidance constitutes the default approach presently, but ongoing research s uggests that improved methods are or soon will be available and recommended for use by the IMPR OVE committee, EPA, and other knowledgeable groups. Thus, any changes to the default method (e.g., deviation from the use of the EPA monthly f

(RH) CALPOST Method 6) will have to be justif ied in the source-specific modeling protocol and evaluated on a case-by-case basis.

Currently, the IMPROVE Steering Committee, EPA, the Electric Power Research Institute, the Federal Land Managers, the Region al Planning Organizations and other scientists are discussing whether refinements are warranted to the method recommended in EPA’s guidance for calculating light extinction (i.e., the IMPROVE equation). Among the topics bei ng considered are:

> Increase OC to OC Mass multiplier from 1.4 to 2; > Use formula to provide for varying sulfate and nitrate extinction efficiencies depending on concentration; > Accounting for anthropogenic transboundary pollution’ > Corrections to the statistical assumptions used to estimate 20% best and 20% worst days; and > Use actual IMPROVE derived relative humidity data rather than EPA’s “climatological” values

Other refinements under consideration, which al so apply to the CALPOST extinction formula (Equation 3-1), include the following:

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

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

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

> Revising some of the extinction efficiencies (the numerical consta nts in Equation 3-1), including making those for sulfates, nitrates, and or ganic carbon vary with concentration

Presumably any changes that are made by the IM PROVE Steering Committee would also be reflected in the EPA’s approach. If this is the case, then CENRAP source-sp ecific modeling protocol s should adopt the emergent recommendations unless the sour ce can justify, and the state approves, a different approach. Thus, for example, in the source-specifi c modeling, consideration of sea salt in the natural background, elevation effects on Rayleigh scattering (s ee below) and natural weather effects such as7-9 fog, rain and snow might be appropriate. Specific configuration settings for these and other model features should be proposed in the source-specific modeling protocol and negotiated with the state, regional EPA office and the FLM.

7.4.4 Estimating Rayleigh Scattering

.

The Rayleigh scattering coefficient fo r clear air has a default value 10 Mm -1 (see Equation 3-1). This default value is appropriate for an elevation of 1600 m (about 5000 ft). Since the correct value at sea level is about 12 Mm -1 , the default value could never be attained at low altitude sites and therefore the relative impact (in dv) of a sour ce on haze would be overstated by us ing the default Ra yleigh value. Publicly available codes are avai lable for making the elevation-depe ndent adjustments to the high elevation default value of 10 Mm -1 . For the source-specifi c analyses, an effort should be made to be consistent with the approach ta ken by the CENRAP and the other RPOs in defining the Rayleigh scattering coefficient for thei r regional analyses.

7.4.5 Natural Baseline Conditions

.

The visibility impacts of BART-eligible sources must be evaluated against natural conditions. Default values of natural conditions have been sp ecified in EPA guidance documents. These values are held as constant throughout the East, which result in natural background extincti on that varies spatially and temporally only in response to the spatial dist ribution and monthly varia tion of climatologicallyrepresentative relative humidity values. (This default definition of natural conditions does not take into account meteorologically caused visibility impairment such as fog and rain.)

The EPA allows refinements to the default natura l conditions values, with adequate scientific justification. Three changes seem to have wide support: (a) increasing the multiplier of the measured organic carbon concentration from 1.4 to a larger value, probably in the ra nge of 1.8 to 2.1; (b) changing the estimates of natural conditions extincti on on the 20% clearest and haziest days to better reflect the statistical distribution of extinction; and (c) including sea salt in the IMPROVE/EPA formula, as discussed in the preceding section. Th e organic carbon multiplier adjustment applies to all BART-related calculations. Since mo st of the extinction under natura l conditions is attributed to organic carbon, the first change w ill substantially increase the natu ral background level of extinction from the default level. This, in turn, will reduce th e modeled percentage or deciview impacts of most sources, which may affect their BART status.

The statistical adjustment applies for the calc ulation of conditions on the 20% clearest and haziest days. The clearest days adjustment is releva nt if the baseline for ev aluating source impacts in the BART exclusion modeling is based on the 20% clearest days. It would increase the deciview impact of a source’s emissions. The haziest days adju stment is of relevance for the assessment of the benefits of the application of BART, for which th e 20% haziest days are the baseline. It slightly increases the default haziness on th ose days and therefore reduces the absolute deciview impact of a source’s emissions. Finally, the sea salt adjustment a pplies to the description of natural conditions, and has effect mainly near the seacoast.

