APPENDIX H 

VISTAS Area of Influence Analyses 

Based on “VISTAS Area of Influence Analyses for Sulfur Dioxide” by Archuleta, Adhloch, 

Mansell, Stella and Brewer (February 28, 2007) and “Procedures for Developing and Displaying 

AoI Back-trajectories Residence Time GIS Data by Mansell (August 29, 2007) 
 H-1 

H.1 Introduction 

 

The objective of the VISTAS Area of Influence analysis is to identify the geographic source 

regions that are contributing to visibility impairment at the Class I areas on the worst 20 percent 

visibility days. This information is being used by the VISTAS states as part of the evaluation 

and demonstration of reasonable progress toward visibility improvement in Class I areas. 

VISTAS’ contribution assessment based on IMPROVE monitoring data (Brewer and Adhloch, 

2005) that demonstrated ammonium sulfate is the major contributor to PM2.5 mass and visibility 

impairment at Class I areas in the VISTAS and neighboring states. As illustrated in Figure H-1, 

on the worst 20 percent visibility days during the 2000-2004 5-year Baseline, ammonium sulfate 

accounted for greater than 70% of the calculated light extinction at Class I areas in the Southern 

Appalachians and greater than 60% of the calculated light extinction for coastal sites in the 

VISTAS states (excepting Everglades). In contrast, ammonium nitrate contributed less than 5% 

of the calculated light extinction at VISTAS Class I areas on the worst 20 percent visibility days. 

(Nitrate has a somewhat larger contribution on the worst 20 percent days at Class I areas in 

neighboring states.) Particulate organic matter carbon (OMC) accounted for 10-20% of light 

extinction on the worst 20 percent visibility days (except Everglades where organic carbon 

accounted for 40%). 

Figure H-1. Average extinction for the worst 20 percent visibility days in 2000-2004 using the 

new IMPROVE equation (data from VIEWS September 2006).

VISTAS emissions sensitivity analyses using the CMAQ regional air quality model project that 

reductions in SO2 emissions from Electric Generating Utilities (EGU) and non-EGU industrial 

point sources will result in the greatest improvements in visibility at VISTAS Class I areas. EGU 

and non-EGU industrial point sources comprise 95+% of the SO2 emissions inventory in the 

VISTAS states. The emissions sensitivity analyses also indicate that improvements in visibility 

from reductions in NOx or primary carbon emissions from ground- level sources or point sources 

would be small. Based on these analyses, VISTAS states chose to focus their reasonable 

0

50

100

150

200

250

300

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

CADI1

BRIG1

BRET1

MING1

HEGL1UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm -1)

Sea Salt

CM

Soil

EC

POM

Amm NO3

Amm SO4

Rayleigh

0

50

100

150

200

250

300

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

CADI1

BRIG1

BRET1

MING1

HEGL1UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm -1)

Sea Salt

CM

Soil

EC

POM

Amm NO3

Amm SO4

Rayleigh
 H-2 

progress evaluation on potential controls of SO2 emissions from EGU and non-EGU point 

sources. To select the specific point sources that would be considered for each Class I area, 

VISTAS first identified the geographic area that was most likely to influence visibility in each 

Class I area and then identified the major SO2 point sources in that geographic area. 

H.2 Back Trajectory Analyses 

In support of VISTAS’s Area of Influence (AoI) analysis, Air Resource Specialists (ARS) 

generated meteorological back trajectories for IMPROVE sites in VISTAS and neighboring 

states. Back trajectory analyses use interpolated measured or modeled meteorological fields to 

estimate the most likely central path of air masses that arrive at a receptor at a given time. The 

method essentially follows a parcel of air backward in hourly steps for a specified length of time. 

Back trajectories account for the impact of wind direction and wind speed on delivery of 

emissions to the receptor, but do not account for chemical transformation and dispersion of 

emissions. 

Trajectories were generated using the Hybrid-Single Particle Lagrangian Integrated Trajectory 

(HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s 

(NOAA) Air Resources Laboratory (ARL). HYSPLIT uses archived 3-dimensional 

meteorological fields generated from observations and short-term forecasts. HYSPLIT can be 

run to generate forward or backward trajectories using several available meteorological data 

archives. 

