May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-1 5.0 MODEL INPUT PREPARATION PROCEDURES

In this section we describe the procedures to be used to develop the CMAQ model inputs for the Phase II 2002 annual and potentially additional ~60 days epis odic simulations. We also discuss how the CAMx model inputs will be generate d if the optional task to run an alternative model to corroborate the CMAQ results is selected. The development of the CMAQ meteorological and emissions inputs are discusse d first followed by the science options to be used by CMAQ. The procedures for devel oping the initial and boundary conditions and photolysis rates inputs are then discussed along with the model application procedures.

5.1 Meteorological Inputs to Emi ssions and Air Quality Models

The emissions and air quality models require certain meteorological input data including wind fields, estimates of turbulent eddy disper sion, humidity, temperature, clouds, and actinic flux. Spatially gridded and hourly varying meteorological data are needed to estimate biogenic, mobile source emissions, and plume-rise for large, elevated point sources. Meteorological data are needed to drive chemical transport models for solving atmospheric diffusion and chemistry equations for model species. Because observed da ta are not available fo r the full gridded model domain, numerical meteorol ogical models are used to provide these inputs.

The National Center for Atmospheric Resear ch (NCAR)/Pennsylvania State University (PSU) Fifth-Generation Mesoscale Model (MM5 ) (v3.6) is being used by the VISTAS meteorological modeling contactor, Baron Advan ced Meteorological Systems (BAMS) (Olerud, 2004) to simulate meteorology at a 36-km reso lution for calendar year 2002 over the entire

continental United States and incl uding portions of Canada, Mexic o, and the Atlantic and Pacific Oceans. MM5 is also being applied over the s outheastern U.S. using a 12 km resolution grid. The MM5 is a three-dimensional prognostic mete orological model that is used not only for meteorology studies but also for air quality studies. Some of th e physics used in the simulation include one-way nesting; nonhydrostatic dynamics; four-dimensional data assimilation of wind, temperature, and mixing ratio; explicit treatmen t of moisture; cumulus cloud parameterization; vertical mixing of momentum in the mixed laye r; PBL process paramete rization; atmospheric radiation; sea ice treatment; and snow cover (see Chapter 2 for more details).

After the MM5 simulation is completed, the MM5 output files are transferred to the emissions and air quality modeling team and anal yzed by the Meteorological Gatekeeper. The Meteorological Gatekeeper performs two main role s. To provide an independent evaluation of

the 2002 MM5 simulation that also serves to determine whether the MM5 data have been transferred correctly from the VISTAS meteorol ogical modeling contractor . And to process the 2002 MM5 output using Version 2.2 of the Mode ls-3 CMAQ Meteorological-Chemical Interface Processor (MCIP) to generate meteorological fields that will be used for emissions processing and air quality simulation.

5.1.1 MCIP Reformatting Methodology

The Models-3 Community Multiscale Air Qual ity (CMAQ) modeling system is designed to simulate multiscale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particles) air quality problems. But befo re running the CMAQ Chemical Transport Model (CCTM), the MM5 generated mete orological data must be pr e-processed and converted toMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-2 Models-3 consistent data stru ctures. MCIP version 2.2 will be used to preprocess the MM5 meteorological output. The “pass through” option in MCIP will be used in the VISTAS Phase II modeling. One of MCIP’s functions is to translate meteorological parameters from the output of the Pennsylvania State University/National Ce nter for Atmospheric Research (PSU/NCAR) Mesoscale Modeling System Generation 5 (MM5) to the Models-3 input/output applications program interface (I/O API format) which is required for operation of Models-3 CMAQ processors. Some other necessary parameters not available from the meteorological model are estimated with appropriate diagnostic algorithms in the program. The key functions of MCIP

include:

1. Reading in meteorological model output files 2. Extraction of meteorological data for CTM window domain 3. Interpolation of coarse meteorol ogical model output for finer grid 4. Collapsing of meteorological pr ofile data if coarser vertical resolution data is requested 5. Computation or passing through surface and PBL parameters 6. Diagnosing of cloud parameters 7. Computation of species-speci fic dry deposition velocities 8. Generation of coordinate dependent meteorol ogical data for the generalized coordinate

CCTM simulation 9. Output meteorological data in Models-3 I/O API format

The MCIP processor transforms the data into I/O API format while also calculating several new data fields (e.g. low, middle, and high cloud fr actions) that are not readily available in the raw MM5 output. It also inter polates temperature and wind spee d to observation height (1.5m and 10m, respectively). The MCIP processor cu lls a minimum of six cells about the domain periphery to minimize edge eff ects in the MM5 simulation. MCIP can be used to further reduce the rows or columns in the MM5 data so that the domain definition for the MCIP output files precisely matches the domain used in the air quality modeling. MCIP also allows MM5 layers to be “collapsed” (i.e., some layers can be aggregated). When feasible it is desirable to use the same layer structure in the air qual ity model as in the MM5 to prevent errors associated with aggregating layer data and to maintain consis tency between data produced by the meteorological model and those used by the chem istry-transport model. Howeve r, due to computational costs associated with using large number of vertical layers, ve rtical layer collapsing is typically used to reduce the total number of layers used by the CCTM. In the VISTAS Phase II modeling we will collapse from 34 layers in MM5 output into 19 layers for the CMAQ air quality simulations. The first 8 layers of CMAQ, up to approximately 450 m AGL, will match th e MM5 vertical layer structure exactly. The MM5 layers are then “doubled up” in CMAQ, up to a height of approximately 3,500 m AGL. The region top for CMAQ is the same as used by MM5, 100mb (approximately 15 km AGL). The 36 km analys is domain contains 148 columns, 112 rows, and 19 layers. The 12 km analysis domain covers 168 columns, 177 rows, a nd 19 layers. More details on the CMAQ modeling domain definitions ar e provided in Chapter 4 with the vertical layer structure of MM5 and MCIP/CMAQ shown at Table 4-3.

5.1.2 Products of the Meteorological Input Development Process

The meteorological input development proce ss produces three two-dimensional and four three-dimensional daily meteorological and ge ophysical output data in the Models-3 I/O API format. These CCTM-ready meteorological input f iles are used in both emissions processing andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-3 the CCTM simulations. The met fields are 36 km and 12 km horizontal resolution on a Lambert Conformal Projection (LCP) coordina te system with 19 vertical si gma layers extending from the surface to the 100 mb pressure level. The data f iles include three-dimensional gridded fields of u- and v-wind components, vertical velocity, te mperatures, Jacobian, Jacobian weighted air density, total air density, water vapor, cloud wate r content, rain water content, ice and snow mixing ratio, layer heights, and vertical exchange coefficients. Two-dime nsional gridded fields of latitude and longitude, squared map-scaled fa ctor, surface temperatures and pressures, 1.5 and 10 meter temperature, planetary boundary heights, rainfall, tota l cloud fraction, snow cover, deposition velocities, u* and w*, surface roughness length, as well as dominant land use category are also developed.

Table 5-1 shows the configuration to be used in MCIP version 2.2 for processing the 2002 MM5 output to produce CCTM-ready meteorology input files.

Table 5-1. MCIP V2.2 Configuration used In the VISTAS Phase II Modeling.
Module or option Values or setting Additional Information

PBL value computation option 1 Use PBL value from input meteorology

Radiation fields 1 Use radiation fields from input meteorology

Dry deposition option 2 Use Models-3 (Pleim) dry deposition

routine

Output interval 60 Unit is in minutes

Vertical layer structure 19 layers See Chapter 4

5.2 Development of Emissions Model Inputs and Resultant Inventories

The base year emissions inventory for Phas e II of the VISTAS m odeling will be founded on revised 2002 emissions developed by VISTAS em ission inventory cont ractors in NEI Input Format (NIF) 3.0. These revised emissions we re reviewed by VISTAS stakeholders and considered complete in March of 2004.

Non-VISTAS state emissions are based on inventories obtained by the Study Team and determined to be representative of the 2002 episod e year. Western State base year point and area source emissions for 2002 were provided by the WR AP RPO. Additionally, an inventory of point source resolved agricultural fire emissi ons were provided by WRAP and utilized in the modeling. The CENRAP RPO provided VISTAS with an inventory of 2002 area source

ammonia emissions which were also used in developing the remaining non-VISTAS emissions.

For the remaining U.S. domain, point s ource projections were based on EPA’s 2001 modeling platform inventories a nd area source, nonroad mobile, a nd fire emissions were based on EPA’s preliminary 2002 NEI. VMT and MOBI LE input files were taken from EPA’s preliminary 2002 FTP site and used in running MOBILE6 nationally.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-4 Mexican and Canadian emissions are be ba sed on the latest available inventories obtainable by the Study Team in formats lending themselves to emissions modeling. At this time,

these inventories are the same as in Phase I.

For purposes of air quality model validati on, actual 2002 calendar year emissions for EGU and fire activity will be used, while during strategy and future year emission runs, “typical year” emissions for these categories will be processed.

A final revised 2002 VISTAS state emission inventory is expected by September 2004 and will be used in the final model performan ce demonstration and configuration expected to begin in October 2004. Non-VISTAS state emi ssions are expected to be based on State

submittals to EPA under the CERR and will be augmented with additional data provided by RPO, State, and international sources. As in the initial modeling, the revised modeling will use actual 2002 calendar year emissions for EGU and fi res, while “typical year” emissions for these categories will be processed during the strategy runs.

These emissions will then be converted to Inventory Data Analyzer (IDA) formatted versions and the data will be processed for ai r quality modeling using Version 2.0 of the Sparse Matrix Operating Kernel Emissions (SMOKE) model. Included in these runs will be the temporal, spatial, and speciation profiles and cross-reference data currently provided with the 2.0 release of the model augmented with any r ecommended and approved emission profile data provided by the emissions inventor y contractor or obtained from EPA prior to initial emissions modeling. The processing will be adjusted for e ach run to account for the specific air quality model (AQM) input required by CMAQ.

5.2.1 Emissions Modeling Methodology

Emissions inventory development for photoc hemical modeling must address several source categories including: (a) st ationary point sources, (b) ar ea sources, (c) on-road mobile sources, (d) non-road mobile sources, and (e) biogeni c sources. For this analysis, these estimates must be developed to support the episode that is being modeled (i.e., the historical base year when the episode actually occurred; 2002).

Development of an emissions inventory cust omized for the VISTAS region requires a merging of: (a) the most recent pertinent regional inventory and (b ) available high-resolution, locale-specific emissions estimated by local, state, and regional agencies in the VISTAS region. Local air regulatory and transportation planning agencies are generally the best sources of domain specific activity and cont rol factors to use in developing the base year emissions. Often,

these local emissions data sets come from a vari ety of sources, frequently in different formats.

The study team will acquire emissions es timate data from Emissions Inventory Contractor, in the NIF 3.0 format for purposes of generating the emission inventory base year files necessary for Phase II of the VISTAS contr act. These data will be augmented with highway

mobile source data submitted to the VISTAS em ission inventory contractors from the VISTAS participating States.

Contacts with VISTAS’ emission inventory contractors and the U.S. EPA will be established and formal requests made for inve ntory corrections, updates and ancillary dataMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-5 pertinent to the modeling of emissions in their jurisdictions. Where feasible and consistent with project resources and schedule, these updated data sets will be acquired and will be used to create day-specific modeling inventories specific to the VISTAS domain for the base year episodes to be modeled.

5.2.2 Set-up of SMOKE Over the VISTAS Domain

SMOKE will be configured to generate point, area, nonroad, highway, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs will be maintained in separate source category files in order to allow maximum flexibility in producing alternate strategies. Settings for each of the source categori es are discussed in relevant sections below. With the exception of biogenic and highway mob ile source emissions that are generated using the, respectively, BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions will be processed using the month, day, and hour specif ic temporal profiles of the SMOKE model.

To produce an emissions inventory to support annual modeling, representative time periods will be selected and modeled. Area, nonroad, and point sources will be modeled as a block of Thursday, Friday, Saturday, Sunday, Monda y one per month (total of 60 days modeled). On-Road motor vehicles will be represented by a entire single week for each month. This selection criteria allows for the representation of day-of-the-week vari ability in the on-road motor vehicles, and models a representation of th e meteorological variability in each month. Holidays will be modeled as Sundays. A list of modeled holidays is provided in Table 5-2. The biogenic emissions will be modeled on a day specific basis (365 days).

Table 5-2. SMOKE Modeled Holidays.

Date Julian Day Holiday Description January 1, 2002 2002001 New Year's Day March 29, 2002 2002089 Good Friday May 27, 2002 2002147 Memorial Day July 4, 2002 2002185 July 4th September 2, 2002 2002245 Labor Day November 28, 2002 2002332 Thanksgiving Thurs November 29, 2002 2002333 Thanksgiving Fri December 24, 2002 2002358 Christmas Eve December 25, 2002 2002359 Christmas Day

Population will be used as a gridding defau lt for all source categories when the assigned surrogate would cause SMOKE to drop emissions . This can be a case when the county-level emission inventories are prepared using surroga tes other than those available for modeling

purposes.

The domain for the Phase II episode will be identical to the Phase I domain, which is based on the EPA’s 36-km national CMAQ domain, illu strated in Figure 5-1 below (details on the modeling domains are provided in Chapter 4).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-6 Figure 5-1. EPA 36-km National CMAQ Domain.

The parameters for the SMOKE runs are as follows:

Episodes : 2002 Calendar Base Year. Optional tasks for episodic modeling of up to 60 days that will likely be from 2003.

Future Years : To be determined.

Output Time Zone : Greenwich Mean Time (zone 0)

Projection : Lambert Conformal with Alpha=33, Be ta=45, Gamma=-97, and center at (- 97,40).

Domain :

• 36 Kilometer Grid: Origin at (-2736, - 2088) kilometers with 148 rows by 112 columns and 36-km square grid cells.

• 12 Kilometer Grid: Origin at (108, -1620) kilometers with 168 rows by 177 columns and 12-km square grid cells.

Layer Structure : The CMAQ layer structure will be 19 layers, with specific layer positions defined in the meteorology files (see Chapter 4).

CMAQ Model Species : The CMAQ initial configuration will be for the CB-IV chemical mechanism with PM. The model species in the emission input files will be: CO, NO,May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-7 NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, SULF, PEC, PMFINE, PNO 3, POA, PSO4, and PMC.

Meteorology Data : Daily (25-hour). SMOKE requires th e following five types of MCIP outputs: (1) Grid cross 2-d, (2) Grid cross 3-d, (3) Met cross 2-d, (4) Met cross 3-d, and (5), Met dot 3-d. These files need to matc h the grid projection and overlap with the emissions modeling region but can be larger in the horizontal directions than the modeling region shown in Figure 1. Therefore, the data files for the 36 Kilometer grid domain will be at least 90 columns by 132 rows

Elevated Sources : All sources will be treated by SMOKE as potentially elevated. No plume-in-grid sources will be modeled. Wildfi re emissions will be handled as point

sources.

Producing 365 day-specific input files for a ll source categories pl aces a burden on available computing facilities, da ta management systems, and w ould adversely affect the Phase II schedule. Selecting representati ve model days for some or all of the source categories reduces the processing and file handling requirements to a more manageable level, and in most cases does not compromise the accuracy of the emissions files.

Other current or recent projects undertak en by EPA, WRAP and LADCo have used a selection approach for all of the source categorie s (except biogenics) that use a representative weekday/Saturday/Sunday either for each month or each season to model all of the emissions files. In an attempt to better represent the level of temporal and spatial de tail available for each source category, we have develope d a more detailed strategy.

Biogenic emissions will be modeled for each episode day, using the daily meteorology. Point sources, including CEM and fire emissions will be modeled for each episode day to take advantage of the available day-specific emissi ons and meteorology. Area sources, including

non-road mobile and dust emissions do not util ize meteorological data, and are temporally allocated by monthly, daily and hour ly profiles. Reviewing these profiles indicate that maximum temporal definition can be achieved by selecti ng representative Thursday, Friday, Saturday, Sunday, and Monday profiles for each month.

Motor vehicle emissions are influenced by me teorological variabilit y, but the processing requirements for daily motor vehicle emissions were determined to be prohibitive under the current schedule. Rather than utilizing averag ed meteorological data or pre-calculated motor vehicle emissions, a single week per month was se lected for modeling. This week was selected from mid-month, to try to best represent the av erage temperature ranges for the month, and also adjusted to exclude holidays that would require atypical processing. The area source modeling

dates were also selected from these ra nges to simplify data handling procedures.

2002 On-Road Mobile Sources Represented by the Following Weeks : January 13-19 February 10-16

March 10-16 April14-20 May 12-18May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-8 June 9-15 July 14-20 August 11-17 September 15-21

October 13-19

November 10-16

December 15-21

5.2.3 Development of Point Source Emissions

Stack parameters are often more important to the reliability of the air quality modeling results than the emissions rates themselves. Stack parameter data are frequently incorrect, especially in some of the current regional modeling inventories and careful QA is required to assure that the point source emi ssions are properly located both hor izontally and vertically on the modeling grid. To screen for si mple, but potentially serious invent ory errors such as these, the study team has modified procedures originally developed by EPA to quality assure, augment, and where necessary, revise, stack parameters to examine the accuracy of the point source emissions, as well as standardize procedures to identify and correct stack data errors. These procedures will be implemented in the NIF to IDA conversion step of the inventory development. Additionally, SMOKE has a number of built-in QA procedures designed to catch missing or out-of-range stack parameters. Thes e procedures will also be invoked in the processing of the point source data.

For the Phase II initial base line modeling, we will be se parating the point source emissions into EGU and non-EGU categories. Th e non-EGU category will not be using any day or hour-specific emissions. All non-EGU point sour ce emissions will be te mporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allocation

factors. These factors will be based on the cros s-reference and profile data supplied with the SMOKE 2.0 version and will be supplemented with relevant data provided to the study team by

VISTAS or its contractors.

For EGU sources with EPA reported CEM data or with hourly emissions provided by stakeholders, actual hourly data will be used. For those s ources where EPA CEM data are utilized, NOx, SO2, and heat input-based hour-sp ecific profiles were developed and applied to NOx, SO2, and all other emissions , respectively. This ensured th at the annual emission values provided by the EI contractor were maintained, but distributed using hourly to annual profiles. For sources providing hour-specifi c data and where they were approved by the State in which they operated, those data were substituted fo r EPA CEM-based emissions and distributions.

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input

data were collected from the 2002 Continuous Em issions Monitoring (CEM ) datasets, and used to develop unit-level temporal distributions. The hour, day of week, and monthly specific temporal profiles will be used in conjunction with th e EI supplied emissions data to calculate hourly EGU emissions by unit.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-9 All point sources will be spatially allocated in the domain based on th e stationary source geographic coordinates. If a point source is missi ng its latitude/longitude coordinates, the source will be placed in the center of its respective county.

5.2.4 Development of Area and Non-Road Source Emissions

All area and non-road source emissions will be temporally allocated to month, day, and hours using annual emissions and source category code (SCC) based allo cation factors. These

factors will be based on the cross-reference and profile data supplied with the SMOKE 2.0 beta version and will be supplemented with relevant data provided to the stu dy team by VISTAS or

its contractors. Area and non-road sources will be spatially allocated in the domain based on SCC-based spatial allocation factor files. If an area or non-road source SCC does not have an existing cross-reference profile assigned to it, the county-level emissions will be allocated by population density in the respective county.

A crustal PM transport factor will be applied to fugitive dust emission sources that have been identified in U.S. EPA modeling to have on ly a portion of its mass transportable from the source of the emission generation. The EPA’s studi es indicate that 60 to 90 percent of PM emissions from fugitive dust sources do not reach an elevated level necessary to be transported or modeled in an episodic simulation. For th is reason, the VISTAS emissions contractor will apply county-specific fugitive dust emissions trans port factors to these sources to adjust PM emissions accordingly. The fugitive dust emissions adjustment is done by the VISTAS emissions contractor prior to receipt by the emissions and air quality modeling Team. Thus, no additional adjustment will be made in the emissions modeling. (for adjustment factors see: http://www.epa.gov/ttn/chief/emch /invent/statusfugdustemissions_082203.pdf ).

5.2.5 Development of On-Road Mobile Source Emissions

The MOBILE6 module of SMOKE will be used to develop the base year on-road mobile source emissions estimates for CO, NOx, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates w ill be combined with gridded, episode-specific temperature data to calculate th e gridded, temporalized emission estimates. Of note, whereas the on-network emissions estimates are spatially a llocated based on link loca tion and subsequently summed to the grid cell level, the off-network em issions estimates are spatially allocated based on a combination of the FHWA version 2.0 high way networks and population. For the VISTAS 36/12 km modeling, no link based data will be us ed. The MOBILE6 emissions factors are based on episode-specific temperatures predicted by th e meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:

• Hourly and daily minimum/maximum temperatures;

• Facility speeds;

• Locale-specific inspection/maintenance (I/M) control programs, if any;

• Adjustments for running losses;

• Splitting of evaporative and exhaust emi ssions into separate source categories;

• VMT, fleet turnover, and changes in fu el composition and Reid vapor pressure (RVP).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-10 The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and maintenance program in effect, type of oxygenated fuel program in effect, alternative vehicle mix pr ofiles, RVP of in-use fuel, operating mode) that

direct the calculation of the MOBILE6 emissions factors.

5.2.6 Development of Biogenic Source Emissions

A revised version of a commonly used biogenic emissions model, the Biogenic Emissions Inventory System (BEIS), has recen tly been developed and tested by EPA over two separate modeling domains/episodes. This vers ion of the model (BEIS-3, v0.9) contains several changes over BEIS-2, including the following:

• Vegetation input data -- are now base d on a 1-km Biogenic Emissions Landuse Database (BELD3) vegetation data base,

• Emission factors – many updates includi ng some recent NARSTO modifications,

• Environmental algorithm -- includes a sunl it/shaded leaf solar radiation model.

A series of sensitivity modeli ng simulations has been complete d and concluded that the more recent BEIS-3 methodology will impact base case model ozone predictions in most parts of the U.S. The preliminary tests have also shown that the newer biogenic emissions do not appear to have a large effect on: 1) the control signal response, 2) relative reduction factors resulting from a projected emissions change, or 3) overall regi onal model performance in the eastern U.S.

For this particular application of BEIS-3, version 0.9 as currently incorporated in the SMOKE processor will be used. This means that: 1) soil NO emissions shall be prepared without the input of specific soil moisture a nd precipitation data and 2) MEOH emissions will not be modeled explicitly. Otherwise, the mo deling should be identical to a BEIS-3 (v1.0) application.

The BELD-3 landuse data on a Lambert confor mal grid at 1-km resolution have already been developed, are available, and will be used to estimate biogenic emi ssions in this study. The BEIS model also requires as input hourly, gri dded temperature and solar radiation data to estimate biogenic emissions, and these data will be derived from the MM5 predictions.