Other refinements to natural conditions estimates may be appropriate at sp ecific locations or at specific times of the year. Also, any refinements to the light extinction estimation formula of Equation 3-1 would be reflected in natura l conditions extinction estimates.7-10 7.4.6 Visibility Impact Assessment

.

As recommended in the final BART guidance, the test for evaluating whether a source is contributing to visibility im pairment is based on the 98 th percentile modeled va lue (rather than the highest predicted value used for the screening evalua tion). States or sources can accomplish this in one of two ways. First, a coding change can be ma de to the CALPOST postprocessor to allow the 98 th percentile change in extinction to be computed. Th is may be accomplished by using a specific program that post-processes the CALPOS T output (CDPHE, 2005). Altern atively, because the CALPOST processor produces visibility imp acts in deciviews, these impacts can be externally processed in a spreadsheet to calculate the cha nges in deciviews. Table 7-2 s hows a hypothetical example of the ranked visibility impacts (change in dv) for each of three years at si x different Class I areas. The 98 th percentile (8 th highest value) in the sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example shown in Table 7-2, the sour ce passes the refined analys is because the highest 98 th percentile visibility impact is below the contribu tion threshold of 0.5 dv. The actual procedure selected by the State or source should be set fo rth clearly in the protoc ol. Any CALPUFF code changes should be documented and copies of the s ource code and input/outpu t files provided to the reviewing agency for corroboration.

7.5 Assessment of Modeling Results

The report accompanying the source-specific CALPUFF modeling s hould provide a clear description of the modeling procedur es followed and the results of the analysis. Any departures from the approved modeling protocol should be specifica lly discussed and justifie d. The report should also include a discussion of uncertainty in the modeling re sults. There are many sources of uncertainty in modeling due to factors such as errors and approxima tions used in the calculation of emission rates, speciation of the emissions, particle size distribu tions, meteorological data inputs including the MM5 data sets used for determining the wind fields, th e representativeness of me teorological observations used in the analysis, uncertainties in the mete orological and dispersion model itself due to its parameterizations of transport, di spersion and chemical transformati on, and in the methods used to compute light extinction from particulate matter c oncentrations predictions. In addition, the use of monthly average relative humidity in the hygroscopi c aerosol growth equations results in error. Furthermore, grid resolution affects the ability of the model to resolve terrain features and for wind and other meteorological fields to re spond to geophysical features su ch as terrain, land-sea boundaries, surface characteristics such as roughness, albedo and other parameters.

As appropriate, the main sources of uncertainty in the BART modeling should be addressed in the final report. Good discussions of uncertainty in air quality modeling are readily available, e.g., Russell and Dennis (2000) and the cita tions in the reference section of this document. The uncertainty analysis is not intended to be exhaustive but rather shed light on important areas of model uncertainty and the likely range of app licability of the CALPUFF (or alternative model) results.

Accompanying the modeling report should be an electronic archive (C Ds, DVDs, or removable USB/IEEE 1394 hard drives as appropriate) that in cludes the full set of CA LPUFF inputs and model output fields and well as any pre- or post-processor codes used to genera te the results. While it is not necessary to archive the MM5 data files used to generate the CALMET fi elds, the CALPUFF-ready meteorological fields should be included in the archive. The m odeling data archive should be sufficiently complete as to allow an independent group to corroborate the m odeling results developed in the source-specific analysis.7-11 Table 7-1. Contents of a Typical Source-Specific BART Modeling Protocol.

1.0 INTRODUCTION 1.1 Objectives 1.2 Location of Source an d 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 Aff ecting Same Class I Areas

3.0 MODEL INPUT DATA 3.1 Modeling Domain 3.2 Terrain and Land Use 3.3 Emissions Data Base 3.3.1 Stack Parameters 3.3.2 Emissions Rates 3.3.3 Condensable Emissions 3.3.4 Speciation and Size Distributions

3.4 Refined Meteorological Modeling Base 3.4.1 MM5 Simulations 3.4.2 CALMET Data Sets 3.4.2 Observational Data

3.5 Air Quality Data Base 3.5.1 Ozone Concentrations 3.5.2 Ammonia Concentrations 3.5.3 Concentrations of Other Pollutants 3.5.5 CENRAP Regional Modeling Data Sets

