# Appendix D.4a Emissions Modeling Documentation

### Background

The Sparse Matrix Operator Kernel Em issions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, point, fire and biogenic emission sources for photochemical grid models. SMOKE is the fa stest emissions proces sing tool currently available to the air quality modeling communit y. The sparse matrix approach utilized throughout SMOKE permits both rapid and flexib le processing of emissions data. The processing is rapid because SMOKE utilizes a se ries of matrix calculations instead of less efficient algorithms used in previous system s. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separated into i ndependent operations wherever possible. The results from these st eps are merged together at a final stage of processing. Each of these emissions processing steps are detailed below.



### Temporal Allocation

VISTAS 2002, 2009 and 2018 annual emissions m odeling was configured to generate point, area, nonroad mobile, on-road mobile, an d biogenic source emissions. In addition, certain subcategories, such as fires and EGUs were maintained in separate source category files in order to allow maximum fl exibility in producing alternate strategies. With the exception of biogenic and on-road mobile source emissions that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions were processed using the month, day, and hour specific temporal profiles of the SMOKE model. Point and biogenic sources were modeled for each day of the annual period while area and nonroad sources were modeled as a block of Thursday, Friday, Saturday, Sunday, Monday, one per month (total of 60 days modeled).

VISTAS based its temporal profiles and source category cross-reference files on the USEPA CAIR/CAMR/CAVR modeling platform with files located on USEPA’s CAIR file transfer website ( ftp://www.airmodelingftp.com/ ). Modifications were made to reflect State specific profiles or upda ted state of knowledge appli cation of these profiles.

New temporal profiles used by VISTAS during modeling of the 2002, 2009 and 2018 EGU emissions took the place of USEPA provide d default temporal profiles that are generally accepted as not accura tely depicting temporal dist ribution of emissions from EGUs in the U.S. (see Attachment 1 by Stella et. al.). VISTAS EGU temporal profiles were developed using hourly CEM data as reported to USEPA’s Clean Air Market’s Division (CAMD) for the Acid Rain Program.

The work conducted in this process had th e main objective of developing temporal profiles for VISTAS EGUs necessary to a pply in the generati on of SMOKE PTHOUR formatted emissions. Additionally, State- level monthly, day-of-week, and diurnal profiles were developed for application to non-CEM matched units in the VISTAS emissions inventory. These temporal distri butions represent a significant improvement over the USEPA defaults.On-road mobile modeling in SMOKE was done for selected weeks (seven days) of each month - using these days as a “representative week” of the entire month. This selection allows for the representation of day-of-the-week variability in the on-road motor vehicles, and models a representation of the meteorological variability in each month. The modeled weeks were selected from mid-mont h, avoiding inclusion of major holidays. Holidays were modeled as the Sunday of the representative week, while the day after a holiday was modeled as a Monday. VISTAS ex ecuted sensitivity tests to examine this “representative week” methodology versus an everyday on-road mobile modeling method. VISTAS determined that the use of representative week on-road mobile emissions produced ozone and particulate matte r concentrations (and thus regional haze) that were nearly indistinguishable from the “everyday” mobile method. VISTAS determined that the difference in the mode led air quality - resulting from the on-road mobile modeling methods - was insignificant.

On-road mobile emissions are represented by the following weeks per month: January 15-21 February 12-18 March 12-18 April16-22 May 14-20 June 11-17 July 16-22 August 13-19 September 17-23 October 15-21 November 12-18 December 17-23



### Speciation

Speciation is the process of disaggregating i nventory pollutants into individual chemical species components or groups of species. The need for speciation is determined by the inventory purpose. Inventory applications that require detailed speciation include photochemical modeling, air toxi cs inventories, chemical mass balance modeling, and visibility modeling.

