### 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 identif y the geographic source regions that are contributing to vi sibility 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 IMPR OVE monitoring data (Brewer and Adhloch, 2005) that demonstrated ammonium sulf ate is the major contributor to PM 2.5 

### mass and visibility impairment at Class I areas in the VISTAS and neighboring states. As il lustrated 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 cont rast, 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.) Particulat e organic matter carbon (OMC) ac counted 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 th e CMAQ regional air quality model project that
reductions in SO2 emissions from Electric Ge nerating Utilities (EGU) and non-EGU industrial point sources will result in the greatest improveme nts 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 emissi ons from ground- level sources or point sources would be small. Based on these analyses, VI STAS states chose to focus their reasonable 0 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1CADI1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt

CM

Soil

EC

POM

Amm NO3
Amm SO4 Rayleigh

0 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1CADI1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt

CM

Soil

EC

POM

Amm NO3
Amm SO4 Rayleigh

### H-2 progress evaluation on potential controls of SO2 emissions from E GU and non-EGU point sources. To select the specific point sources th at would be considered for each Class I area, VISTAS first identified the geographic area that wa s most likely to influence visibility in each Class I area and then identified the major SO 2 point sources in th at 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 site s in VISTAS and neighboring states. Back trajectory analyses use interpolat ed 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 fo r chemical transformation and dispersion of
emissions.

Trajectories were generated using the Hybrid-Si ngle Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laborat ory (ARL). HYSPLIT uses archived 3-dimensional meteorological fields generated from observations and short-term forecasts. HYSPLIT can be run to generate forward or backward trajectorie s using several availabl e meteorological data
archives.

The data archives used in this analysis were the National Weather Service's National Centers for Environmental Prediction Eta Data Assimilation Sy stem (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 horizont al 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 thos e 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 tr ajectories generated a nd the final attribution analyses. In particular, trajectories tend to be come increasingly uncertain the further back in time they are used. Vertical motion in the model is sometimes best represented by following
actual vertical motion measuremen ts (represented by model data), surfaces of constant entropy, or surfaces of constant pressure, depending on th e meteorological conditions at a given location and time. The impact of receptor height (or e nd 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 bound ary layer flow above the local terrain.

For the years 2000 through 2004, back trajectori es 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 tr ajectories, ARS generate d extinction-weighted
residence time data for each IMPROVE site. Each hourly trajectory point was tagged with its associated sample (or end) date. Back trajec tories with end dates corresponding to the worst 20 percent visibility days were weighted with ca lculated extinction values and summed into 1 degree horizontal grid cells of latitude and longitude.

Extinction data is based on the IMPROVE Regiona l 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 or iginal 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. Othe r components were filled based on data from nearby sites.

Trajectory data were weighted by total extinction, and by ex tinction components including ammonium sulfate (AmmSO4), ammonium nitrate (AmmNO3), organic matter carbon (OMC), Light Absorbing Carbon (LAC or EC), Soil, and Coarse Mass (CM). Wei ghting 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 id entified 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 charge d with developing residence time plots to define the geographic area w ith 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 sin ce it does not account for emissions and removal processes, it does not necessa rily 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 an alysis on those geographic regions and source categories which most impact a particular Cla ss I Area. The sources of data used in these analyses include back-trajector y residence time data, provided by ARS and described above, and the VISTAS 2002 and 2018 36-km gridded emissi ons data (Base G, August 2006 inventory version).

### H-5 Extinction weighted back-trajectory residence ti me 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., geod etic lat/lon 1 degree by 1 degree) grid and
transferred to ENVIRON for furthe r 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 an alysis 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-p rojected extinction weighted back-trajectory residence times were then plotted on the VIST AS 12 km modeling domain. As sulfate is the primary visibility impairing component of th e total extinction in the Southeast, the SO4- weighted residence time trajectories were chosen as the basic data set fo r display. For clarity, these data are scaled by the maximum value of the total extinction-weighted residence time within the modeling domain. Fi gure 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 s caled 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-tra jectories 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 a nd/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-weight ed 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 Uti lities and industrial point sources are the largest
contributors to SO4 levels at th e Class I areas. These two source categories make up 95% of the VISTAS SO2 inventory. Therefore these two sour ce categories were identified as the highest priority for evaluation of reasonable progr ess in 2018 at the Class I areas.

The potential impacts of SO2 point source emissi ons 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 w ith both the 2002 and 2018 base year emission inventories. Figures H-4 and H-5 display th e gridded point source SO2 emissions from the VISTAS modeling databases, for 2002 and 2018, resp ectively. 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 em issions 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 applie d. The distance-weighted point source SO2 emissions are then combined with the gridded extinction-weighted back -trajectory residence times at a spatial reso lution of 36 km.