5.2.7 Wildfires, Prescribed Burns, Wind Blown Dust and Sea

Salt Source Emissions

Wildfires and Prescribed Burns

Wildfire and prescribed burn emissions will be handled separately from the standard area source input files. The study team expects to receive monthly estimates of fire emissions from the EI contractor, which include burn acreage an d biomass loading information for the VISTAS states. Depending on the completeness and quality of the data received, attempts will be made to calculate spatial and temporal dist ributions of the fire emissions, rather than relying on standard distribution profiles. Also, the study team will a ttempt to calculate vertical distribution of the fire emissions, based on fire size and bi omass involvement. The SMOKE 2.0 can model fire plume rise if provided with the following variables:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-11 PTOP – Top of the fire plume profile (meters above ground level) PBOT – Bottom of the fire plume pr ofile (meters above ground level) Lay1 – The percent of the emissions en trained in the first modeling layer

The WRAP Fire Emissions Joint Forum Em issions Inventory Report (FEJF,2002) has documented an approach to calculating these plum e descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to ca lculate representative hourl y plume profiles. These profiles are then used by SMOKE2.0 to distribute the vertical em issions for the fires. To successfully model fires as elevated point source s, the data provided by the EI contractor will need to include both the day or da ys on which the fire occurs, and a spatial identifier of the fire location. At a minimum, a latitude and longitude of the fire location can be used, while a polygon coverage would be preferable.

In addition, wildfire and prescribed burn data, including emissions estimates and plume rise distributions, will be obtai ned from the WRAP contractor and used to supplement the inventory for the WRAP states.

Windblown Dust

PM 10 and PM 2.5 emissions from wind erosion of natural geogenic sources (SCCs 2730100000 [total] and 2730100001 [dust devils]) will be excluded from the resulting modeling files using a 100 percent reducti on in the control packets.

Sea Salt

CMAQ currently treats sea salt as an inert PM species. That is, the sea salt is not allowed to chemically interact with other species, such as producing particulate sodi um nitrate. There are plans to update CMAQ to have chemically active sea salt, but it is unclear whether such an update will occur during the Phase II modeling. Accordingly, the initial Phase II modeling will be conducted without any sea salt emissions. If CMAQ is updated to treat chemically active Sea Salt, or if CAMx is run using its full-science op tions, then Sea Salt emissions will be generated using appropriate procedures (e.g., as was done in Phase I).

5.2.8 Speciation and Reform atting of Emissions

SMOKE will be run to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM-IV , CB-IV or CB4). Note that VISTAS is evaluating whether the CB-IV or SAPRC99 photochemical mechanisms are more appropriate for the Phase II modeling and may switch to the SAPRC99 mechanism for the revised base case modeling that will be initiated in September 2004. The SMOKE model will also reformat the emissions estimates for use in CMAQ modeling. For each model-ready emissions inventory, SMOKE will produce at a minimum five (5) separa te air quality model-ready files: low-level point source, area source, elevated point source, mobile source, and biogenics. Other source categories, such as EGU and fire emissions may also be handled as separate air quality modelready files.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-12 5.2.9 Development of Modeling Inventories

The emissions inventories modeled for the VISTAS Phase II can be grouped into three distinct types: 1) 2002 annual i nventories, 2) Future Year epis odic inventories, using Phase I definitions, and 3) Alternate Base Year Episodic Inventories. In all cases, the Study Team expects to receive the emissions inventory data for the VISTAS states from the Emissions Inventory contractor, a dd non-VISTAS states and Canadian and Mexican data acquired from alternate EPA and/or RPO sources, and produce the CMAQ ready emissions files.

5.2.9.1 2002 Annual Inventories

The initial 2002 annual base inventory will be produced in two phases, coinciding with the delivery of the MM5 model results for the first 8 months and last 4 months of the 2002 calendar year. The initial inventor y will consist of all of the re quired source categories: area, nonroad, point, motor vehicle and biogenics.

A revised 2002 annual inventory will be modeled late r in the project , using updated emissions as supplied by the EI contractor. This may require re-production of all or some of the source categories. A “typical” inventory will be produced, based on the revised 2002, and replacing the revised actual emissions for EGUs and fires with “typical” EGU and fire emissions, as provided by the EI contractor. The Study Team will also produce up to 3 future year scenario annual runs, based on the 2002 meteorology. The composition of these future year runs is yet to

be determined.

5.2.9.2 Future Year Phase I Episodic Inventories

Utilizing emissions data provided by the EI contractors, the Study Team will model up to 2 alternate future-year scenar ios, using 2 of the Phase I episodes and the accompanying MM5 meteorological data: (1) January 2- January 21, 2002, and (2) July 7 to July 28, 2001. This may require re-modeling of all of the source categories, with th e exception of the biogenics.

5.2.9.3 Alternate Year Episodic Inventories

To capture visibility conditions that are not otherwise represented by 2002 conditions, we may perform episodic air quality modeling for up to 60 additional episode days selected from 2003 or another year. Emissions inventories for these additional days will be prepared by the Study Team and the requisite meteorological mode ling results would be provided prior to any modeling. The additional episode days will be modeled for a single base case inventory.

5.2.10 Products of the Emissions Inventory Development Process

In addition to the CMAQ-ready input files ge nerated for each hour of the days modeled in the Phase II annual run, a number of quality assura nce (QA) files will be prepared and used to check for gross errors in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distri bution of the emission provides insight into the quality and accuracy of the emissions inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-13 C

Visualizing the model-ready emissions with the scale of the plots set to a very low value, we can determine whether there are areas omitted from the raw inventory or if emissions sources are erroneously lo cated in water cells.

C

Spot-check the holiday emissions files to c onfirm that they are temporally allocated like Sundays.

C

Producing pie charts emission summaries that highlight the contribution of each emissions source component (e.g. nonroad mobile).

C

Normalizing the emissions by population for each state will illustrate where the inventories may be deficient and provide a reality check of the inventories.

C

Spot check vertical allocati on of point sources using PAVE. We will use state inventory summaries prepared prior to the emissions processing to compare against SMOKE output report totals generated after each major step of the emissions generation

process.

To check the chemical speci ation of the emissions to CB-IV terms and the vertical allocation of the emissions, we will compare re ports generated with SMOKE reports to target these specific areas of the processing. For spec iation, we will compare th e inventory import state totals versus the same state totals with the speciation matrix applied.

For checking the vertical allocation of the emissions, we will creat e reports by source, hour, and layer for randomly selected states in the domain. We will create these reports for a representative weekday in each of the episodes for each of these selected states.

The quantitative QA analyses of ten reveal significant deficienci es in the input data or the model setup. It may become necessary to tailo r these procedures to tr ack down the source of each major problem. As such, we can only outline the basic quantitative QA steps that we will perform in an attempt to reveal the underlying problems with the inve ntories or processing. Following are some of the reports that may be generated to review the processed emissions:

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State and county totals from i nventory for each source category

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State and county totals after spatia l allocation for each source category

C

State and county totals by day after tempor al allocation for each source category for representative days

C

State and county totals by model species after chemical specia tion for each source

category

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State and county model-ready totals (after spatial allocation, te mporal allocation, and chemical speciation) for each source categ ory and for all source categories combined

C

If elevated source selection is chosen by user, the report indicati ng which sources have

been selected as elevated and plume-in-grid will be included.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-14 C

Totals by source category code (SCC) from the inventory for area, mobile, and point

sources

C

Totals by state and SCC from the inventory for area, mobile, and point sources

C

Totals by county and SCC from the invent ory for area, mobile, and point sources

C

Totals by SCC and spatial surrogates code for area and mobile sources

C

Totals by speciation profile code for area, mobile, and point sources

C

Totals by speciation profile code and SCC for area, mobile, and point sources

C

Totals by monthly temporal profile c ode for area, mobile, and point sources

C

Totals by monthly temporal profile code and SCC for area, mobile, and point sources

C

Totals by weekly temporal profile c ode for area, mobile, and point sources

C

Totals by weekly temporal profile code and SCC for area, mobile, and point sources

C

Totals by diurnal temporal profile c ode for area, mobile, and point sources

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Totals by diurnal temporal profile code and SCC for area, mobile, and point sources

C PAVE plots of gridded inventory pollutants for all pollutant s for area, mobile, and point

sources

5.3 Quality Model Configuration and Input Preparation

5.3.1 CMAQ Science Configuration

This section described the model configurati on and science options to be used in the Phase II modeling effort. The recommendations ar e based on testing and model evaluations of several models or model configurations carried out in the VISTAS Phase I study. Table 5-3 summarizes the proposed configuration for CMAQ. The latest version of CMAQ is currently

Version 4.3 that was released October 2003. Howe ver, EPA is expected to release an updated version of CMAQ in mid 2004, and this version will likely be used in the Phase II modeling. In addition, we will perform simulations for at le ast January and July using the CMAQ 4.3 and the new versions of CMAQ to determine if the CMAQ updates substantially change the model predictions or performance.

In the CMAQ base configuration we will run both the 36 km and 12 km grids using oneway grid nesting where the boundary conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ BCON processor. The base configuration of CMAQ will use 19 vertical layers up to a region top of 100 mb (approximately 15 km AGL).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-15 The PPM advection solver would be used al ong with the spatially varying (Smagorinsky) horizontal diffusion approach. K-theory will be used for vertical diffusion with a minimum eddy diffusion constant (Kz) of 0.1 m 2 /s. Note that the CMAQ default minimum Kz value is 1.0

m 2 /s. However, based on theoretical considera tions and the Phase I testing and evaluation a 0.1

m 2 /s minimum Kz values was selected for Phase II modeling.

The MCIP2.2 will be used to process the MM5 data using the “pass through” option.

The AERO3/ISORROPIA aerosol chemistry sche me will be used for inorganic aerosol thermodynamics. During the Phase I testing of the CMAQ model it was noted that the

AERO3/ISORROPIA aerosol modules failed to c onserve sulfur and nitrogen mass. A mass conservation patch developed by the Georgia In stitute of Technology was added to the model that renormalized the total sulfur, reactive nitroge n and reduced nitrogen species after the call to

the aerosol modules to conserve sulfur and nitrogen mass in the model. Sensitivity simulations were performed with and wit hout the mass conservation patch. Although the mass conservation patch did not affect model performance, the VISTAS science team believes that mass

conservation is a fundamental char acteristic of the real-world atmosphere so have retained the mass conservation patch for the Phase II mode ling. The SORGAM scheme, which includes a reversible thermal equilibrium, will be used for secondary organic aerosols.

Phase I testing included comparisons of the three gas phase chemistry mechanisms: the Carbon Bond Mechanism version 4 (CB4, Gery et al., 1989), a recent update of CB4 (CB4-2002, Jeffries et al., 2002), and the SAPRC99 (Carter, 1999). Although Phase I testing did not show notably better performance for any one mechan ism, the use of either CB4-2002 or SAPRC99 chemistry in the Phase II modeling is desirable because these mechanisms employ the most

recent available chemical kinetics data and are mo re scientifically defensible than the older CB4 mechanism. The computational cost of the SAPRC 99 chemistry is nearly twice that of the CB4. Since CB4-2002 is only implemented in the slower SMVGEAR chemistry solver, the computational penalty of using CB4-2002 chemistry over using CB4 with the EBI chemistry solver is even greater than that for SAPRC99. Given that the initial te sting of the CMAQ model

in the first half of 2004 will be more of a sh akedown of the modeling system, including the 2002 MM5 data and initial 2002 emission estimates, and the three chemical mechanisms produce similar model performance, then we propose to use the CB4 chemical mechanism for the initial 2002 base case modeling. In the mean time, under Task 7 of the Phase II work effort the sensitivity of the CMAQ model to emission re ductions using the CB4 and SAPRC99 chemistries will be evaluated using the January 2002 and July 2001 Phase I episodes to determine whether the two mechanisms have different responses. Based on this evaluation, and considerations of whether CB4-2002 has been implemented in CMAQ using the faster EBI chemistry solver, a decision will be made on the choice of the ga s-phase photochemical mechanism for the revised 2002 base case modeling to be initiated in September 2002.

Finally, recent environmental chamber expe riments at UCR suggest that both CB4-2002 and SAPRC99 may underestimate th e reactivity of arom atic compounds. UCR and others expect

to test new versions of CB4 and SAPRC chem istry during 2004 independently of the VISTAS work effort. Although updates in aromatic chemis try will primarily affect urban areas, it is desirable to perform Phase II modeling using th e most up-to-date version of the chemistry possible. We will report any pertinent findings or new chemical mechanisms to VISTAS as they

become available.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-16 Table 5-3. Proposed Phase II Model Configuration for the CMAQ. Model Option CMAQ

Model Version Version 4.4 (April 2004)

Horizontal Resolution 36/12 km No. Vertical Layers NZ = 19

Horizontal Advection PPM

Vertical Advection PPM

Horizontal Diffusion Spatially Varying

Vertical Diffusion K V (Eddy Diffusion) MM5 Configuration Pleim-Xiu/ACM MM5 Processing MCIP2.2 Pass Through Gas-Phase Chemistry CB4 Gas-Phase Chemistry Solver MEBI/Hertel Secondary Organic Aerosol SORGAM Aqueous-Phase Chemistry RADM Aerosol Chemistry AE3/ISORROPIA Dry Deposition Pleim-Xiu

Plume-in-Grid Off

Initial Concentrations CMAQ Default Boundary Conditions Initial: Seasonal 2001 GEOS-CHEM Revised: 3-Hourly 2002 GEOS-CHEM

Emissions NEI 1999 v2 CMU NH3 Adjustments

5.3.2 Spin-Up Initialization

For the 2002 annual CMAQ modeling, the model will be exercised separately for four quarters. The 2002 MM5 modeling started on D ecember 17, 2001 at 12Z. Thus, allowing for 12 hours of spin up of the MM5 model, CMAQ will be initialized at 00Z on December 18, 2001. This results in a 13 day spin up period for CMAQ and the first quarter run segment of 2002. For the other quarter run segments of 2002, CMAQ w ill be initialized with a 15 day spin up period.

5.3.3 Boundary Conditions

The Phase I modeling included comparisons of several schemes for producing boundary

conditions for the 36-km doma in, including the following:

• Fixed, default values at each edge, with ve rtical variation based on CMAQ default data.

• Clean boundary conditions.

• Seasonal mean output from the GEOS -CHEM model for a 2001 simulation

In addition, for the July 2001 episode we exam ined the following three boundary conditions:

• Fixed CMAQ default values;

• 3-hourly temporally and spatially vary ing BCs based on the 2001 GEOS-CHEM output

• Monthly average GEOS-CHEM output for July 2001May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-17 Sensitivity experiments showed th at model performance in the VI STAS states during conditions of high visibility impairment was relatively in sensitive to variations in the boundary conditions, although differences in concentrations did occur. However, it is likely that boundary conditions will have a larger effect on visibili ty for clean days and for future conditions as visibility begins to improve. Moreover, boundary conditions will have a more significant effect on natural background visibility. In fact the BCs had a la rger effect on the cl eaner January 2002 model estimates than the dirtier July 1999 and 2001 episodes. Therefore, as a long-term effort we recommend that boundary conditions for the 36 km continental US domain be obtained from a global model such as GEOS-CHEM. However, because global model results are not yet available for 2002, in the initial Phase II modeling we will use seasonal average diurnally varying data from a 2001 GEOS-CHEM data to provide boundary conditions for the 36 km

domain. If 2002 GEOS-CHEM results become av ailable, the BCs will be updated with high temporal resolution (e.g., 3-hourly) day-specif ic GEOS-CHEM output. Boundary conditions for the 12 km nest will be obtained from the CMAQ 36-km simulation.

5.3.4 Photolysis Rates

Several chemical reactions in the atmosphere are initiated by th e photodissociation of various trace gases. To accurately represent the complex chemical transformations in the atmosphere, accurate estimates of these photodisso ciation rates must be made. The Models-3 CMAQ system includes the JPROC processor, whic h calculates a table of clear-sky photolysis rates (or J-values) for a specific date. JPROC uses default values for to tal aerosol loading and provides the option to use default column O3 data or to use TOMS data for total column O3. Previously, in the Phase I modeling we used TO MS data, and we will continue to use TOMS data for Phase II. There are a few days in 2002 fo r which TOMS data is not available, and we will used default column O3 data or previous days data for those days.

JPROC produces a "look-up" tabl e provides the photolysis rate s as a function of latitude, altitude, and time (in terms of the number of hours of deviation from local noon, or hour angle). In the current CMAQ implementation, the J-values are calculated for six la titudinal bands (10o, 20o, 30o, 40o, 50o, and 60o N), se ven altitudes (0 km, 1 km, 2 km, 3 km, 4 km, 5 km, and 10 km), and hourly values up to " 8 hours of deviation from local noon. During model calculations, photolysis rates for each model grid cell are estimated by first interpolating the clear-sky photolysis rates from the look-up table using the grid cell latit ude, altitude, and hour angle, followed by applying a cloud correction factor.

The photolysis rates input file must be pr epared as separate look-up tables for each simulation day. The modeling team has already prep ared scripts to automate the production of photolysis rate files for each day of the annual s imulation. Photolysis files are ASCII files, and these will be visually checked for selected days to verify that p hotolysis are within the expected

ranges.

5.4 CAMx Model Inputs

If the optional task to apply the CAMx m odel to corroborative the CMAQ results were selected, then the model inputs would be prepared in a manner consistent with the CMAQ inputs. The same modeling domain and grid structure as used for the CMAQ modeling of 2002 wouldMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 5.doc 5-18 be used for CAMx (see Chapter 4). Meteorol ogical inputs would be generated using the MM5CAMx processor using options similar to the MCIP “pass through” option and with a 0.1

m 2 /s minimum Kz. The CAMx initial and boun dary conditions (IC/BC) inputs would be generated from the CMAQ IC/BC inputs using the CMAQ-to-CAMx IC/BC processors. The CAMx emission inputs would be generated us ing the CMAQ-ready emission inputs and the CMAQ-to-CAMx emission processor.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-1 6.0 QUALITY ASSURANCE PLAN

In this section we discuss the quality assuran ce procedures that will be used in the Phase II modeling. More details are provided in the VISTAS Phase II Quality Assurance Project Plan (QAPP) that is under preparation.

6.1 Quality Assurance Objectives

In December 2002, the USEPA publish exte nsive guidance on developing a Quality Assurance Project Plan (QAPP) for modeling st udies (EPA, 2002). The objective of a QAPP is to ensure that a modeling study is scientifically sound, robust, and defensible. The new EPA guidance suggests that a QAPP should include the following elements :

• a systematic planning process including id entification of assessments and related performance criteria;

• peer reviewed theory and equations;

• a carefully designed life-cy cle development process that minimizes errors;

• clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can fully unde rstand the model output;

• input data and parameters that are accurate and appropriate for the problem;

• output data that can be used to help inform decision making.

• documentation of any changes from th e original quality assurance plan;

Moreover, the EPA guidance specifies that different levels of QAPP may be required depending on the intended application of the model, with a modeling study designed for regulatory purposes requir ing the highest level of quality assurance.

The QAPP also provides a valuable resource for project management. It can be used to document data sources and assumptions used in the modeling study, and it can be used to guide project personnel through the data processing and model applica tion process to ensure that

choices are consistent w ith the project objectives.

The modeling team has already developed QA documents and procedures in the Phase I

effort. For the Phase II effort these will be enhan ced and modified to assure that it conforms to the EPA QAPP guidance.

The guidance document also addresses mode l development, coding and selection of models, and model performance requirements. For the Phase II modeling we are using an existing EPA sponsored model. Thus, our QAPP will focus primarily on documenting data sources and QA of data processing performed by the model team. In addition, because no official EPA guidance currently exists for visibility mo del performance, a major objective of our QAPP will be to propose and define model performa nce evaluation procedures. QA objectives for specific aspects of the project are discussed below, and these will be incorporated into a QAPP that conforms to the EPA guida nce document for modeling studies.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-2 6.2 Emissions Model Inputs and Outputs

Emissions Quality Assurance (QA) and Quality Control (QC) are the single most critical step in performing air quality modeling studies . Because emissions processing is tedious, time consuming and involves complex ma nipulation of many different type s of large data sets, errors are frequently made in emissions processing and, if rigorous QA measures are not in place, these errors may remain undetected.

As part of the VISTAS Phase II QA effort, an “Emissions Gatekeeper” function will be implemented similar to that used effectively in Phase I. The Study Team envisions the role of this Gatekeeper as one to perform quality assurance activ ities on the following emission inventory (EI) data:

(1) EI data obtained from the VISTAS emissions inventory contractors; and (2) The emission inventory to be used for m odeling outside of the St ates in the VISTAS region.

Specifically, the Emissions Gatekeeper will re view the content and format of the provided emission inventories ensuring an appropriate appr aisal of the emissions data and estimates for the VISTAS States. Other tasks will include any additional translation from mass emissions files into the emissions modeling input file struct ure necessary for mode ling. The Study Team will supplement these activities with QA checks on the intermediate and model output files using internal and public domain visua lization and diagnostic packages.

We propose to continue with multistep emi ssions the QA/QC approach applied in the Phase I modeling. This includes the initial emissions QA/QC by the Emissions Gatekeeper described above, as well as QA/QC by the Emissions Modeler during the processing of emissions and then additional QA/QC by the air quality modeler of the processed model ready emission files. This multistep process with thre e separate groups involved in the QA/QC of the emissions is much more likely to catch any erro rs prior to the air qua lity model simulations.

6.2.1 Emissions Modeling QA/QC

EMS and EPA Input Screening Error Checking Algorithms : Although the SMOKE emissions model will be used for emissions processing, so me of the more advanced EMS input error checking algorithms will be used to screen the data and identify potential emission input errors. Additionally, EPA has issued a revised stack QA and augmentation procedures memorandum that will be used to identify and augment any outlying stacks.

SMOKE error messages : SMOKE provides various cautionary or warning messages during the emissions processing. We will redirect the SMOKE output to log files and review the log files for serious error messages. An archive of the lo g files will be maintained so that the error messages can be reviewed at a later date if necessary.

SMOKE emissions summaries : We will use QA functions built into the SMOKE processing system to provide summaries of processed emissions as daily totals according to species, source category and county and state boundaries. These summaries will then be compared withMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-3 summary data prepared for the pre-processe d emissions, e.g., state and county totals for emissions from the augmented emissions data.

6.2.2 QA of the Model-Ready Emissions Impacts

The goal of the post-processed emissions summary QA is to detect possible errors in the final, model-ready binary emissions files by pr eparing summary plots that characterize spatial and temporal patterns in the emi ssions data. This step is designed to catch errors that may be missed in the internal SMOKE QA procedures. We will use a QA/QC post-processing program that read the CMAQ-ready I/O API emissions file formats for each of the major source categories (mobile, area, point, biogenic , fire) and produce the following plots.

