# Appendix M

. 3

VISTAS Area of Influence Analyses for Sulfur Dioxide

Tennessee Department of Environment and Conservation
Division of Air Pollution Control
9 th 

## Floor L&C Annex
401 Church Street Nashville, TN 37243

# 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

## Introduction

The objective of the VISTAS Area of Influen ce analysis is to id entify 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 majo r contributor to PM2.5 mass and visibility impairment at Class I areas in the VI STAS 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 calculate d light extinction at Class I areas in the Southern Appalachians and greater than 60% of the calcu lated light extinction for coastal sites in the VISTAS states (excepting Everglades). In c ontrast, ammonium nitrate contri buted less than 5% of the calculated light extinction at VIST AS Class I areas on the 20% wors t visibility days. (Nitrate has a somewhat larger contribution on 20% worst da ys at Class I areas in neighboring states.) Particulate organic matter (org anic 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 Vi sibility Days in 2000-2004 using New IMPROVE Equation (data from VIEWS Sep 2006).

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

## VISTAS emissions sensitivity analyses using the CMAQ regional air quality model project that reductions in SO2 emissions from Electric Genera ting 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 sour ces comprise 95+% of the SO2 emissions inventory in the VISTAS states. The emissi ons sensitivity analyses also indicate that improvements in visibility from reductions in NOx or primary carbon emissions from groundlevel sources or point sources w ould be small. Based on these analyses, VISTAS states chose to focus their reasonable progress evaluation on poten tial controls of SO2 em issions 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 geogr aphic area that was most likely to influence visibility in each Class I area a nd then identified the major SO2 point sources in that geographic

area.

Back Trajectory Analyses

In support of VISTAS’s Area of Influen ce (AoI) analysis, Air Resource Specialists (ARS) generated meteorological back trajec tories for IMPROVE sites in VISTAS and neighboring states. Back trajectory analys es 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 fo llows a parcel of air backward in hourly steps for a specified length of time. Back trajectori es 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 Hybr id-Single Particle La grangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Labora tory (ARL). HYSPLIT uses archived 3- dimensional meteorological fields generated fr om observations and short-term forecasts. HYSPLIT can be run to generate forward or b ackward 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 A ssimilation 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 horizo ntal 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 th is analysis include t hose listed in Table 1.

## 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 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 the meteorological conditions at a given location and time. The impact of receptor height (or e nd 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 bound ary 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)

## Based on the five years of individual backtrajectories, 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 Regi onal 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 devel oped and applied a data substitution protocol to fill missing data. Organic carbon and elemental car bon 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 tota l extinction, and by extinction components including ammonium sulfate (A mmSO4), ammonium nitrate (A mmNO3), 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 import ance 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.

## 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 20% worst days dominated by sulfat e. Residence time over an area is indicative of general flow patterns, but si nce it does not account for emissions and removal processes, it does not necessarily imply speci fic areas contributed significan tly to haze compounds at a receptor site.

Area of Influence Data and Displays

The primary objective of the Area of Influence (AO I) displays and data sets are to assist the States in focusing their reasonable progress an alysis on those geogra phic 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 emissions data (Base G, Aug 2006 inventory version).

Extinction weighted back-traject ory 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-proje cted (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 Co nformal Projection domain. Once the data were re-projected, the data were mapped to the 36-km grid cells in the RPO pr ojection using an areaweighted mapping routine. The re-projected extinction weighted back-trajectory residence times were then plotted on the VISTAS 12-km modeling do main. As sulfate is the primary visibility impairing component of the tota l extinction in the Southeast, the SO4-weighted residence time trajectories were chosen as the basic data set for display. For clarity, thes e data are scaled by the maximum value of the total extinction-weighted residence time within the modeling domain. Figure 3 displays an example of th e back-trajectory residence time plot for Sipsey, Alabama.

## Figure 3. Sulfate-weighted back-trajectory residence time (RT) displays for Sipsey, AL. (Red outline delineates the region with RT > 10%; oran ge outline (grey cells) delineates regions with RT > 5% )

As an aid to the analysis, all regions with sc aled residence time values greater than 5% and greater than 10% were noted on the displays as shown in Figur e 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 imp act from emission sources within the regions identified from the extinction-weight ed back-trajectory residence time displays and analyses.

Previous VISTAS contribution assessment, em issions sensitivity modeling, and inventory analyses have indicated that SO4 is the larges t contributor to haze on th e 20% worst visibility days and that Electric Generating Utilities and industrial point s ources are the largest
contributors to SO4 levels at th e Class I areas. These two sour ce categories make up 95% of the VISTAS SO2 inventory. Therefore these two so urce 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 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 di splay 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 su mming across all days to obtain annual emission totals for each grid cell.