Depending on the purpose of a particular emi ssions inventory, the inventory may include TOG, NOx, sulfur oxides (SOx), CO, to tal suspended particulate matter (TSP), particulate matter less than 10 micrometer s in aerodynamic diameter (PM10), or ammonia (NH3). However, modeling inventor ies may require these emissions to be expressed in terms of other pollutants. A dditionally, for some models, NOx emissions may need to be specified as NO and NO2. Also, PM may need to be separated into various fractions, such as PM10 and PM less than 2.5 micrometers in aerodynamic diameter (PM2.5).SMOKE was configured to speciate the emissions estimates according to the requirements of the Carbon Bond Mechanism version four (CBM-IV, CB-IV or CB4). The SMOKE model reformats the emissions estimates for use in CMAQ modeling based on source category code (SCC) and speciati on profile cross-reference files. The speciation profiles and source category cross-references use in VISTAS modeling are based on USEPA’s CAIR/CAVR/CAMR mode ling platform with files located on USEPA’s CAIR file transfer website ( ftp://www.airmodelingftp.com/ ). Minor modifications were made to reflect State sp ecific profiles or upda ted state of knowledge application of these profiles. One major ch ange made in the VI STAS modeling was the modification of coal combustion cross-refe rence from speciation profile “NCOAL” to profile “22001.”



### Spatial Allocation

Because air quality modeling strives to re plicate the actual physical and chemical processes that occur in an i nventory domain, it is important that the physical location of emissions be determined as accurately as pos sible. In an ideal situation, the physical location of all emissions would be known ex actly. In reality, however, the spatial allocation of emissions in a modeling inventor y only approximates th e actual location of emissions.

Gridding surrogates are used to spatially allocate emission sources from a coarse geographic area to finer grid cells used for modeling. There can be hundreds of unique source categories in an emi ssions inventory, which is typi cally developed for counties, states, or other areas. The exact location of most major emission sources is known and their geographic coordinates are usually cont ained in the inventory. These usually are referred to as major point sources and incl ude electric utilities and major industrial facilities. However, other emission sources are estimated for the entire county or other area as an aggregate since the exact locati ons of each source are not included in the modeling inventory. Surrogate s are human activities or la nd use information that are used to represent a more precise location of emission source categor y groups. A gridded surrogate ratio is the ratio of the amount of a surrogate in a modeling grid cell to the total amount of that surrogate in a county. Grid cell emissions are calculated by multiplying the cell’s gridded surrogate ratio by the county emissions.

These surrogates and their associated SCC cro ss-references were or iginally developed by USEPA ( http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html ) and converted to the gridded domain definitions of the VISTAS model requirements.



### Development of Gri dded Surrogate Files

The general process for creating the SMOKE -ready gridded surrogate files from the ArcGIS shape files is as follows:

1. Overlay the grid on the surrogates. Gene rate the grid polygons (36/12km) with the specifications of the VISTAS domain and spat ially overlay (intersect) the grid onto thesurrogate area polygons or points. The re sulting geodatabase contains, for each surrogate, the county FIPS code, the grid co lumn and row number, and the amount (area, miles or count) of the county’s portion of the surrogate in that cell.

2. Extract and convert each geodatabase table to a useful dataset. E ach table contains the gridded area, miles or count in each county for a specifi c surrogate. The variables include FIPS code, column number, row number and area, miles or count.

3. Calculate surrogate ratios. Surrogate ratios are calculated for each surrogate using a series of program files. The programs sum the surrogates for each county and calculate each the ratio by dividing the county cell surro gate value by the total county surrogate value. Combination surrogates where both ar e of the same type (i.e., Heavy and High Tech Industrial are both area) were summed prior to calculating the ratio. Combination surrogates with unlike data (i.e., 3/4 Roadway Miles plus 1/4 Populati on are line and area data) were summed after calculating the ratio s and then normalized. The surrogate crossreference code was also assigned here.

4. Gap-fill surrogates for counties missing data . There will be many instances where inventory emissions exist for a particular county but there is no data, for that county, for the surrogate assigned. For example, a count y with class 1 locomotive emissions may not have data for the class 1 railroad surrogate. In this case we have sele cted to incorporate, within the assigned surrogate, a different sour ce of data (a different surrogate) for that particular county. We incorporate secondary surrogates even if there is no emission source that requires it for that particular county. We denote th is process as “gap-filling.” All surrogates resulting from the gap-fill ing process have ratios for all counties.