Spatial Summary : We will sum the emissions for all layers and for all 24 hours that is used to prepare a PAVE plot showing the daily total emissions spatial distribution. For a 20 day simulation this produces approximately 20 days x 20 species x 5 emissions categories = 2,000 plots. In our base case simulations these plots will be presented as tons per day. The objective of this step is to identify errors in spatial distribution of emissions.

Vertical Profile : For point sources the emissions total fo r each layer will be summed and plotted

to show the vertical distributi on of emissions. These plots show the emissions on the x-axis for each model layer on the y-axis. The objective of this st ep is to identify possible errors in vertical

distribution of emissions.

Short Term Temporal Summary : The total domain emissions for each hour will be accumulated and time series plots prepared that display the di urnal variation in total hourly emissions. The objective of this step is to identi fy errors in temporal profiles.

Long Term Temporal Summary : The total domain emissions for each day will be accumulated and displayed as time series plots that show th e daily total emissions across the domain as a function of time. The objective of this step is to identify particular da ys for which emissions appear to be inconsistent with other days for no reason (e.g., not a weekend) and compare against the general trend.

Control Strategy Spatial Displays : Spatial summary plots of the da ily total emissions differences between a control strategy and base case emission s scenarios will be generated. These plots can be used to immediately identify a problem in a control strategy. Fo r example, if a VISTAS states

SO 2 control strategy is being analyzed and there ar e changes in emissions for other pollutants or

for SO 2 outside of the VISTAS states problems in em issions processing can be identified prior to the air quality model simulation.

6.3 Meteorological Model Outputs

As part of the VISTAS Phase II QA effort, a “Meteorological Gatekeeper” function will be implemented similar to that used effectivel y in Phase I (Tesche and McNally, 2002a,b,c). The task of the Gatekeeper is to provide an i ndependent review and qua lity assurance of the meteorological modeling and related data sets developed by the VISTAS meteorological modeling contractor (BAMS) and used subsequently by the emi ssions and air quality modeling teams. This Gatekeeper QA review serves two sp ecific purposes: (a) to en sure that any potentialMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-4 problems with the data sets (should they exist) are identified and corrected in a timely manner, and (b) to provide the study team with information to support ongoing CMAQ model performance testing and sensitivity analyses. In the case of meteorology, the Gatekeeper’s independent QA analysis of the MM5 meteorological data sets serves to provide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model performance and sensitivity analyses.

In addition to having personal responsibility for the quality and chain of custody of the meteorological data sets supplie d by other VISTAS contractors, the Meteorological Gatekeeper will be responsible for ensuring and maintaining th e integrity of the data files uploaded to the project website. This website, hosted by UCR, serves as the repository of data for the ENVIRON/UCR/Alpine m odeling centers and for the VISTAS TAWG participants. In performing the Gatekeeper quality assurance activ ity, one of the first steps is to conduct an independent operational evaluation on the MM5 model results at 36 km and 12 km grid scale.

This evaluation covers surface and aloft wind dir ection, temperature, mixing ratio, precipitation, and planetary boundary layer (PBL) depths on a c ontinental scale (36 km) and subregional scale (12 km) basis. The specific techniques to be used are described in the MM5 model performance protocol prepared for EPA for annual modeling (M cNally and Tesche, 2002). The Gatekeeper will also perform supplemental, ad hoc analys is of pertinent MM5 fields (e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. Another task of the Gatekeeper will be to exercise the Meteorological Chemistry Interface Processor (MCIP) version 2.2 is to read the MM5 outputs from BAMS and produce binary input files for the CMAQ Chemical Transport Model (CCTM) to provide the complete set of parameters necessary in the emissions processing and air quality modeling.

In summary, the quality assurance plan fo r the meteorological data will include the following elements:

3⁄4 Upon receiving the MM5 and MCIP 2.2 output files from BAMS, we will verify the integrity of the file transfer (e.g., no missing and/or corrupted files);

3⁄4 Since the CMAQ modeling domain is a subset of the MM5 domain, we will verify that the modeling domain and vertical layer st ructures in the MCIP files are identical to the CMAQ modeling domain;

3⁄4 We will select several days of the MM5 output and reprocess the MM5 files with MCIP v2.2 using the predetermined MCIP op tions. We will then compare the MCIP files with those provided by BA MS to verify that we obtain identical results from the MCIP processing.

3⁄4 We will create horizontal and vertical plot s of temperature, pressure, precipitation, modeled flow patterns, PBL heights, etc. to assess whether the MC IP output fields are reasonable;May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-5 3⁄4 The VISTAS 2002 MM5 simulation will be evaluated using the same surface observations, subdomains and procedures as used to evaluate the WRAP 2002 MM5 simulation as an independent QA and evaluation of the database.

3⁄4 We will make the plots available on the VISTAS website for viewing and download.

6.4 Air Quality Model Inputs and Outputs

Key aspects of QA for the CMAQ input a nd output data include the following:

• Verification that correct conf iguration and science options are used in compiling and running each model of the in the CMAQ m odeling system, where these include the MCIP, JPROC, ICON, BCON and the CCTM.

• Verification that correct input data se ts are used when running each model.

• Evaluation of CCTM results to verify that m odel output is reasonable and consistent with general expectations.

• Processing of ambient monitoring data for use in the model performance evaluation.

• Evaluation of the CCTM results against concurrent observations.

• Backup and archiving of cr itical model input data.

The most critical element in the QA plan for CMAQ simulations is the QA/QC of the meteorological and emissions input files. The majo r QA issue specifically associated with the air quality model simulations is verification that the correct science options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model we employ a system of naming conventions using environment variables in the compile and run scripts that guarantee that correct inputs and science options are used. We also employ a redundant naming system so that the name of key science options or inputs are included in the name of CMAQ executable program, in the name of the CMAQ output files, and in the name of the directory in which the files are located. This is accomplished by using the environment variables in the scripts to specify the names and locations of key input files. For example, if a model simulation is performed using the CB4 mech anism, all compile and run scripts contain the variable definition “$MECH = CB4”, and this vari able is hard coded into the script for the executable name, the output file name, and the ou tput directory name. This procedure produces long file/directory names but it eff ectively prevents mistakes or ma kes mistakes readily apparent if they do occur.

A second key QA procedure is to never “recycle” run scripts, i.e., we always preserve the original runs scripts and direct ory structure that were used in performing a model simulation. For example, if we perform simula tion with the SAPRC mechanism, instead of editing the original scripts to specify “$MECH = SAPRC” we will creat e a parallel directory structure with a new set of scripts to perform the SAPRC simulations. This provides a pe rmanent archive of the scripts that were used in performing model simulations . In addition, output from the model simulationMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 6.doc 6-6 will be directed to a log file th at provides a record of input file names, warning messages etc that

will be archived.

We will also perform a post-processing QA of the CMAQ output files similar to that described for the emissions processing. We will generate animated gif files using PAVE that can be viewed to search for unexpected patterns in the CMAQ output files. In the case of model sensitivity studies, the animated gifs will be prepared as difference plots for the sensitivity case minus the base case. Often, errors in the em issions inputs can be di scovered by viewing the animated GIFs. Finally, we will produce 24 hour average plots for each day of the CMAQ simulations. This provides a summary that can be useful for more quick ly comparing various

model simulations.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-1 7.0 MODEL PERFORMANCE EVALUATION

7.1 OVERVIEW

As noted in Chapter 1, Appendix A presents a summary of the Scope of Work (SOW) for the VISTAS Phase II Emissions and Air Quality Modeli ng activities. This Phase II SOW delineates the tasks, approach and schedule for carrying out the various technical activities described in greater detail in this modeling protocol. The SOW in A ppendix A is broken down into a base effort that focuses on annual modeling of 2002 and several opti onal tasks to be evaluated by VISTAS. The ultimate objective of the Phase II work effort in 2004 is to obtain a properly evaluated operational emissions and air quality modeling system for the 2002 annual calendar year so that control strategy evaluation can begin in 2005. Given the formal cont ractual role that the SOW plays in guiding the work products of the Emissions and Air Quality Modeling Team in Phase II, where ambiguities might arise regarding the Task 4a/4b model eval uation effort, the SOW takes precedence over this Protocol Document. Consistent with the spir it of a modeling protocol for regulatory decisionmaking, this document lays out the ‘roadmap’ for achieving an adequately tested modeling system for regulatory usage. But, obviously, this does not m ean that every analysis identified in this chapter will be carried out or is indeed even possible gi ven the Phase II schedule and resources, the existing aerometric data bases, and present technology c onstraints. The roadmap guides the way to the

desired destination – in this cas e, an evaluated, operational PM/regional haze modeling system – but does not commit the driver to exploring every side street and back country road along the way. Indeed, one expectation of the VISTAS TAWG is a close working relationship with the modeling

team to ensure that the available resources and sc hedule are applied most efficiently in reaching the aforementioned goal.

This chapter describes a range of model testing methodologies potentially available to the Emissions and Air Quality Modeling study team in its efforts to adequately ev aluate the performance of the CMAQ air quality modeling system for th e 2002 annual period. Since one cannot know at this juncture the specific performance problem s that may arise in the initial 2002 CMAQ testing (Task 4a) or the final year 2002 evaluation (Task 4b), we set forth in this chapter a broad range of methods and techniques that may be brought to bear in examini ng CMAQ model performance. We identify the core operational evaluation procedures , recommended in EPA (2001) guidance, that will be performed in Tasks 4a and 4b. We also descri be a broad range of additional performance testing methods that may be worth considering, if necessa ry. Implementation of one or several of these various techniques would fall under Optional Task 14 (Enhanced Model Performance Evaluation). The Optional Task 14 represents a means for ma rshalling additional VISTAS funding, or perhaps supplemental funding from other stakeholder gr oups, for the purpose of enhancing the core operational evaluation presently budgeted for Tasks 4a and 4b. Should portions or all of Optional Task 14 be invoked, we strongly recommend that this be guided by a cooperative effort by the modeling team and the TAWG to ensure that th e highest priority “supplemental evaluations” be selected and properly carried out.

Clearly, not all of the supplemental evaluative techniques identified in this chapter will ultimately be performed. There are three reasons for this:

3⁄4 The Phase II SOW places clear limits on the resources available to perform model evaluation analyses. Accordingly, some ev aluation steps, while desirable, simplyMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-2 may not be possible given current funding levels;

3⁄4 The Phase II SOW places stringent schedul e demands on the model evaluation. A number of the model performance evaluation methods introduced in this chapter (e.g. Weight of Evidence analyses, diagnostic testing with individual measurement networks, PM indicator species and ratios analyses, use of the alternative CAMx model) could very likely require more time to carry out given their quasi-research

nature. Since VISTAS Phase II is not a m odel research and development effort, but rather an operational evaluation of ex isting modeling systems for regulatory decision-making, some interesting, but time consuming analyses simply may not be possible given the present schedule; and

3⁄4 To conform to the EPA PM guidance docum ents requirements for PM model testing, it may not be necessary to conduct many of the diagnostic and Weight of Evidence tests identified in this protocol. Indee d, an adequate evaluation of the Phase II modeling system may be possible through st raightforward application of the core operational performance evaluation pro cedures identified in EPA’s 2001 draft guidance.

At a minimum, the evaluation of the CMAQ modeling system for the Phase II annual 2002 simulation (and for any additional episodes that might be chosen by VISTAS) w ill be consistent with EPA’s draft guidance on PM model testing. This guidance essentially calls for an operational evaluation of the model focusing on a specific set of gas phase and aerosol chemical species and a suite of statistical metrics for quantifying model response over the annual cycle. The emphasis is on assessing: (a) How accurately the model predicts observed concentrations? and, (b) How accurately does the model predict responses of predicted air quality to changes in inputs? States are encouraged to utilize the evaluation procedures set forth in the earlier 1991 guidance document (EPA, 1991) for gas phase species and the newer ( 2001) guidance of PM species. Thus, in carrying out the initial Task 4a operational evaluation and the subsequent Task 4b final evaluation, we will implement the suggested EPA performance testing methodologies for the key gas phase and aerosol species. Since these methods are explicitly presente d in EPA’s guidance docum ent, there is no need to repeat them here.

Subject to the availability of time and Opti onal Task 14 resources, the Phase II evaluation will also attempt to employ other testing methods beyond those in the EPA guidance document. In addition, if the initial Task 4a evaluation evolves smoothly without problems arising in the early performance testing efforts, resources and/or time c onserved in such a situation might then be used to underwrite on or more scientifically rigorous model performance investigations (e.g., those afforded by the rich observational networks such as the continuous speciation SEARCH measurements and the EPA Supersite data). To this end, we discuss in this chapter various supplemental performance testing methods that mi ght be worth pursuing gi ven adequate, additional

resources and time.

We conclude by again emphasizing that most important goal of the Phase II CMAQ evaluation is to determine whether the aggregate m odeling system (model codes plus input data sets and observational data for testing) offers suffi ciently reliable and accurate results that public decision-makers may have reasonable confidence in using the model to help choose betweenMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-3 alternative regional haze reduction scenarios. If the CMAQ model evaluation, as outlined in this chapter, provides sufficient evidence that the modeling system is operating reliably and in conformance with measurements and scientific exp ectations, then specific justifications explaining why the model is acceptable for developing regiona l haze strategies will be offered in the 2002 CMAQ Modeling Summary Report. Conversely, should the evaluation determine that the modeling system suffers from important flaws or errors that undermine its reliability or use, these findings will also be documented, together with recommendations regarding the use of alternate methods, steps to improve the model and/or data base, or other approaches.

7.2 Context for the VISTAS Phase II Model Evaluation

We begin the discussion of the Phase II modeling evaluation met hodology by reviewing how the CMAQ model output is used in regional haze applications to project changes in visibility 1

. When designing a model performance evaluation, it is important to understand how the modeling results will ultimately be used. EPA has published tw o versions of draft guidance for fine particulate and regional haze modeling (EPA, 2000; 2001), utilizi ng a Fine Particulate Guidance Workgroup to provide technical input in the development of both documents 2 . More recently, EPA has provided an informal update on the PM/regional haze m odeling guidance (Timin, 2002) and conducted a PM model evaluation workshop (see, for example, Timin, 2004; Boylan, 2004) shedding additional light on what the final guidance document might contain.

A key concept in EPA’s guidance for addressing regional haze issues is that the modeling

results should be used in a relative sense to scal e or roll back the observed particulate matter (PM) concentrations from which light extinction is estimated. Adopting the recommendations from the Federal Land Managers’ Air Quality Related Values Workgroup (FLAG), estimates of visibility are obtained from observed fine particulate concentr ations using the following IMPROVE reconstructed mass extinction equation (FLAG, 2000):

b ext = 3{f(RH)[(NH 4 ) 2 SO 4 ]} + 3{f(RH)[NH 4 NO 3 ]} + 4{f’(RH)[OC]} + 10[EC] + 1[IP] + 0.6[CM] + b rayleigh

where:

b ext is the estimated extinction coefficient (Mm -1 ); [SO 4 ] is the sulfate concentration assumed to be ammonium sulfate; [NO 3 ] is the particulate nitrate concentration assumed to be ammonium nitrate; [OC] is the organic carbon concentration; [EC] is the elemental carbon concentration;

1 Details on the definitions of model performan ce evaluation statistical measures and graphical displays in three reports to VISTAS: (a) the Ph ase I Task 2 model configuration report (ENVIRON, 2003b), (b) the Task 4a/b model sensitivity a nd CMAQ configuration report (ENVIRON, 2003d), and (c) the Final Phase I Evaluation and Model Sensitivity Analysis report (ENVIRON, 2004). Additional details are also presented in Table 7-4.

2 Members of the VISTAS modeling team participat ed on this work group over the two-year span of

its activities.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-4 [IP] is the inorganic primary fine particulate (< 2.5 : ) concentration excluding primary sulfates and nitrates; [CM] is the coarse particulate (> 2.5 : and < 10 : ) concentration;

b rayleigh is the light-scattering due to Rayleigh scattering (assumed to be 10 Mm -1 ); f(RH) is a relative humidity adjustment factor for the sulfate and nitrates; and f’(RH) is a relative humidity adjustment factor for OC that is assumed to be 1.0.

The relative humidity (RH) values used in th e above equation are monthly- specific and Class I area-specific values based on a long term average (EPA, 2003a).

The regional haze rule expresses reasonable progress in terms of changes in the deciview ( ) dV) from the current to future year conditions. Deciview (dV) is expressed as the natural logarithm of the extinction coefficient (b ext ) to Rayleigh scattering:

Deciview = 10 ln(b ext /10)

) dV = 10 ln[(b ext ) future / (b ext ) base ]

In this framework, changes in visibility in te rms of percentage changes in extinction over a natural “clean” background visibility rather than ch anges in deciview. Changes in deciview or extinction are essentially mathematically iden tical with a 1% change in deciview being approximately equivalent to a 10% change in extinction. Both visibility parameters will be calculated in the Phase II model applications.

The VISTAS Phase II model testing will concen trate on an operational evaluation of those model predictions that are most necessary for estimating visibility (i.e., NH 4, SO 4, NO 3, OC, EC, IP and CM). Where feasible and supported by sufficien t measurement data, we will also evaluate the modeling system for its ability to accurately estimate gas-phase oxidant and precursor/product species since correct, unbiased simulation of gas- phase photochemistry is a necessary element of reliable regional haze predictions. This evalua tion will be carried out across the full VISTAS domain for the entire year and also on subdomai ns and month-by-month basis to help build confidence that the modeling system is operating corr ectly. With this context in mind, we next turn to the philosophy of the model evaluation process.

7.3 Multi-Layered Model Testing Process

EPA’s “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) affirms the recommendations of numerous modeling scientists over the past decade (see, for example, Dennis et al., 1990; Tesche et al., 1990, 1994; Seigneur et al., 1998, 2000; Russell and Dennis, 2000; Ar nold et al., 2003; Boylan et al., 2003; Tonnesen, 2003) that a comprehensive, multi-layered approach to m odel performance testing should be performed, consisting of the four components: operational, diagnostic, mechanistic (or scientific) and probabilistic. As applied to regional PM/visibility models, this multi-layered framework may be viewed conceptually as follows:

> Operational Evaluation : Tests the ability of the model to estimate PM concentrations (both fine and coarse) and the components at PM 10 and PM 2.5 including the quantitiesMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-5 used to characterize visibility (i.e., sulfate, nitrate, ammonium, or ganic carbon, elemental carbon, PM 2.5 , and PM 10 ). This evaluation examines whether the measurements are properly represented by the model predictions but does not necessarily ensure that the model is getting “the right answer for the right reason”;

> Diagnostic Evaluation: Tests the ability of the model to predict visibility and extinction, PM chemical composition including PM precursors (e.g., SOx, NOx, and NH 3 ) and associated oxidants (e.g., ozone and nitric acid) ; PM size distribution; temporal variation; spatial variation; mass fluxes; and component s of light extinction (i.e., scattering and absorption);

> Mechanistic Evaluation : Tests the ability of the model to predict the response of PM and visibility to changes in variables such as emissions and meteorology; and

> Probabilistic Evaluation: Takes into account the uncertainties associated with the model predictions and observations of PM and visibility.

Within the constraints of the Phase II schedule a nd budget resources the VISTAS Phase II effort will attempt to include elements of each of these co mponents. The operational evaluation will obviously receive the greatest attention since this is the primarily thrust of EPA’s 2001 PM guidance. However, we will consider, where feasible and appropriate, diagnostic and mechanistic tests (e.g., use of probing tools, indicator species and ra tios, aloft model evaluations, urban vs. rural performance analyses), traditional sensitivity simu lations to explore uncertainty, and the use of alternative science platforms (e.g., CAMx). The scope of these additional diagnostic and mechanistic tests will be shaped by the resources available in Optional Task 14 and the timing of when such analyses are commissioned relative to the Phase II schedule and resources.

Before discussing the types of testing pro cedures available for the above evaluation components, we first identify the surface and aloft data sets that are available to support these comparisons.

7.4 Development of Consistent Evaluation Data Sets

7.4.1 Surface Measurements

The ground-level model evaluation database w ill be developed using several routine and research-grade databases. The first is the routine gas-phase concentrati on measurements for ozone, NO, NO 2 and CO archived in EPA’s Aerometric Information Retrieval System (AIRS/AQS)

database. Other sources of information come from the various PM monitoring networks in the U.S., with particular emphasis in the central and easte rn U.S. These include the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends Network (CASTNET), (c) Southeastern Aerosol Re search and Characterization (SEARCH), (d) EPA

PM 2.5 and PM 10 Mass Networks (EPA-FRM), (e) EPA Sp eciation Trends Network (STN); (f) National Acid Deposition Network (NADP) and (g) EPA Supersites (EPA-SPEC) networks. Typically, these networks provide ozone, other gas phase precursors and product species, PM, and visibility measurements. Noteworthy is the continuous speciated PM 2.5 measurements availableMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-6 from the SEARCH network for 2002 modeling period. For 2003 episode modeling, additional continuous PM speciated data will be available from the FOCUS network.

As an example, the IMPROVE network give s daily (24-hour) average mass concentrations every 3 days for SO 4 , NO 3 , organic carbon (OC), elemental carbon (EC), soil (IP), CM, PM 2.5 and

PM 10 . These data are available at approximately 38 sites in the VISTAS 12 km domain. In addition, hourly values of light extinction and deciview are av ailable at several of th ese sites. The SEARCH network provides 24-hour as well as continuous (hourly) speciated measurements of PM 2.5 components and other specifics from 8 stations, depending on the time period (Hansen et al., 2003).

We will use data from these and the other observa tional databases listed in Table 7-1, supplemented with the routine AIRS/AQS data, as appropriate, for CMAQ model performance testing.

Care must be taken in selecting data that are representative of regional concentrations and the prediction of regional haze at the sensitive Class I areas within the VISTAS (see Table 7-3). For example, the criteria and other pollutants in the AIRS/AQS database are t ypically urban-oriented. Thus, would not expect CMAQ predictions for thes e species using a 36/12 km grid mesh to be ideally suited to simulating urban-scale concentra tions. With other data sets (e.g., SEARCH) it may be possible to reproduce the urban-to-rural gradients in PM 2.5 components. While finer grid meshes (e.g., 4 km) might be more appropr iate, high-resolution modeling at 4 km scale on an annual time frame for the VISTAS region is prohibitive given current computer technology and model run times. Accordingly, some selectivity is needed in assembling pertinent measurement sites for comparisons with regional-scale model predictions and the interpretation of the modeled and measured comparisons must be made with care.

Another important consideration is that diffe rent PM monitoring networks may use different measurement approaches that “measure” different amount s of the same species th at are also different from the modeled species. For example, the IMPROVE network only speciates PM 2.5 so any sulfate or nitrate in the coarse mode (PM 2.5-10 ) is included in the CM species. As was done in Phase I, the model will be evaluated separately for each networ k. While Phase II is clearly not aimed at new model development or algorithm refinement, information discerned from model performance across

the various networks should be useful in late r model refinement activities and network design improvements. Finally, the mapping of the modeled species to the monitored data will also have to be performed in a consistent fashion.