The data archives used in this analysis were the National Weather Service's National Centers for 

Environmental Prediction Eta Data Assimilation System (EDAS). The EDAS fields are archived 

by the ARL across the continental U.S., including a buffer zone, at a horizontal resolution of 80 

km before 2004, and at a horizontal resolution of 40 km starting in 2004. Detailed information 

regarding the trajectory model and these data sets can be found on NOAA’s Web site 

(http://www.arl.noaa.gov/ready/hysplit4.html). 

The major model parameters selected for this analysis include those listed in Table H-1. 

Table H-1. Back Trajectory Model Parameters Selected for VISTAS AoI Analysis. 

Model Parameter Value 

Trajectory duration 72 hours backward in time 

Top of model domain 14,000 meters 

Vertical motion option used model data 

Meteorological Field EDAS 

End Times 0600, 1200, 1800 and 2400 EST 

End Heights 100 and 500 m 

The choice of these parameters affects the trajectories generated and the final attribution 

analyses. In particular, trajectories tend to become increasingly uncertain the further back in 

time they are used. Vertical motion in the model is sometimes best represented by following 

actual vertical motion measurements (represented by model data), surfaces of constant entropy, 

or surfaces of constant pressure, depending on the meteorological conditions at a given location 

and time. The impact of receptor height (or end height) on an individual trajectory is also 
 H-3 

important. Low-ending trajectories represent air parcels nearer to ground level and high-ending 

trajectories may represent more accurate boundary layer flow above the local terrain. 

 

For the years 2000 through 2004, back trajectories were computed for the following 22 

IMPROVE sampling sites in and around the VISTAS region: 

1. Breton, LA (BRET1) 

2. Brigantine NWR, NJ (BRIG1) 

3. Cadiz, KY (CADI1) 

4. Caney Creek, AR (CACR1) 

5. Cape Romain NWR, SC (ROMA1) 

6. Chassahowitzka NWR, FL (CHAS1) 

7. Cohutta, GA (COHU1) 

8. Dolly Sods Wilderness, WV (DOSO1) 

9. Everglades NP, FL (EVER1) 

10. Great Smoky Mountains NP, TN (GRSM1) 

11. Hercules-Glades, MO (HEGL1) 

12. James River Face Wilderness, VA (JARI1) 

13. Linville Gorge, NC (LIGO1) 

14. Mammoth Cave NP, KY (MACA1) 

15. Mingo, MO (MING1) 

16. Okefenokee NWR, GA (OKEF1) 

17. Shenandoah NP, VA (SHEN1) 

18. Shining Rock Wilderness, NC (SHRO1) 

19. Sipsy Wilderness, AL (SIPS1) 

20. St. Marks, FL (SAMA1) 

21. Swanquarter, NC (SWAN1) 

22. Upper Buffalo Wilderness, AR (UPBU1) 

Based on the five years of individual back trajectories, ARS generated extinction-weighted 

residence time data for each IMPROVE site. Each hourly trajectory point was tagged with its 

associated sample (or end) date. Back trajectories with end dates corresponding to the worst 20 

percent visibility days were weighted with calculated extinction values and summed into 1 

degree horizontal grid cells of latitude and longitude. 

Extinction data is based on the IMPROVE Regional Haze Rule dataset, as available from the 

VIEWS website (http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx) in 

November 2005 and is calculated based on the original IMPROVE algorithm. For sites with less 

than three complete years of data, ARS developed and applied a data substitution protocol to fill 

missing data. Organic carbon and elemental carbon values were filled based on correlations to 

hydrogen and sulfate values for the same site. Other components were filled based on data from 

nearby sites. 

Trajectory data were weighted by total extinction, and by extinction components including 

ammonium sulfate (AmmSO4), ammonium nitrate (AmmNO3), organic matter carbon (OMC), 

Light Absorbing Carbon (LAC or EC), Soil, and Coarse Mass (CM). Weighting trajectories for 

the worst 20 percent days by sulfate extinction gave greater importance to days influenced by 

SO2 emissions and lesser importance to days influenced by organic carbon emissions. This 

allowed a geographic area of influence to be identified based on sulfate contributions and not 

skewed by contributions from fire. 
 H-4 

 

Figure H-2. Example Back trajectories for 20% worst visibility days in 2002 for Sipsey 

Wilderness Area. 