For each surrogate, we assign a secondary or tertiary surrogate where needed for gap filling. For the class 1 railroad surrogate example mentioned above, we chose total railroads as the secondary surrogate. The s econdary or tertiary su rrogate chosen would be the same across all countie s and apply to all SCCs that use the particular primary surrogate. We pull in and s ubstitute the secondary surrogate for counties where the primary surrogate is missing. Tertiary surroga tes will then be assigned to those counties that are still w ithout surrogates.

For identified counties having no values for each surrogate, we assign the data based on the appropriate secondary or te rtiary surrogate to these coun ties. A check to see that surrogate ratios for each county sum to a pproximately 1.00 is also performed in our surrogate development. Ratios will not always sum exactly to 1.00 due to rounding. However, SMOKE will normalize surrogates greater than 1.00.

5. Create SMOKE-formatted spatial surrogate file s. The resulting data from the previous steps is then reconfigured into SMOKE-ready format and used in the spatial allocation
process.


### Treatment of Large Fire Plume RiseWildfire, agricultural, and prescribed burn em issions were handled separately from the standard area source input files. We used day specific or monthly estimates of fire emissions from VISTAS, which include burn acreage and biomass loading information for the VISTAS states. Depending on the comp leteness and quality of the data received, VISTAS-specific calculations were made to calculate spatial and te mporal distributions of the fire emissions, rather than relying on st andard distribution prof iles. We calculated vertical distribution of the fire emissions, based on fire size and biomass involvement. SMOKE v2.1 can model fire plume rise when provided with the following variables:

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

For those fires as having the necessary data elem ents to site these files with distinct time and space coordinates, these variables were prepared and included in the modeling files used to process this emission source type.

The WRAP Fire Emissions Joint Foru m Emissions Inventory Report (see http://www.wrapair.org/forums/fejf/doc uments/WRAP_2002_PhII_EI_Report_20050722 .pdf) has documented an approach for calcula ting these plume 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 calculate representative hourly plume profiles. These profiles ar e then used by SMOKE 2.1 to distribute the vertical emissions for the fires. To successfu lly model fires as elevat ed point sources, the data included both the day or days on which the fire occurs, and a spatial identifier of the fire location.



### Quality Assurance

The Quality Assurance (QA) is one of the most important steps in performing an air quality modeling study. Because emissions inventory development is tedious, time consuming and involves complex manipulation of many different types of large data sets, errors are frequently made and, if rigorous QA measures are not in place, these errors may remain undetected.

A number of QA files were prep ared 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 distribution of the emission provides insight into the quality and accuracy of the emissions inputs. Some of the a dditional steps for checking the emissions are summarized in the bulleted list below.

• 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 err oneously located in water cells.

• Spot-check the holiday emissions files to confirm that they are temporally allocated like Sundays.• Producing pie charts emission summaries th at highlight the contribution of each emissions source component (e.g. nonroad mobile).

• Normalizing the emissions by population fo r each state will illu strate where the inventories may be deficient and provide a reality check of the inventories.

• Spot check vertical allocation of point sources using PAVE.

State inventory summaries prepared prior to the emissions processing were compared against SMOKE output report totals generated after each major step of the emissions generation process.

For speciation, a comparison of the inventory stat e totals versus the sa me state totals with the speciation matrix applied was completed.

For checking the vertical allocation of the emissions, reports by source, hour, and layer for randomly selected states in the domain we re created. These repo rts were created for a representative weekday in each of the ep isodes for each of these selected states.

The quantitative QA analyses often reveal signifi cant deficiencies in the input data or the model setup. It may become necessary to tailor these procedures to track down the source of each major problem. As such, we will only outline the basic quantitative QA steps that were performed in an attempt to reveal th e underlying problems with the inventories or processing.

Following are some of the reports that were generated to review the processed emissions:

• State and county totals from inve ntory for each source category

o (example provided in Table 1 below for Area sources)

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

• State and county totals by day after tem poral allocation for each source category for representative days

• State and county totals by model species after chemical speciation for each source category

• State and county model-ready totals (after spatial allo cation, temporal allocation, and chemical speciation) for each source category and for all source categories combined

• If elevated source selection is chosen by user, the report indicating which sources have been selected as elevated and plume-in-grid will be included.