The VISTAS Phase I air quality data asse ssment report (ENVIRON, 2003c) provides more details on the ambient monitoring data available for Phase II modeling.

7.4.2 Aloft Measurements

In recent years, the use of instrument aircraft in support of regulatory monitoring and research programs has become much more commonplace. Ind eed, in the upper Midwest, the Lake Michigan Air Directors Consortium (LADCo) has been centr ally involved in aircraft programs to support model development and applications studies for seventeen (17) years, beginning with pioneering flights in 1987. Supplementing the long-term sampling performed by LADCo in the Midwest, there

have been other occasional intensive airborne sampling campaigns throughout the easter n U.S. (e.g., the 1999 SOS field program which provided aloft data for our evaluation of CMAQ for the July ’99 episode), that have produced very useful inform ation for air quality model performance testing.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-7 Fortunately, during CY-2002, there were at least tw o mature airborne field programs underway in the eastern U.S. One was centered over the Midwest, the other on the mid-Atlantic coast. A brief characterization of these potentially valuable CMAQ m odel evaluation data sets is given here. Note that the advanced modeling evaluation using non-r outine data sets like aircraft data, would be covered under Optional Task 14 (see Appendix A).

During 2002, the Wisconsin Department of Natural Resources (WDNR) and the Midwest RPO (MRPO) (who funded the Jacko aircraft) co llaborated on the support of airborne sampling using two aircraft that, along with ground-ba sed measurements, provided a 3-dimensional representation of air pollution concentrations acr oss the upper Midwest with some flight paths extending south to include the Mammoth Cave, KY and Dolly Sods, WV Class I areas in the VISTAS domain. The goal of the WDNR/MRPO flights was to collect aloft air quality and meteorological data to support model evaluation and da ta analyses. The aircraft flights were aimed at: (1) characterizing high fine particle and ozone episodes, (2) characterizing air quality over the Class I areas in the upper Midwest (Isle Royale National Park and Seney National Wildlife Refuge in northern Michigan) on both clean and hazy da ys, and (3) characterizing urban areas in the

Midwest.

As indicated in Table 7-2, airborne sampli ng was performed over a broad region of the Midwest (including portions of the VISTAS states) from 1 June to 22 November. Lasting 3-5 hours, the WDNR and Jacko aircraft sampled a variety of aerometric parameters (depending upon the flight and aircraft) including wind speed, wind directi on temperature, dew point, relative humidity, pressure, O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg, SO 4 , OC, EC,

PM 2.5 , and light scattering (Neph). Still photographs documenting visibility were also collected. Presently, the full WDNR/MRPO aircraft database , from the first flights in 1987 to the recent sampling in 2003 is being aggregated into a master data base archive.

At the University of Maryland, research ers have been using ground-based monitors, radiosondes, profilers, and instrumented aircra ft to make observations each year since 1992. Parameters measured included meteorology; selected trace gases; fine particulate chemistry, microphysics and optical properties across broad regions of the middle Atlantic coast. During 2002, the University Research Foundation’s Aztec- F aircraft instrument suite included O 3 , NO, CO, SO 2 samplers, as well as a NO 2 closed-path tunable diode laser sy stem, and a differential GPS-based meteorology (T, RH) and horizontal wind ( u and v horizontal components) data system. Aztec-F flights were made from 23 May to 3 October, typically lasting 3 hours.

The volume of aircraft information availabl e for CMAQ performance testing during 2002 is quite significant and, as discussed later, this data set will be used as extensively as possible. Historically, aircraft data sets have been used only sporadically in evaluating model performance aloft. To our knowledge, the VI STA Phase II evaluation under optional Task 14 could represent the most thorough utilization of aircraft data fo r regional model ozone/PM model evaluation yet attempted.

7.5 Model Evaluation Tools

This section introduces the various statistical m easures, graphical tools, and related analytical procedures that have proven useful over the year s in evaluating grid-based chemical transportMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-8 models. Many of the methodologies mentioned belo w were utilized to one degree or another in Phase I. Where appropriate, they will also be us ed in the 2002 annual and ep isodic evaluation of the CMAQ modeling system. However, as with Phase I, while we plan on calculating a rich variety of statistical performance metrics, only a very limited subset of these measures will actually be relied upon to form judgments concerning model acceptability and in the final reporting.

The current methodologies and statistical me trics may be augmented, as necessary and appropriate, by new measures that may become available during the course of the Phase II modeling. Definitions of the statistical measures for gas-phase species such as ozone are provided in the Phase I Task 4 report (ENVIRON, 2003d); measures geared more toward fine particulate aerosol species are identified later in this chapter. In sum, th e Phase II evaluation will employ similar methods and

evaluation tools used in Phase I but the evaluation will remain open to emerge nt methods for testing model performance where necessary and consistent with the project schedule and resources.

7.5.1 Statistical Performance Metrics

EPA’s 2001 PM and regional haze guidance suggests a suite of metrics for use in evaluating model performance. The standard set of statis tical performance measures suggested by EPA for evaluating fine particulate models include: (a) normalized bias; (b) normalized gross (unsigned) error; (c) fractional bias; (d) fractional gross error; and (e) fractional bias in standard deviations.

These measures are subsumed within the list of me trics that were calculated on a routine basis in Phase I using the UCR analysis package. (These are identified in Table 7- 4). The UCR evaluation software will generate these statistical measures for each model simulation performed for each analysis region (see below). In parallel, the AG MAPS software will be used to generate complimentary statistical measures which combine the methods recommended in the EPA ozone guidance (EPA, 1991; 1999) and the newer 2001 PM guidance. From past regional PM model

evaluations we have found the fractional bias and fractional error to be the most useful summary measures and we will focus mainly upon them in Phase II, but not to the exclusion of others that are found to yield discriminating power. For oz one and other gas pha se species (NO, NO 2 , SO 2 ) we will

include use the traditional statistical measures (Tesche et al., 1990; EPA, 1991, 1999) develop with MAPS as an adjunct to the UCR analysis measures.

Typically, the statistical metrics are calcula ted at each monitoring site across the full computational domain for all simulation days. In the Phase II CMAQ evaluation, we will stratify the performance statistics across relevant space and time scales. As part of the Task 4a/b operational evaluation, the gas-phase and aerosol statistical m easures shown in Table 7-4 will be computed for the full 36 km and 12 km domains, as well as fo r the individual RPOs (including VISTAS of course) and on other subdomains as appropriate. Temporally, we will compute the statistical measures for the appropriate averaging times: 1 hr for ozone, and gas-phase precursors such as NO,

NO 2 , CO, SO 2 ; 8-hr for ozone, and 24 hr for sulfate, nitr ate, PM and other aerosol species. These results will then be averaged over annual, m onthly, and seasonal periods for display, further analysis, and reporting. Should it become necessary as part of model performance diagnosis, we will consider aggregating the statistics in othe r ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipitation vs. non-precipitation da ys, (d) month of the year, and (e) the 20% haziest/cleanest days, in order to help elucidate model performance problems. Absent performance difficulties, these supplemental time/space analyses would only be considered if supported under directed Optional Task 14 funding. In subregional performance testing, the focus would likely be onMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-9 the Class I areas and sites where enhanced m onitoring (EPA STN locations, the hourly/daily SEARCH sites) within the VISTAS 12 km domain (Hansen et al., 2003) is available.

As part of the operational evaluation, the metric s defined in Table 7-4 will be calculated for each gas phase species and each fine particulate species in the extinction equation as well as separately for SO 4 , NO 3 and ammonium (NH 4 ) on both the 36 km and 12 km domains. In any diagnostic evaluations that are performed, we will examine the model’s ability to estimate the gaseous species listed above from EPA’s guidan ce (EPA, 2001). However, in reality ambient gaseous species in 2002 are principally available for ozone, NO 2 , SO 2 , and CO. Since most of the gaseous air quality monitors are located in urban settings, these data sets will be of somewhat restricted value in evaluating the 36/12 km regi onal-scale CMAQ predictions. Under Optional Task 14, we would consider the merits of assembling a rural-oriented gaseous species observational model performance evaluation database for use w ith CMAQ. This database could be composited with measurement from rural sites from the AQS, CASTNet, NPS, STN, and SEARCH networks. If commissioned under Optional Task 14 by VISTAS, this rural vs. urban scale operational evaluation would be conducted to augment the more traditional operational evaluation usi ng all valid data sets.

7.5.2 Graphical Representations

The Task 4a/b core operational air quality model evaluation will utilize numerous graphical displays to facilitate quantitative and qualitativ e comparisons between CMAQ predictions and measurements, many of which were used in the Phase I Final Report (ENVIRON, 2004). Together with the statistical metrics listed in Table 7-4, th e graphical procedures are intended to help: (a) identify obviously flawed model simulations, (b ) guide the implementation of Task 4a/4b performance improvements in the 2002 model input files in a logical, defensible manner, and (c) to help elucidate the similarities and differences between the alternative CMAQ simulations. These graphical tools are intended to depict the model’s ability to predict the observed fine particulate and gaseous species concentrations.

The Phase I modeling helped to refine the su ite of graphical tools most effectively in assessing model performance and the differences between the baseline CMAQ runs and sensitivity experiments. The core graphical displays to be considered for use in Phas e II include the following:

> Spatial mean concentration time series plots; > Time series plots at monitoring locations; > Ground-level gas-phase and particulate concentration maps (i.e., tile plots); > Concentration scatterplots stratified by station, by time, and by network; > Bias and error stratified by concentration; > Bias and error stratified by time; > Histogram plots of the statistical metric s, stratified by day, by pollutant, by subregion (e.g., 12 km vs. 36 km, by RPO), and by monitoring network; and > Quantile-Quantile (Q-Q) plots.

These graphical displays will be generated, were a ppropriate for the full annual cycle as well as for monthly and seasonal periods. The displays will be generated with a consistent suite of products including the UCR analysis tools, Alpine MAPS software and ENVIRON evaluation software.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-10May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-11 7.5.3 MAPS/Flying Data Grabber Routines

This section describes the procedures we w ould implement for the aloft gas phase and PM model evaluation with CMAQ using aircraft da ta should Optional Task 14 be commissioned by VISTAS. This aloft performance evaluation would employ aircraft data sets from various sampling programs carried out over the Midwest and eastern U.S. during 2002. Details on how these data are used to evaluate CMAQ performance aloft together with findings from our aloft model evaluations with the 10-21 July 1999 episode are described in the Phase I Final Report (ENVIRON, 2004).

The principal challenge in using the aircraft data for meteorological and photochemical

model evaluations relates to the ‘incommensurab ility’ of Lagrangian aircraft observations with Eulerian (i.e., fixed location) volume-averaged model estimates (see, for example, Hanna, 1994). Aircraft data are essentially continuous, high fr equency Lagrangian samples having response times

on the order of 30 seconds or less. In contra st, CMAQ model estimates represent hourly-averaged values. Thus, the aircraft data must be averaged in some manner to yield quantities that are at least qualitatively comparable to the air quality fields estimated by CMAQ in the grid volume(s) through which the aircraft passes. The objective is to de velop hourly-average time series of measurements and model estimates that are as nearly comparable as possible.

The procedures proposed for processing the al oft meteorological and air quality observations and CMAQ model predictions have been described in several science reports prepared in connection with the Lake Michigan Ozone Study (Tesche and McNally, 1993a-d, 2001) and more recently the Houston-Galveston 1-hr ozone SIP modeling that utilized the TexAQS 2000 data base (Tesche and Jeffries, 2002). These methods, formalized within AG’s Flying Data Grabber (FDG ) model, were employed in Phase I for the 13-21 July 1999 episode. The methods used are substantial extensions of the techniques pioneered a decade ago by Sche re and Wayland (1989) for the Regional Oxidant Model (ROM2.0) evaluation agains t the NEROS database and by Barchet and Dennis (1990) for the RADM/ADOM evaluation (Dennis et al., 1990).

For a typical aircraft (or helicopter flight), the Flying Data Grabber first identifies the specific time interval during which the aircraft was located in a given CMAQ model grid cell along the flight path. The observations are then integr ated to produce mean, standard deviation, bias and

error estimates for the variable measured within each grid cell of the flight path. This averaging process produces an observed, averaged time series for the above statistical quantities along the flight path. Note, that these time intervals are characteristically much smaller than the one-hour model averaging time. Flight path statistics, together with the mean modeled and observed horizontal winds along the flight paths are also produced by the FDG. The maximum and minimum values during each time segment within a grid cell are also recorded.

The FDG methodology further assumes that the air quality model estimates vary approximately linearly during each hour. It is then straightforward to construct an estimated time series of the modeled values that corresponds to the above-described observed time series derived from the aircraft data. The measurements and model estimates, now on roughly comparable time and space scales, are subsequently processed with the MAPS statistical/graphical software tools described in the Phase I Task 4a report (ENVIRON, 2003d). The statistics of principal interest are

the mean values of the observed and modeled concen trations together with estimates of bias andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-12 imprecision (i.e., gross error). A va riety of graphical representations are also produced to facilitate evaluation an intercomparison.

7.5.4 Use of Multiple Evaluation Packages

In Phase I model evaluation (ENVIRON, 2004) relied principally on the UCR evaluation package that was originally developed for WRAP and then enhanced by VISTAS. This package produce scatter plots by site, day or all sites and days , time series plots and statistical measures. In Phase I modeling an error was introduced in the exte nsion of the package to treat 12 km grids that caused some delays as we attempte d to resolve the 36 km and 12 km performance issues. In Phase II we will attempt to prevent such a reoccurrence by using additional software evaluation packages specifically, the Alpine MAPS package will be used to not only corroborate the UCR results but also provide some additional model evaluation measures and displays (e.g. spatial average time series plots). In addition; ENVIRON’s evaluation software that focuses on the eval uation of the model at Class I areas for the 20% best and 20% worst days will be employed to identify how the model is predicting PM species on these key days and iden tify any days from the 20% worst and 20% best days 3 where model performance is so suspect that use of the modeling results on these days to project visibility improvements may not be desirable.

7.5.5 Probing Tools and Allied Methods

The CMAQ model evaluation in Tasks 4a and 4b will employ routine operational evaluation methods and standard statistical metrics (Table 7- 4) and graphical displays to support the assessment

of whether the model is shown to perform with sufficient accuracy and reliably for its intended purpose. Ideally, this operational evaluation will confirm that the modeling system is performing consistent with its scientific formulation, technical implementation, and at a level that is at least as

reliable as other current state-of-science methods . Should unforeseen model performance problems arise in the initial or refined year 2002 model simu lations, it may be necessary to draw into the evaluation supplemental diagnostic tools to aid in model testing. These diagnostic techniques are loosely referred to as “probing tools”. The actual n eed for their use, if any, can only be determined once the initial 2002 CMAQ operational evaluation is completed. Should such diagnostic methods actually be needed, their usage would be funde d by Optional Task 14 and/or Optional Task 15 (Contingency). The scope of these additional analys es into the Task 4a/b evaluation process will be closely coordinated with the VISTAS TAWG, with due consideration being given to the Phase II schedule and resources. Below, we identify the type s of probing tools that could be brought to bear under Optional Tasks 14 and/or 15 should their use become necessary at some later time.

Current ‘One-Atmosphere” models such as CMAQ and CAMx have been outfitted with a number of “probing tools” that have proven to be very useful in testing and improving model performance and in evaluating emissions control strategies. Among the probing tools available in one or both models are: (a) ozone source appor tionment technology (OSAT) algorithms, (b) process analysis (PA), and (c) the direct decoupled met hod (DDM) for sensitivity analysis. In addition, the

3 Mention here and elsewhere in this protocol of evaluation efforts for the 20% best and worst days is in addition to the planned evaluation of all days in the annual cycle. While EPA visibility guidance addresses the 20% days, and our evaluation is tailored to follow the guidance, the overall CMAQ evaluation will address all days in the year.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-13 project team is implementing PM Sour apporti onment techniques in CMAQ and CAMx. VISTAS may choose to evaluate these tools as part of the Phase II modeling exercise. It is currently unclear what funding will be available under Optional Tasks 14 and/or 15 for these options.

Source Apportionment Technology: CAMx contains a suite of “source attribution”

methods. One such method is Ozone Source Apportionment Technology (OSAT). OSAT tracks ozone formation based on how groups of ozone precursors contributed to ozone formation. Thus, OSAT decides whether ozone formation is NOx or VOC limited in each grid cell at each time step,

and bases ozone attributions on the relative amounts of the limiting precursor from different sources that are present in that grid cell at that time step. These incremental ozone attributions are integrated throughout the model run. The method is generally applicable and has been widely used to aid model diagnosis in the performance testing phase and to guide control strategy formulations as well.

A new PM Source Apportionment Technology (PSAT) is being implemented in CAMx funded by the MRPO that is in its final stages of tes ting. A Tagged Species S ource Apportionment (TSSA) approach has also been implemented in CM AQ and tested for nitrate (Tonneson, 2004, personal communication).

Decoupled Direct Method (DDM): Various forms of the Decoupled Direct Method (DDM) have been installed in CMAQ and CAMx, based on the original work of Dunker and co-workers (Dunker, 1981; 1984; Dunker et al., 2002) and research ers at Georgia Institute of Technology (GIT). In general, the DDM method: (a) calculates first order sensitivities dC/dP where C is a concentration output and P an input parameter 4 , (b) promotes accuracy by using consistent numerical methods and the same time steps for concentrati ons and sensitivities, (c) optimizes the code for efficiency, but not at expense of accuracy, and (d) calculates sensitivities with respect to parameters representing pollutant sources – emissions, BCs and ICs. Finally, the DDM provides a flexible and powerful user interface for defining various sensitivities including:

> Emissions resolved by geographic area. > Emissions resolved by source category. > BCs optionally resolved by boundary edge (N, S, E, W, Top). > All sensitivities available relative to sources of individual species (NO, PAR, etc.) or species group (VOC, NOx or ALL). > Simultaneously calculate sensitivities to many initial condition, boundary condition and emissions parameters.

In recent comparisons between CAMx DDM sensitivities and brute-force sensitivities (calculated from +/- 20% perturbations) Dunker et al., (2002a,b) reported that sensitivities of ozone with respect to area source NOx and VOC emissions were calculated and results indicated that the agreement between DDM and brute force sensitiv ities is excellent. DDM implementation into CMAQ is reported by Kumar (2003).

Process Analysis (PA): Photochemical air quality model simulations are usually evaluated primarily in terms of their ability to simulate observed O 3 data. There is an increasing awareness that

4 Recent research by Prof. Russell and coworkers at GIT has led to the extension of the CMAQ DDM method to include second order sensitivity coefficients (see, Hakami et al., 2003).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-14 chemical mechanisms, and air quality models must also be evaluated in terms of their ability to simulate the fundamental chemical processes that control O 3 formation and the sensitivity of O 3 to emissions reductions (Arnold et al., 1998). Proce ss analysis is a method for explaining model simulations by adding algorithms to the AQM to store the integrated rates of species changes due to

individual chemical reactions and other sink a nd source processes (Jeffries and Tonnesen, 1994; Tonnesen, 1995). By integrating these rates over time and outputting them at hourly intervals, process analysis provides diagnostic outputs that can be used to explain a m odel simulation in terms of the budgets of free radicals, production and loss of odd oxygen and O 3 , and conversion of NOx to inert forms, as well as the effects of transpor t and other sink and source terms. Of particular importance to the VISTAS modeling, process analysis can also improve model diagnosis and performance evaluation efforts by identifying proce sses that are ‘out of balance’ (Tesche and Jeffries, 2002), by identifying situations for whic h the model formulation and/or implementation should not be expected to apply and by suggesting how ambient data can be used to evaluate model accuracy for key terms in the chemical processing of VOC and NOx (e.g., Imre et al., 1998).

Process Analysis (PA) is implemented in both CMAQ and CAMx and each model supports three complementary aspects of the method: (a) th e integrated process rate (IPR), (b) integrated reaction rate (IRR) and (c) chemical process analysis (CPA). Several versions of process analysis (PA) have been implemented in air quality m odels (AQMs) including both trajectory models (Tonnesen, 1990, 1995) and grid models (Jang et al., 1995, Tonnesen and De nnis, 2000; Arnold et al., 1998; and Wang, 1997).

The fundamental approach in all versions of PA is similar: The AQM is modified to calculate the integral over time of the individua l sink and source processes and each chemical reaction. These integrated sink/source process rate s (IPR) and integrated reaction rates (IRR) can then be stored to a file and analyzed using a pos t-processor, or some processing can be performed internally in the model and a more limited set of process diagnostic information is output directly by the AQM. Chemical process analysis (CPA) is an improvement on the IRR method whereby some of the processing of IRR information is intern alized within the AQM to output chemically meaningful parameters directly (e.g., budget terms for O 3 , NOx and odd oxygen).

Process analysis measures for aerosol chemis try have not been analyzed as much as for ozone chemistry. Although the ozone chemistry proce ss analysis is directly related to secondary sulfate and nitrate formation, there is additional process analysis information available in the aerosol modules that are not extracted in either CMAQ or CAMx. In particular, information on sulfate formation and oxidants from the aqueous-phase m odule and on the sulfate/nitrate equilibrium from the aerosol thermodynamics module would be a usef ul addition to the current process analysis

output.

Because application of all three of thes e probing tools--source apportionment, DDM, and Process Analysis—are computational intensive and require a fair amount of analysis time to reap the benefits of using the methods, they do not lend themselves directly to annual simulations. However, each method has potential for use in addressing key episodic periods or geographical locations in the VISTAS domain where performance in the 2002 simulation may present a problem or where particular attention needs to be focused on emissions controls (a specific Class 1 area for example). In such focused applications, one or more of these probing tools may indeed serve a purpose and will be considered where appropriate.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-15May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-16 7.6 Phase II Model Evaluation Procedures

EPA guidance (EPA, 2001, pg. 227) suggests that the performance evaluation focus on two

aspects:

> How well is the model able to replicate observed concentrations of components of

PM 2.5 , and total observed mass of PM 2.5 ? and

> How accurately does the model characterize the sensitivity of ch anges in component concentrations to changes in emissions?

Recognizing that the former is much easier to accomp lish than the latter, EPA goes on to declare that testing of a model’s reliability in estimating the actual effects of emissions changes is the more important. Over the past 20 years, a substantia l body of information and analytical techniques has been developed to address the first aspect. Unfortunately, even today there are little rigorous methods available for quantifying the accuracy and precision of a model’s predictions of ozone, PM or visibility changes as the result of emissions cha nges. In this section we explain how the VISTAS Phase II testing will address the first aspect of the performance evaluation, i.e., how does the model compare against observed data. In section 7.9 we consider the second performance consideration.

