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Documentation of 2018 Emissions from Electric Generating Units 

in the Eastern United States for 

MANE-VU’s Regional Haze Modeling

Revised Final Draft 

28 April 2008 

Prepared for: 

Mid-Atlantic / Northeast Visibility Union (MANE-VU) 

Prepared by: 

Alpine Geophysics, LLC 

387 Pollard Mine Road 

Burnsville, NC 28714 

Mid-Atlantic Regional Air Management Association (MARAMA) 

8900 LaSalle Road 

Suite 636 

Towson, MD 21286 
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ACKNOWLEDGEMENTS 

Alpine Geophysics, LLC (Alpine), was under contract with the Mid-Atlantic Regional Air 

Management Association (MARAMA) to assist in the revision, analysis, and adjustment of 

emission inventories and associated factors necessary to provide data and inputs required for air 

quality analyses and state implementation plan (SIP) submittals. 

Greg Stella of Alpine prepared a final draft of this document for MANE-VU/MARAMA under 

Work Order #7 of the MANE-VU Future Year Emissions Inventory Umbrella Contract. This 

work was supported by funds provided to MARAMA by the Ozone Transport Commission 

(OTC) through their memorandum of agreement. OTC's funding for this work was provided by 

the U.S. Environmental Protection Agency under assistance agreement XA 97318101. 

MARAMA further revised Section 5 of the document after receipt of Mr. Stella’s final work 

product. MARAMA provided text and tables to reflect emissions inventory analysis that 

continued after the expiration of Alpine’s contract. 

Special recognition is given to Julie McDill, MARAMA’s project manager, who initiated the 

collection and aggregation of documentation used to prepare this report. Ms. McDill and Susan 

Wierman, MARAMA’s Executive Director provided analytical and editorial contributions to the 

preparation of this document, and Patrick Davis of MARAMA and Dr. John Graham of 

NESCAUM contributed to emissions inventory data analysis and summaries. 

This report was prepared for use by the MANE-VU States and does not necessarily represent the 

position of the U.S. Environmental Protection Agency. 
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TABLE OF CONTENTS 

1 INTRODUCTION ................................................................................................................. 1

1.1 BACKGROUND ...................................................................................................................... 1

2 PREPARATION OF EGU FORECASTS ........................................................................... 2

2.1 EGU FORECAST METHODS DOCUMENT ............................................................................... 3

2.2 THE INTEGRATED PLANNING MODEL (IPM) ........................................................................ 4

2.3 U.S. EPA USE OF IPM ......................................................................................................... 4

2.3.1 EPA’s Base Case 2004 ............................................................................................................................ 5

2.3.2 EPA CAIR Case ....................................................................................................................................... 6

2.3.3 EPA’s CAIR Modeling Limitations .......................................................................................................... 6

2.4 RPO USE OF IPM – PHASE I ................................................................................................ 7

2.5 RPO ADJUSTMENTS TO IPM – PHASE II .............................................................................. 8

2.6 STATE RESULTS – PHASE II .................................................................................................. 9

2.7 MANE-VU SPONSORED CAIR PLUS IPM MODELING ...................................................... 11

3 POST PROCESSING OF IPM OUTPUT ......................................................................... 12

3.1 USE OF SMOKE EMISSIONS PROCESSING MODEL ............................................................. 12

3.2 PREPARING IPM OUTPUT FOR USE IN SMOKE MODEL ..................................................... 12

3.2.1 IPM to NIF ............................................................................................................................................. 13

3.3 STATE RESULTS – PHASE II AUGMENTED .......................................................................... 18

3.4 NIF TO IDA ....................................................................................................................... 18

4 MODIFICATIONS BY OTHER REGIONS .................................................................... 20

4.1 EMISSION CONTROL MODIFICATIONS WITHIN VISTAS, MRPO, AND CENRAP ............... 20

4.2 EMISSION FACTOR AND CONTROL MODIFICATIONS FOR VISTAS EMISSION SOURCES ..... 20

4.3 EMISSION INVENTORY REPLACEMENT WITHIN WRAP DOMAIN ........................................ 21

4.4 ELIMINATING DOUBLE COUNTING OF EGU UNITS ............................................................ 21

4.5 PRELIMINARY RESULTS FROM PHASE II ADDITIONAL MODIFICATIONS ............................. 22

4.6 REVISED RESULTS – VISTAS BASE G2 ADJUSTMENT ....................................................... 24

5 ADDITIONAL ADJUSTMENTS BY NORTHEASTERN STATES AND MODELERS 

FOR REGIONAL HAZE SIP MODELING ................................................................... 26

5.1 INTRODUCTION .................................................................................................................. 26

