### APPENDIX 

### H

VISTASArea 

### ofInfluence Analyses

Based on "VISTAS Area of Influence Analyses for SulfurDioxide" by Archuleta, Adhloch, Mansell, Stella and Brewer (February28,2007) and "Procedures for Developing andDisplaying AoI Back-trajectoriesResidence Time GIsData by Mansell (August 29,2007)

### H.1 Introduction

The objective of the VISTAS Area of Influence analysis is to identify the geographic source regions that are contributing to visibility impairment at the Class I areas onthe worst 20 percent visibility days. This information is being used by the VISTAS states as part of the evaluation and demonstration of reasonableprogress toward visibility improvement in Class I 

### areas.

VISTAS' contribution assessment based on IMPROVE monitoring data (Brewer and Adhloch, 2005) that demonstrated ammonium sulfate is the major contributor to PM2.5

### mass and visibility impairment at Class I areas in the VISTAS and neighboring states. As illustrated in Figure H-I, on the worst 20 percent visibility days during the 2000-2004 5-year Baseline, ammonium sulfate accounted for greater than 70% ofthe 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 extinctionat VISTAS Class I areas on the worst 20percent visibility days. (Nitrate has a somewhat larger contribution on the worst 20 percent days at Class I 

### areas in neighboring states.) Particulate organic matter carbon (OMC) accounted for 10-20% of light extinction on the worst 20 percent visibility days (except Everglades where organic carbon accounted for 40%).

Soil EC El POM
RAmm NO



### 1 VISTAS coastal 

### 1 VISTAS inland 

### 1 non-VISTAS 

### I L -I Figure H-1. Average extinction for the worst 20 percent visibility days in 2000-2004 using the
new IMPROVE equation (data from VIEWS September2006).



### 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 sourceswill result in the greatest improvements 

### invisibility at VISTAS Class 1

### 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 inNOx 

### orprimary carbon emissions from ground- level sourcesorpoint sources would be small. Based on these analyses, VISTAS states chose to focus their reasonableprogress 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 identifiedthe major SO2point sources in that geographic area.



### H.2 Back Trajectory Analyses

In support of VISTAS'S Area of Influence (AoI) analysis, Air Resource Specialists (ARS) generated meteorological back trajectories for IMPROVE sites in VISTAS and neighboring states. Back trajectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air masses that arrive at a receptor at a given time. The method essentially follows a parcel of airbackward in hourly steps for a specified length oftime. 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). TheEDAS 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 

### (11tt~:~lwww.arl.i1oaa.~oviready~hvsplit4.11tml).

Themajor model parameters selected for this analysis include those listed inTableH-1.

IAnalysis.

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 alsoimportant. Low-ending trajectories represent air parcels nearer to ground level and high-ending trajectories may represent more accurate boundary layer flow above the local terrain.

For the years 2000 through 2004, back trajectories were computed for the following 22 IMPROVE sampling sites in and around the VISTAS region:

Breton, LA (BRET1) Brigantine NWR, 

### NJ (BRIGI) Cadiz, 

### KY (CADI1) Caney Creek, AR (CACRI) CapeRomain NWR, SC (ROMA1) Chassahowitzka NWR, FL (CHAS1) Cohutta, GA (COHU1) Dolly Sods Wilderness, WV (DOSOI) Everglades NP, FL (EVER1 ) Great Smoky Mountains NP, TN (GRSMI) Hercules-Glades, MO (HEGL1) James River Face Wilderness, VA (JARII) Linville Gorge, NC (LIGO1) Mammoth CaveNP, 

### KY (MACA1) Mingo, MO (MING1) OkefenokeeNWR, GA (OKEF1) Shenandoah NP, VA (SHEN1) Shining Rock Wilderness, NC (SHRO1) Sipsy Wilderness, AL (SIPS 1) St.Marks, FL (SAMA1) Swanquarter, NC (SWAN1) Upper Buffalo Wilderness, AR (UPBU1)

Based on the five years of individual back trajectories, ARS generated extinction-weighted residence time data for each IMPROVE site. Each hourly trajectory point was tagged with its associated sample (or end) date. Back trajectories with end dates corresponding to the worst 20 percent visibility days were weighted with calculated extinction values and summed into 1 degreehorizontal grid cells oflatitude and longitude.