7.6.1 Assessment of Ground-Level Gas-Phase and Aerosol Species

Given that visibility is expressed in terms of extinction built off of individual components of fine particulate matter, the model should be evaluate d separately for each of the key fine particulate matter components that make up the extinction coeffi cient. Current EPA guidance suggests that the

model should also be evaluated for ammonium as well as several key gas-phase species that are important for fine particulate modeling. For particulate species this includes SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , elemental carbon (EC), organic carbon (OC), IP, mass of individual constituents of IP, and coarse matter (CM). The gaseous species include ozone (O 3 ), HNO 3 , NO 2 , PAN, NH 3 , NOy, SO 2 , CO, and H 2 O 2

.

As indicated above, for some of the VISTAS subregions there are very few measurements, if any, for most of the gaseous species. But ozone measurements are available and should be integrated into the model performance evaluation. Given the importance of the photochemistry and the radical cycle in forming secondary fine par ticulate matter, the ozone evaluation will provide some insight into the model’s ability in this area. However, given the coarse grid resolution to be used (36 km and 12 km) perhaps greater weight s hould be given to the model evaluation results at the more rural ozone monitors (e.g., CASTNet) comp ared to urban ozone monitors. In addition to the list of PM and gaseous species from EPA’ s guidance document listed above, the Task 4a/4b evaluation will also examine model’s ability to estimate PM 2.5 mass, PM 10 mass, extinction (b ext ) and deciview (dV) using the equation above to obtain ex tinction from fine particulate concentrations.

At some of the IMPROVE sites there are also direct measurements of hourly extinction using transmissometer or nephelometer instruments. 5 Thus, it would be scientifically interesting to

5 The Phase I modeling did not utilize the hourly extinction data in the operational evaluation of the CMAQ and CAMx models due to schedule and resource constraints and because the modelsMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-17 evaluate the model estimated extinction with the hourly measured values at these sites. If such a comparison were to be made, day-specific hourly a nd site-specific RH and f( RH) values could be used to convert model estimated fine particulate matter to extinction rather than the monthly f(RH) EPA recommends for projecting visibility improveme nts (EPA, 2003a). Furthermore, there have been some discrepancies reported in the past between the extinction calculated using the reconstructed mass data and the hourly measurements . Thus, it would be interesting to calculate day-specific extinction using day-specific f(RH) values and the reconstructed mass data for comparison with the direct measurements and the m odel estimates. Whether and to what extend this more detailed investigation into light extincti on is pursued would depend upon the availability of Optional Task 14 authorization.

As part of the CMAQ operational evaluation, model outputs will be compared statistically and graphically to observational data obtaine d from the IMPROVE, SEARCH, CASTNet, EPAFRM, EPA-STN, and other monitoring networks. These monitoring data will be obtained from AIRS, VIEWS, and other appropriate organizations. These comparisons will likely include:

> Daily monthly, seasonal and annual averages for SO 2 , SO 4 , NO 3 , EC, OC, PM 2.5 , and

PM 10 , taking care to exclude periods of sampli ng interference in the observational data. We will look for systematic biases between the model results and IMPROVE observations, and if biases are found, identif y possible sources of error in the model inputs.

> Hourly, high resolution PM species and gase ous species concentrations at sites where available (e.g., SEARCH, AIRS and EPA-Supersites).

> At sites with contrasting aerosol mass loadi ngs, analysis of the temporal behavior of the major scattering and absorbing aerosol constituents along with the visibility trends, to

establish correlations.

The optional Phase II CMAQ diagnostic model evaluations may entail several components, many of which can be identified presently. Of course, the actual diagnostic analyses to be performed and the scope of such analyses can onl y be determined once the initial Task 4a model testing is underway. These diagnostics analyses – to be funded through Optional Task 14 (Enhanced Model Performance Evaluation)-- will need to be carefully defined and rank-ordered in terms of their priority to ensure that they can be accommodated within available resources and schedule. Among the diagnostic model evaluation analyses that could be considered are:

> Evaluate seasonal trends in observations of organic and inorganic aerosol precursors and their effects on PM composition and visibility, and evaluate the ability of the model to capture these seasonal trends.

> Evaluate how well the model simulates various physicochemical processes by: (a) examining observed and modeled correlati ons between various species pairs, and

performance for the major PM components was deemed more important in terms of evaluating alternative model science configurations.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-18 (b) comparing model-predicted ratios of vari ous species (individual or families) with observations to evaluate gas/particle partitioning (e.g., nitrate/total nitrate, SO 4 /SO

x ).

> Investigate the performance of the model at selected observational sites characterized by different chemical regimes that may be encountered either spatially or during different seasons to help identify any inadequacies in the model and to provide a better understanding of conditions under which model inferences may be weak.

> Compare hourly 24-hour average, and episode averages of the PM constituents for the 2002 Phase I VISTAS annual modeling period as well as the 2003 episodes across all

sites.

> Create scatter plots of modeled vs. obser ved data and hourly and 24-hour averages by site and subregion to help identify any site-specific biases.

> Create time series plots of predicted and observed concentrations as appropriate.

> Evaluate for total sulfur (SO 2 + SO 4 ), nitrate (HNO 3 + NO 3 ) and ammonia (NH 3 + NH 4 ).

> Compare observed versus modeled mass fractions of PM constituents at various sites that are characterized by their proximity or re moteness relative to sources, or by specific meteorological conditions (e.g., frontal passa ge, stagnation, precipitation); these will

enable identification of trends in the mode l of over- or under-prediction of specific PM

constituents under these conditions.

> Calculate the measured and predicted relative abundance of key PM components and compare with EPA guideline recommendations and emergent alternative science recommendations (e.g., removing the soil co mponent from the calculations, use of alternative relative importance equations [i.e., Boylan, 2004]).

> Pay particular close atte ntion to the model performance at the Class I areas for SO 4 , NO 3 , EC, OC, IP and CM on the 20% best and 20% worst days to document whether certain of these days should be eliminated from the visibility projections due to inadequate model performance.

The suite of statistical metrics and graphical t ools identified in the previous section for the core operational evaluation efforts under Task 4a/ 4b would likely also be used to diagnose performance problems with the CMAQ simulations s hould they exist and to highlight differences between model runs. Experience in ozone/PM m odeling is the best basis upon which to identify obviously flawed simulation results. Efforts to improve the CMAQ model’s base case performance will be made, where necessary, warra nted (i.e., to reduce the discrepancies between model estimates and observations), and consistent with the proj ect resources and schedule; however, these model performance improvements efforts must be based on sound scientific principles. “Curve-fitting”

exercises will be avoided.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-19 7.6.2 Assessment of Aloft Gas-Phase and Aerosol Species

A substantial number of aircraft flights were conducted during 2002 over the Midwest and Eastern U.S. Should VISTAS elect to fund the op tional aloft model performance evaluation, we will

endeavor to obtain this information and use it in a scientific performance evaluations of aloft gasphase and aerosol species (see section 7.3.2).

7.7 Performance Goals and Benchmarks

Establishment of performance goals and benc hmarks for regulatory m odeling is a necessary but difficult activity. Here, performance goals re fer to targets that we believe a good performing model should achieve, where as performance benchm arks are based on historical model performance measures for the best performing simulations. Perf ormance goals are necessary in order to provide consistency in model applications and expectations across the country and to provide standardization in how much weight may be accorded modeling study re sults in the decision-making process. It is a problematic activity, though, because many areas present unique challenges (e.g., Houston, San Joaquin Valley, Los Angeles) and no one set of perfor mance goals is likely to fit all needs. Equally concerning is the very real danger that modeling studies will be truncated when the ‘statistics look right’ before full assessment of the model’s reliability is made. This has the potential from breeding built-in compensating errors (Reynolds et al., 1996) as modelers strive to get good statistics as opposed to searching for the explanations for poor performance and then rectifying them. A

NARSTO review of more than two-dozen urban-scal e ozone SIP applications found this tendency to be all too prevalent in the regulatory modeling of the 1990s. (Roth et al, 1997).

Nearly 15 years ago, research sponsored by the California Air Resources Board (Tesche et al., 1990) led to the agency’s adop tion of three performance goals for 1-hour ozone modeling in the

state:

> Unpaired (in time and space) peak prediction accuracy ( ≤ ± 20%); > Mean normalized bias in hourly averaged concentrations ( ≤ ± 15%); and > Mean normalized gross error in hourly concentrations ( ≤ 35%).

These performance goals for 1-hr ozone concentra tions were adapted from previous surveys of several dozen urban-scale photochemical grid modeling studies (principally in California) focusing on ozone episodes of 1 to at most 3 days in dura tion. A surprising number of these studies did not include biogenic VOC emissions in the inventory under the then prevailing belief that biogenics were a negligibly small source category compared to automobile emissions. Most of the studies (Tesche, 1985, 1988; Tesche et al., 1985; 1990) compri sing the data base from which the California ozone performance goals were derived entailed hourly ozone concentrations well above background levels (~40-50 ppb). As a result, it was common practice to use a “cutoff values” ranging between 40 ppb to 60ppb to eliminate prediction-observations pairs that would cause these bias and error residual statistics to become extraordinarily larg e when measured concentrations were low.) Accordingly, normalized statistics such as bias and error proved to be suitable in most applications since the observed concentrations were genera lly high. These three California ozone model performance goals were adopted by EPA (1991) as part of the nationwide photochemical modeling guidelines and have been heavily used since.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-20 However, when these evaluation metrics and goals were later adapted to PM and PM species, difficulties arose because performance statistics that divide by low concentration observations become much less useful. Indeed, some PM species may approach zero (e.g., NO 3 ). In time, this

has led to the introduction of the fractional and norma lized mean bias and error metrics in addition to the mean normalized metrics and related perfor mance expectations based on these alternative

measures.

While the 1-hr metrics and goals still have va lue in interpreting ozone and some gas-phase species performance, it has been necessary to de velop new performance metrics and goals for fine particulates. EPA’s PM guidance document (EPA , 2001) guidance document identifies particulate matter components of interest to include: SO 4 and/or S, NH 4 , NO 3 , mass associated with SO 4 , mass associated with NO 3 , EC, OC, IP, and mass of individual constituents of inorganic primary particulate matter (i.e., IP). Gaseous pollutants of interest include ozone, HNO 3 , NO 2 , PAN, NH 3 , NO y , SO 2 , CO, and H 2 O 2 . In addition, EPA guidance identifies several potentially useful statistical measures including: (a) accuracy of spatially averag ed concentrations near a monitor, (b) fractional bias in means and standard deviations of pred ictions and observations, (c) normalized bias, (d) normalized gross error, (e) unpaired comparisons between predicted and observed peak concentrations. (Interested readers are referred to the EPA guidance document on the details of these metrics including mathematical formulae and implementation methods.)

As with ozone in the 1980s, actual experience with PM models has led to the development of the current performance expectations for these models. For example, PM 10 SIP model performance goals for mean normalized gross error of < 30% for southern California (SCAQMD, 1997; 2003)

and < 50% for Phoenix (ENVIRON, 1998) have been us ed. As correctly poi nted out by Seigneur and co-workers (2003), the current ability of regi onal PM models to predicting regional PM and visibility is an area of research with improve ments needed for characterizing meteorology and emissions as well as PM models themselves. To this list we would add the need for improvements in model evaluation methodologies as well.

When EPA’s draft guidance was developed nearly f our (4) years ago, an interim set of fine particulate modeling performance goals were suggested for aggregated mean normalized gross error

and mean normalized bias as follows:

Pollutant Gross Error Normalized Bias

PM 2.5 ~30-50% ~ " 10%

Sulfate ~30-50% ~ " 20-30%

Nitrate ~20-70% ~ " 15-50%

EC ~15-60% NA

OC ~40-50% ~ " 38%

Because regional-scale fine particulate and regi onal haze modeling is an evolving science, and considerable practical application and performance testing has transpired in the intervening years since these goals were postulated, we consider th em general guidelines. As part of the Phase II model evaluation effort, we will compare the EPA goals in the 2001 guidance document with the results of the CMAQ/CAMx Phase I modeling, toge ther with other recent PM modeling studies (e.g., WRAP, MRPO) to advance a proposed set of pe rformance goals most re levant to the VISTASMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-21 study. Results of the Phase I model evaluation together with recommendations from science workshops (e.g., EPA’s PM Model Performance Evaluation Workshop in February 2004) and recently published scientific studies (e.g., Boylan, 2004) will be used to provide support to these

recommendations.

We regard the above goals as simply general interim guidance. Cert ainly, more information on likely performance expectations will be ava ilable once the Phase I CMAQ/CAMx ozone and PM performance evaluation and sensitivity results ar e fully analyzed. In the VISTAS Phase II modeling, we will generate the model performance statistics listed in Table 7-4 and make comparisons with EPA’s interim goals as one means for:

> Establishing a benchmark on the annual 2002 VISTAS modeling episode, and

> Guiding the interpretation of the CMAQ modeling results.

Equally as useful, we will endeavour to compare th e results of the Phase II multi-species evaluations of CMAQ with the Phase I results for the Jan ’02, Jul ’99 and Jul ’01 episodes as well as modeling results from other studies (e.g., BRAVO, EPA, CRC, etc.), other RPOs, and the modeling research community in general. This will allow judgmen ts about the adequacy of the Phase II modeling results to be placed in the most current perspective possible.

7-8. Diagnostic and Sensitivity Testing

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even most) model performance e xpectations. Indeed, our experience has been that initial simulations that ‘look very good’, usually do so as the result of comp ensating errors. The norm is to engage in a logical, documented process of model performan ce improvement wherein a variety of diagnostic probing tools and sensitivity testing methods are us ed to identify, analyze, and then attempt to remove the causes of inadequate model performan ce. This is invariably the most technically challenging and time consuming phase of a modeling study. We anticipate that the annual CMAQ

model base case simulations will present some performance challenges that may necessitate focused diagnostic and sensitivity testing in order for them to be resolved. Hopefully, these diagnostic and/or sensitivity tests can be adequately carried out with in the resources and schedule of Tasks 4a/4b. If not, then it may be necessary to draw upon the Optional Task 14 (Enhanced Model Performance Evaluation) and/or Optional Task 15 (Contingenc y) resources to conduct the necessary work. Where practical, diagnostic or sensitivity analyses , if needed, could be performed on selected episodes within the annual cycle, thereby avoi ding the time-consuming task of running CMAQ for the fully 2002 period. Below we identify the types of diagnostic and sensitivity testing methods that might be employed in diagnosing inadequate mode l performance and devisi ng appropriate methods for improving the model response.

7.8.1 Traditional Sensitivity Testing

Model sensitivity experiments are useful in three distinct phases or ‘levels’ of an air quality modeling study and all will be used as appropriate in the VISTAS Phase II modeling with CMAQ.

These levels are:May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-22 > Level I

. Model algorithm evaluation and configuration testing;

> Level II . Model performance testing, uncertainty analysis and compensatory error diagnosis, and

> Level III. Investigation of model output respons e (e.g., ozone, aerosol, deposition) to changes in precursors as part of emissions control scenario analyses.

Most of the Level I sensitivity tests with CMAQ have already been completed in the Phase I configuration and diagnostic analyses. However, given that open community nature of CMAQ and the frequent science updates to the model and s upporting data abases, it is possible that some additional configuration sensitivity testing will be necessary in the early months of Phase II Potential Level I sensitivity runs would be carried out at one or more of the Team’s three modeling

centers.

Potential Level II sensitivity analyses might be helpful in accomplishing the following tasks:

> To reveal internal inconsistencies in the model; > To provide a basis for compensatory error analysis; > To reveal the parameters (or inputs) th at dominate (or do not dominate) the model’s operation; > To reveal propagation of errors through the model; and > To provide guidance for model refinement and data collection programs.

At this time, it is not possible to identify one or more Level II sensitivity runs that might be needed to establish a reliable annual 2002 CMAQ base case. The merits of performing Level II sensitivity testing will depend upon whether performance problem s are encountered in Tasks 4a/4b. Also, the number of tests possible, should performance di fficulties arise, will be limited by the available schedule and Phase II resources under Optional Task 14 (Extended Model Performance Evaluation). Thus, at this juncture, one cannot be overly pr escriptive on the number and emphasis of sensitivity runs that may ultimately be desirable in Phase II. However, from past experience with CMAQ and other models, experience it is possible to identify examples of sensitivity runs could be useful in model performance improvement exercises with the annual 2002 CMAQ simu lation. These include:

> Modified biogenic emissions estimates; > Modified on-road motor vehicle emissions; > Modified air quality model vertical grid structure; > Modified boundary conditions; > Modified fire emissions; > Modified EGU emissions;

> Modified ammonia emission estimates.

> Modified aerosol/N 2 O 5 /HNO 3 chemistry; and

> Modified NH 3 and HNO 3 deposition velocities.

Note that in a few cases (e.g., vertical grid structure, NH 4 emissions estimates), some sensitivity experimentation has already been carried out in Phase I with the Jan ’02, Jul ’01 and Jul ’99 episodes. To the extent that this Phase I in formation can help guide the Phase II diagnostics analyses, we will capitalize on this earlier work.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-23 If necessary, Process Analysis extraction output s can be included in these Level II diagnostic sensitivity simulations in order to provide insight into why the model responds in a particular way to each input modification. Again, the number, co mplexity, and importance of these types of traditional sensitivity simulations can only be determined once the initial CMAQ annual 2002 simulation(s) are executed.

Level III sensitivity analyses have two main pur poses. First, they facilitate the emissions control scenario identification and evaluation pr ocesses. Today, four complimentary sensitivity “tools” can be used in regional photochemical models depending upon the platform being used. These methods include: (a) traditional or ‘brute force’ testing, (b) the direct decoupled method (DDM), (c) Ozone Source Apportionment Technology (OSAT) and PM Source Apportionment Technology (PSAT), and (d) Process Analysis (PA). Each method has its strong points and they will be employed in Phase II where needed. The sec ond purpose of Level III sensitivity analyses is to help quantify the estimated reliability of the ai r quality model in simulating the atmosphere’s response to significant emissions changes. This important model evaluation need is addressed in

further detail in section 7.9 below.

Based on experience in other regional studies , examples of Level III monthly or annual sensitivity runs for Phase II might include:

> Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO 2 emissions; > Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point

source NO

x emissions; > Ozone, sulfate, nitrate, ammonium a nd other aerosol sensitivities to ground level

NO

x emissions; and > Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia.

Of course, traditional ‘brute force’ sensitivity experiments is just one way of quantifying

these or other Level III sensitivities. Other met hods that can be applies include DDM, OSAT, or

PSAT simulations.

The need to perform sensitivity experiment ation (Levels I, II, or III) will depend on the outcome of the Task 4a/4b operational performance evaluations. If such a need arises, the ability to actually carry out selected sensitivity and/or dia gnostic experiments will hinge on the availability of Optional Task 14 and/or 15 resour ces and sufficient time to carry out the analyses. Clearly, selection of the specific analysis method will de pend upon the nature of the technical question(s) being addressed at the time. Note that as part of VISTAS Phase II modeling, Georgia Institute of Technology will be performing emissions sensitivities with CMAQ using the Phase I databases.

7.8.2 Diagnostic Tests

A rich variety of diagnostic probing tools are available for investigating model performance issues and devising appropriate means for improving the model and/or its inputs. Previously, in section 7.4.4 we introduced the suite of ‘probing t ools’ available for use in the CMAQ and CAMx modeling system for use in Phase II. Where the need exists (i.e., if performance problems are encountered) and assuming VISTAS elects to fund th e use of the probing tool applications (under Optional Task 14 or elsewhere), these techniques coul d be employed as appropriate to assist in theMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-24 model performance improvement efforts a ssociated with the annual 2002 CMAQ basecase development. Here we describe an additional diagnostic method – indicator species and species ratios -- that is potentially useful not only in m odel performance improvement activities but also in judging the models reliability in estimating the imp acts on air quality from future emissions. This method involves the use of so-called ‘indicator species’ and species ratios. If, during the conduct of Phase II, we determine that application of indicat or species and species ra tio techniques would be beneficial to the study (and if existing project res ources allow), we will di scuss with the TAWG the merits of including this additional probing tool as part of Optional Task 14.

Beginning in the mid 1990s, considerable interest arose in the calculation of indicator species and species ratios as a means of diagnosing phot ochemical model performance and in assessing model credibility in estimating the effects of em issions changes. Major contributions to the development and refinement of this general dia gnostic method over the past decade have been made many scientists including Milford et al., ( 1994), Sillman (1995, 1999), Sillman et al., (1997), Blanchard (2000), Blanchard and Fairley (2001), and Arnold et al., (2003). Indeed, a recent evaluation of CMAQ using indicator species ratios such as O 3 /NO

x , NO

z /NO y (a measure of chemical aging), and O 3 /NO

z (a measure of the ozone production efficiency per NO

x converted), showed not only good agreement with measurements (Arnold et al., 2003) but also convincingly demonstrated the utility of the method for diagnosing model performance in a variety of ways.

Traditionally, indicator species analyses have focused on ozone and its precursor and product species. However the method is e qually applicable to PM species and species ratios given sufficient measurement data for comparisons. With some of the high-resolutions monitoring data available from the SEARCH program and the EPA Supersites, it is indeed feasible to compute relevant indicator species and ratios for PM and its com ponent species. For example, Ansara and Pandis (1998) demonstrated how indicator species ratios c ould be applied to show how the modeled mass of PM might respond to sulfate, nitrate and ammonia emissions-related reductions.

7.9 Corroborative and Weight of Evidence Modeling Analyses

This section identifies additional modeling an alyses that might be worth pursuing under Optional Task 14 (Extended Model Performance Evaluation) to add strength to the core model evaluation efforts already planned as part of Tasks 4a and 4b.

7.9.1 Corroborative Models

Noteworthy in EPA’s new ozone, PM, and regional haze guidance documents is the encouragement of the use of alternative modeli ng methods to corroborate the performance findings and control strategy response of the primary air qua lity simulation model. This endorsement of the use of corroborative methodologies, stems fr om the common understanding that no single photochemical modeling system can be expected to provide exact predictions of the observed ozone and PM species concentrations in a region the si ze of VISTAS, especially over time scales spanning 1-hr to 1 year. Although the photochemical/PM models identified in EPA’s PM/regional haze guidance document possess many up-to-date science and computational features, there still can be important differences in modeled gas-phase and aerosol predictions when alternative models are exercised with identical inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-25 As we discovered in the Phase I CMAQ/CAMx inter-comparisons, the general levels of

difference revealed between the two model’s ozone and PM predictions is typical of what one encounters when inter-comparing alternative state- of-science regional models. These differences provide some insight into the current limits of predictability and reproducibility of today’s best photochemical/PM models. In light of these unders tandable differences in modeling results between state-of-science “One Atmosphere” models, not onl y is the issue of model selection for VISTAS

Phase II critical 6, but the procedures for the selected model (CMAQ in this instance) and interpreting its output are important as well. Thus, recognizi ng the uncertainty that attends even the most sophisticated models, the EPA’s draft PM/regiona l haze modeling guidance explicitly addresses the issue of modeling uncertainty by recommending that alternative models (photochemical and observation-based) be considered in the attainment demonstration ‘weight of evidence’ analyses. Indeed, the Relative Reduction Factor (RRF) appro ach in the formal attainment demonstration process is designed to address the fact that no mode l is fully capable of giving precise predictions. In fact the RRF approach was developed in respons e to the findings of many modelers (as we have also demonstrated in Phase I) that alternative, comparable models give somewhat divergent ozone and secondary aerosol predictions.