3.6 Natural Conditions at Class I Areas

4.0 CALMET MODELING METHODOLOGY 4.1 Domain Configuration 4.2 MM5 Extraction Procedures 4.3 CALMET Configuration 4.3.1 Terrain 4.3.2 Land Use 4.3.3 Vertical Layer Structure 4.3.4 Diagnostic Model Settings 4.3.5 BIAS, RMIN2, IXTERP settings 4.3.6 R1, R2, RMAX1, RMAX2, RMAX3 settings 4.3.7 Surface Stations 4.3.8 Upper Air Stations7-12 4.3.9 Precipitation Stations

4.4 Evaluation of CALMET Datasets

5.0 CALPUFF MODELING METHODOLOGY 5.1 Model Selection 5.2 Domain Configuration and Receptors 5.3 CALPUFF Configuration 5.3.1 CALPUFF model options 5.3.2 Visibility Modeling Domain 5.3.3 Dispersion - Building downwash - Puff representation - Puff Splitting

5.3.4 Chemistry 5.3.5 Chemical Mechanism 5.3.6 Speciation 5.3.7 Background Ozone Concentrations 5.3.8 Background NH 3 Concentrations 5.3.9 Background H 2 O 2 Concentrations

5.4 Post Processing Methodology 5.4.1 POSTUTIL Parameters 5.4.2 CALPOST Parameters

5.5 Light Extinction and Haze Impact Calculations 5.5.1 Natural Background Conditions 5.5.2 Visibility Impact Methodology 5.5.3 Ammonia Limiting Method

5.6 Modeling Results 5.7 Uncertainty Analysis

6.0 EXEMPTION MODELING (if performed) 7.0 BART CONTROL MODELING RESULTS 8.0 REPORTING REFERENCES

APPENDIX A: CALMET Inputs APPENDIX B: CALPUFF Inputs APPENDIX C: POSTUTIL Inputs APPENDIX D: CALPOST Inputs7-13 Table 7-2. Example of Hypothetical Visibili ty Impact Rankings at Five CENRAP Class I Areas.

Class I Area 2001 2002 2003

DeltaDeciview Ranks 1-8 DeltaDeciview Ranks 1-8 DeltaDeciview Ranks 1-8

Caney Creek 0.99 0.88 0.62 0.59 0.55 0.52 0.48 0.47 0.95 0.63 0.51 0.50 0.46 0.42 0.37 0.36 1.20 0.90 0.73 0.72 0.59 0.47 0.45 0.42

Upper Buffalo 0.67 0.45 0.43 0.33 0.29 0.27 0.25 0.23 0.81 0.69 0.65 0.50 0.45 0.33 0.31 0.29 0.76 0.47 0.37 0.35 0.31 0.30 0.28 0.28

Boundary Waters 0.66 0.43 0.41 0.35 0.26 0.24 0.23 0.22 0.73 0.69 0.63 0.52 0.46 0.34 0.29 0.26 0.75 0.45 0.36 0.34 0.28 0.27 0.26 0.25

Voyageurs 0.26 0.23 0.22 0.21 0.20 0.19 0.18 0.16 0.54 0.47 0.43 0.37 0.37 0.31 0.31 0.30 0.61 0.42 0.30 0.29 0.28 0.28 0.25 0.25

Hercules Glade 0.34 0.33 0.31 0.26 0.24 0.20 0.18 0.17 0.52 0.43 0.32 0.31 0.30 0.28 0.24 0.24 0.27 0.24 0.23 0.20 0.14 0.13 0.11 0.107-14 Figure 7-1. POSTUTIL Processing of the Ammonia Limiting Method (ALM), NO 3 /HNO 3 Repartitioning for an Individual Source, and Estimating Ammonia Consumption from Modeled Sources and Boundary Conditions

. (Source: Escoffier-Czaja and Scire. 2005.) CALPUFF new source(s): CALPUFF.CON NO 3 _1; HNO 3 _1, TNO 3 _1=NO 3 _1+HNO 3 _1 CALPUFF Incoming Boundary: BOUNDARY.CON

NO 3 _2; HNO 3 _2; TNO3_2=NO 3 _2+HNO 3 _2

POSTUTIL Step 1 TNO 3 _1+TNO 3 _2 are repartitioned in new NO 3 _total/HNO 3 _total using NH 3 available, hourly Temperature and Relative Humidity

POSTUTIL Step 2 NO 3 _total and HNO 3 _total are renamed NO 3 ALL and HNO 3 ALL

POSTUTIL Step 3 NO 3 ALL/(NO 3 ALL+HNO 3 ALL)= NO 3 src/(NO 3 _1 +HNO 3 _1) NO 3 src; HNO 3 src are the new output

TNO 3 src=NO 3 src+HNO 3 src=TNO 3

_ 1