5.2 BEST AVAILABLE RETROFIT TECHNOLOGY (BART) ......................................................... 26

5.3 MANE-VU STATE MODIFICATIONS OF IPM RESULTS ...................................................... 26

5.4 MANE-VU EGU STRATEGY ............................................................................................. 27

5.5 IMPLEMENTATION OF MANE-VU CONTROL STRATEGY FOR KEY EGUS .......................... 28

5.6 STATE RESULTS – NORTHEASTERN STATE ADJUSTMENTS ................................................. 30

6 EGU PREPARATION TIMELINE ................................................................................... 33

7 REFERENCES .................................................................................................................... 34
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LIST OF TABLES 

Table 1. State Level Fuel Use and Emission Summary; 2018 VISTASII_PC_1f.xls. 10 

Table 2. SCC Default Heat Content and Stack Parameters from IPM to NIF Conversion. 15 

Table 3. EPA-Approved Emission Factor File for CO, VOC, filterable PM, and NH3. 16 

Table 4. EPA-approved condensable PM emission factor assignment. 17 

Table 5. State Level Emission Summary; 2018 VISTASII_PC_1f with Pollutant 

Augmentation. Modeling file ida_egu_18_basef_2453605.txt from VISTAS 

BaseF. (fossil-only) 19 

Table 6. State Level Emission Summary; 2018 VISTAS Base G Modeling file 

ptinv_egu_2018_11sep2006.txt. Based on 2018 VISTASII_PC_1f with adjustments 

from VISTAS, MRPO, and WRAP. 23 

Table 7. State Level Emission Summary; 2018 VISTAS Base G2 Modeling file 

egu_18_vistas_g2_20feb2007.txt. Based on 2018 VISTASII_PC_1f with 

adjustments from VISTAS, MRPO, and WRAP. 25 

Table 8. Comparison of Regional SO2 Emissions Estimates (1000 tons per year) 

 29 

Table 9. State Level 2018 Emission Summary; March 2008 MANE-VU EGU Modeling 

Inventory 31

LIST OF APPENDICES 

Appendix A. Top Electric Generating Emission Points Contributing to Visibility 

Impairment in MANE-VU in 2002 34 
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1 INTRODUCTION 

1.1 Background 

Development of an emissions inventory is an important foundation for performing regional scale 

atmospheric modeling for regulatory air quality management. The accuracy of the atmospheric 

model’s prediction of air quality depends, in part, on the accurate representation of emissions 

from a variety of source sectors including point, area, non-road, on-road and biogenic sources. 

Electric generating units (EGUs) are an important point source sector and are often considered 

for controls to meet air quality objectives. Therefore, it is especially important to accurately 

represent and document EGU emissions and associated characteristics in a regulatory modeling 

application. This report is intended to describe the development of future year EGU emission 

estimates for use in Mid-Atlantic/Northeast Visibility Union (MANE-VU) 2018 regional haze 

modeling. 

This document synthesizes information from several documents that already describe parts of the 

process of preparing emissions estimates and provides information not yet included in other 

documents. It covers the following: preparation of the inter-Regional Planning Organization 

(RPO) Integrated Planning Model® (IPM) runs commonly referred to as the Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) IPM runs, the postprocessing of those runs to create Sparse Matrix Operator Kernel Emissions (SMOKE) input 

files, the modification of those files to reflect state estimates of emissions, and the adjustments 

made by MANE-VU modelers to maintain the Clean Air Interstate Rule (CAIR) cap. It also 

provides background information about preparing EGU forecasts and related work by the U.S. 

Environmental Protection Agency (EPA). 
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2 PREPARATION OF EGU FORECASTS 

Emission projections for point sources are dependent upon changes in source level activity, the 

emission factors or installed controls. The approach taken to project point source emissions 

depends on the level of detail necessary in the projection year file. Changes in point source 

emissions are accounted for by a combination of growth, control, and retirement rates. Growth 

rates are applied to estimate the overall change in activity, while retirement rates are applied to 

estimate the decrease in emissions activity from existing sources. Retirement (and replacement 

of these sources with new sources) must be considered because regulations affecting new sources 

may differ from those affecting existing sources. 

