Appendix M.3

VISTAS Area of Influence Analyses for Sulfur Dioxide 

Tennessee Department of Environment and Conservation 

Division of Air Pollution Control 

9th Floor L&C Annex 

401 Church Street 

Nashville, TN 37243

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Appendix M.3 - 1

January 18, 2008
VISTAS Area of Influence Analyses for 

Sulfur Dioxide 

Report prepared for VISTAS by 

Cassie Archuleta and Joe Adlhoch, Air Resource Specialists 

Gerry Mansell, ENVIRON Corporation 

Greg Stella, Alpine Geophysics 

Pat Brewer, VISTAS Technical Coordinator 

Feb 28, 2007 

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Appendix M.3 - 2

January 18, 2008
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 20% worst 

visibility days. This information will be 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 

Adlhoch, 2005) demonstrated that 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 1, on the 20% worst visibility days in 2000-2004, 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 20% worst visibility days. (Nitrate 

has a somewhat larger contribution on 20% worst days at Class I areas in neighboring states.) 

Particulate organic matter (organic carbon) accounted for 10-20% of light extinction on 20% 

worst visibility days (except Everglades where organic carbon accounted for 40%). 

Figure 1. Average Extinction for 20% Worst Visibility Days in 2000-2004 using New 

IMPROVE Equation (data from VIEWS Sep 2006). 

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

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Appendix M.3 - 3

January 18, 2008
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 groundlevel sources or point sources would be small. Based on these analyses, VISTAS states chose to 

focus their reasonable 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. 

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 1. 

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Appendix M.3 - 4

January 18, 2008
Table 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 

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 – 2004, back trajectories were computed for the following 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) 

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Appendix M.3 - 5

January 18, 2008
Based on the five years of individual back trajectories, ARS generated extinctionweighted 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 

20% worst 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), particulate organic 

material (POM), Light Absorbing Carbon (LAC), Soil, and Coarse Mass (CM). Weighting 

trajectories for the 20% worst 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. 

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

Wilderness Area. 

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Appendix M.3 - 6

January 18, 2008
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 20% worst 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. 

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, Aug 2006 inventory 

version). 

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 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 areaweighted 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 3 displays an example of the back-trajectory residence time plot for Sipsey, Alabama. 

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Appendix M.3 - 7

January 18, 2008
Figure 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 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. 

Previous VISTAS contribution assessment, emissions sensitivity modeling, and inventory 

analyses have indicated that SO4 is the largest contributor to haze on the 20% worst 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 4 and 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. 

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Appendix M.3 - 8

January 18, 2008
Figure 4. Annual point source SO2 emissions for the VISTAS region for 2002 in tons/year. 

(2002 BaseG) 

Figure 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, deposition, and chemical transformation of 

point source emissions along the path of the trajectories, these data were weighted by 1/d, where 

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Appendix M.3 - 9

January 18, 2008
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 6 and 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 3). As with the residence time analysis, the results of the combined 

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

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

emissions and control options for specific sources. 

 

Figure 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% ) 

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Appendix M.3 - 10

January 18, 2008
Figure 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. 

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

domain. Because the SMOKE-ready files were used in this analysis, the particulate matter 

transport factor 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. 

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Appendix M.3 - 11

January 18, 2008
Table 2. VISTAS 2002 Base Year Annual Emissions Summary. 

Table 3. 2018 Base Case Annual Emissions Summary. 

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 4-8. 

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 4-8. 

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)

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Appendix M.3 - 12

January 18, 2008
Table 4. 2018 Base G2 Annual Emissions (Tons) by Source Category in SO2 Area of Influence 

for Sipsey Wilderness Area 

Table 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 (%)

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Appendix M.3 - 13

January 18, 2008
Table 6. 2018 Base G2 Annual Emissions (Tons) for Top Three Source Categories in SO2 Area 

of Influence for Sipsey Wilderness Area 

Table 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 potential. 

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 (%)

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Appendix M.3 - 14

January 18, 2008
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, 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 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 8. VISTAS Base G2 SO2 Emissions Contributions by Major Source Sector. 

Process in Preparing Files for Cost Effectiveness Curve Development 

The 2018 IPM file used by VISTAS for EGU sources (RPO 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 8. 

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

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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 final EPA 2002 NEI) were assigned to boilers using a primary (highest emitting) SCC. 

Table 9 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 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 

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, Alpine 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 

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Appendix M.3 - 16

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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. 

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Table 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) 

(Bituminous 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: Circulating Bed (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 

(Subbituminous 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 

Pre-Hearing DraftRegional Haze SIP for TN Class I Areas
18 

Development of AOI-Based Cost Curves 

Each Class I area in the VISTAS modeling domain had an associated AOIs as identified 

earlier in this document. 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 will allow VISTAS States to determine if, 

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 emission reduction 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 non-EGU point source within the geographic domain of the AOI. Incremental controls on 

area and mobile sources were not considered in this analysis. An illustrative example of the 

steps involved with the cost effectiveness curve design can be found in the Appendix A of this 

document. 

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/d^2), 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. 

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