Mindful of EPA’s endorsement of corroborativ e modeling methods and the rigorous use of ‘weight of evidence’ investigations, we recommend that the most recent version of CAMx may be used in supportive, corroborative ro le to CMAQ in Phase II . Among other things, this will permit us to more explicitly identify the expected range of model uncertainty and to corroborate the general effectiveness of the Phase II CMAQ regional haze c ontrol strategies. Other corroborative modeling methods such as the CMAQ-AIM and CMAQ-MADRID should also be considered. However, as these models are derivatives of CMAQ they woul d not provide as robust independent corroboration

as CAMx.

Thus, we recommend CMAQ as the primary photoc hemical model for use in VISTAS Phase II and CAMx as a supportive model to be used fo r corroboration and are in the identification of compensation errors in the MM5/SMOKE/CMAQ m odeling system. The use of CAMx is under Optional Task 11 in the Phase II SOW that is cu rrently not funded (see Appendix A). Additional

corroborative models should also be considered.

7.9.2 Weight of Evidence Analyses

EPA’s guidance recommends three general types of ‘weight of evidence’ analyses in support of the attainment demonstration: (a) use of ai r quality model output, (b) examination of air quality and emissions trends, and (c) the use of corrobora tive modeling such as obse rvation-based (OBM) or observation-driven (OBD) models. We will consider the use of one or more methods in conducting the Phase II CMAQ modeling because it could significantly strengthen the credibility and reliability of the modeling available to the states for their subs equent use. The exact de tails of the ‘weight of evidence’ analyses must wait until Phase II evolves fu rther. It is premature to prescribe which, if any of the WOE analyses would be performed sin ce the model’s level of performance with the 2002 episode is obviously not known at this time and th e time and remaining project resources available to support WOE analyses is unknown as well. Nonetheless, we outline below our thoughts

6 Model selection is a key issue that all RPOs have addr essed and some such as WRAP and VISTAS have chosen CMAQ. Others, such as the MRPO have adopted CAMx inst ead. Still other RPO’s have yet to decide between these two or another model. Clearly, there does not appear to be any one ‘right’ selection at the present time.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-26 regarding what would likely be considered shoul d the operational CMAQ mode l evaluation need to be bolstered with WOE analyses.

Use of Air Quality Models

. As just discussed, we recommend augmenting the CMAQ annual 2002 and episodic simulations with the use of CAMx to provide additional information on model uncertainty and sensitivity. More specifica lly, we propose to exercise CAMx on a somewhat limited basis to corroborate the key model perfor mance evaluation results and emissions control findings of the primary model, CMAQ. Sec ond, we propose to apply the DDM and OSAT/PSAT methods to develop corroborative informati on on source-receptor relationships and model sensitivities. These supplemental calculations would be performed with one or both models (CMAQ does not contain OSAT/PSAT presently) for one or more key episodes within the annual 2002 cycle. The results of this additional modeli ng would be used directly in the ‘weight of evidence’ analyses to quantify the degree of modeling uncertainty and to corroborate appropriateness of the subsequent PM emissions reductions scenarios.

Use of Emissions and Air Quality Trends. A limited scope emissions and trend analysis could be employed in Phase II to support the ‘wei ght of evidence’ determinations. However, traditionally, these types of analyses are performe d by the lead agency’s own staff. With this expectation, we would coordinate our efforts with the VISTAS TAWG to develop a trends analysis supporting the future year applications of CMAQ.

Use of Corroborative Observational Modeling

. While regulatory modeling studies for ozone attainment demonstrations have traditionally relied upon photochemical models to evaluate ozone control strategies, there has recently been growing emphasis on the use of data-driven models to corroborate the findings of air quality models. As noted, EPA’s guidance now encourages the use

of such observation-based or observation-driven m odels (OBMs/ODMs). As part of Phase II, we will consider the merits of using these techni ques as supportive weight of evidence. While the OBD/OBM models cannot predict future year air qua lity levels, they do provi de useful corroborative information on the extent to which specific s ubregions may be VOC-limited or NOx-limited, for example, or where controls on ammonia or SO2 em issions might be most influential in reducing

PM 2.5 . Information of this type, together with results of DDM and traditional ‘brute-force’ sensitivity simulations, can be extremely helpful in postulating emissions control scenarios since it helps focus on which pollutant(s) to control.

7.10 Assessing Model Reliability in Estimating the Effects of Emissions Changes

EPA identifies three methods (EPA, 2001, pg. 228) potentially useful in quantifying a model’s reliability in predicting air quality respons e to changes in model inputs, e.g., emissions.

These include:

> Examination of conditions for which subs tantial changes in (accurately estimated) emissions occur;

> Retrospective modeling, that is, modeling before and after historical significant changes in emissions to assess whether the observed air pollution changes are adequately simulated; andMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-27 3⁄4 Use of predicted and observed ratios of ‘chemical indicator species’.

We note that in some urban-scale analyses, th e use of weekday/weekend information has been helpful in assessing the model’s response to emissi ons changes. However, we suspect that this approach would not prove feasible with an a nnual episode over geographic domain as large as

VISTAS.

The first two methods have actually been cons idered for over 15 years and were the subject of intensive investigations in the early 1990s in Southern California in studies sponsored by the South Coast Air Quality Management District (Tes che, 1991) and the American Petroleum Institute (Reynolds et al., 1996). To date, neither method has proven useful largely because of the great difficulty in developing historical emissions inve ntories of sufficient quality to make such an analysis credible and the difficulties in removi ng the influences of different meteorological conditions such that the modeling signal reflects only the model’s response to emissions changes. It is difficult enough to construct re liable emissions inventories using today’s modeling technology let alone construct retrospective inventories 5-10 year s ago prior to the implementation of significant emissions control programs or major land use changes. The use of indicator species, however, offers some promise.

However, recent analytical and numerical modeli ng studies have demonstrated how the use of ambient data and indicator species ratios can be us ed to corroborate the future year control strategy estimates of Eulerian air quality models. Blanch ard et al., (1999), for example used data from environmental (i.e., smog) chambers and photochemi cal models to devise a method for evaluating the 1-hr ozone predictions of models due to changes in precursor NOx and VOC emissions. Reynolds et al., (2003) followed up this analysis, augmented with process analysis, to assess the reliability of SAQM photochemical model estimate of 8-hr ozone to precursor emissions cutbacks. With respect to secondary aerosol PM, the recen t CMAQ evaluation by Arnold et al. (2003) clearly demonstrated how the use of indicator species anal ysis could be use to develop insight into the expected reliability and adequacy of a photoche mical/PM model for simulating the effects of emissions control scenarios. These researchers used three indicator ratios (or diagnostic ‘probes’) to quantify the model’s response to input changes:

> The ozone response surface probe [O 3 /NO

x ]; > The chemical aging probe [NO

z /NO y ]; and > The ozone production efficiency probe [O 3 /NO

z ].

By closely examining CMAQ’s response to key input changes, properly focused in time and spatial location, Arnold et al., (2003) were able to conclude that the photochemical processing in CMAQ was substantially similar to that in the atmosphere

Thus, the extension of these techniques to address CMAQ predictions for secondary aerosols will doubtless be quite challenging, but the use of indicator species (e.g., ammonia or HNO 3 limitation for nitrate particle formation) and species ra tios appears to offer, at this time, the only real opportunity to quantify the expected reliability of the air quality model to correctly simulate the effects of emissions changes. In the Phase II CMAQ model evaluation, we will remain alert to opportunities to extend the indicator species ratio an alyses to the problem of fine particulate and regional haze. This is one area where technical collaboration between the Em issions and Air QualityMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-28 Modeling team and the VISTAS TAWG can be especially fruitful in terms of identifying and testing emergent methods for challenging the model’s ability to correctly simulate the effects of future year emissions changes. Finally, we note that this is truly a current research area and as such falls outside the scope of the Phase II modeling effort. However, given its importance, we will remain alert to opportunities to utilize newly available methods s hould this prove feasible within the Phase II

resources and schedule.

Table 7-1 . Ground-Level Ambient Data Monitoring Networks and Stations Available in VISTAS 12
Km Domain for CY-2002. Monitoring

Network Chemical Species Measured Sampling Frequency;

Duration Approximate

Number of

Monitors

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr 38

CASTNET Speciated PM 2.5 , Ozone Hourly, Weekly; 1 hr, Week 74

SEARCH 24.hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); and Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) Daily, Hourly; 8

NADP WSO4, WNO3, WNH4 Weekly 100

EPA-FRM Only total fine mass (PM 2.5 ) 1 in 3 days; 24 hr 313 (?)

EPA-SPEC Speciated PM 2.5 Varies; Varies 43 (?)

AIRS/AQS CO, NO, NO 2 , NO

x , O 3 Hourly; Hourly 6,407

Table 7-2 . Aircraft Sampling Programs Performed in the VISTAS 12 Km Domain for CY-2002.

Aircraft Program Meteorological Parameters & Chemical Species Measured Sampling Program & Flight Duration Approximate

Number of Flights; Days;

Aircraft University of Maryland (UMD);

Univ. Research Foundation (URF) Meteorology: WS, WD, Temp, RH, Air Quality : O 3 , NO, NO 2 , CO,
SO 2, aerosol absorption, aerosol scattering. 23 May to 3 Oct; Typically 3 hrs 54 flights, 54 days,
1 aircraft

Midwest RPO &

Wisconsin DNR Meteorology : WS, WD, Temp, RH, dew point, pressure Air Quality : O 3 , NO, NO 2 , NO

x , NO y , speciated VOCs, carbonyls, HNO 3 , NH 3 , Hg,
SO 4 , OC, EC, PM 2.5 , light scattering (Neph), visibility pictures. 1 June to 22 Nov; Typically 3-5 hrs 133 flights; 29 days;
2 aircraft (WDNR and Jacko Aircraft)May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-30 Table 7-3. Federal Mandated Class I Areas in the VISTAS States.

State Acreage Federal Land Manager Public Law

Alabama

Sipsey Wilderness Area 12,646 USDA-FS 93-622

Arkansas

Caney Creek Wilderness Area 7,344 USDA-FS 93-622

Upper Buffalo Wilderness Area 9,912 USDA-FS 93-622

Florida

Chassahowitzka Wilderness Area 23,360 USDI-FWS 94-557

Everglades NP 1,397,429 USDI-NPS 73-267

St. Marks Wilderness Area 17,745 USDI-FWS 93-632

Georgia

Cohotta Wilderness Area 33,776 USDA-FS 93-622

Okefenokee Wilderness Ar ea 343,850 USDI-FWS 93-429

Wolf Island Wilderness Area 5,126 USDI-FWS 93-632

Kentucky

Mammoth Cave NP 51,303 USDI-NPS 69-283

Louisiana

Breton Wilderness Area 5,000+ USDI-FWS 93-632

North Carolina

Great Smoky Mountains NP {1} 273,551 USDI-NPS 69-268

Joyce Kilmer-Slickrock Wilderness Area{2} 10,201 USDA-FS 93-622

Linville Gorge Wilderness Area 7,575 USDA-FS 88-577

Shining Rock Wilderness Area 13,350 USDA-FS 88-577

Swanquarter Wilderness Area 9,000 USDI-FWS 94-557

{1} Great Smoky Mountains National Park, 514,758 acres overall, of which 273,551 acres are in North Carolina, and 241,207

acres are in Tennessee. {2} Joyce Kilmer-Slickrock Wilderness, 14,033 acres overall, of which 10,201 acres ar e in North Carolina, and 3,832 acres are

in Tennessee.

South Carolina

Cape Romain Wilderness 28,000 USDI-FWS 93-632

Tennessee

Great Smoky Mountains NP {1} 241,207 USDI-NPS 69-268

Joyce Kilmer-Slickrock Wilderness{2} 3,832 USDA-FS 93-622

{1} Great Smoky Mountains National Park, 514,758 acres overa ll, of which 273,551 acres are in North Carolina, and 241,207

acres are in Tennessee. {2} Joyce Kilmer Slickrock Wilderness, 14, 033 acres overall, of which 10,201 acres ar e in North Carolina, and 3,832 acres are

in Tennessee.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-31 Virginia

James River Face Wilderness 8,703 USDA-FS 93-622

Shenandoah NP 190,535 USDI-NPS 69-268

West Virginia

Dolly Sods Wilderness 10,215 USDA-FS 93-622

Otter Creek Wilderness 20,000 USDA-FS 93-622

Table 7-4. Core Statistical Measures to be used in the VISTAS Phase II Air Quality Model Evaluation with Ground-Level Data (see ENVIEON, 2003b,d for details).

Statistical

Measure Shorthand

Notation Mathematical Expression Notes

Accuracy of paired peak ( A p ) Paired_Peak

peak peak


## O OP

− P peak = paired (in

both time

and space) peak prediction

Coefficient of determination ( r 2 ) Coef_Determ



# ∑ ∑ ∑

= = =



## − −      

−−

N

i N

i i i N

i i i



## OOPP OOPP

1 1 2 2 2

1



## )()( ))(( P i = prediction at

time and

location i ; O i = observation

at time

and location

i ;

P = arithmetic average of P i , i=1,2,..., N;

O = arithmetic average of O i , i=1,2,...,N

Normalized Mean Error ( NME ) Norm_Mean_Err



# ∑ ∑

=

= 

## −

N

i i N

i ii



## O OP

1 1 Reported as %

Root Mean Square Error ( RMSE ) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

## 1      

− 

# ∑

= N

i ii 

## OP N Reported as %

Fractional Gross Error ( F E ) Frac_Gross_Err 

# ∑

= 

## + − N

i ii ii 

## OP OP

N 1 

## 2 Reported as %May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 7.doc 7-32 Mean Absolute
Gross Error ( MAGE ) Mean_Abs_G_Err 

# ∑

= 

## − N

i ii 

## OP N 1 

## 1

Mean Normalized
Gross Error ( MNGE ) Mean_Norm_G_Err 

# ∑

= 

# − N

i i ii



# O OP

N 1



# 1 Reported as %

Mean Bias ( MB ) Mean_Bias 

# ( ) 

# ∑

= 

## − N

i ii 

## OP N 1 

## 1 Reported as

concentration (e.g., μg/m 3 )

Mean Normalized Bias ( MNB ) Mean_Norm_Bias 

# ( ) 

# ∑

= 

## − N

i i ii



## O OP

N 1 

## 1 Reported as %

Mean

Fractionalized Bias (Fractional Bias, MFB ) Mean_Fract_Bias 

# ∑

= 

##        

+ − N

i ii ii 

## OP OP

N 1 

## 2 Reported as %

Normalized Mean

Bias ( NMB ) Norm_Mean_Bias



# ∑ ∑

=

= 

## −

N

i i N

i ii



## O OP

1 1 

## )( Reported as %

Bias Factor ( BF ) Bias Factor

1 

## 1 N

i

i i 

## P

N O

= 

##       

# ∑ Reported as

BF:1 or 1: BF or
in fractional notation (BF/1 or 1/BF).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-1 8.0 VISIBILITY ASSESSMENT AND COMPARISON

This chapter provides a summary of how the modeling results will be used to satisfy the requirements of the Regional Haze Rule (RHR) as part of the Section 308 visibility State Implementation Plans (SIPs) and Tribal Implemen tation Plans (TIPs) due in 2007. Note that the VISTAS Phase II modeling results will also likel y be used in some capacity as the regional component in some State’s fine particulate (PM 2.5 ) and 8-hour ozone SIPs that are also due in 2007. However, how the modeling resu lts will be used to address PM 2.5 and 8-hour ozone issues is not discussed in this chapter and will be addre ssed by the pertinent States at a later time. The Phase II modeling results generated in 2004 will lik ely not be the final basis for the Section 308

SIPs as the results will be updated and refined. However, it is important to start planning how the modeling results will be used to project visibility changes, optimize the presentation of results and identify any potential pitfalls early on.

8.1 Regional Haze Rule Requirements

The Regional Haze Rule (RHR) regulations were published by EPA in 1999 (Federal Register 35769, July 1, 1999) and are designed to address the requirements of Section 169A and 169B of the Clean Air Act (CAA). The CAA and RH R call for the protection of visibility at the 156 “mandatory Federal Class I areas.” 1 Section 169A of the Clean Air Act Amendments (CAAA) established a national visib ility goal to remedy existing impa irment due to air pollution at the Class I areas. This is accomplished by defining a visibility goal of “nat ural conditions” to be

achieved at each Class I ar ea by 2064. The RHR requires States with Class I areas to develop SIPs that include reasonable progress goals for improvi ng visibility in each Class I area and emission reduction measures to meet those goals. For the initial control strategy SIPs due in 2007, States are required to adopt progress goals for improvi ng visibility from baseline conditions. The 2000- 2004 five-year period is used to de fine baseline conditions and the first future progress period is 2018, that has been interpreted as the 2013-2017 or 2014-2018 five year periods. A State is required to set progress goals for each Class I ar ea in the State for tw o visibility metrics:

• Provide for an improvement in visibility for the most impaired (i.e., 20% worst) visibility days; and

• Ensure no degradation in visibi lity for the least impaired (i.e., 20% best) visibility days.

The reasonable progress goals must provide for a rate of improvement sufficient to attain “natural conditions” by 2064, or justify any altern ative rate. States are to defi ne controls to meet progress goals every 10 years, starting in 2018, that defines progress periods ending in 2018, 2028, 2038, 2048, 2058 and finally 2064. States will determine whether they are meeting their goals by comparing visibility conditions from one five -year period to another (e.g., 2000-2004 to 2013- 2017). As stated in 40 CFR 51.308 (d) (1), baseli ne visibility conditi ons, progress goals, and

1 Areas designated as mandatory Class I areas are t hose National Parks exceeding 6, 000 acres, wilderness areas and national memorial parks exceeding 5,0 00 acres, and all international parks th at were in existence on August 7, 1977. Since then some new Class I areas have been defined and some existing Class I areas have been expanded. However, these areas are not included as part of the 156 “Federal mandated Class I areas” for which the RHR applies. In this document the term “Class I area” re fers to the 156 “Federal mandated” Class I Areas.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-2 changes in visibility must be expressed in term s of deciview (dv) units. The deciview unit of visibility impairment is de rived from light extinction (b ext ) as follows:

dv = 10 ln (b ext /10)

Where light extinction (b ext ) is expressed in terms of inverse megameters (Mm -1 = 10 -6 m -1 ).

Section 8.2.1.1 below discusses how b ext is calculated using the observed fine particulate

concentrations from the IMPR OVE monitors and S ection 8.2.1.2 below defines how the modeling results will be mapped to the IMPROVE measurements for the pu rpose of projecting visibility improvements.

8.2 EPA Guidance for Complying with Regional Haze Rule

EPA has published three guidance documents that relate to how modeling results should be used to demonstrate compliance with the RHR:

“Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze, Draft 2.1 (EPA, 2001).

“Guidance for Tracking Progress Under the Regional Haze Rule” (EPA, 2003a).

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule” (EPA, 2003b).

The first EPA modeling guidance document lis ted above (EPA, 2001) discusses the use of modeling results to demonstrate prog ress toward the RHR goals. This is a draft document that will be updated during 2004. When the draft modeling guidance is updated that may necessitate an update of this Modeling Protocol to keep it cons istent with current EPA guidance. The second document (EPA, 2003a) focuses on monitored visibili ty and how to define the visibility baseline and how to track visibility goals. The third EPA guidance document discusses procedures for defining “natural conditions” for a Class I area that is the visibility goal in 2064. In the discussion below we are assuming that other VISTAS particip ants would define the “natural conditions” for each VISTAS Class I area. In this Chapter we a ddress the use of the mode led and monitored fine particulate concentrations at the Class I areas for projecting visibility cha nges from the current to future years.

VISTAS is currently evaluating EPA’s assumptions for projecting visibility improvements in their guidance documents (EPA, 2001; 2003a,b). If technically justif ied, VISTAS may make modifications to EPA’s procedures based on thei r analysis. For example, recent measurements suggest that, at least for the VIST AS region, sulfate is not always fully neutralized by ammonium

and that ammonium bisulfate and su lfuric acid can exist, especially in the summer. Thus, the EPA approach assuming that sulfate is always comple tely neutralized by amm onium may have to be updated. The current EPA approach also does not account for sea salt that can be a contributor to visibility impairment, especially at coastal sites. VISTAS is also evaluatin g EPA’s default “natural conditions” and its appropriateness for the VISTAS region. Belo w we describe EPA’s approach for projecting visibility improvements, but be aware they may be modified by VISTAS at a later

date.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-3 8.2.1 Procedures for Projecting Visibility Changes

EPA guidance recommends using the model in a relative sense to project future-year visibility conditions (EPA, 2001; 2003a). This is done through the use of Rela tive Reduction Factors (RRFs) that are defined as the ratio of th e future-year to the current-year modeling results. The RRFs are applied to the baseline visibility c onditions to project future-year visibility. The major features of EPA’s recommended visibil ity projections are as follows (EPA, 2003a,b):

• Monitored data should be used to define current air quality.

• Monitored concentrations of PM 10 are divided into six major components, the first

five of which are assumed to be PM 2.5 and the sixth is PM 2.5-10

. 3⁄4 SO4 (sulfate);
3⁄4 NO3 (particulate nitrate);
3⁄4 OC (organic carbon);
3⁄4 EC (elemental carbon);
3⁄4 OF (other fine particulate); and
3⁄4 CM (coarse matter).

• Models are used in a relative sense to develop relative reduc tion factors (RRFs) between future and current predicte d concentrations of each component.

• Component-specific relative reduction fact ors are multiplied by current monitored values to estimate future component concentrations.

• Estimates of future component concentra tions are consolidated to provide an estimate of future air quality, which can be related to a goal for regional haze.

• Future estimated air quality is compared wi th the goal to see if the simulated control strategy results in the goal being met.

• It is acceptable to assume that all measur ed sulfate is in the form of ammonium sulfate [(NH 4 ) 2 SO 4 ] and all particulate nitrate is in the form of ammonium nitrate [NH 4 NO 3 ].

In order to facilitate tracking visibility progress, three important vi sibility parameters are required

for each Class I area:

Baseline Conditions : Baseline Conditions represent visi bility for the 20% best and 20% worst days for the initial five-year baseline period of the regional haze program. Baseline

Conditions are calculated us ing monitor data collected during the 2000-2004 five-year period.