The projection year control factor accounts for both changes in emission factors due to 

technology improvements and new levels of control required by regulations. The control factor 

accounts for three variables: regulation control, rule effectiveness, and rule penetration. 

Control factors are closely linked to the type of emission process (identified by Source 

Classification Code (SCC)) and secondarily to the type of industry identified by Standard 

Industrial Classification (SIC). Point source projections should account for Federal, State, and 

local regulations affecting these categories. 

A complicating factor is the requirement for emission offsets in nonattainment areas through 

New Source Review requirements. This may be accounted for by 1) restricting growth under the 

assumption that it will be offset; 2) applying reductions to selected source categories to account 

for the emission growth which must be offset; or 3) selecting the individual sources, based on a 

cost analysis, from which offsets are likely to come. 

When projecting Electricity Generating Unit (EGU) emissions in the Eastern United States, 

emission trading should be considered. There are three general approaches to performing 

projections while accounting for such trading schemes. The first option is to optimize control 

levels across the domain based on the cost of alternative controls. The second option is to survey 

individual sources to determine how they will comply (will they apply controls and sell or buy 

allowances) and use this as the basis for the future year control level. The third option is to apply 

the control level used to establish the budget to all affected sources and ignore which sources 

may choose to buy or sell credits/allowances. 

Other factors which must be considered include programs, such as fuel switching, designed to 

provide source flexibility in meeting future air quality requirements. Fuel switching refers to 

instances where a unit historically burned one primary fuel, such as coal, and under a "fuel 

switching" program the unit would burn an alternate fuel, such as natural gas, during a certain 

period of time and may switch back to the "historic" fuel for some or all of the year. Fuel 

switching is often done in cases where sources average their emissions to meet federal mandates. 

Fuel switching may also be used as a seasonal compliance strategy (e.g., switching from residual 

fuel oil to natural gas in order to reduce NOx emissions during the ozone season. The variation in 

emissions over the course of the year caused by fuels switching must be calculated properly in 

projections. 
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Repowering is another example of a planned change in emission rates which should be 

considered. In this case, the unit may be switching entirely from coal to natural gas or may be 

completing a major modification which would lower the emission rate. 

Spatial allocation is another factor which must be considered, particularly if air quality modeling 

will be performed using the projection. For point sources, important questions are which 

facilities will retire and where new growth will occur. Changes in land use patterns may also 

impact the location of point source emissions. As undeveloped and rural areas become suburban 

and urban areas, the number of point sources in that area will increase. 

As can be seen from the discussion above, any number of complicating issues can lead to 

emission forecasts which may differ from user to user. An inconsistent decision made between 

two parties can lead to significant differences in growth, control, or placement of emissions from 

point source forecasts. For this reason, the RPOs made a conscious decision to utilize consistent 

forecasting methods for EGU emissions, as they are one of the most significant contributors to 

regional haze in the United States. This decision, to coordinate on the projection of EGU source 

emissions, led to the preparation of an EGU forecast methods document from which a 

coordinated decision was made on methods to develop EGU emissions in future years. 

2.1 EGU Forecast Methods Document 

Early in the planning process there was a joint agreement by the RPOs to work together to 

develop future year EGU emissions estimates based on the use of the Integrated Planning 

Model® (IPM). The decision to use IPM modeling resulted in part on a study of EGU forecast 

methods by E.H. Pechan and Associates, Inc. (Pechan) for the Midwest Regional Planning 

Organization (MRPO) (Pechan, 2004), which recommended IPM as a viable methodology. 

Although IPM results were available from work conducted by EPA to support their rulemaking 

for the Clean Air Interstate Rule (CAIR), the RPOs concluded that certain model inputs needed 

to be revised. Thus, the RPOs decided to work together to hire contractors to conduct new IPM 

modeling and to post-process the IPM results. This section describes the recommendation to use 

IPM. 