The final step in the development of the displa ys 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 ba sis. 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 th e outlined regions (in red and light orange) represent the extent of the >10% and >5% sulfate extinction-weighted resi dence times, determined from the first step in the analysis (see Figure H-3). As with the resi dence time analysis, the results of the combined distance-weighted SO2 emissions and extinction-weighted reside nce 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 reside nce time weighted by sulfate extinction plots of gridded SO2 emissions and define d 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 de sign is a thorough review of the emission inventories that are used in the modeling of the fu ture year base case. These inventories can shed light on the residual emissions from sources or source categories de fined to be within areas of transport or impact of a Class I area. We us ed 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 f iles 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 fo r 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 su mmaries. This factor is applied to account for the removal of a substantial portion of f ugitive dust emissions near a source by surrounding vegetation and structures when such emissions are used in regional s cale modeling analyses.

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

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

State V OC 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

V ISTA S 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 V OC 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

V ISTA S 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 st arting 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 high est 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 th e 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. Tie r V OC NOX CO SO2 PM-10 PM-2. 5 NH3 Fuel Comb. Elec. Util. 7,23 7 236,192 82,293 542,524 74,612 56,145 3,91 6 Fuel Comb. Industrial 14,210 181,413 168,051 198,03 3 29,686 19,351 59 2 Fuel Comb. Other 61,63 5 55,145 178,412 57,140 27,763 25,516 87 3 Chemical & Allied Pr oduct Mf g 19,70 7 6,225 61,613 16,31 5 6,046 4,038 1,64 3
Metals Processin g 12,06 9 8,866 235,955 48,88 9 23,309 19,855 778.7 8 Petroleum & Related Industries 2,37 2 1,689 3,353 5,70 9 998 643 0.0 2 Other Industrial Processes 112,62 9 59,626 94,469 42,121 77,815 36,776 3,13 3 Solvent Utilization 583,810 969 574.93 12.8 7 2,387 1,935 105.74 Storage & Transport 84,17 7 462 422.07 122.1 4,637 2,668 3.5 9 Waste Disposal & Recyclin g 46,900 14,606 310,604 2,43 8 53,825 50,254 16 9 Highway Vehicles 231,42 6 220,157 3,159,96 8 3,31 5 10,934 5,399 33,044 Off-highwa y 151,510 186,975 2,304,37 2 7,78 9 13,846 13,083 204.24 Miscellaneous 39,73 3 13,403 642,523 3,640 580,930 117,56 6 308,84 8

V ISTAS Total 1,367,414 985,728 7,242,611 928,04 6 906,789 353,22 9 353,311 A

nnual 2018 BaseG2 Emissions (Tons)

Tie r V OC NO X 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 Pr oduct Mf g 1% 1% 1% 2% 1% 1% 0%
Metals Processin g 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 Utilizatio n 43% 0% 0% 0% 0% 1% 0% Storage & Transpor t 6% 0% 0% 0% 1% 1% 0% Waste Disposal & Recyclin g 3% 1% 4% 0% 6% 14% 0% Highway Vehicles 17% 22 % 44% 0% 1% 2% 9% Off-highwa y 11% 19 % 32% 1% 2% 4% 0%
Miscellaneous 3% 1% 9% 0% 64% 33% 87%

V

ISTAS Total 100 % 100 % 100 % 100 % 100 % 100 % 100 % A

nnual 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 resi dual emission categories in the futu re 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 s ource 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-effectiv e emission reduction potential.

Finally, once each subcategory was identified, unit level tables of emission comparisons from 2002 to 2018 were developed allowing VISTAS Stat es to review existing emission reductions and providing the ability to assign new cost-effective controls to un its using the best control for the scenario. These tables presented comp arisons of 2002 and 2018 emission levels, for SO2, Tier V OC NOX CO SO2 PM-10 PM-2. 5 NH3 Fuel Comb. Elec. Util.-Coal 5,83 8 222,105 43,332 538,424 70,940 52,634 988 Fuel Comb. Elec. Util.-Oil 6 5 548 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 3,24 8 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 2,13 8 1,30 5 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 5,14 9 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 29 5 0 Fuel Comb. Other- 131 3,076 1,193 32,056 1,602 96 8 149 Fuel Comb. Other- 65 8 11,045 7,131 946 962 89 8 23 Fuel Comb. Other-Misc. Fuel Comb. 1,14 5 3,811 21,618 1,649 95 8 68 9 3 Fuel Comb. Other-Residential Wood 57,645 3,034 129,21 5 431 21,242 20,34 5 304 Fuel Comb. Other-Residential Other 1,753 28,742 18,249 3,272 2,560 2,323 395

Top Tier Total 83,082 472,750 428,75 6 797,69 7 132,061 101,01 3 5,381 A

nnual 2018 BaseG2 Emissions (Tons)

Tier V OC NO X 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 % A

nnual 2018 BaseG2 Emissions (%)