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

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

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

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

• Totals by speciation profile code fo r area, mobile, and point sources

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

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

sources

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

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

sources

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

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

sources

• PAVE plots of gridded inventory pollutants for all pollutants for area, mobile, and point sourcesTable 1. Example of a State to tals report for Area Sources

Stationary area Processed as Area sources Base inventory year 2018 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from Thursday July 18, 2002 at 000000 to Thursday July 18, 2002 at 230000 Annual total data basis in report Date Region State CO NOX VOC NH3 SO2 PM10 PM2_5 PMC [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day]

------------------------------------------------------------------------------------------------------------------------------- ----------------------------------- 07/18/200 2 1000 Alabama 5.21E+01 2.19E+01 3.88E+02 2.16E+02 9.34E+01 3.66E+01 2.52E+01 1.14E+01 07/18/200 2 4000 Arizona 4.34E+02 1.94E+02 3.55E+02 1.22E+02 1.01E+01 5.17E+01 4.84E+01 3.24E+00 07/18/200 2 5000 Arkansas 4.48E+02 6.06E+01 2.14E+02 4.51E+02 7.90E+01 7.08E+01 6.25E+01 8.30E+00 07/18/200 2 6000 California 8.67E+02 3.73E+02 1.48E+03 9.55E+02 2.65E+01 2.71E+02 2.18E+02 5.26E+01 07/18/200 2 8000 Colorado 9.01E+00 1.01E+01 1.83E+02 2.16E+02 2.80E+00 2.39E+00 2.30E+00 9.67E-02 07/18/200 2 9000 Connectic u 6.37E+00 1.05E+01 1.18E+02 1.99E+01 5.39E-01 3.81E+00 3.54E+00 2.69E-01 07/18/200 2 10000 Delaware 6.56E+00 4.74E+00 2.36E+01 6.12E+01 1.48E+00 7.90E+00 2.75E+00 5.15E+00 07/18/200 2 11000 District of C 1.74E+00 1.99E+00 1.33E+01 2.68E-02 2.32E+00 9.36E-01 8.92E-01 4.37E-02 07/18/200 2 12000 Florida 1.14E+02 8.36E+01 1.35E+03 1.08E+02 1.36E+02 3.07E+01 2.56E+01 5.03E+00 07/18/200 2 13000 Georgia 1.63E+02 8.88E+01 8.71E+02 3.11E+02 1.68E+02 6.60E+01 5.05E+01 1.55E+01 07/18/200 2 16000 Idaho 1.06E+02 1.32E+02 7.85E+02 2.00E+02 2.93E+00 9.50E+01 6.70E+01 2.81E+01 07/18/200 2 17000 Illinois 8.25E+01 6.82E+01 5.73E+02 2.14E+02 1.50E+01 2.69E+01 2.40E+01 4.38E+00 07/18/200 2 18000 Indiana 1.27E+02 6.27E+01 5.45E+02 2.69E+02 1.58E+02 1.56E+02 8.07E+01 7.51E+01 07/18/200 2 19000 Iowa 4.81E+01 6.41E+00 3.07E+02 7.24E+02 2.23E+00 1.86E+01 1.65E+01 2.19E+00 07/18/200 2 20000 Kansas 2.26E+03 1.09E+02 3.71E+02 2.32E+02 3.86E+01 2.86E+02 2.19E+02 6.72E+01 07/18/200 2 21000 Kentucky 8.95E+01 1.21E+02 2.70E+02 1.24E+02 1.02E+02 3.00E+01 2.55E+01 4.50E+00 07/18/200 2 22000 Louisiana 1.66E+03 3.31E+02 3.21E+02 2.24E+02 2.51E+02 1.93E+02 1.65E+02 2.81E+01 07/18/200 2 23000 Maine 2.74E+01 7.23E+00 1.21E+02 3.06E+01 1.53E+01 1.19E+01 1.09E+01 1.04E+00 