The Lake Michigan Air Directors Consortium (LADCO) sought contractor assistance in 

reviewing emissions inventory growth for existing and new EGUs (Pechan, 2004). Because the 

results of EGU emission forecasts are used in urban or regional scale air quality modeling 

exercises to estimate future year air pollutant concentrations, growth methods are needed to 

supply model-ready emission model inputs. The purpose of LADCO’s project was to begin to 

examine EGU growth methods. 

The primary pollutants of interest were sulfur dioxide (SO2), oxides of nitrogen (NOx), 

particulate matter (PM), ammonia (NH3), and mercury (Hg). Projection years of interest included 

2009 (the approximate time for ozone and PM2.5 attainment) and 2018 (a longer term regional 

haze planning horizon). The geographic area of interest was the eastern half of the United States 

(to capture the trading issues affecting the Midwest States). 
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This 2004 Pechan report provided a detailed evaluation of three EGU growth modeling methods 

of interest to the LADCO States for consideration in developing its own approach. These 

evaluations addressed the following attributes of each modeling approach: 

• Description of primary analytical modeling methods; 

• Geographic areas of application; 

• Advantages; and 

• Disadvantages. 

The material in this evaluation was intended to be used to determine which of the currently 

available modeling approaches might be best suited for use by the LADCO States (and other 

RPOs) for future state implementation plan (SIP) and air dispersion modeling work. The models 

evaluated in this report included the IPM, the National Energy Modeling System (NEMS), and 

the Electric Power Market Model (EPMM). 

Based on the conclusions and summary of the report (Pechan, 2004), the four participating RPOs 

(MANE-VU, MRPO, VISTAS, and the Central Regional Air Planning Association, CENRAP) 

decided to use IPM as the tool for forecasting EGU emissions. 

2.2 The Integrated Planning Model (IPM) 

IPM was developed by ICF Consulting, Inc. (ICF) and used to support public and private sector 

clients. This model is a multi-regional, dynamic, deterministic linear programming model of the 

U.S. electric power sector. It provides forecasts of least-cost capacity expansion, electricity 

dispatch, and emission control strategies for meeting energy demand and environmental, 

transmission, dispatch, and reliability constraints. It can be used to evaluate the cost and 

emissions impacts of proposed policies to limit emissions of SO2, NOx, carbon dioxide (CO2), 

and Hg from the electric power sector. The IPM model was a key analytical tool used by EPA in 

developing CAIR and the Clean Air Mercury Rule (CAMR). 

Among the factors that make IPM particularly well suited to model multi-emissions control 

programs are (1) its ability to capture complex interactions among the electric power, fuel, and 

environmental markets; (2) its detail-rich representation of emission control options 

encompassing a broad array of retrofit technologies along with emission reductions through fuel 

switching, changes in capacity mix and electricity dispatch strategies; and (3) its capability to 

model a variety of environmental market mechanisms, such as emissions caps, allowances, 

trading, and banking. IPM's ability to capture the dynamics of the allowance market and its 

provision of a wide range of emissions reduction options are particularly important for assessing 

the impact of multi-emissions environmental policies like CAIR and CAMR. 

2.3 U.S. EPA Use of IPM 

The U.S. EPA uses IPM to analyze the projected impact of environmental policies on the electric 

power sector in the 48 contiguous states and the District of Columbia. 
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2.3.1 EPA’s Base Case 2004 

The EPA’s Base Case 2004 (EPA, 2005a) served as the starting point against which EPA 

compared various policy scenarios. It is a projection of electricity sector activity that takes into 

account federal and state air emission laws and regulations whose provisions were either in effect 

or enacted and clearly delineated at the time the base case was finalized in August 2004. 

Regulations mandated under the Clean Air Act Amendments of 1990 (CAAA), but whose 

provisions have not yet been finalized, were not included in the base case. These include: 

• Measures to Implement Ozone and Particulate Matter (PM) Standards: EPA Base Case 

2004 predates and so does not include the provisions of CAIR, the primary federal 

regulatory measure for achieving the National Ambient Air Quality Standards (NAAQS) 

for ozone (8-hour standard of 0.08 ppm) and fine particles (24-hour average of 65 ug/m3 

or less and annual mean of 15 ug/m3 for particles of diameter 2.5 micrometers or less, 

i.e., PM2.5). EPA Base Case 2004 was used to evaluate policy alternatives which 

ultimately resulted in CAIR. The final CAIR was issued on March 10, 2005. EPA Base 

Case 2004 includes measures to implement ozone and particulate matter standards to the 

extent that some of the state regulations included in EPA Base Case 2004 contain 

measures to bring non-attainment areas into attainment. Individual permits issued by 

states in response to ozone and particulate matter standards are not captured in the base 

case. 