Extinction data is based on the IMPROVE Regional Haze Rule dataset, asavailable from the VIEWS website (httv 1,vutn cmcolostate eduviews/Web~IMPKOVE SumrnarsL>ata.aspx)in November 2005 and is calculated based on the original IMPROVE algorithm. For siteswith less than three complete years ofdata, ARS developed and applied adata 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 (AmmS04), ammonium nitrate (AmmN03), organic matter carbon (OMC), Light Absorbing Carbon (LAC or EC), Soil, and Coarse Mass (CM). Weighting trajectories for the worst 20 percent days by sulfate extinction gave greater importance to days influenced by SO2 emissions and lesser importance to days influenced by organic carbon emissions. This allowed a geographic area of influence to be identified based on sulfate contributions and not skewed by contributions from fire.SIPS - 2002 72-hr BackTrajectory 20% Highest (Extinction)

EndTime: 1200 EST

EndHe~ght lOOm Vertical Motion: Data 

# a site Location

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

These data files were provided to ENVIRON. ENVIRON was charged with developing residence time plots to define the geographic area with highest probability of influencing the receptor on the worst 20 percent days dominated by sulfate. Residence time over an area is indicative of general flow patterns, but since it does not account for emissions and removal processes, it does not necessarily imply specific areas contributed significantly to haze compounds at a receptor site.



### H.3 Area ofInfluence 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 analysesinclude back-trajectory residencetime data, providedby ARS and described above, and the VISTAS 2002 and 2018 36-km gridded emissions data (Base 

### G, August 2006 inventory version).Extinction weighted back-trajectory residence time plots were provided by ARS for all Class I Areas inthe 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 MherGIsprocessing and analysis. Back-trajectories were run for 100-meterand 500-meter start heights. Because the back-trajectories were to be combined with gridded emissions data, the first step in the analysis and GIsprocessing involved re-projecting these data to the 36 

### km Inter-EWO Unified Lambert Conformal Projection domain. Once the data were re-projected, the data were mapped to the 36

### km grid cells inthe RPO projection using



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

residence times were then plotted on the VISTAS 12 

### km modeling domain. As sulfate is the primary visibility impairing component of the total extinction in the Southeast, the S04- weighted residence time trajectories were chosen as the basic data set for display. For clarity, these data are scaled by the maximum value of the total extinction-weighted residence time within the modeling domain. Figure H-3 displays an example of the back-trajectory residence time plot for Sipsey, Alabama.

Figure H-3. Sul ipsey,AL. (Red outline delineatesthe 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 greaterthan 10% were noted onthe displays as shown inFigure H-3. Finally, county boundaries

were overlaid to identify those counties and 36 

### km modeling grid cells for which the extinctionweighted residence time back-trajectories exceeded 

### 5% and 10%. These data were then exported

as comma-delimited ASCII data files for Wher analyses. These residence time displays are used primarily to determine the regions (counties andlor grid cells) impacting each Class I Area. 

### a he next step in the analysis is to determine the potential impact from emission sources within the regions identified fiom 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 worst 20 percent visibility days and that Electric Generating Utilities and industrial point sources are the largest contributorsto SO4 levels at the Class I 

### areas. These two source categoriesmake 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 atthe Class

### I areas.

The potential impacts of SO2 point source emissions for each Class 

### I Area are determined by combining gridded emissions data and weighted back-trajectory residence times for the 

### 36 

### km grid cells. The analysis was carried out with both the 2002 and 2018 base year emission inventories. Figures H-4 and 

### H-5 display the gridded point source SO2 emissions from the VISTAS modeling databases, for 2002 and 2018, respectively. These data were processed by summing all vertical layers for each grid column and then summing across all days to obtain annual emission totals foreachgrid cell.

n tonslyear. (2002 BaseG).DRAFT V1SIAS 2018PointSowca302EmiaQEons

on for 2018 in tonslyear. (201 8 BaseG).

As a way of incorporating the effects of transport, dispersion, deposition, and chemical transformation of point source emissions along the path of the trajectories, these data were weighted by lld, 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 119 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 (lld) gridded point source SO2 emissions are multiplied by the total extinction-weighted back-trajectory residence times (SO2 

## * Bext-weighted RT 

## * (lld)) 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 20 18base year inventories. Figures 

### H-6 and 

### H-7 display the result ofthis final analysis step. Note that the outlined regions (inred and light orange) represent the extent of the >lo% and 

### >5% sulfate extinction-weighted residence times, determined from the first step in the analysis (see Figure 

### H-3). As with the residence time analysis, the results of the combined distance-weighted SO2 emissions and extinction-weighted residence times are overlaid with county boundaries and exported to comma-delimited ASCII data files for further analyses of emissions and control options for specific sources.nce-weighted residence

> 10%; orange outline

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 regionswith 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 greaterthan lo%,based on trajectories with start height of 100mand 4 starttimes per day.