07/18/200 2 24000 Maryland 2.49E+02 2.66E+01 2.06E+02 9.56E+01 2.21E+01 3.43E+01 3.31E+01 1.14E+00 07/18/200 2 25000 Massachu s 9.95E+01 6.14E+01 2.80E+02 3.87E+01 3.46E+01 5.01E+01 4.36E+01 6.46E+00 07/18/200 2 26000 Michigan 4.64E+01 5.89E+01 5.22E+02 1.39E+02 1.06E+02 1.98E+01 8.99E+00 1.08E+01 07/18/200 2 27000 Minnesota 1.80E+02 1.41E+02 4.52E+02 3.66E+02 3.55E+01 4.87E+01 4.19E+01 6.78E+00 07/18/200 2 28000 Mississippi 4.91E+00 3.25E+00 3.41E+02 1.76E+02 7.73E-01 5.19E+00 1.50E+00 3.69E+00 07/18/200 2 29000 Missouri 2.32E+02 7.43E+01 4.40E+02 3.90E+02 1.24E+02 6.70E+01 5.39E+01 1.31E+01 07/18/200 2 30000 Montana 4.60E+01 3.25E+01 1.02E+02 9.16E+01 2.63E+00 8.46E+00 7.97E+00 4.88E-01 07/18/200 2 31000 Nebraska 1.38E+02 4.25E+01 2.07E+02 3.43E+02 2.68E+01 2.20E+01 1.80E+01 4.06E+00 07/18/200 2 32000 Nevada 1.44E+02 1.95E+01 1.35E+02 2.41E+01 8.17E+00 1.77E+01 1.71E+01 6.02E-01 07/18/200 2 33000 New Ham p 3.82E+01 2.61E+01 8.11E+01 7.21E+00 7.62E+00 2.30E+01 2.06E+01 2.40E+00 07/18/200 2 34000 New Jerse y 1.45E+01 2.24E+01 2.70E+02 5.55E+01 8.55E+00 1.94E+01 9.83E+00 9.72E+00 07/18/200 2 35000 New Mexi c 8.55E+01 7.68E+01 1.48E+02 1.37E+02 1.78E+01 1.52E+01 1.37E+01 1.55E+00 07/18/200 2 36000 New York 7.14E+01 1.15E+02 7.02E+02 2.64E+02 2.28E+02 9.92E+01 4.78E+01 5.14E+01 07/18/200 2 37000 North Car o 1.08E+03 1.12E+02 4.50E+02 5.32E+02 1.63E+01 1.97E+01 1.53E+01 4.51E+00 07/18/200 2 38000 North Dak o 1.80E+01 5.18E+01 1.28E+02 1.25E+02 1.43E+02 7.04E+00 3.74E+00 3.30E+00 07/18/200 2 39000 Ohio 4.02E+01 5.19E+01 6.51E+02 2.81E+02 3.48E+01 5.26E+00 4.70E+00 5.61E-01 07/18/200 2 40000 Oklahoma 1.00E+03 3.38E+02 5.98E+02 3.30E+02 3.23E+01 1.48E+02 1.18E+02 2.97E+01 07/18/200 2 41000 Oregon 1.77E+02 5.42E+01 5.19E+02 9.80E+01 5.96E+01 7.30E+01 6.81E+01 4.93E+00 07/18/200 2 42000 Pennsylva n 3.47E+02 5.72E+01 5.80E+02 3.18E+02 7.39E+01 7.30E+01 5.08E+01 2.22E+01 07/18/200 2 44000 Rhode Isla 1.11E+01 5.57E+00 1.29E+02 2.85E+00 5.43E+00 2.17E+00 2.00E+00 1.65E-01 07/18/200 2 45000 South Car o 1.23E+02 5.25E+01 3.95E+02 9.36E+01 3.12E+01 2.27E+01 1.95E+01 3.20E+00 07/18/200 2 46000 South Dak o 1.73E+01 1.43E+01 8.98E+01 2.69E+02 5.49E+01 5.92E+00 4.44E+00 1.48E+00 07/18/200 2 47000 Tennesse e 8.91E+01 3.53E+01 4.42E+02 8.82E+01 7.95E+01 3.51E+01 2.80E+01 7.16E+00 07/18/200 2 48000 Texas 1.71E+03 2.24E+02 1.18E+03 8.96E+02 1.87E+01 1.96E+02 1.65E+02 3.32E+01 07/18/200 2 49000 Utah 1.99E+02 5.54E+01 1.62E+02 9.43E+01 2.39E+01 2.68E+01 2.54E+01 1.41E+00 07/18/200 2 50000 Vermont 3.95E+01 4.55E+00 3.79E+01 4.14E+01 1.05E+01 6.09E+00 5.48E+00 6.09E-01 07/18/200 2 51000 Virginia 1.98E+02 1.26E+02 3.34E+02 1.40E+02 2.87E+01 5.71E+01 2.31E+01 3.40E+01 07/18/200 2 53000 Washingto 3.64E+02 4.41E+01 3.26E+02 1.21E+02 4.90E+00 6.00E+01 5.81E+01 1.92E+00 07/18/200 2 54000 West Virgi n 3.84E+01 3.07E+01 1.57E+02 3.65E+01 3.15E+01 1.56E+01 1.36E+01 2.03E+00 07/18/200 2 55000 Wisconsin 8.57E+01 3.00E+01 4.35E+02 3.56E+02 9.08E+00 1.62E+01 1.49E+01 1.30E+00 07/18/200 2 56000 Wyoming 5.24E+01 2.14E+02 4.57E+01 6.93E+01 3.83E+01 7.36E+00 6.09E+00 1.27E+00