These data files were provided to ENVIRON. ENVIRON was charged with developing 

residence time plots to define the geographic area with highest probability of influencing the 

receptor on the worst 20 percent days dominated by sulfate. Residence time over an area is 

indicative of general flow patterns, but since it does not account for emissions and removal 

processes, it does not necessarily imply specific areas contributed significantly to haze 

compounds at a receptor site. 

H.3 Area of Influence Data and Displays 

The primary objective of the Area of Influence (AOI) displays and data sets are to assist the 

States in focusing their reasonable progress analysis on those geographic regions and source 

categories which most impact a particular Class I Area. The sources of data used in these 

analyses include back-trajectory residence time data, provided by ARS and described above, and 

the VISTAS 2002 and 2018 36-km gridded emissions data (Base G, August 2006 inventory 

version). 
 H-5 

Extinction weighted back-trajectory residence time plots were provided by ARS for all Class I 

Areas in the VISTAS region as well as for the surrounding neighboring states. These trajectories 

were processed on an un-projected (i.e., geodetic lat/lon 1 degree by 1 degree) grid and 

transferred to ENVIRON for further GIS processing and analysis. Back-trajectories were run for 

100-meter and 500-meter start heights. Because the back-trajectories were to be combined with 

gridded emissions data, the first step in the analysis and GIS processing involved re-projecting 

these data to the 36 km Inter-RPO Unified Lambert Conformal Projection domain. Once the 

data were re-projected, the data were mapped to the 36 km grid cells in the RPO projection using 

an area-weighted mapping routine. The re-projected extinction weighted back-trajectory 

residence times were then plotted on the VISTAS 12 km modeling domain. As sulfate is the 

primary visibility impairing component of the total extinction in the Southeast, the SO4-

weighted residence time trajectories were chosen as the basic data set for display. For clarity, 

these data are scaled by the maximum value of the total extinction-weighted residence time 

within the modeling domain. Figure H-3 displays an example of the back-trajectory residence 

time plot for Sipsey, Alabama. 

Figure H-3. Sulfate-weighted back-trajectory residence time (RT) displays for Sipsey, AL. (Red 

outline delineates the region with RT > 10%; orange outline (grey cells) delineates regions with 

RT > 5% ). 

As an aid to the analysis, all regions with scaled residence time values greater than 5% and 

greater than 10% were noted on the displays as shown in Figure H-3. Finally, county boundaries 

were overlaid to identify those counties and 36 km modeling grid cells for which the extinctionweighted residence time back-trajectories exceeded 5% and 10%. These data were then exported 

as comma-delimited ASCII data files for further analyses. These residence time displays are 

used primarily to determine the regions (counties and/or grid cells) impacting each Class I Area. 

The next step in the analysis is to determine the potential impact from emission sources within 

the regions identified from the extinction-weighted back-trajectory residence time displays and 

analyses. 
 H-6 

Previous VISTAS contribution assessment, emissions sensitivity modeling, and inventory 

analyses have indicated that SO4 is the largest contributor to haze on the worst 20 percent 

visibility days and that Electric Generating Utilities and industrial point sources are the largest 

contributors to SO4 levels at the Class I areas. These two source categories make up 95% of the 

VISTAS SO2 inventory. Therefore these two source categories were identified as the highest 

priority for evaluation of reasonable progress in 2018 at the Class I areas. 

The potential impacts of SO2 point source emissions for each Class I Area are determined by 

combining gridded emissions data and weighted back-trajectory residence times for the 36 km 

grid cells. The analysis was carried out with both the 2002 and 2018 base year emission 

inventories. Figures H-4 and H-5 display the gridded point source SO2 emissions from the 

VISTAS modeling databases, for 2002 and 2018, respectively. These data were processed by 

summing all vertical layers for each grid column and then summing across all days to obtain 

annual emission totals for each grid cell. 

Figure H-4. Annual point source SO2 emissions for the VISTAS region for 2002 in tons/year. 

(2002 BaseG). 
 H-7 

Figure H-5. Annual point source SO2 emissions for the VISTAS region for 2018 in tons/year. 