• Mercury Regulations on Electric Steam Generating Units: EPA Base Case 2004 

predates both CAMR, which was issued by EPA on March 15, 2005 and the “Maximum 

Achievable Control Technology” (MACT) standards, which were scheduled to be 

promulgated by December 15, 2004, but, pending litigation, have been superseded by 

CAMR. Consequently, this base case does not include any federal regulatory measures 

for mercury control. (CAMR was vacated in 2008.) 

• Clean Air Visibility Rules: On July 1, 1999, EPA issued Regional Haze Regulations to 

meet the national goal for visibility established in Section 169A of the CAAA, which 

calls for “prevention of any future, and the remedying of any existing, impairment of 

visibility in Class I areas (156 national parks and wilderness areas), which impairment 

results from manmade air pollution.” The regulations required states to submit revised 

SIPs that (1) establish goals that provide for reasonable progress towards achieving 

natural visibility conditions at Class I areas, (2) adopt a long-term control strategy that 

includes such measures as are necessary to achieve the reasonable progress goals, and (3) 

require Best Available Retrofit Technology (BART) for sources in listed source 

categories placed in operation between 1962 and 1977. 

In effect, EPA Base Case 2004 offered a snapshot projection of the electric sector assuming that 

the only future environmental regulations were those with provisions known at the time that the 

base case assumptions were finalized. While not necessarily an accurate reflection of what would 

actually occur, this assumption ensured that the base case was policy neutral with respect to 

future environmental policies. 
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2.3.2 EPA CAIR Case 

On January 30, 2004, EPA proposed CAIR, which set emission reduction requirements for 29 

States and the District of Columbia. Those emission reduction requirements were based on 

achieving highly cost-effective emission reductions from large electricity generating units. 

While EPA believed that the modeling it initially performed for the January 2004 proposal 

provided a reasonable estimate of the impact of requiring highly cost-effective emission 

reductions from electricity generating units, it did not exactly model the proposed control region. 

For both SO2 and NOx, EPA used modeling assumptions that differed slightly from the January 

2004 CAIR proposal. For SO2 in particular, EPA modeled the program assuming a cap on 

national emissions rather than in the 29 States proposed. Although EPA believed the modeling 

done at that time provided a reasonable approximation of the impacts of the original CAIR, 

because 92 percent of the SO2 emissions in the 48 contiguous States occur in the 28 States that 

were covered by the proposal, EPA completed additional analysis. This additional analysis 

examined the effect of covering the geographic region proposed in the January 30, 2004 proposal 

using the NOx emissions cap and a close approximation of the SO2 cap proposed for CAIR (EPA, 

2005a). 

For the supplemental proposal, EPA performed refined modeling of the emission reduction 

requirements proposed on January 30, 2004. In this refined modeling, EPA modeled the exact 

control regions for both SO2 and NOx, as proposed. 

2.3.3 EPA’s CAIR Modeling Limitations 

The U.S. EPA’s modeling was based on its best judgment for various input assumptions that 

were uncertain, particularly assumptions for future fuel prices and electricity demand growth 

(EPA, 2004). In addition, modeling using IPM did not take into account the potential for 

advancements in the capabilities of pollution control technologies for SO2 and NOx removal as 

well as reductions in their costs over time. 

Retirement Ratios: EPA issued a CAIR supplemental notice of proposed rulemaking that 

proposed two alternatives for how the SO2 reduction target would be achieved. The proposal 

took comment on implementing the reduction requirements in the second phase either by using a 

2.86 to 1 ratio (which would match the 65 percent SO2 reduction target) of acid rain allowances 

to emissions, or alternatively, by implementing the reductions using a 3 to 1 ratio (for 

administrative simplicity) and then letting States create and distribute additional allowances 

equal to the surplus created by the 3 to 1 ratio to achieve the proposed 65 percent reduction. In 

either case, the effective cap on SO2 emissions from the power sector would be the same. 