### H.4 Summary ofEmission Inventories Used in Cost Curve Analysis
A necessary component of control strategy design is a thorough review of the emission inventories that areused in themodeling ofthe 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 201

### 8base case and 2002 base year emissions to conduct this cost curve analysis.

TheNIF formatted and SMOKE-ready modeling files forboth 2002 and 201

### 8base 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 ofinterest.

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 ths analysis, the fugitive dust transport factor (FDTF) is included in the PM emission summaries. This factor is applied to account for the removal of a substantial portion of fbgitive dust emissions near a source by surrounding vegetation and structures when such emissions areused inregional scalemodeling analyses.

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

# - 2002 Typical (Tons) State VOC NOx CO SO2 PM-I0 PM-2.5 NH3 AL 445,497 459,880 2,534,778 584,665 232,229 105,341 67,970

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

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

# - 2018 Base Case (Tons) 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

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

### 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

areillustrated for sources in the SO2Area ofInfluence 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 (Qld) 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 throughH-8.

Table H-4. 2018 Base G2 Annual Emissions (Tons) by Source Category in SO2 Area of lnfluence for Si~sev WildernessArea.



# . . Annual 2018 BaseGZEnissions(Tons) Tier VOCI NO4 

### col so21 MlOl PIK2.4 Fuel Comb. Elec. Util. 74.6121 .%. 1451

I I I I I I I VISTASTotal 

### 1 1,367,414 985,7281 7,242,6111 928,- 906,789( 353,229( 353,311

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

## - ~i~hway vehicles ' 231.426' 2203157 3,1591968' 3,315 10,934 5,399 13,083 117,566 Of-highway Miscellaneous 3,044 204.24 308,848 7,789 3,640 151,510 39,733 13,846 580,930 186,975

13,403 2,304,372 642,523Table H-6. 2018 Base G2 Annual Emissions (Tons) for Top Three Source Categories in SO2 Area of Influencefor SipsevWildernessArea.

FuelComb.Other-ResidentialWcod 57,645 3,034 129,215 20,345 304 FuelComb.Wr-Residential Other 1,753 28,742 18,249 2,323 395

Table H-7. 2018 Base G2 Annual Emissions (Percentage) for Top Three Source Category in SO2 Area of lnfluence for Sipsey WildernessArea.

I 1 I I TopTierTotal 4%l 47x1

, 6%1 85%1. 14741 28x1 1%

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 futureyear base casehad the potential for reduction orif source types already reduced have reached the fullcost-effective emission reduction potential.
Finally, once each subcategory was identified, unit level tables of emission comparisons from 2002 to 201 8 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 S02,and hture 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 theVISTAS emissions inventory contractor, MACTEC.
Using these emission reviews and summaries like those presented in Figure H-8, VISTAS states made a decision to further evaluate reasonable measures for SO2 emissions controls beyond the On-The-Books scenario in 2018 for EGU and non-EGU point sources only. Ultimately, the long-term strategy will include emissions controls on the BART applicable sources. However, determinations ofBART controls were not available for this analysis.

Miscellaneous - Nonroad Onroad Industrial FuelCombustion

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



### H.5 Processin Preparing Files for Cost Effectiveness CurveDevelopment

The20 18 IPM file used by VISTAS for EGU sources (RPOIPM version 2.1.9) was obtained and matched to the 2018 base case inventory of EGU sources. This step was conducted to ensure that incremental controls assigned to these source types did not duplicate existing base case assumptions. Because IPM does not assign a control efficiency with each control device applied to SO2andNOx, we made some assumptions, based on IPM documentation, as to what pollutant specific level ofreduction was applied in the future year base case runs. These assumptions, by primary and secondary controldevice codecombinations for SO2,arepresented inTableH-8.