### Additional State QA Procedures

Once the on-road mobile SMOKE outputs were acquired by TDEC-APC, a number of metrics were generated to further QA and summarize the emissions. Those included:

• County emissions totals, bar charts to vi sually examine whether the counties with the highest emissions are c onsistent with what was expected from county VMT distribution. (example provided in Figure 1)

• County emissions by SCC code (vehicle and facility) were examined with pie charts to ensure distribution of emissi ons by vehicle type f its the conceptual model.

• PAVE plots were generated to check to ensure emissions where showing up in all counties in NC. Scale was lowered to make sure no emissions were omitted.

• PAVE plots were animated over a 24-hr period to ensure diurnal changes in emissions were as they should be.

• PAVE plots were visually inspected to ma ke sure emissions were highest/lowest in logical places.

The following QA checks were performed both together and separa te for EGU and nonEGU point sources:

• Data product summaries and raw NIF 3.0 data files were examined.

• County emissions totals were examined to assure the counties with the highest emissions were consistent with what was expected.

• PAVE plots were generated to check to ensure emissions where showing up in all counties in NC. Scale was lowered to make sure no emissions were omitted.

• PAVE plots were animated over a 24-hr period to ensure diurnal changes in emissions were as they should be.

• PAVE plots were visually inspected to ma ke sure emissions were highest/lowest in logical places.

• Errors detected in earlier model runs we re rechecked with eac h successive model run to assure their correction was ca rried forward in subsequent runs.

• NIF files were examined to identify probl ems with latitude and longitude, as well as, stack parameters.

• Parsed files were examined (i.e., Excel sp readsheets that provide unit-level results derived from the model plant projections obtained by the IPM) for accuracy.

• Facility level emission summaries for 2018 were examined for both the base case and CAIR case to ensure that emissions were consistent and that there were no missing sources.

• Ensured that stack parameters were modi fied appropriately and where necessary at facilities where new controls are scheduled to be installed.

• Input files were examined to assure there were no double counted facilities (example would be if a facility was known by two different names and counted under each).Typical fire emissions SMOKE output in th e VISTAS states were acquired by TDECAPC from Alpine Geophysics. The plots and summary reports for these area source fire emissions were spot-checked for QA and included:

• County emissions totals and County em issions by SCC code, were visually examine whether the counties with the highe st emissions are cons istent with what was expected.

• PAVE plots were generated to check to ensure emissions where showing up in all counties in NC. Scale was lowered to make sure no emissions were omitted.

• PAVE plots were animated over a 24-hr period to ensure diurnal changes in emissions were as they should be.

• PAVE plots were visually inspected to ma ke sure emissions were highest/lowest in logical places.