Appendix D.4a 

Emissions Modeling Documentation 
Background 

The Sparse Matrix Operator Kernel Emissions (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 fastest emissions processing tool currently 

available to the air quality modeling community. The sparse matrix approach utilized 

throughout SMOKE permits both rapid and flexible processing of emissions data. The 

processing is rapid because SMOKE utilizes a series of matrix calculations instead of less 

efficient algorithms used in previous systems. The processing is flexible because the 

processing steps of temporal projection, controls, chemical speciation, temporal 

allocation, and spatial allocation have been separated into independent operations 

wherever possible. The results from these steps 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 modeling was configured to generate 

point, area, nonroad mobile, on-road mobile, and biogenic source emissions. In addition, 

certain subcategories, such as fires and EGUs were maintained in separate source 

category files in order to allow maximum flexibility 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 updated state of knowledge application of these profiles. 

New temporal profiles used by VISTAS during modeling of the 2002, 2009 and 2018 

EGU emissions took the place of USEPA provided default temporal profiles that are 

generally accepted as not accurately depicting temporal distribution 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 the main objective of developing temporal 

profiles for VISTAS EGUs necessary to apply in the generation 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 distributions 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-month, 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 executed 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 matter concentrations (and thus regional haze) 

that were nearly indistinguishable from the “everyday” mobile method. VISTAS 

determined that the difference in the modeled 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 inventory 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 toxics inventories, chemical mass balance modeling, and 

visibility modeling. 

Depending on the purpose of a particular emissions inventory, the inventory may include 

TOG, NOx, sulfur oxides (SOx), CO, total suspended particulate matter (TSP), 

particulate matter less than 10 micrometers in aerodynamic diameter (PM10), or 

ammonia (NH3). However, modeling inventories may require these emissions to be 

expressed in terms of other pollutants. Additionally, 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 speciation profile cross-reference files. The 

speciation profiles and source category cross-references use in VISTAS modeling are 

based on USEPA’s CAIR/CAVR/CAMR modeling platform with files located on 

USEPA’s CAIR file transfer website (ftp://www.airmodelingftp.com/). Minor 

modifications were made to reflect State specific profiles or updated state of knowledge 

application of these profiles. One major change made in the VISTAS modeling was the 

modification of coal combustion cross-reference from speciation profile “NCOAL” to 

profile “22001.”

Spatial Allocation 

Because air quality modeling strives to replicate the actual physical and chemical 

processes that occur in an inventory domain, it is important that the physical location of 

emissions be determined as accurately as possible. In an ideal situation, the physical 

location of all emissions would be known exactly. In reality, however, the spatial 

allocation of emissions in a modeling inventory only approximates the 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 emissions inventory, which is typically developed for counties, 

states, or other areas. The exact location of most major emission sources is known and 

their geographic coordinates are usually contained in the inventory. These usually are 

referred to as major point sources and include 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 locations of each source are not included in the 

modeling inventory. Surrogates are human activities or land use information that are 

used to represent a more precise location of emission source category 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 cross-references were originally 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 Gridded 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. Generate the grid polygons (36/12km) with the 

specifications of the VISTAS domain and spatially overlay (intersect) the grid onto the 
surrogate area polygons or points. The resulting geodatabase contains, for each 

surrogate, the county FIPS code, the grid column 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. Each table contains the 

gridded area, miles or count in each county for a specific 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 surrogate value by the total county surrogate 

value. Combination surrogates where both are 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 Population are line and area 

data) were summed after calculating the ratios 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 county with class 1 locomotive emissions may not 

have data for the class 1 railroad surrogate. In this case we have selected to incorporate, 

within the assigned surrogate, a different source 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 this process as “gap-filling.” 

All surrogates resulting from the gap-filling 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 secondary or tertiary surrogate chosen would 

be the same across all counties and apply to all SCCs that use the particular primary 

surrogate. We pull in and substitute the secondary surrogate for counties where the 

primary surrogate is missing. Tertiary surrogates will then be assigned to those counties 

that are still without surrogates. 