Since many of the control technology control cost equations within AirControlNET require additional unit-level characteristic data, we also made matches of the SMOKE IDA files to VISTAS NIF, EPA NEI, or EPA CAMD CEM data setsto obtain these variables when missing. Unit level boiler capacity (MMBtuhr) 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 thefinal EPA 2002 NEI) were assigned to boilers using aprimary (highest emitting) SCC. Table 

### H9 presents these weighted capacities. Additionally, stack flow, sulfur content, and primary SCC assignment were necessary to cross-reference available incremental control technologies to the base case emissions inventory data. These variables were obtained where matches could be found, inpriority orderofVISTAS, CAMD, and other EPA datasets, respectively.

H.6 

### Application ofAirControlNET 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, PMlo,PM2.5, SO2,NOX, as well as visibility impairment (regional haze) from point, area, and mobile sources. PMlo 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 ofEPA emission inventories, direct use of the model in this analysis was not possible. However, VISTAS received approval from EPA's Innovative Strategies and Economics Group (ISEG) to modify the AirControlNET version 4.1 source code and data tables in order to make it useful to this study (Sorrels, 2006). The results of the application of this modified version of the code still retain the applicability, efficiency, and cost information from the unmodified version of the source code, but were applied to the VISTAS modeling inventories withupdated 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.EmissionsWeighted NETDC (MW)Association. Description 

## I NETDC (MW) External Combustion Boilers; Electric Generation; BituminouslSubbituminousCoal; Pulverized Coal: Wet Bottom (Bituminous Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminousCoal; Pulverized Coal: Dry Bottom (Bituminous Coal) ExternalCombustion Boilers; Electric Generation; Bituminous1SubbituminousCoal; Cyclone Furnace (Bituminous Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) (Bituminous Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminousCoal; Cell Burner (Bituminous Coal) External Combustion Boilers; Electric Generation; Bituminous1Subbituminous Coal; Atmospheric Fluidized Bed Combustion: Circulating Bed (Bitum.Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminousCoal; Pulverized Coal: Dry Bottom (Subbituminous Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminousCoal; Cyclone Furnace (Subbituminous Coal) External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Dry Bottom Tangential (Subbituminous Coal) External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Normal Firing External Combustion Boilers; Electric Generation; Residual Oil; Grade 6Oil: Tangential Firing External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1and 2 Oil External Combustion Boilers; Electric Generation; Natural Gas; Boilers> 100 Million Btulhr except Tangential External Combustion Boilers; Electric Generation; Process Gas; Boilers > 100 Million Btulhr External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes External Combustion Boilers; Electric Generation; Solid Waste; Tire Derived Fuel : Shredded External Combustion Boilers; Commercialllnstitutional;Landfill Gas; Landfill Gas lnternal Combustion Engines; Electric Generation; DistillateOil (Diesel); Turbine

F:\VJSTASPhaseJI\TSD\draft#3\AppendixxHHAOI3.doc Internal Combustion Engines; Electric Generation; DistillateOil (Diesel); Turbine: Exhaust Internal Combustion Engines; Electric Generation; Natural Gas; Turbine All other boilers 200 200 100

### H.7 Development ofAOI-Based Cost Curves

Each Class I area in the VISTAS modeling domain had an associated AOIs. In order to best determine where emission reduction has the greatest benefit, this geography was designed to limitthe available source type list from including all sources withinthe entire domain.
These marginal cost curves include the application of all available control measures to all applicable point sources within the AOI. The curves allow VISTAS States to determine if emission controls, based on the generic application of SO2 control technologies from the associations originally developed in AirControlNET and improved upon with stakeholder input, will help attain reasonable further progress objectives while presumably minimizing the number and type of controlled sources in each AOI.
Using a geocoded county list from these AOIs, we parsed the master list of incremental control

measures from all point sources located within the boundaries of the AOIs. This parsed list was then sorted on in incremental cost-effectiveness (marginal cost) basis to determine the most cost effective control suite available to attain emission reduction targets for SO2 within each AOI. Each individual source had its own cost effectiveness curve generated.
In aggregate, the results ofthese applications are SO2 emission cost curves forall EGU and nonEGU point source withthe geographic domain of the AOI. Incremental controls on area and mobile sources were not considered in this analysis.

With each AOI's cost curve, additional metrics calculated for the distance from the emission release point to the Class I area IMPROVE monitor (in 

### km), the 2018 residual emissions and distance (Qld) or squared distance (~ld*), 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 forindividual Class 

### I areas.

F:\VISTASPhase II\TSD\draft#3\Appendix-H-AOI3.doc H-15