Modelers assumed a 3 to 1 Title IV allowance retirement ratio for 2015 and beyond to implement 

the reductions in the proposed control region. The model did not add back the 130,000 tons of 

SO2 from over-compliance that would result from this ratio. Therefore, in this modeling, EPA 

analyzed slightly greater SO2 emission reductions than required by the proposal. This assumption 

was made for modeling simplicity and was expected to result in a slight overestimate of costs for 

the proposal and of the emissions reductions achieved. 
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BART: The EPA did not incorporate any best achievable retrofit technology (BART) modeling 

in this analysis. BART would achieve reductions in non-CAIR States and had the potential to 

mitigate leakage issues. 

Demand Response: EPA’s 2004 CAIR case includes a demand response to increased gas prices 

but not electricity prices. In the model, increased gas prices would prompt the public to curtail 

their use of gas and encourage them to seek substitutes. However, no provision for demand 

response was included for electricity prices. If demand had been allowed to change in response 

to increasing prices of electricity,, one can assume that consumers would have reduced their 

demand for electricity, lowering electricity prices and reducing generation and emissions to some 

extent. 

State Rules: Only some State adopted rules were incorporated into EPA’s modeling framework. 

A list of the State Multi-pollutant regulations used in IPM 2.1, IPM 2.1.6, and IPM 2.1.9 can be 

located in Appendix 3-2 of EPA’s Standalone Documentation for EPA Base Case 2004 (v.2.1.9) 

Using the Integrated Planning Model (EPA, 2005a). 

Because of the limitations noted above, the RPOs decided to initiate their own IPM modeling 

based on the EPA’s latest update of the IPM input framework, called IPM 2.1.9. EPA completed 

the input framework for IPM 2.1.9 in March of 2003. 

2.4 RPO Use of IPM – Phase I 

In August 2004, VISTAS contracted with ICF to run IPM to provide revised utility forecasts for 

2009 and 2018 under two future scenarios – Base Case and CAIR Case (ICF, 2004). The Base 

Case represented the current operation of the power system under laws and regulations as known 

at the time the run was made, including those that come into force in the study horizon. The 

CAIR Case was the Base Case with the proposed CAIR rule superimposed. Run results were 

parsed at the unit level for the 2009 and 2018 run years. 

In August 2004, MRPO contracted with Pechan to post-process the VISTAS’ IPM outputs to 

provide the (National Emission Inventory Input Format) NIF formatted emission files needed for 

the regional inventory. The IPM output files were delivered by ICF to VISTAS in November 

2004 and the post-processed data files were delivered by Pechan to the MRPO in December 

2004. 

These IPM runs (VISTAS_CAIR_2) and the NIF files that were generated from the parsed data 

sets are commonly referred to as the Phase I Inter-RPO runs. The Phase I runs were ultimately 

not used in RPO modeling of regional haze, as further revisions to the inputs were necessary 

once CAIR was adopted. 
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2.5 RPO Adjustments to IPM – Phase II 

On March 10, 2005, EPA issued the final CAIR. A consortium of RPOs, (MANE-VU, VISTAS, 

MRPO, and CENRAP) conducted another round of IPM modeling which reflected changes to 

control assumptions based on the final CAIR as well as additional changes to model inputs based 

on state and local agency and stakeholder comments. Several conference calls were conducted in 

the spring of 2005 among the participating RPOs to discuss and provide comments on IPM 

assumptions related to six main topics: power system operation, generating resources, emission 

control technologies, set-up parameters, financial assumptions, and fuel assumptions. Based on 

these discussions, VISTAS sponsored a new set of IPM runs to reflect the final CAIR 

requirements as well as certain changes to IPM assumptions that were agreed to by the RPOs. 

ICF performed the following four runs using IPM during the summer of 2005. This set of IPM 

runs is referred to as the VISTAS Phase II analysis or Inter-RPO v.2.1.9 runs. 

• Base Case with EPA 2.1.9 coal, gas, and oil price assumptions (VISTASII_BC_1Z1). 

• Base Case with EPA 2.1.9 coal and gas supply curves adjusted for the U.S. Energy 

Information Administration’s most recent Annual Energy Outlook (AEO 2005) reference 

case price and volume relationships (VISTASII_BC_2Y). 

• Strategy Case with EPA 2.1.9 coal, gas and oil price assumptions (VISTASII_PC_1f). 