Current Conditions : Current Conditions for the best and worst visibility days are calculated from the latest five-year averag e based on the latest five-years of monitored data (which is currently 1998-2002, but expected to be 1999-2003 by the time the initial future-year Phase II visibility projections will be made in late 2004)..May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-4 Natural Conditions : Estimates of natural visibility conditions for the 20% best and 20% worst days at a Class I area that is the goal of the RHR is 2064 that has been defined as visibility conditions that would be expe rienced in the absence of human-caused impairment” (EPA, 2003b).

8.2.1.1 Calculation of Baseline Conditions

Baseline and Current Conditions for Class I ar eas are calculated using fine and coarse particulate concentration measured at Interagenc y Monitoring of Protected Visual Environments (IMPROVE) monitors. Currently, each Class I area in the VISTAS domain has an associated

IMPROVE PM monitor. The IMPROVE monitors do not directly measure visibility, but instead measure speciated fine particulate (PM 2.5 ) and total PM 2.5 and PM 10 mass concentrations from which visibility is obtained through the IMPROV E reconstructed mass extinction equation.

Visibility conditions are estimated starting with the IMPROVE reconstructed 24-hour average particulate matter (PM) mass measurements for six PM species:

• Sulfate [SO 4 ];

• Particulate Nitrate [NO 3 ];

• Organic Matter [OM];

• Elemental Carbon [EC];

• Other Fine Particulate [Soil]; and

• Coarse Matter [CM].

The IMPROVE monitors do not directly monitor some of these species so assumptions are

made as to how the IMPROVE measurements can be adjusted and combined to obtain these six components of light extinction. For example, sulf ate and particulate nitr ate are assumed to be completely neutralized by ammonium and are assume to occur solely in the fine particulate mode (that is any coarse mode sulfate and nitrate in the real atmosphere may be present in the CM IMPROVE measurement). Concentrations for the above six components of light extinction in the IMPROVE reconstructed mass ( RCM) extinction equation are obtained from the IMPROVE measured species as follows:

Table 8-1. Definition of IMPROVE Reconstructed Mass (RCM) Species from Measured IMPROVE Species.

IMPROVE RCM IMPROVE Measured Species

SO 4 1.375 x ( 3 x S)

NO 3 1.29 x NO3_

OM 1.4*OC1 + 1.4*OC2 + 1.4*OC3 + 1.4*OC4 + 1.4*OP

EC EC1 + EC2 + EC3 – OP

Soil 2.2*AL + 2.49*SI + 1.63*CA + 2.42*FE + 1.94*TI

CM MT – MF

Where:

• S is elemental sulfur as determined fr om proton induced x-ray emissions (PIXE) analysis of the IMPROVE Module A that is multiplied by 3 to account the presence of oxygen, if S is missing then the sulfate (SO4) measured by ion chromatography analysis of the Module B is us ed to replace (3 x S). It is assumed to be completely neutralized by ammonium (1.375 x SO4).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-5 • NO3_ is the particulate nitrat e measured by ion chromatogr aphy analysis of the Module B. It is assumed to be complete ly neutralized by ammonium (1.29 x).

• The IMPROVE Organic Carbon (OC) measur ements are multiplied by 1.4 to obtain Organic Matter (OM) to adjust the OC mass for other elements assumed to be

associated with OC.

• Elemental Carbon (EC), which is also referre d to as Light Absorbing Carbon (LAC) is determined by TOR analysis and is the su m of EC fractions minus the pyrolized

fraction.

• Soil is determined as a sum of the ma ss of those elements (measured by PIXE) predominately associated with soil, whos e mass is adjusted for oxygen in the common compounds.

• MT and MF are total PM 10 and PM 2.5 mass, respectively.

Associated with each PM species is an extincti on coefficient that converts concentrations (in

 g/m 3 ) to light extinction (in inverse mega meters, Mm -1 ). Sulfate and nitrate are hygroscopic so relative humidity adjustment factors [f(RH)] are us ed to modify the extinction coefficients that increase the particle’s extinction efficiency with increa sing RH to account for the particles taking on water and having higher light scattering properties. Note that some Organic Matter (OM) compounds may also have hygroscopic properti es, but the IMPROVE reconstructed mass extinction equations assume OM is non-hygroscopic.

B Sulfate = 3 x f(RH) x [SO 4 ]

b Nitrate = 3 x f(RH) x [NO 3 ]

b EC = 10 x [EC]

b OM = 4 x [OM]

b Soil = 1 x [Soil]

b CM = 0.6 x [CM]

Monthly average f(RH) factors are used as recommended in EPA’s guidance (EPA, 2003a). These values have been recently updated (SAIC, 2003) and are available at: ftp://ftp.saic.com/raleigh/Regiona lHaze_2002FRHcurve/fRH_analysis/

.

The total light extinction (b ext ) is assumed to be the sum of the light extinction due to the six PM species listed above plus Rayleigh (blue sky) background (b Ray ) that is assumed to be 10

Mm -1

.

B ext = b Ray + b Sulfate + b Nitrate + b EC +b OM + b Soil + b CM

The total light extinction (b ext ) in Mm -1 is related to visual range (VR) in km using the following relationship:

VR = 3912 / b ext

The Regional Haze Rule requires that visibility be expressed in terms of deciview (dv) that uses natural logarithms of the extinction as follows:

dv = 10 ln(b ext /10)May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-6 The equations above using data from the associated IMPROVE mo nitor are used to estimate the daily average visibility at each Class I area. For each year from the 2000-2004 baseline these daily average visi bility values, in terms of decivi ew (dv), are then ranked from highest to lowest. The 20% worst days visibility for each given year is given as the average visibility across the 20% highest visibility days in deciview. Similarly, the 20% best days are given as the average visibility across the 20% lo west visibility days in terms of deciview. The Baseline Conditions is the average of the 20% be st and 20% worst days across the five-years of

2000-2004. The Current Conditions is the average of the 20% best and 20% worst days based on the latest five-years of available data, which would be 1999-2003 for the VISTAS Phase II analysis being conducted in late 2004.

8.2.1.2 Mapping of Modeling Results to the IMPROVE Measurements

As noted above, to project future-year visibil ity at Class I areas the modeling results are

used in a relative sense to scale current obser ved visibility for the 20% best and 20% worst visibility days using RRFs that ar e the ratio of modeling results for the future-year to current-year. This scaling is done separately for each of the six components of light extinction in the IMPROVE reconstructed mass extinction equa tions. The modeled species do not necessarily exactly match up with the IMPROVE reconstructed mass species, thus assumptions must be made to map the modeled species to the IMPROVE reconstructe d mass species for the purpose of projecting visibility improvements. For example, the models explicitly simulates ammonium and sulfate may or may not be fully neutralized in the model, whereas the IMPROVE rec onstructed mass equations assume sulfate is fully neutralized by ammonium. For the CMAQ Version 4.3 model that was used in VISTAS Phase I modeling, the mapping of modeled species to IMPROVE reconstructed mass (RCM) species listed in Table 8-2 is propose d to be used in the VISTAS Phase II modeling.

Table 8-2. Proposed Mapping of CMAQ V4.3 Modeled Species Concentrations to IMPROVE Reconstructed Mass (RCM) Species.

IMPROVE RCM CMAQ V4.3 Species

SO 4 1.375 x (ASO4J + ASO4I)

NO 3 1.29 x (ANO3J + ANO3I)

EC AECJ + AECI

OM AORGAJ + AORGAI + AORGPAJ + AORGPAI + AORGBJ + AORGBI

Soil A25J + A25I

CM ACORS + ASEAS + ASOIL

If a different model is used (e.g., CAMx or CMAQ-AIM) or updates to CMAQ change its species definitions, then the speci es mappings would have to be defined specific for that model. Note that in the above species mapping it is assu med that all of the CMAQ estimated sulfate and particulate nitrate are in the fi ne mode. If a fully sectional model was used (e.g., CMAQ-AIM or CAMx), then a decision would have to be made whether to map the coarse mode sulfate and

nitrate to the IMPROVE fine SO 4 and NO 3 species (as implicitly assumed for CMAQ) using mappings in Table 8-2) or to include it in the IMPROVE CM species. In fact this is also an issue for the CMAQ V4.3 modal approach in which th e lognormal size distribution for sulfate and nitrate in the accumulation mode (i.e., ASO4J and ANO3J) may have some mass greater than 2.5

: m, however the current usual convention for CMAQ is to assume all the sulfate and nitrate are in

the fine mode.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-7 8.2.1.3 Using Modeling Results to Project Changes in Visibility

Modeling results are used in a relative fashion to project future-year visibility using relative reduction factors (RRFs). RRFs are expressed of the ratio of the modeling results for the futureyear to the results of the base year and are Class I area and PM species specific. RRFs are applied to the Baseline Conditions observed PM species to project future-year PM levels from which visibility can be assessed usi ng the IMPROVE RCM extinction equations listed above. EPA has identified the following six steps to project futu re-year visibility for the 20% best and 20% worst days (EPA, 2001;2003a):

1. For each Class I area visibility is ranked using IMPROVE reconstructed PM mass (RCM) extinction equation for each year that compri ses the five-year Baseline Conditions (i.e., 2000-2004).

2. Calculate the arithmetic average of the 20% be st and 20% worst visibility days, in deciview (dv), for each year of the five-year baseline period and then calculate the five-year average of the 20% best and 20% worst days. Document which days during th e five-year baseline period comprise the 20% best and 20% worst observed visibility days.

3. Use an air quality model to simulate th e baseline period emissions and future-year emissions. Extract 24-hour average PM species concentrations “near” each Class I area. Calculate the average PM species concentratio n estimates in the current and future-year

simulation across the 20% best and 20% worst obs erved visibility days for each year in the five-year baseline period. Average across the five-years the average PM species

concentrations for the 20% best and 20% wors t days and for each year in the five-year baseline period. Calculate Cla ss I area and best/worst 20% da ys RRFs for each of the six PM components as the ratio of the five-year average estimated PM species concentration in the future-year to current year.

4. Multiply the 20% best/worst days and PM species dependent RRFs by the observed 24- hour PM species concentrations for each day from the 20% best/wor st days to obtain future-year daily average PM species concentr ations for each day from the 20% best and 20% worst visibility days from the five-year baseline period.

5. Using the future-year estimated 24-hour PM species concentrations for the best/worst 20% observed days, calculate extinction using the IMPROVE reconstructed mass extinction equation and daily deciview, perf orm annual averaging of the visibility (dv) estimates for the 20% best/worst days for each year and then obtain the five-year average visibility (dv) for the future-year.

When selecting model estimated PM species c oncentrations “near” the monitor, EPA (2001) recommends taking a spatial average of PM concen trations across a grid cell resolution dependent NX by NY array of cells centered on the grid contai ning the monitor. For the VISTAS Phase II 36 km modeling, just the model estimates for the grid cell containing the monitor will be used (i.e., NX=NY=1). For the VISTAS Ph ase II 12 km modeling, EPA recommends that NX=NY=3, that is the model estimated PM species concentrations are averaged across 9 grid cells centered on the IMPROVE monitor (EPA, 2001).May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-8 8.2.1.4. Exclusion of Days from 20% Best/Worst Conditions

As discussed in Chapter 7, the model perfor mance evaluation will pay particular close attention to the model’s PM performance at ea ch Class I area for the 20% Best and 20% Worst days. If the model performance is extremely poor fo r one or more of these days at a Class I area, it may be desirable to exclude that day when calcu lating the RRFs for visibility projections. For example, if a day is one of th e Worst 20% days at a Class I area and the model estimates clean background concentrations then it would be inappropriate to use the modeling results, even in a relative fashion through the RRFs, to project future-year visibi lity. The modeling Team would identify such suspect days and work with the VI STAS TAWG on whether theyshould be excluded from the visibility projecti ons on a case-by-case basis.

8.3 VISTAS Phase II Future-Year Modeling

The VISTAS Phase II future-year modeling will use the 2002 MM5 meteorological conditions. That is, we will assume that the me teorological conditions for the future-year are the same as for 2002. This will allow for the comparison of the changes in visi bility at Class I areas from the current (2002) to future-y ear due to changes in emissions. This means that the effects of climate change and climatic variations will not be accounted for. Several other decisions concerning the future-year to be modeled, model(s) to be used and modifications to the model inputs to reflect future years do need to be made such as:

8.3.1 Future-Year to be Modeled

Visibility projects are needed for the 20% be st and 20% worst days from the five-year baseline period of 2000-2004 to 2018, which has been interpreted as the five-year future-year periods of 2013-2017 or 2014-2018. The EPA guidance documents are inconsistent on what future-year should be used for the assessment. The 2001 modeling guidance says that the mid-year from the 2013-2017 five-year proj ection period should be used, wh ich would be 2015 (EPA, 2001, pg. 221). The 2003 guidance for tracking progress doesn ’t discuss the future-year that should be modeled, but does mention that go als should be compared across 5- year planning periods “(e.g., 2000-2004 to 2013-2017) and progress should be measur ed as improvement from 2004 to “2018” (EPA, 2003, pg. 1-6). VISTAS and other RPOs will be looking to EPA for providing more definitive guidance as to which future-year should be used for modeling visibility progress and this Modeling Protocol will be updated when such inform ation is available. Thus, in the discussion below we just refer to the “future-year” with th e actual year to be determined during 2004. There may also be some benefits to projecting visibili ty improvements for an interim future-year between 2002 and 2018 to help track progress to the 2018 visibility targets.

8.3.2 Future-Year Emissions

The future-year emissions will be provided to the emissions and air quality modeling team (ENVIRON/Alpine/UCR) by the VIST AS emissions projection contr actor (MacTec). They would be processed into the gridded speciated h ourly three-dimensional emissions inputs for photochemical grid modeling using the SMOKE emi ssions model and the proc edures discussed in Section 5.2. The same biogenic emissions as used in the 2002 Base Case modeling will be used for the future-year modeling. This assumes that the same land use and biomass distribution as

used in the 2002 Base Case emissions would exis t in the future-year emission scenarios. TheMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-9 effects of urban sprawl, increased agricultural, defo restation, locations of new roads, etc. between the current (2002) and future-year would not be accounted for. The same Typical Year fire emissions that were used in the 2002 Typical Year emissions scenario would be used in the futureyear. Future-year Typical Year emissions for Electrical Generating Units (EGUs) would also be used for the future-year scenarios, however for EGUs these would not be identical to the 2002 Typical Year EGU emissions.

8.3.3 Future-Year Initial and Boundary Conditions

The same Initial Conditions (ICs) as used in the 2002 Base Case would be used in the future-year modeling. Because a ~15 day spin up period is being used, ICs will have minimal if any influence on the model estimated concentrations.

As discussed in Section 5.3, the source of the boundary conditions (BCs) to be used along the lateral edges of the continental US nationa l RPO 36 km modeling domain for current (2002) and future-year modeling is being studied by VI STAS. The VISTAS Phase I modeling analyzed several different types of boundary conditions, including the CMAQ default values and seasonal, monthly and 3-hour average values estimate d by the GEOS-CHEM globa l model for a 2001

annual simulation.

The effects of using time varying (3- hourly) BCs produced by the 2001 GEOS-CHEM global model simulation versus the seasonal GE O-CHEM BCs were analyzed using the CMAQ model and the VISTAS Phase I July 2001 episode. These results support the notion that a 2002

GEOS-CHEM simulation would be beneficial to support regional air quality modeling of the 2002 calendar year. Accordingly, VISTAS is pur suing performing a 2002 GEOS-CHEM simulation,

whose results would be used to define BCs for the CMAQ revised basecase simulation in the fall

of 2004.

Initially, BCs based on seasonal average re sults from the GEOS-CHEM 2001 simulation will be used for the VISTAS Phase II CMAQ modeli ng of the initial 2002 base case. If a 2002 GEOS-CHEM simulation is ready in time, then the Phase II modeling 2002 BCs would be updated

with that data. If a 2002 GEOS-CHEM simulati on is performed, there is the possibility that GEOS-CHEM would also be run for the future-year emissions scenario in which case the futureyear CMAQ BCs would be based on the future-year GEOS-CHEM simulation. In the absence of a future-year emissions scenario GEOS-CHEM si mulation, we recommend that the same CMAQ BCs be used in the future-year as used in the 20 02 base year as any adjust ments to them would be arbitrary and speculative.

8.3.4 Other Future-Year Modeling Inputs

All other future-year CMAQ m odeling inputs would be identic al to the 2002 Base Case simulation including meteorology, photolysis rates, geophysical, and other inputs.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-10 8.4 Presentation of Results

There are various ways that the future-year m odeling results can be presented to display the visibility projections and convey the key fi ndings. Although the proj ection of visibility improvements at the Class I areas is a key elemen t of the Regional Haze Rule (RHR), analyzing the results solely at the Class I areas is a very narrow view of them, thus we intend to present the future-year modeling results in other forms to convey a more complete picture.

8.4.1 Projection of Visibility Improvements at Class I Areas

The results of the visibility projections at each Class I area in and around the VISTAS domain would be displayed graphically as well as in tabular summaries. Spread sheets of the results would be generated ordered by state and pr ovided to VISTAS. The vi sibility projections at each Class I area would be compared with the 2 018 visibility progress goal calculated following EPA’s “Guidance for Tracking Progress Unde r the Regional Haze Rule (EPA, 2003a).

Figure 8-1 displays a sample plot of a visi bility progress goal prepared for the Grand Canyon National Park as part of the WRAP Section 309 SIP work effort. The modeled projected visibility could be plot ted on the same figure as a graphical way of presenting the results. Shown as a straight line near the botto m of Figure 8-1 is the EPA defaul t “natural conditions” for the 20% worst visibility days as the green line with green triangles that represents the visibility goal at Grand Canyon of 6.97 dv in 2064. The black diamonds on the left side of the plot are the “current

conditions” that are based on IMPROVE observations for the 20% worst days for five-year periods ending in 1993 (i.e., 1989-1993) to 2001 (i.e., 1997-2001). The “curr ent conditions” for the latest five years of data, which is 1997-2001 in this gr aph (the Section 309 SIP analysis was performed

in 2003 before the 2002 IMPROVE data were availa ble), is assumed as the “baseline conditions” (i.e., 2000-2004) that is the star ting point in 2004 (12.00 dv) for th e glide path or linear uniform rate of progress to natural cond itions in 2064 (purple line with s quares). In this example, the visibility progress goal in 2018 would be 10.83 dv so that a 1.17 dv reduction in visibility is needed by 2018 to meet the visibility progress target in 2018 at the Grand Canyon National Park (GMNP). Note that we would exp ect higher observed visibility le vels for the 20% worst days at Class I areas in VISTAS due to higher density population and higher sulfate concentrations in the eastern US, but Figure 8-1 illustrates the types of gra phs that can be utilized to display visibility progress goals combined with vi sibility progress projections.

Tabular summaries could list for each Class I area, in terms of deciview, the observed Baseline Conditions, the 2018 Progress Goal, the difference (i.e., visi bility progress target) and then the modeled difference for each future-year emissions scenario. Such a table would clearly state which future-year emission scenarios met the RHR 2018 Progress Go als at which Class I

areas.

Another useful display of the projected visibility improvement s are stacked bar charts of extinction showing the contributio ns of each of the six major PM components to light extinction for the IMPROVE measured current period to th e future-year period and the 20% best and 20% worst days. These side–by-side current to futu re-year extinction compar isons can graphically display which extinction components are getting better and which are getting worse. They can also be used to identify why the emission cont rols may not have had the desired effect. ForMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-11 example, an SO 2 emissions control strategy may have had the desired effect in reducing sulfate concentrations but the visibility estimates re main relatively unchanged as the lower sulfate concentrations frees up ammonia that then forms pa rticulate nitrate that compensates for the lower sulfate in the visibility equations. Such a chem ical shift in the extinction budget would be readily apparent in the stacked bar charts. The most useful form s to convey the modeling results will evolve over the project.

Figure 8-1. Example Calculation Of Visibility Pr ogress Goals Using IMPROVE Data Collected At The Grand Canyon National Park For The 20% Worst Days S howing EPA Default “Natural Conditions” (Green Triangles) For The 20% Worst Days That Is The 2064 Goal (6.97 dv),

“Current Conditions” Observed Vi sibility (dv) As Black Triangl es Whose Latest Value (12.00 dv) Is Assumed As The Starting Point For The 2000- 2004 Baseline (First Purple Square), And The

Glide Path From 2004 Baseline To “Natural Cond itions” In 2064 Assuming Linear Uniform Rate Of Progress. 

### Uniform Rate of Reasona ble Progress Glide Path Grand Canyon NP - 20% Worst Days

12.00 11.66 10.83 9.99 9.15 8.31 7.47 6.97

0 5 10 15 20 25 30

1993 1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063

YearHaziness Index Unit:Deciviews

Glide Path Natural Visibility Conditions ObservationMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 8.doc 8-12 8.4.2 Spatial Maps of Results

Spatial maps of changes in individual species, total PM mass, extinction and deciview from the 2002 Typical Year to future-year simulations w ould be used to provide a big picture view of the changes in regional air quality and visibility from 2002 to th e future–year. The display of hourly and 24-hour animations as well as longer-term averages (e.g., monthly, seasonal and annual) may provide more insight into the modeling results.

8.4.3 Time Series Plots

Time series plots of concentrations extinction and deciview at key receptor locations, such as Class I areas, may also provide additional insight into the modeling results. Hourly time series for selected periods and locations may provide insi ght into short-term changes, whereas changes in 24-hour concentrations for the year provides inform ation for all days, not just the ~40 days that constitute the 20% best/worst days of the year.

8.4.4 Box Plots and Q-Q Plots

Box and whisker plots of the projected extinctio n and deciview at Class I areas as well as absolute modeled extinction and deciview at all sites can be used to see changes in visibility across all concentrations, not just the 20% best/worst da ys. Quantile-Quantile plots of the changes in frequency distribution projected using RRFs and absolute results from the model would also provide an indication of the change s in visibility from the curren t to future-year across all days.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-1 9.0 DATA MANAGEMENT

Data management and data security procedur es are critical components of the VISTAS regional fine particulate and haze modeling. Very la rge data files are used in each component of the modeling process, including processing of the meteorology data, emissions processing, and visibility modeling with CMAQ. An annual simu lation on the VISTAS 36-km domain requires approximately 2 Terabytes (Tb) of disk storag e while an annual simulation on the VISTAS 12- km domain requires over 3 Tb of disk storag e. This chapter describes data management practices.

For all critical files we w ill maintain backup copies eith er on tapes or redundant disk systems. In addition, because model simulations will be performed separately by ENVIRON, UCR and AG, each institution will maintain its own copy and backup of critical input and output files. Because there are differences in system configurations at each of the 3 modeling centers (ENVIRON, UCR, and Alpine Geophysics) the data backup and archiving are discussed separately for each center, below.