(2018 BaseG). 

As a way of incorporating the effects of transport, dispersion, deposition, and chemical 

transformation of point source emissions along the path of the trajectories, these data were 

weighted by 1/d, where d was calculated as the distance between the 36 km grid cell centers for 

the source and the Class I area monitor, in kilometers. For the grid cell containing the Class I 

Area monitor, a weighting of 1/9 was applied. The distance-weighted point source SO2 

emissions are then combined with the gridded extinction-weighted back-trajectory residence 

times at a spatial resolution of 36 km. 

The final step in the development of the displays and datasets involve the combination of the 

residence times and gridded emission data. The distance weighted (1/d) gridded point source 

SO2 emissions are multiplied by the total extinction-weighted back-trajectory residence times 

(SO2 * Bext-weighted RT * (1/d)) on a grid cell by grid cell basis. These results are then 

normalized by the domain-wide total and displayed as a percentage. The analysis is carried out 

for both the 2002 and 2018 base year inventories. Figures H-6 and H-7 display the result of this 

final analysis step. Note that the outlined regions (in red and light orange) represent the extent of 

the >10% and >5% sulfate extinction-weighted residence times, determined from the first step in 

the analysis (see Figure H-3). As with the residence time analysis, the results of the combined 

distance-weighted SO2 emissions and extinction-weighted residence times are overlaid with 

county boundaries and exported to comma-delimited ASCII data files for further analyses of 

emissions and control options for specific sources. 

 
 H-8 

Figure H-6. Normalized 2002 SO2 point source emissions times distance-weighted residence 

time display for Sipsey, AL. (Red outline delineates the region with RT > 10%; orange outline 

delineates regions with RT > 5% ). 

Figure H-7. Normalized 2018 SO2 point source emissions times distance-weighted residence 

time display for Sipsey, AL. (Red outline delineates the region with RT > 10%; orange outline 

delineates regions with RT > 5% ). 

VISTAS states evaluated these plots of residence time weighted by sulfate extinction plots of 

gridded SO2 emissions and defined the SO2 Areas of Influence as those areas with residence 

time greater than 10%, based on trajectories with start height of 100m and 4 start times per day. 
 H-9 

H.4 Summary of Emission Inventories Used in Cost Curve Analysis 

A necessary component of control strategy design is a thorough review of the emission 

inventories that are used in the modeling of the future year base case. These inventories can shed 

light on the residual emissions from sources or source categories defined to be within areas of 

transport or impact of a Class I area. We used the Base G (fall 2006) version of the VISTAS 

2018 base case and 2002 base year emissions to conduct this cost curve analysis. 

The NIF formatted and SMOKE-ready modeling files for both 2002 and 2018 base year and base 

cases were obtained from VISTAS emissions development and modeling contractors and 

converted to annual emissions and summed for the geography and domain of interest. 

Tables H-2 and H-3 present the State breakdown of these emissions for the entire VISTAS 

domain. Because the SMOKE-ready files were used in this analysis, the fugitive dust transport 

factor (FDTF) is included in the PM emission summaries. This factor is applied to account for 

the removal of a substantial portion of fugitive dust emissions near a source by surrounding 

vegetation and structures when such emissions are used in regional scale modeling analyses. 

Table H-2. VISTAS 2002 Base Year Annual Emissions Summary. 

Table H-3. 2018 Base Case Annual Emissions Summary. 

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

AL 327,850 268,067 1,994,607 305,938 258,179 114,681 84,513

FL 990,629 448,443 6,099,425 260,963 483,328 226,529 73,455

GA 585,528 357,169 3,820,095 358,570 472,376 211,406 124,237

KY 236,983 277,271 1,356,496 320,525 166,918 58,559 63,959

MS 247,932 173,893 850,056 62,627 205,761 54,912 76,040

NC 421,965 275,907 2,702,315 164,067 208,015 100,253 196,577

SC 300,291 193,247 1,527,341 163,491 189,358 88,891 42,883

TN 374,093 247,827 1,692,551 228,035 187,066 84,117 46,984

VA 311,835 276,525 2,001,939 294,209 131,708 57,993 64,276

WV 107,675 150,777 576,198 190,683 54,736 31,678 14,400

VISTAS 3,904,780 2,669,125 22,621,023 2,349,107 2,357,445 1,029,017 787,325

BaseG Annual Emissions - 2018 Base Case (Tons)