• Strategy Case with EPA 2.1.9 coal and gas supply curves adjusted for AEO 2005 

reference case price and volume relationships (VISTASII_PC_2C). 

The above runs were parsed for 2009 and 2018 run years. The output taken from the Strategy 

Case with EPA 2.1.9 coal, gas, and oil price assumptions (VISTASII_PC_1f) is also referred to 

as the Inter-RPO CAIR Case IPM 2.1.9 and is the basis for discussion in the remainder of this 

report. 

The Phase II scenarios were based on VISTAS Phase I and EPA IPM 2.1.9 assumptions (EPA, 

2005b). Additional changes that were implemented in the above four runs are summarized below 

and in associated documentation (ICF, 2007): 

• Unadjusted AEO 2005 electricity demand projections were used. (U.S. EPA runs were 

adjusted to reflect reduced demand due to voluntary conservation projects sponsored by 

U.S. EPA) 

• Gas supply curves were adjusted for AEO 2005 reference case price and volume 

relationships. The EPA 2.1.9 gas supply curves were scaled such that IPM solved for 

AEO 2005 gas prices when the power sector gas demand in IPM is consistent with AEO 

2005 power sector gas demand projections. 

• The coal supply curves used in EPA 2.1.9 were scaled such that the average mine mouth 

coal prices that the IPM was solving in aggregated coal supply regions were comparable 

to AEO 2005. Coal grades and supply regions contained in AEO 2005 and EPA 2.1.9 

were not directly comparable. An iterative approach was used to obtain comparable 

results. The coal transportation matrix was not updated with Energy Information 

Administration (EIA) assumptions due to significant differences between the EPA 2.1.9 

and EIA AEO 2005 coal supply and coal demand region configurations. 
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• The cost and performance of new units were updated to AEO 2005 reference case levels. 

• The run years 2008, 2009, 2012, 2015, 2018, 2020 and 2026 were modeled. 

• The AEO 2005 life extension costs for fossil and nuclear units were incorporated. 

• The extensive NEEDS comments provided by VISTAS, MRPO, CENRAP and MANEVU were incorporated into the Phase I NEEDS input file. 

• MANE-VU’s comments in regards to the northeast state regulations were incorporated. 

• Northeast Renewable Portfolio Standards (RPS) were modeled based on the Regional 

Greenhouse Gas Initiative analysis. A single RPS cap was modeled for MA, RI, NY, NJ, 

MD, and CT. These states could buy credits from NY or from the PJM Interconnection 

and New England model regions. 

• Selective Catalytic Reduction (SCR) and Scrubber Feasibility Limits: No limits were 

applied in 2008, 2009 and 2010 to the capacity for installing these emissions controls. 

• The Clean Air Visibility Rule (CAVR) was not modeled. 

• Modelers assumed a Title IV SO2 Bank for 2007 of 4.98 million tons. 

• The investments required under the Illinois Power, Mirant and First Energy NSR 

settlements (as identified during spring 2005) were incorporated in the above runs. 

For the Phase II inter-RPO set of IPM runs, ICF generated two different parsed files for each of 

the two scenarios. One file includes all fuel burning units (fossil, biomass, landfill gas) as well as 

non-fuel burning units (hydro, wind, etc.). The second file contains just the fossil-fuel burning 

units (e.g., emissions from biomass and landfill gas are omitted). In all RPOs the fossil-only file 

was used for modeling. This is consistent with EPA, since EPA used the fossil only results for 

CAIR analyses. 

2.6 State Results – Phase II 

Table 1 presents unmodified State level fuel use and emission results from the 2018 Inter-RPO 

CAIR Case IPM v. 2.1.9 fossil-only parsed file (VISTASII_PC_1f). Note that IPM produces 

only NOx and SO2 emissions estimates. 
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Table 1. State Level Fuel Use and Emission Summary; 2018 VISTASII_PC_1f.xls. (fossil only) 