### H-12 and future year control technology assignment ( by IPM forecasting and State modification) for point sources. Non-EGU unit-spec ific 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 emissi ons 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 contro ls 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 sour ces (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 contro l efficiency with each control device applied
to SO 2 

### and NO X 

### , we made some assumptions, based on IP M documentation, as to what pollutant specific level of reduction was applied in the futu re year base case runs. These assumptions, by primary and secondary control de vice code combinations for SO 2, 

### are presented in Table H-8.

Since many of the control technology control cost equations within AirControlNET require additional unit-level characteristic data, we al so 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 calc ulations for many of the SO2 applied technologies. In cases where these nameplate capacity values could not be identified, emission weighted (based on the A

l a b

a m

a
F l o r i d

a

K e n t u c k

y
G

e o r g i a

M i s s i s s i p p i

S o u t h

C

a r o l i n a

N

o r t h

C

a r o l i n a

T e n n e s s e e V i r g i n i a

W e s t V

i r g i n i a 

### 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU 2

0

0

2

2

0

0

9

2

0

1

8

A

l a b

a m

a
F l o r i d

a

K e n t u c k

y
G

e o r g i a

M i s s i s s i p p i

S o u t h

C

a r o l i n a

N

o r t h

C

a r o l i n a

T e n n e s s e e V i r g i n i a

W e s t V

i r g i n i a 

### 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU 2

0

0

2

2

0

0

9

2

0

1

8 Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU 2

0

0

2

2

0

0

9

2

0

1

8

### H-13 final EPA 2002 NEI) were assigned to boilers usi ng a primary (highest emitting) SCC. Table H9 presents these weighted capacities. Additionall y, 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 va riables 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 SO 2 

### 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 t ool developed to support the U.S. EPA in its analyses of air pollution policies and regulatio ns (Pechan, 2005). The tool provides data on emission sources, potential pollutio n control measures and emissi on reductions, and the costs of implementing those controls.

The core of AirControlNET is a relational databa se system in which control technologies are
linked to sources within EPA emi ssions inventories. The system contains a database of control measure applicability, efficiency, and cost inform ation for reducing the em issions contributing to ambient concentrations of ozone, PM 10 

### , PM 2.5 

### , SO 2 

### , NO X 

### , as well as visibility impairment (regional haze) from point, ar ea, and mobile sources. PM 10 

### and PM 2.5 

### 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 prepro cessed application of control technologies to a predetermined se t of EPA emission inventories, di rect use of the model in this analysis was not possible. However, VI STAS received approval from EPA’s Innovative Strategies and Economics Group (ISEG) to modify the AirControlNET vers ion 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-effectivene ss based control strategies by
source or domain.F:\VISTAS Phase II\TSD\draft#3\Appendix_H_AOI3.docH-14Table H-9.Emissions Weighted NETDC (MW) Association.SCCDescriptionNETDC (MW)10100201 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal) 20010100202 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Bituminous Coal) 50010100203 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Cyclone Furnace (Bituminous Coal) 20010100212External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) (BituminousCoal)50010100215 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Cell Burner (Bituminous Coal) 130010100218External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Atmospheric Fluidized Bed Combustion: Circulating Bed(Bitum. Coal)20010100222 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Subbituminous Coal) 40010100223 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Cyclone Furnace(Subbituminous Coal)40010100226External Combustion Boilers; Electric Generation; Bituminous/SubbituminousCoal; Pulverized Coal: Dry Bottom Tangential (SubbituminousCoal)50010100401 External Combustion Boilers; Electric Generation; Residual Oil;Grade 6 Oil: Normal Firing 40010100404 External Combustion Boilers; Electric Generation; Residual Oil;Grade 6 Oil: Tangential Firing 50010100501 External Combustion Boilers;Electric Generation;Distillate Oil; Grades 1 and 2 Oil 40010100601 External Combustion Boilers; Electric Generation; Natural Gas; Boilers > 100 Million Btu/hr except Tangential 40010100701 External Combustion Boilers; Electric Generation; Process Gas;Boilers > 100 Million Btu/hr 20010100801 External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes 60010101204 External Combustion Boilers; Electric Generation; Solid Waste;Tire Derived Fuel : Shredded 20010300811 External Combustion Boilers; Commercial/Institutional; Landfill Gas; Landfill Gas 20020100101 Internal Combustion Engines;Electric Generation; Distillate Oil (Diesel); Turbine 20020100109 Internal Combustion Engines; Electric Generation; DistillateOil (Diesel); Turbine: Exhaust 20020100201 Internal Combustion Engines; ElectricGeneration; Natural Gas; Turbine200All other boilers100F:\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 great est 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 applica tion 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 appli cation of SO2 control technologies from the associations originally deve loped in AirControlNET and impr oved 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 th e 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 emi ssion reduction targets for SO2 within each AOI.
Each individual source had its own co st 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 consid ered 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/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 in cremental review and c onsideration. States can
use these values to further determine control st rategy development for individual Class I areas.