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

AL 445,497 459,880 2,534,778 584,665 232,229 105,341 67,970

FL 1,291,562 930,086 7,710,376 589,069 408,902 205,450 61,643

GA 765,063 623,645 5,013,024 653,362 408,736 195,654 100,134

KY 286,752 466,403 1,730,622 580,341 148,290 50,981 57,220

MS 302,374 239,547 1,203,899 103,679 180,816 43,533 63,765

NC 655,950 616,583 4,097,444 546,429 179,242 84,513 172,976

SC 382,836 323,976 2,064,423 286,781 165,699 78,356 35,400

TN 473,244 511,151 2,601,059 442,589 165,095 75,458 42,738

VA 448,205 461,628 3,034,414 416,190 109,848 47,004 55,057

WV 135,819 348,576 802,262 569,373 48,423 27,807 12,341

VISTAS 5,187,303 4,981,474 30,792,301 4,772,477 2,047,281 914,096 669,243

BaseG Annual Emissions - 2002 Typical (Tons)
 H-10 

Our review was conducted in a top down fashion starting with an analysis of the major source 

categories in the domains of interest to determine which major categories had the highest 

residual contribution to the area. Once the highest source types were identified, subcategories 

within those source types were reviewed. Again, a ranking of the highest residual sub source 

types was performed and additional analyses on these categories were conducted. These analyses 

are illustrated for sources in the SO2 Area of Influence for Sipsey in Tables H-4 through H-8. 

For purposes of listing sources within the Area of Influence, data were provided for all sources 

with emissions over distance (Q/d) greater than 5 and maximum residence time (RTmax) greater 

than 5. States independently defined the criteria for selecting which sources from this list would 

be considered for reasonable control measures. RTmax greater than 10 was used to define 

emissions included in Tables H-4 through H-8. 

Table H-4. 2018 Base G2 Annual Emissions (Tons) by Source Category in SO2 Area of 

Influence for Sipsey Wilderness Area. 

Table H-5. 2018 Base G2 Annual Emissions (Percentage) by Source Category in SO2 Area of 

Influence for Sipsey Wilderness Area. 

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 7,237 236,192 82,293 542,524 74,612 56,145 3,916

Fuel Comb. Industrial 14,210 181,413 168,051 198,033 29,686 19,351 592

Fuel Comb. Other 61,635 55,145 178,412 57,140 27,763 25,516 873

Chemical & Allied Product Mfg 19,707 6,225 61,613 16,315 6,046 4,038 1,643

Metals Processing 12,069 8,866 235,955 48,889 23,309 19,855 778.78

Petroleum & Related Industries 2,372 1,689 3,353 5,709 998 643 0.02

Other Industrial Processes 112,629 59,626 94,469 42,121 77,815 36,776 3,133

Solvent Utilization 583,810 969 574.93 12.87 2,387 1,935 105.74

Storage & Transport 84,177 462 422.07 122.1 4,637 2,668 3.59

Waste Disposal & Recycling 46,900 14,606 310,604 2,438 53,825 50,254 169

Highway Vehicles 231,426 220,157 3,159,968 3,315 10,934 5,399 33,044

Off-highway 151,510 186,975 2,304,372 7,789 13,846 13,083 204.24

Miscellaneous 39,733 13,403 642,523 3,640 580,930 117,566 308,848

VISTAS Total 1,367,414 985,728 7,242,611 928,046 906,789 353,229 353,311

Annual 2018 BaseG2 Emissions (Tons)

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 1% 24% 1% 58% 8% 16% 1%

Fuel Comb. Industrial 1% 18% 2% 21% 3% 5% 0%

Fuel Comb. Other 5% 6% 2% 6% 3% 7% 0%

Chemical & Allied Product Mfg 1% 1% 1% 2% 1% 1% 0%

Metals Processing 1% 1% 3% 5% 3% 6% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Other Industrial Processes 8% 6% 1% 5% 9% 10% 1%