## 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 e ffects of transport, deposition, and chemical transformation of point source emissions along the path of the trajec tories, these data were weighted by 1/d, where

## d was calculated as the distance between the 36-k m grid cell centers for the source and the Class I area monitor, in kilometers. For the grid ce ll containing the Class I Area monitor, a weighting of 1/9 was applied. The distan ce-weighted point source SO2 emissions are then combined with the gridded extinction-weighted back -trajectory residence times at a sp atial resolution 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 extinc tion-weighted back-traj ectory residence times (SO2 * Bext-weighted RT * (1/d)) on a grid ce ll 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 th e result of this final analysis step. Note that the out lined regions (in red and light ora nge) represent the extent of the
>10% and >5% sulfate extinction-we ighted residence times, determined from the first step in the analysis (see Figure 3). As w ith 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 delineate s the region with RT > 10%; orange outline delineates regions with RT > 5% )

### Figure 7. Normalized 2018 SO2 point source emissions times distance-weighted residence time display for Sipsey, AL. (Red outline delineate s 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 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 c ontrol strategy design 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 modeli ng 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 f ugitive dust emissions near a source by surrounding vegetation and structures when such emissions are used in regional s cale modeling analyses.

### 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 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 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 dist ance (Q/d) greater than 5 and maximum residence time (RTmax) greater than 5. States independent ly defined the criteria for selec ting which sources from this list
would be considered for reasonable control meas ures. RTmax greater than 10 was used to
define emissions included in Tables 4-8. 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)

## Table 4. 2018 Base G2 Annual Emissions (Tons) by S ource 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 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 (%)

### Table 6. 2018 Base G2 Annual Emissions (Tons) for T op 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 alread y 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 reducti on or if source types already reduced have reached the full cost-effective potential. 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 (%)

### Finally, once each subcategory was identified, unit level tables of emission comparisons from 2002 to 2018 were developed allowing VIST AS 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 pres ented comparisons of 2002 and 2018 emission levels, for SO2, and future year control technolog y assignment (by IPM forecasting and State modification) for point sources. Non-EGU unit-specific technology assignments were identified using the control efficiency ta bles provided by the VISTAS em issions 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 fo r EGU and non-EGU point sources only. Ultimately, the long-term strategy will include em issions controls on the BART applicable sources. However, determinations of BART cont rols were not available for this analysis.

Figure 8. VISTAS Base G2 SO2 Emissions Co ntributions by Major Source Sector.

Process in Preparing Files for Cost Effectiveness Curve Development

The 2018 IPM file used by VISTAS for EG U 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 cont rol 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 device code combinations for SO 2, 

### are presented in Table 8. Al

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 2002

2009

2018

Al

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 2002

2009

2018 Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU Miscellaneous

Nonroad
Onroad

Industrial
Other Fuel Combustion

EGU 2002

2009

2018

### Since many of the control technology contro l 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 SO 2 applied technologies. In cases where these nameplate capacity values coul d 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. Addi tionally, stack flow, sulfur content, and primary SCC assignment were necessary to cross-referen ce available incremental control technologies to
the base case emissions inventor y 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 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



### 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 regul ations (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 pr eprocessed application of control technologies to a predeter mined set of EPA emission inventor ies, 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 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 ab ove, 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 en tire VISTAS 36 km domain necessary for VISTAS States to design command- and-control or cost-effectiveness based control strategies by source or domain.Table 9.Emissions Weighted NETDC (MW) Association.SCC DescriptionNETDC (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; ElectricGeneration; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Tangential)(Bituminous Coal)50010100215 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Cell Burner (Bituminous Coal)130010100218External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Atmospheric Fluidized Bed Combustion:Circulating Bed (Bitum. Coal)20010100222External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (SubbituminousCoal)40010100223 External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Cyclone Furnace (Subbituminous Coal)40010100226External Combustion Boilers; ElectricGeneration; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom Tangential(Subbituminous Coal)50010100401 External Combustion Boilers; Electric Generation; Residual Oil;Grade 6 Oil: Normal Firing40010100404 External Combustion Boilers; Electric Generation; Residual Oil;Grade 6 Oil: Tangential Firing50010100501 External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1 and 2 Oil40010100601 External Combustion Boilers; Electric Generation; Natural Gas; Boilers > 100 Million Btu/hr except Tangential40010100701 External Combustion Boilers; Electric Generation; Process Gas; Boilers > 100 Million Btu/hr20010100801 External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes60010101204 External Combustion Boilers; Electric Generation; Solid Waste;Tire Derived Fuel : Shredded20010300811 External Combustion Boilers; Commercial/Institutional; Landfill Gas; Landfill Gas20020100101 Internal Combustion Engines;Electric Generation; Distillate Oil (Diesel); Turbine20020100109 Internal Combustion Engines; Electric Generation; DistillateOil (Diesel); Turbine: Exhaust20020100201 Internal Combustion Engines; ElectricGeneration; Natural Gas; Turbine200All other boilers100

### 18 Development of AOI-Based Cost Curves

Each Class I area in the VISTAS modeling domai n 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 th e 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 curv es will allow VISTAS States to determine if, based on the generic application of SO2 control t echnologies from the associations originally developed in AirControlNET and improved upon w ith 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 boundari es of the AOIs. This parsed list was then sorted on in incremental cost-effectiveness (marginal cost) basis to determine the
most cost effective control suit e 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 applicati ons 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 e ffectiveness curve design can be found in the Appendix A of this
document.

With each AOI’s cost curve, additional me trics 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 dist ance (Q/d^2), and the normalized 2018 SO2 point source emissions times distance-weighted resi dence 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 fu rther determine control strategy development for
individual Class I areas.