For identified counties having no values for each surrogate, we assign the data based on 

the appropriate secondary or tertiary surrogate to these counties. A check to see that 

surrogate ratios for each county sum to approximately 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 files. 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 Rise 
Wildfire, agricultural, and prescribed burn emissions 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 completeness and quality of the data received, 

VISTAS-specific calculations were made to calculate spatial and temporal distributions 

of the fire emissions, rather than relying on standard distribution profiles. 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 profile (meters above ground level) 

Lay1 – The percent of the emissions entrained in the first modeling layer 

For those fires as having the necessary data elements 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 Forum Emissions Inventory Report (see 

http://www.wrapair.org/forums/fejf/documents/WRAP_2002_PhII_EI_Report_20050722

.pdf) has documented an approach for calculating 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 are then used by SMOKE 2.1 to distribute the 

vertical emissions for the fires. To successfully model fires as elevated 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 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 distribution of the emission provides insight into the quality and accuracy 

of the emissions inputs. Some of the additional 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 erroneously located in water cells. 

• Spot-check the holiday emissions files to confirm that they are temporally 

allocated like Sundays. 
• Producing pie charts emission summaries that highlight the contribution of each 

emissions source component (e.g. nonroad mobile). 

• Normalizing the emissions by population for each state will illustrate 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 state totals versus the same 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 were created. These reports were created for a 

representative weekday in each of the episodes for each of these selected states. 

The quantitative QA analyses often reveal significant 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 the 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 inventory for each source category 

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

• State and county totals after spatial allocation for each source category 

• State and county totals by day after temporal 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 allocation, 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 inventory for area, mobile, and point sources 

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

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

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

• Totals by speciation profile code and SCC 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 sources
Table 1. Example of a State totals 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]

------------------------------------------------------------------------------------------------------------------------------------------------------------------

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 9000 Connecticu 6.37E+00 1.05E+01 1.18E+02 1.99E+01 5.39E-01 3.81E+00 3.54E+00 2.69E-01

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 25000 Massachus 9.95E+01 6.14E+01 2.80E+02 3.87E+01 3.46E+01 5.01E+01 4.36E+01 6.46E+00

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 33000 New Hamp 3.82E+01 2.61E+01 8.11E+01 7.21E+00 7.62E+00 2.30E+01 2.06E+01 2.40E+00

 07/18/2002 34000 New Jersey 1.45E+01 2.24E+01 2.70E+02 5.55E+01 8.55E+00 1.94E+01 9.83E+00 9.72E+00

 07/18/2002 35000 New Mexic 8.55E+01 7.68E+01 1.48E+02 1.37E+02 1.78E+01 1.52E+01 1.37E+01 1.55E+00

 07/18/2002 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

 07/18/2002 37000 North Caro 1.08E+03 1.12E+02 4.50E+02 5.32E+02 1.63E+01 1.97E+01 1.53E+01 4.51E+00

 07/18/2002 38000 North Dako 1.80E+01 5.18E+01 1.28E+02 1.25E+02 1.43E+02 7.04E+00 3.74E+00 3.30E+00

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 42000 Pennsylvan 3.47E+02 5.72E+01 5.80E+02 3.18E+02 7.39E+01 7.30E+01 5.08E+01 2.22E+01

 07/18/2002 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

 07/18/2002 45000 South Caro 1.23E+02 5.25E+01 3.95E+02 9.36E+01 3.12E+01 2.27E+01 1.95E+01 3.20E+00

 07/18/2002 46000 South Dako 1.73E+01 1.43E+01 8.98E+01 2.69E+02 5.49E+01 5.92E+00 4.44E+00 1.48E+00

 07/18/2002 47000 Tennessee 8.91E+01 3.53E+01 4.42E+02 8.82E+01 7.95E+01 3.51E+01 2.80E+01 7.16E+00

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 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

 07/18/2002 54000 West Virgin 3.84E+01 3.07E+01 1.57E+02 3.65E+01 3.15E+01 1.56E+01 1.36E+01 2.03E+00

 07/18/2002 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

 07/18/2002 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 visually examine whether the counties with 

the highest emissions are consistent 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 emissions by vehicle type fits 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 make sure emissions were highest/lowest 

in logical places. 

The following QA checks were performed both together and separate 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 make sure emissions were highest/lowest 

in logical places. 

• Errors detected in earlier model runs were rechecked with each successive model 

run to assure their correction was carried forward in subsequent runs. 

• NIF files were examined to identify problems with latitude and longitude, as well 

as, stack parameters. 

• Parsed files were examined (i.e., Excel spreadsheets 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 modified 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 the 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 emissions by SCC code, were visually 

examine whether the counties with the highest emissions are 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 make sure emissions were highest/lowest 

in logical places.