Solvent Utilization 43% 0% 0% 0% 0% 1% 0%

Storage & Transport 6% 0% 0% 0% 1% 1% 0%

Waste Disposal & Recycling 3% 1% 4% 0% 6% 14% 0%

Highway Vehicles 17% 22% 44% 0% 1% 2% 9%

Off-highway 11% 19% 32% 1% 2% 4% 0%

Miscellaneous 3% 1% 9% 0% 64% 33% 87%

VISTAS Total 100% 100% 100% 100% 100% 100% 100%

Annual 2018 BaseG2 Emissions (%)
 H-11 

Table H-6. 2018 Base G2 Annual Emissions (Tons) for Top Three Source Categories in SO2 

Area of Influence for Sipsey Wilderness Area. 

Table H-7. 2018 Base G2 Annual Emissions (Percentage) for Top Three Source Category in 

SO2 Area of Influence for Sipsey Wilderness Area. 

In addition to reviewing the residual emission categories in the future year base, it was important 

to identify reductions that had already occurred within each category or at specific units. This 

allowed VISTAS States to determine if certain source categories that had yet to be controlled 

under the future year base case had the potential for reduction or if source types already reduced 

have reached the full cost-effective emission reduction potential. 

Finally, once each subcategory was identified, unit level tables of emission comparisons from 

2002 to 2018 were developed allowing VISTAS States to review existing emission reductions 

and providing the ability to assign new cost-effective controls to units using the best control for 

the scenario. These tables presented comparisons of 2002 and 2018 emission levels, for SO2, 

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util.-Coal 5,838 222,105 43,332 538,424 70,940 52,634 988

Fuel Comb. Elec. Util.-Oil 65 548 9 52 2 1 0

Fuel Comb. Elec. Util.-Gas 66 1,598 609 3,782 323 216 113

Fuel Comb. Elec. Util.-Other 16 235 101 6 76 46 0

Fuel Comb. Elec. Util.-Internal 1,252 11,706 38,242 260 3,272 3,248 2,815

Fuel Comb. Industrial-Coal 1,884 41,177 29,545 135,360 13,952 6,177 13

Fuel Comb. Industrial-Oil 231 12,471 4,455 36,115 2,138 1,305 97

Fuel Comb. Industrial-Gas 6,001 56,185 74,037 17,184 7,836 7,381 482

Fuel Comb. Industrial-Other 3,377 18,346 48,181 9,188 5,149 3,902 0

Fuel Comb. Industrial-Internal 2,717 53,233 11,833 185 611 587 0

Fuel Comb. Other- 302 5,437 1,005 18,786 440 295 0

Fuel Comb. Other- 131 3,076 1,193 32,056 1,602 968 149

Fuel Comb. Other- 658 11,045 7,131 946 962 898 23

Fuel Comb. Other-Misc. Fuel Comb. 1,145 3,811 21,618 1,649 958 689 3

Fuel Comb. Other-Residential Wood 57,645 3,034 129,215 431 21,242 20,345 304

Fuel Comb. Other-Residential Other 1,753 28,742 18,249 3,272 2,560 2,323 395

Top Tier Total 83,082 472,750 428,756 797,697 132,061 101,013 5,381

Annual 2018 BaseG2 Emissions (Tons)

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util.-Coal 0% 23% 1% 58% 8% 15% 0%