CMAQ generates large output files of which most information is rarely used (for example, model output for layers other than the surface layer). Under Phase I, we extracted layer 1 data for analysis and then deleted the 3- dimensional CMAQ instantaneous concentration CONC output files. However, we permanently sa ved the script, executables and input data so that 3-D files can be regenerated if neede d. During Phase I a ne w version of CMAQ was released that outputs hourly average layer 1 c oncentrations for selected species (ACONC) in

addition to the 3-D instantaneous concentration CONC files. For Phase II we propose to output and archive all species on the layer 1 ACONC file and extract and archive 3-D CONC files just for the last hour of each month so that monthly se nsitivity tests could be performed. In the event the Optional Task 14 is funded to evaluate CMAQ estimates alof t, we will save the 3-D CONC files for periods with aircraft flights. We do not pl an redundant archiving for model output files (including output from MCIP and SMOKE) because these files can be regenerated by repeating model simulations, and this is approach more ef ficient and more cost effective than redundant archiving.

To promote efficient, reliable communicat ion among project participants, the modeling

team has created 4 different listservs to aid in dissemination of information and as a primary means for distributing emissions and air quality modeling information to the VISTAS TAWG.

The listservs are:

• vistas-all@cert.ucr.edu: ge neral project information fo r all interested persons.

• vistas-modeling@cert.ucr.edu: private list for the VISTAS project management team and modeling contractors.

• vistas-emissions@cert.ucr.edu: list for sharing information on emissions processing and QA.

• vistas-met@cert.ucr.edu: list for shari ng information on meteorology modeling and processing of MM5 data using MCIP.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-2 These separate listservs are aimed at provi ding better organization of communications and allowing for detailed discussions of specific topics such as emissions QA.

9.1 Project Website

For the VISTAS Phase I modeling effort, a pr oject webpage at UCR has been used to publish and disseminate all project results. The webpage address is: www.cert.ucr.edu/vistas and it includes sections on model input data, emissions QA, visibility modeling results, and project reports and presentations. Because the Phase I we bpage already contains results for a very large number of model simulations, we propose to establish a new, parallel webpage for Phase II using the address www.cert.ucr.edu/aqm/vistas2 that will be identical in design to the Phase I webpage. The advantages of establishi ng a new webpage include the following:

• Separating Phase I and Phase II results will avoid confusion in presenting or interpreting.

Results.

• The Phase II results will be simpler and more accessible.

• All Phase I results will continue to be ava ilable at the previously published web page

addresses.

As in Phase I, the project webpage will be th e primary mechanism for communicating results of model simulations and analysis to funding agencies and other interested parties.

9.2 Data Transfer

Data transfer among the 3 modeling centers an d between other VISTAS participants or contractors will be accomplished using a combinati on of email, ftp downloads and portable disk drives depending on the size of the data transfer. For data files smaller than a few MB email typically works well and is most efficient. For da ta files of less than ab out 500 MB file transfer protocol (ftp) is typically th e fastest and most efficient method. ENVIRON, UCR and AG each maintain webpages and ftp pages that can be us ed for exchanging data . In addition, each modeling center has severa l portable disk drives with both USB2 and firewire interfaces that can be FedEx among project participants to exchange large data sets. Portable disk drives range in size from 80 to 300 GB and are adequate for al l large files data transfers. The approach

described here has been used throughout the VISTAS Phase I project and has proven to be

economical and efficient.

9.3 Data Backup and Archiving

Data backup and archiving will be performed at each of the modeling centers. Copies of critical project data will be maintained at each modeling center to provide redundant backup of key project data. In addition, ea ch modeling center will perform backups of key project data to tape or redundant disk storage systems. Data storage and back up resources at each modeling

center are described next.

ENVIRON

Over 10,000 Gigabytes (>10 Terabytes, Tb) of disk storage are available to the UNIX/Linux workstations. All of the workstations are networked togeth er and are accessibleMay 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 9.doc 9-3 from each employee’s desktop PC. All workstat ions have CD-ROM drives and can access DLT, 4mm DAT and 8mm Exabyte tape drives for data backup and data transfer . ENVIRON can also create CDs (CD-R and CD-RW) and DVDs (DVD+ a nd DVD-) for data bac kup and distribution. For Phase II modeling, most CMAQ simulations would be performed on a new 9 node Beowulf

Linux Cluster that included one ma ster node and 8 processing nodes. Each node consists of two AMD Athlon 2600+ processors. The master node ha s 2 Gb of memory and is connected to a 2.8 Tb RAID disk system. Each secondary pr ocessing node includes 1 Gb of memory. The ENVIRON Novato computing ce nter also includes approxima tely 10 dual processor Linux workstations with processing sp eeds of 1700+ to 3000+. Three olde r Unix workstations are also available, SUN, DEC and SGI. The Linux computer systems are located in their own room with

their own dedicated air conditioni ng (AC) system. The room in cludes a temperature sensitive power shut off device that will shut off the power to all comput ers in case the AC breaks down so that catastrophic failure due to too high temp eratures does not occur. Backups are made on

IDE disk drives that are removed from the com puter and stored on a sh elf to protect against power surges destroying the backup data.

UC Riverside

Data storage systems at UCR include more than 22 TB of disk space configured as RAID5 disk systems. All computers and disk systems are connected using high speed Gigabit

Ethernet for efficient simulati on and analysis of large datase ts. To provide maximum data security the systems are located behind the UCR firewall and an additional firewall internally within the laboratory. A separate system is used for the project websites and ftp site to allow project data to be accessed thought the UCR T1 internet connection. The data backup/archiving system include 8mm tape drives and DLT and S uper DLT auto loading car tridge system capable of performing unattended archive/ backups of over 1 Tb (uncompressed). Key disk systems have hot-swappable hard drives with stand-by spare drives and redundant power supplies. Copies of critical project data are backed up to tape a nd to a redundant RAID5 disk system. The compute clusters and disk systems are located in a locke d, secure room with a dedicated climate control system and with backup air c onditioning. The laboratory has a full time systems administrator to perform system backups, maintenance and upda tes and Dr. Tonnesen’s group includes a second full time systems administrator who performs we ekly backups of critical data to tape.

Alpine Geophysics

Alpine Geophysics' computing facilities c onsist of SUN Micros ystems SPARCstation computers and a very powerful array of 19 mu ltiprocessor Linux-based workstations. Disk storage systems include 12 Tb of aggregate disk space and over 8 Tb of SCSI and IDE Raid-5 protected space. All client data is stored on at least one RAID-5 protected di sk array. To further protect client data, two of our main servers b ackup their disk drives on a weekly basis to a second server that is physically disconnected from the power supply when not doing an active transfer. This way a catastr ophic power failure will not comp romise the ability for Alpine Geophysics to deliver.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 10.doc 10-1 10.0 DOCUMENTATION

This section describes the documentation that be provided during VISTAS Phase II and the potential for modifications to this Modeling Protoc ol and QA Plan that might become necessary as this phase of the study unfolds.

10.1 Planned Documentation

Documentation associated with the emissi ons and air quality modeling performed during Phase II will include all relevant input data base s and scripts associated with the pre- and postprocessing associated with model input developm ent, model application, sensitivity and diagnostic analyses, and performance evaluations. Interim re ports describing the methodologies and results of the model performance evaluation, model interc omparison, and visibility assessment will be provided. As shown in Appendix A, the interim reports for many of the subtasks will consist of PowerPoint presentations and/or results posted to the project website. For the major tasks, however, we do intend to document the work activities in more formal reports. Such planned reports for the

base effort include:

• Phase II Modeling Protocol (this document)

• Phase II Quality Assurance Project Plan (QAPP)

• 2002 emissions modeling draft and final reports

• Initial 2002 CMAQ modeling summary report

• Revised 2002 CMAQ modeling summary report

• Future-year visibility projections modeling report

• Draft and final Executive Summary report

• Optional Journal Article(s)

In addition, copies of all scri pts, reports and documentation provided to the VISTAS TAWG will be maintained on the project website. A final report summarizing all aspects of the project will also be provided. The list of deliverables for this project is described in a summary of the Phase II scope of work provided in Appendix A.

Reporting on each task in Phase II will consist of documentation of the

 data sources,

 methods,

 results, and findings. Individual task deliverab les shall reflect any changes and revisions

that occur over duration of Phase II. At the completion of Phase II, a draft final Executive Summary report will be prepared that details , documents and summarizes the results. This

documentation will conform to the recommendations set forth in EPA’s "Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze (Jan ‘01)” and any subsequent versions. The final Execu tive Summary report will contain: (a)

 an executive summary abstract that provides a brief overview and summa ry of the modeling effort, emissions and air quality models used, model configuration, model performance evaluation overview and results, and rationale for the selected configuration, (b) technical detail covering all relevant aspects of the Phase II emissions and PM grid modeling, and (c) a di scussion on data accessibility and availability for review by the public. The report will be provi ded in electronic form, (e.g., Word 2000 and pdf formats) and shall be submitted to the VISTAS Pr oject Officer and also posted on the project web

site.May 2004

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\Chapter 10.doc 10-2 10.2 Procedures for Updating Modeling Protocol and QA Plan

One of the underlying realizations stemming fr om the Phase I activities was the awareness that the science of ‘One-Atmosphe re’ PM/regional haze modeling is advancing very rapidly. Part of this stems from the parallel activities being carried out by the RPOs; some if it is due to other ongoing 8-hr ozone and PM modeling studies being performed by various states. In addition, EPA is in the process of revising its PM 2.5 and regional haze guidance documents. Collectively, it is quite likely that there will be new opportunities to strengthen the modeling algorithms, input data sets, and evaluation procedures throughout the duration of Phase II. Moreover, the Phase II emissions and air quality modeling activities involve the collaboration with the TAWG as well as other VISTAS contractors who will provide the required emissi ons and meteorological inputs to the SMOKE and CMAQ models. Given the ongoing model refinement activities and the need for strong coordination with other VISTAS contractors, it may be necessa ry to modify certain aspects of this modeling protocol. In this event, modification will be ma de in consultation with the VISTAS TAWG and the revised protocol will be submitted to the workgroup for approval.May 2003

G:\VISTAS Modeling\Task6 Modeling Protocol\Draft#3\References.doc R-1 REFERENCES

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### Appendix A

Summary of VISTAS Phase II Emissions and Air Quality Modeling Scope of Work (SOW) (Updated May 6, 2004)VISTAS Phase II 2004 Atmospheric Modeling Scope of Work ENVIRON/Alpine/UCR Draft Version#5: May 6, 2004

INTRODUCTION

ENVIRON International Corporation, Alpine Geophy sics (Alpine) and the University of California at Riverside (UCR) propose to perform the VISTAS Phase II emissions and air quality modeling as described in the Scope of Work (SOW) given below. As was seen in the VISTAS Phase I modeling, the SOW will likely be refined and modified during the course of the project and the ENVIRON/Alpine/UCR team is flexible in achieving the ultimate objectives and requirements needed by VISTAS. In putting together the VISTAS Phase II SOW, the ENVIRON/Alpine/UCR team is dependent on receiving data and information from other participants. The following are our assumed dates for deliverables that we expect to receive key data sources in developing
our SOW.

• January 31, 2004 – Initial 2002 baseline emissions for VISTAS states delivered by VISTAS contractors. (Achieved February 9, 2004.)

• January 31, 2004 – First 8 months of 2002 MM5 modeling results delivered by VISTAS contractors. (Achieved February 20, 2004)

• March 30, 2004 – Last 4 months of 2002 MM5 modeling results.

• April 15, 2004 -- Initial 2018 Future-Year VISTASA States emissions for one scenario and 2 of the Phase I episodes (Jan ’02 and Jul ’01) from VISTAS contractor. Second 2018 scenario available April 30, 2004.

• September 15, 2004 – Revised 2002 baseline emissions for VISTAS states.

• September 15, 2004 – Revised 2002 base year using typical year EGU and fire inventory for VISTAS states.

• September 15, 2004 – 2002 base year inventory and typical year EGU and fire inventory for non-VISTAS states, Mexico, Canada as available from non-VISTAS sources

• October 15, 2004 – Revised Future-Year emissions for VISTAS states, three future year scenarios and the 2002 annual period.

• October 31, 2004 – MM5 modeling results for ~60 episode days from 2003 (or other years as appropriate) for optional episode modeling task.

Key deliverables for the modeling team are as follows:

• Draft modeling protocol by March 5, 2004, final protocol 2 weeks after VISTAS review

comments received.

• Analysis of GEOS-CHEM 2001 hourly and monthly outputs for CMAQ initial and boundary conditions (IC/BC) and recommendation whether to use 2002 GEOS-CHEMfor IC/BC in CMAQ revised 2002 modeling in fall 2004 – due March 2004.

• 2002 initial inventory for Non-VISTAS States, Mexico, Canada by March 15, 2004.

• 2018 initial emissions estimates for Non-VISTAS States, Canada, Mexico and 2 Phase I episodes (Jan 2002 and Jul 2001) by April 15, 2004.

• 2018 CMAQ-ready emissions from SMOKE fo r 2 Phase I episodes (July 2001 and Jan 2002) for one scenario by May 15, 2004 and the other scenario by June 15, 2004.

• 2002 CMAQ-ready emissions from SMOKE for 2002 annual period by May 31, 2004

• Quality Assurance Project Plan (QAPP) for Phase II modeling (June 2004)

• CMAQ model performance evaluation for annual 2002 runs using initial 2002 inventory by August 30, 2004

• Collection of available 2002 revised inventory and typical year EGU and fire for nonVISTAS states, Canada, Mexico by September 15, 2004

• Future year revised inventory for non-VISTAS states, Canada, Mexico by October 15,
2004 for 3 scenarios

• 2002 revised CMAQ-ready SMOKE outputs usi ng revised 2002 inventories by October 15, 2004

• 2002 revised plus typical EGU and fire CM AQ-ready SMOKE outputs by October 31,

2004

• 2002 revised CMAQ annual run and model per formance evaluation by December 1,

2004

• 2002 revised plus typical EGU and fire CMAQ annual run and model performance evaluation by December 31, 2004

• Future year CMAQ-ready SMOKE outputs using revised inventories for first scenario by November 15, 2004, for second and third by December 15, 2004

• Future year revised emissions CMAQ annual modeling runs for 3 scenarios due by January 31, 2005.

• Optional task CMAQ-ready SMOKE outputs for 2003 episodes after January 1, 2005

Table 1 summarizes the task structure, task leader, schedule and deliverables for the VISTAS Phase II proposed SOW. Figure 1 displays the current proposed schedule for the VISTAS Phase II modeling based on the above assumptions on data deliverables to the Modeling Team. This schedule will likely be revised and updated as more refined delivery dates are developed.Figure 1. Current proposed schedule for the VISTAS Phase II modeling (May 6, 2004).Table 1.Summary of proposed VISTAS Phase II Tasks, Task Managers, Deliverables and Costs (shaded tasks are currently not funded, May6,2004).Work ElementManagerDeliverables (Date)Task 1: Management Ralph Morris•Monthly Conference Calls (2004)•3 Meetings with 2 People in Southeast US (2004)•First Draft Modeling Protocol (03/05/04)•Second Draft Modeling Protocol (03/30/04)•Updated Modeling Protocol (as needed)•QAPP (June 2004)•Draft Final Executive Summary Report (01/15/05)•Final Executive Summary Report (01/31/05)Task 2: IC/BCs Analysis Gail Tonnesen•Initial analysis of the need for 3-hr vs. seasonal BCs for CMAQmodeling (02/12/04).•2002 IC/BC recommendations PowerPoint presentation, whichincludes monthly average vs. 3-hr BC CMAQ comparisons forJuly 2001 episode (March 2004)Subtask3a: Air QualityGatekeeperGail Tonnesen•2002 Air Quality MPE database, QA/QC and PowerPointpresentation (04/30/04)•2002 initial CMAQ IC/BC inputs (04/30/04)•Review of 2002 special study (e.g., aircraft data) dataavailability that would be used in Optional Task 14 Enhancedmodel evaluation (05/30/04)Subtask 3b:MeteorologicalGatekeeperDennis McNally•First eight months 2002 MM5 QA/QC Gatekeeper PowerPointpresentation (03/26/04)•CMAQ-ready meteorological inputs for first six months of 2002(03/31/04)•Last four months 2002 MM5 QA/QC Gatekeeper PowerPointpresentation (04/15/04)•CMAQ-ready meteorological inputs for first six months of 2002(04/15/04)Subtask 3c: EmissionsGatekeeperGregory Stella•Recommendation for Mexico and Canadian Emissions(02/29/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointpresentation on initial 2002 emissions (03/31/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointWork ElementManagerDeliverables (Date)presentation on initial future year emissions (5/15/04)•VISTAS States emissions QA/QC Gatekeeper PowerPointpresentation on revised 2002 and future year emissions(10/15/04)•Initial 2002 Base Case emissions inventory for non-VISTASstates (03/15/04)•Revised 2002 emissions inventory for non-VISTAS States(09/15/04)•Initial future-year inventory for non-VISTAS states (05/15/04)•Three future-year emission inventories for non-VISTAS states(09/15/04)Subtask 3d: EmissionsModelingCyndi Loomis•2002 CMAQ-ready emission input using initial emissions for allof 2002 (05/30/04)•Initial 2002 emissions PowerPoint presentation (05/30/04)•2002 CMAQ-ready emission inputs using revised and typicalemissions (10/15/04)•Revised and typical 2002 emissions modeling PowerPointpresentation (10/31/04)•Future-year CMAQ-ready emissions for two scenarios usingrevised emissions (11/15/04 and 12/15/04)•Draft emissions summary report (11/30/04)•Final Phase II emissions modeling report (12/31/04)Subtask 3e: Future-YearEmissions Modeling for 2VISTAS Phase I EpisodesCyndi Loomis•CMAQ-ready initial future-year emission inputs for two VISTASPhase I episodes (05/15/04)•CMAQ-ready future-year emission inputs for second scenarioand two VISTAS Phase I episodes (06/15/04)•Summary PowerPoint presentation on VISTAS Phase I episodefuture-year emissions (06/15/04)Subtask 3f: InterimFuture-Year Modeling for2002Cyndi Loomis•CMAQ-ready future-year emissions for interim future-year and2002 meteorology (12/15/04)•Interim future-year emissions summary PowerPoint presentation(12/30/04)Subtask 4a: Initial 2002CMAQ Modeling and MPET.W. Tesche•PowerPoint presentation of the initial CMAQ model performancefor 2002 annual period (06/30/04)Work ElementManagerDeliverables (Date)•Summary report of CMAQ initial 2002 model performanceevaluation and sensitivity analysis including recommendationsfor “best” configuration (08/30/04)Subtask 4b: Revised 2002CMAQ Modeling and MPERalph Morris•Power Point presentations on the revised CMAQ base casesimulation and sensitivity analysis (11/30/04)•Draft Report of revised CMAQ 2002 Base Case simulation andMPE (12/15/04)•Report on model sensitivity tests (01/15/05)Subtask 4c: Typical year2002 CMAQ ModelingRalph Morris•PowerPoint presentation on 2002 typical year CMAQ modeling(12/31/04)Subtask 4d: Future-YearCMAQ Modeling for TwoScenariosGail Tonnesen•Draft Report on future-year base case modeling for twoscenarios using 2002 and episodic databases (01/31/05)Subtask 4e. InterimFuture-Year CMAQModelingGail Tonnesen•PowerPoint presentation of Interim Future-Year modelingresults (01/31/05)Optional Task 5: 2003Episodes•Starts after completion of future-year emissions modeling (starts~01/01/05)Subtask 5a: EpisodicEmissions Gatekeeperand ModelingCyndi Loomis•Emissions Gatekeeper summary PowerPoint presentation forrevised episodic modeling emissions (2005)•Revised Episodic CMAQ-ready revised 2003 Base Case andTypical Year emission inputs and summary (2005)•Future-year CMAQ-ready episodic inputs for three scenariosand summary report on the episodic emissions modeling (2005)Subtask 5b: EpisodicMeteorologicalGatekeeperDennis McNally•CMAQ MCIP meteorological modeling inputs (2005)•MM5 Gatekeeper evaluation and QA/QC PowerPointpresentation (2005)Subtask 5c: Episodic AQData GatekeeperGail Tonnesen•MPE air quality data database for episodic modeling periods(2005)•AQ data summary presentation for episode periods (2005)•CMAQ IC/BC inputs for “2003” episode periods (2005)Subtask 5d:Episodic Emissions fornon-VISTAS statesGreg Stella•Revised “2003” Base Case, Typical Year and Future-Yearemissions for episodic modeling (2005)Work ElementManagerDeliverables (Date)Subtask 5e. EpisodicBase Case and TypicalYear CMAQ ModelingGail Tonnesen•CMAQ Base Case and MPE and Typical Year modeling reportfor episode periods (2005)Subtask 5f: EpisodicFuture-Year Modeling forThree scenariosRalph Morris•Summary Report on future-year base case modeling for threescenarios using the episodic databases (2005)Subtask 6a: VISTASModeling WebsiteGail Tonnesen•VISTAS modeling website will be maintained and updatedcontinuously during 2004.Subtask 6b: DataTransfer, Storage andRetrievalGail Tonnesen•Performed continuously during 2004oArchive (2004)oData transfer drive (12/31/04)Optional Task 7Evaluate RelativeReduction Responseusing CB-IV vs. SAPRCfor 2 Phase I episodesGail Tonnesen•PowerPoint presentation documenting the emissions reductionsensitivity tests for the January 2002 and July 2001 episodesusing CB4 and SAPRC99 chemistry (06/30/04)Optional Task 8:Emissions SensitivityModeling using Phase IepisodesRalph Morris•PowerPoint presentation and model output files for the emissionreduction sensitivity simulations using winter and summerPhase I episodes (TBD, ~09/30/04)Optional Task 9:Additional Sensitivityusing VISTAS Phase IEpisodesT.W. Tesche•Summary report of additional Phase I sensitivity tests (TBD,09/30/04???)Optional Task 10: JournalPaper on VISTAS Phase IModelingGail Tonnesen•Draft Journal paper (05/31/04)•Draft final Journal paper submitted to Journal; (06/30/04)•Final Manuscript (TBD, 10/31/04???)Optional Task 11:Corroborate Analysisusing Alternative ModelsRalph Morris•Summary presentation of corroborative modeling analysis usingalternative models (01/31/05)Optional Task 12: NaturalConditions ModelingGail Tonnesen•Natural conditions summary presentation (TBD???)Optional Task 13: 2002GEOS-CHEM BCsGail Tonnesen•2002 CMAQ BCs based on 2002 GEOS-CHEM simulation.Work ElementManagerDeliverables (Date)Optional Task 14:Enhanced ModelPerformance EvaluationT.W. Tesche•Summary presentation of the enhanced model performanceevaluation using initial database (08/31/04)•Summary presentation of the enhanced model performanceevaluation using the revised base case results (11/30/04)Optional Task 14:ContingencyRalph Morris•To be determined.