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 Introduction 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

accounted for 40%). 

Soil 

EC 

El POM 

R Amm 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 September 2006). 

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

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

point sources will result in the greatest improvements in visibility at VISTAS Class 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 in NOx or primary carbon emissions from ground- level sources or point sources 

would be small. Based on these analyses, VISTAS states chose to focus their reasonable 
progress evaluation on potential controls of SO2 emissions from EGU and non-EGU point 

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

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

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

H.2 Back Trajectory Analyses 

In support of VISTAS'S Area of Influence (AoI) analysis, Air Resource Specialists (ARS) 

generated meteorological back trajectories for IMPROVE sites in VISTAS and neighboring 

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

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

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

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

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

emissions. 

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

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

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

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

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

archives. 

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

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

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

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

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

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

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

I Analysis. 

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

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

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

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

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

and time. The impact of receptor height (or end height) on an individual trajectory is also 
important. Low-ending trajectories represent air parcels nearer to ground level and high-ending 

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

For the years 2000 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 (CADI 1) 

Caney Creek, AR (CACRI) 

Cape Romain NWR, SC (ROMA1) 

Chassahowitzka NWR, FL (CHAS 1) 

Cohutta, GA (COHU1) 

Dolly Sods Wilderness, WV (DOSOI) 

Everglades NP, FL (EVER 1 ) 

Great Smoky Mountains NP, TN (GRSMI) 

Hercules-Glades, MO (HEGL 1) 

James River Face Wilderness, VA (JARII) 

Linville Gorge, NC (LIGO 1) 

Mammoth Cave NP, KY (MACA 1) 

Mingo, MO (MING 1) 

Okefenokee NWR, GA (OKEF 1) 

Shenandoah NP, VA (SHEN 1) 

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 

degree horizontal grid cells of latitude and longitude. 

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

VIEWS website (httv 1, vutn cm colostate edu views/Web~IMPKOVE SumrnarsL>ata.aspx) in 

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

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

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

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

nearby sites. 

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

ammonium sulfate (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 Back Trajectory 

20% Highest (Extinction) 

End Time: 1 200 EST 

End He~ght lOOm 

Vertical Motion: Data 

a site Location 

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

Wilderness Area. 

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

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

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

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

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

compounds at a receptor site. 

H.3 Area of Influence Data and Displays 

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

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

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

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

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

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

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

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

transferred to ENVIRON for Mher GIs processing and analysis. Back-trajectories were run for 

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

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

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

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

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

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

primary visibility impairing component of the total extinction in the Southeast, the 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 delineates the region with RT > 10%; orange outline (grey cells) delineates regions with 

RT > 5% ). 

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

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

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

as comma-delimited ASCII data files for 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 

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

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

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

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

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

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

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

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

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

annual emission totals for each grid cell. 

n tonslyear. 

(2002 BaseG). 
DRAFT V1SIAS 2018 Point Sowca 302 EmiaQEons 

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 18 base year inventories. Figures H-6 and H-7 display the result of this 

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

the >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 regions with RT > 5% ). 

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

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

time greater than lo%, based on trajectories with start height of 100m and 4 start times per day. 
H.4 Summary of Emission Inventories Used in Cost Curve Analysis 

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

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

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

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

201 8 base case and 2002 base year emissions to conduct this cost curve analysis. 

The NIF formatted and SMOKE-ready modeling files for both 2002 and 201 8 base year and base 

cases were obtained from VISTAS emissions development and modeling contractors and 

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

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

domain. Because the SMOKE-ready files were used in 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 are used in regional scale modeling analyses. 

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

BaseG Annual 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. 201 8 Base Case Annual Emissions Summary. 

BaseG Annual 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 

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

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

with emissions over distance (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 through H-8. 

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

lnfluence for Si~sev . . Wilderness Area. 

Annual 2018 BaseGZ Enissions (Tons) 

Tier VOCI NO4 col so21 MlOl PIK2.4 

Fuel Comb. Elec. Util. 74.6121 .%. 1451 

I I I I I I I 

VISTAS Total 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 Wilderness Area. 

, - 

~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 

642,523 
Table H-6. 2018 Base G2 Annual Emissions (Tons) for Top Three Source Categories in SO2 

Area of Influence for Sipsev Wilderness Area. 

Fuel Comb. Other-Residential Wcod 57,645 3,034 129,215 20,345 304 

Fuel Comb. 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 Wilderness Area. 

I 1 I I 

Top Tier Total 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 future year base case had the potential for reduction or if source types already reduced 

have reached the full cost-effective emission reduction potential. 

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

2002 to 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 the VISTAS emissions inventory contractor, MACTEC. 

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

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

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

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

determinations of BART controls were not available for this analysis. 

Miscellaneous - 

Nonroad 

Onroad 

Industrial 

Fuel Combustion 

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 20 18 IPM file used by VISTAS for EGU sources (RPO IPM version 2.1.9) was obtained and 

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

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

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

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

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

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

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

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

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

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

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

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

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

H.6 Application of AirControlNET Technologies 

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

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

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

implementing those controls. 

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

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

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

ambient concentrations of ozone, 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 of EPA emission inventories, direct use of the model in this 

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

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

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

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

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

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

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

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

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

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

source or domain. 
Emissions Weighted NETDC (MW) Association. 

Description I NETDC (MW) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Dry Bottom (Bituminous Coal) 

External Combustion Boilers; Electric Generation; Bituminous1Subbituminous Coal; Cyclone Furnace (Bituminous Coal) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) (Bituminous 

Coal) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Cell Burner (Bituminous Coal) 

External Combustion Boilers; Electric Generation; Bituminous1Subbituminous Coal; Atmospheric Fluidized Bed Combustion: Circulating Bed 

(Bitum. Coal) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; Pulverized Coal: Dry Bottom (Subbituminous Coal) 

External Combustion Boilers; Electric Generation; BituminouslSubbituminous Coal; 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 6 Oil: Tangential Firing 

External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1 and 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; Distillate Oil (Diesel); Turbine 

F:\VJSTAS Phase JI\TSD\draft#3\AppendixxHHAOI3.doc 

Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine: Exhaust 

Internal Combustion Engines; Electric Generation; Natural Gas; Turbine 

All other boilers 

200 

200 

100 
H.7 Development of AOI-Based Cost Curves 

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

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

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

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

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

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

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

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

and type of controlled sources in each AOI. 

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

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

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

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

Each individual source had its own cost effectiveness curve generated. 

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

mobile sources were not considered in this analysis. 

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

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

distance (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 for individual Class I areas. 

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