Fuel Comb. Elec. Util.-Oil 0% 0% 0% 0% 0% 0% 0%

Fuel Comb. Elec. Util.-Gas 0% 0% 0% 0% 0% 0% 0%

Fuel Comb. Elec. Util.-Other 0% 0% 0% 0% 0% 0% 0%

Fuel Comb. Elec. Util.-Internal 0% 1% 1% 0% 0% 1% 1%

Fuel Comb. Industrial-Coal 0% 4% 0% 15% 2% 2% 0%

Fuel Comb. Industrial-Oil 0% 1% 0% 4% 0% 0% 0%

Fuel Comb. Industrial-Gas 0% 6% 1% 2% 1% 2% 0%

Fuel Comb. Industrial-Other 0% 2% 1% 1% 1% 1% 0%

Fuel Comb. Industrial-Internal 0% 5% 0% 0% 0% 0% 0%

Fuel Comb. Other- 0% 1% 0% 2% 0% 0% 0%

Fuel Comb. Other- 0% 0% 0% 3% 0% 0% 0%

Fuel Comb. Other- 0% 1% 0% 0% 0% 0% 0%

Fuel Comb. Other-Misc. Fuel Comb. 0% 0% 0% 0% 0% 0% 0%

Fuel Comb. Other-Residential Wood 4% 0% 2% 0% 2% 6% 0%

Fuel Comb. Other-Residential Other 0% 3% 0% 0% 0% 1% 0%

Top Tier Total 4% 47% 6% 85% 14% 28% 1%

Annual 2018 BaseG2 Emissions (%)
 H-12 

and future year control technology assignment (by IPM forecasting and State modification) for 

point sources. Non-EGU unit-specific technology assignments were identified using the control 

efficiency tables provided by the VISTAS emissions inventory contractor, MACTEC. 

Using these emission reviews and summaries like those presented in Figure H-8, VISTAS states 

made a decision to further evaluate reasonable measures for SO2 emissions controls beyond the 

On-The-Books scenario in 2018 for EGU and non-EGU point sources only. Ultimately, the 

long-term strategy will include emissions controls on the BART applicable sources. However, 

determinations of BART controls were not available for this analysis. 

Figure H-8. VISTAS Base G2 SO2 Emissions Contributions by Major Source Sector. 

H.5 Process in Preparing Files for Cost Effectiveness Curve Development 

The 2018 IPM file used by VISTAS for EGU sources (RPO IPM version 2.1.9) was obtained and 

matched to the 2018 base case inventory of EGU sources. This step was conducted to ensure 

that incremental controls assigned to these source types did not duplicate existing base case 

assumptions. Because IPM does not assign a control efficiency with each control device applied 

to SO2 and NOX, we made some assumptions, based on IPM documentation, as to what pollutant 

specific level of reduction was applied in the future year base case runs. These assumptions, by 

primary and secondary control device code combinations for SO2, are presented in Table H-8. 

Since many of the control technology control cost equations within AirControlNET require 

additional unit-level characteristic data, we also made matches of the SMOKE IDA files to 

VISTAS NIF, EPA NEI, or EPA CAMD CEM data sets to obtain these variables when missing. 

Unit level boiler capacity (MMBtu/hr) or NETDC (MW) values are required for capital and 

operating and maintenance cost calculations for many of the SO2 applied technologies. In cases 

where these nameplate capacity values could not be identified, emission weighted (based on the 

Alabama

Florida

Kentucky

Georgia

Mississippi

South Carolina

North Carolina

Tennessee

Virginia

West Virginia

100

200

300

400

500

600

700

Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

Alabama

Florida

Kentucky

Georgia

Mississippi

South Carolina

North Carolina

Tennessee

Virginia

West Virginia

100

200

300

400

500

600

700

Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018
 H-13 

final EPA 2002 NEI) were assigned to boilers using a primary (highest emitting) SCC. Table H9 presents these weighted capacities. Additionally, stack flow, sulfur content, and primary SCC 

assignment were necessary to cross-reference available incremental control technologies to the 

base case emissions inventory data. These variables were obtained where matches could be 

found, in priority order of VISTAS, CAMD, and other EPA datasets, respectively. 

Table H-8. IPM Post Processing Assigned Device Codes and Applied SO2 Control Efficiencies. 

Primary Device Code Secondary Device Code Description CE RE 

0 0 No Control 0 0 

119 0 Dry Scrubber 90 100 

141 0 Wet Scrubber 90 100 

H.6 Application of AirControlNET Technologies 

AirControlNET is a control technology analysis tool developed to support the U.S. EPA in its 

analyses of air pollution policies and regulations (Pechan, 2005). The tool provides data on 

emission sources, potential pollution control measures and emission reductions, and the costs of 

implementing those controls. 

The core of AirControlNET is a relational database system in which control technologies are 

linked to sources within EPA emissions inventories. The system contains a database of control 

measure applicability, efficiency, and cost information for reducing the emissions contributing to 

ambient concentrations of ozone, PM10, PM2.5, SO2, NOX, as well as visibility impairment 

(regional haze) from point, area, and mobile sources. PM10 and PM2.5 as included in 

AirControlNET represent primary emissions of PM. The control measure data file in 

AirControlNET includes not only the technology's control efficiency, and calculated emission 

reductions for that source, but also estimates the costs (annual and capital) for application of the 

control measure. 

Since available versions of AirControlNET contain the preprocessed application of control 

technologies to a predetermined set of EPA emission inventories, direct use of the model in this 

analysis was not possible. However, VISTAS received approval from EPA’s Innovative 

Strategies and Economics Group (ISEG) to modify the AirControlNET version 4.1 source code 

and data tables in order to make it useful to this study (Sorrels, 2006). The results of the 

application of this modified version of the code still retain the applicability, efficiency, and cost 

information from the unmodified version of the source code, but were applied to the VISTAS 

modeling inventories with updated price index scalars to reflect control costs in 2005-dollars. 

Using the modified inventories identified above, we ran every available SO2 control strategy in 

AirControlNET against the EGU and non-EGU point source inventories to develop a master list 

of available, incremental control strategies for the entire VISTAS 36 km domain necessary for 

VISTAS States to design command-and-control or cost-effectiveness based control strategies by 

source or domain. 
F:\VISTAS Phase II\TSD\draft#3\Appendix_H_AOI3.doc 

Table H-9. Emissions Weighted NETDC (MW) Association. 

SCC Description 10100201 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal) 10100202 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Bituminous Coal) 10100203 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Bituminous Coal) 10100212 

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) (Bitum

Coal) 

10100215 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cell Burner (Bituminous Coal) 10100218 

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Atmospheric Fluidized Bed Combustion: Circulatin

(Bitum. Coal) 

10100222 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Subbituminous Coal) 10100223 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Subbituminous Coal) 10100226 

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom Tangential (Subbitum

Coal) 

10100401 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Normal Firing 10100404 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Tangential Firing 10100501 External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1 and 2 Oil 10100601 External Combustion Boilers; Electric Generation; Natural Gas; Boilers > 100 Million Btu/hr except Tangential 10100701 External Combustion Boilers; Electric Generation; Process Gas; Boilers > 100 Million Btu/hr 10100801 External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes 10101204 External Combustion Boilers; Electric Generation; Solid Waste; Tire Derived Fuel : Shredded 10300811 External Combustion Boilers; Commercial/Institutional; Landfill Gas; Landfill Gas 20100101 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine 20100109 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine: Exhaust 20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine  All other boilers 
F:\VISTAS Phase II\TSD\draft#3\Appendix_H_AOI3.doc H-15 

H.7 Development of AOI-Based Cost Curves 

Each Class I area in the VISTAS modeling domain had an associated AOIs. In order to best 

determine where emission reduction has the greatest benefit, this geography was designed to 

limit the available source type list from including all sources within the entire domain. 

These marginal cost curves include the application of all available control measures to all 

applicable point sources within the AOI. The curves allow VISTAS States to determine if 

emission controls, based on the generic application of SO2 control technologies from the 

associations originally developed in AirControlNET and improved upon with stakeholder input, 

will help attain reasonable further progress objectives while presumably minimizing the number 

and type of controlled sources in each AOI. 

Using a geocoded county list from these AOIs, we parsed the master list of incremental control 

measures from all point sources located within the boundaries of the AOIs. This parsed list was 

then sorted on in incremental cost-effectiveness (marginal cost) basis to determine the most cost 

effective control suite available to attain emission reduction targets for SO2 within each AOI. 

Each individual source had its own cost effectiveness curve generated. 

In aggregate, the results of these applications are SO2 emission cost curves for all EGU and nonEGU point source within the geographic domain of the AOI. Incremental controls on area and 

mobile sources were not considered in this analysis. 

With each AOI’s cost curve, additional metrics calculated for the distance from the emission 

release point to the Class I area IMPROVE monitor (in km), the 2018 residual emissions and 

distance (Q/d) or squared distance (Q/d2

), and the normalized 2018 SO2 point source emissions 

times distance-weighted residence time (RTMax) values for the county in which the emission 

release point was located were calculated for incremental review and consideration. States can 

use these values to further determine control strategy development for individual Class I areas.