State RPO Summer Annual Summer NOx Annual NOx Annual SO2

Connecticut MANE-VU 62.1572 142.7141 1,521 3,418 6,697

Delaware MANE-VU 41.9472 92.7542 5,485 12,341 35,442

District Of Columbia MANE-VU 2.0774 4.8716 49 103 83

Maine MANE-VU 21.8494 49.8748 804 1,827 5,436

Maryland MANE-VU 195.3393 437.8991 6,832 14,709 28,065

Massachusetts MANE-VU 188.0653 433.3227 8,004 18,157 17,486

New Hampshire MANE-VU 32.4638 73.8699 1,393 3,089 7,469

New Jersey MANE-VU 140.8000 304.7240 6,432 13,636 32,495

New York MANE-VU 282.4272 669.0821 10,926 24,376 51,445

Pennsylvania MANE-VU 687.1446 1,540.1322 36,329 82,881 135,946

Rhode Island MANE-VU 15.1701 40.0407 244 576 55

Vermont MANE-VU 1.3677 3.0597 74 105 35

MANE-VU Total 1,670.8093 3,792.3450 78,093 175,219 320,651

Alabama VISTAS 605.2513 1,329.1117 19,416 41,715 190,029

Florida VISTAS 831.5942 1,813.5433 26,620 56,506 139,526

Georgia VISTAS 687.9659 1,530.2279 26,228 56,180 178,196

Kentucky VISTAS 494.6026 1,121.9188 27,904 64,099 229,596

Mississippi VISTAS 211.7079 443.3923 4,269 8,895 27,226

North Carolina VISTAS 431.1262 984.5996 25,412 57,774 102,217

South Carolina VISTAS 326.3757 749.2039 20,240 46,318 118,584

Tennessee VISTAS 300.8087 672.6405 13,348 29,873 112,343

Virginia VISTAS 305.6546 710.9991 18,443 43,144 80,602

West Virginia VISTAS 477.7910 1,080.9570 22,556 51,208 124,464

VISTAS Total 4,672.8781 10,436.5940 204,435 455,711 1,302,784

Illinois MRPO 564.3359 1,281.6624 31,214 71,234 241,136

Indiana MRPO 665.8976 1,534.4126 40,820 95,376 376,864

Michigan MRPO 537.6731 1,257.6784 42,629 98,685 398,562

Ohio MRPO 773.6334 1,785.3989 35,888 83,129 215,501

Wisconsin MRPO 303.7451 691.5260 19,794 45,701 155,369

MRPO Total 2,845.2851 6,550.6783 170,345 394,124 1,387,433

Arkansas CENRAP 211.9455 479.1864 14,836 33,097 82,605

Iowa CENRAP 238.7101 548.7369 22,252 51,119 147,305

Kansas CENRAP 213.4288 465.8685 37,207 83,333 81,486

Louisiana CENRAP 225.6282 481.9880 14,240 30,432 74,263

Minnesota CENRAP 175.6582 388.8279 17,940 41,029 85,847

Missouri CENRAP 416.5504 918.5720 34,350 77,660 280,887

Nebraska CENRAP 113.8064 255.2901 22,524 50,781 73,629

Oklahoma CENRAP 357.5522 745.1097 36,695 76,048 113,680

Texas CENRAP 1,710.8244 3,236.6605 79,449 153,837 339,433

CENRAP Total 3,664.1040 7,520.2400 279,493 597,336 1,279,135

Arizona WRAP 442.6160 1,022.0551 36,168 81,858 60,640

California WRAP 602.8505 1,403.6297 10,464 23,767 5,447

Colorado WRAP 215.1782 486.7281 31,074 70,171 87,163

Idaho WRAP 14.5575 34.1372 309 718 0

Montana WRAP 88.4363 200.1442 17,034 38,504 22,066

Nevada WRAP 179.3334 408.0758 20,978 47,404 31,172

New Mexico WRAP 155.2294 344.7868 32,965 74,010 52,917

North Dakota WRAP 131.5025 297.0199 31,745 71,711 108,645

Oregon WRAP 109.6842 255.3128 4,968 11,330 10,034

South Dakota WRAP 16.3929 36.8730 6,457 14,574 12,085

Utah WRAP 146.1278 330.1164 26,905 60,782 37,819

Washington WRAP 155.7190 362.9219 11,625 26,379 12,236

Wyoming WRAP 202.3566 457.1643 35,935 81,182 40,265

WRAP Total 2,459.9843 5,638.9652 266,628 602,390 480,488

National Total 15,313.0609 33,938.8226 998,994 2,224,779 4,770,490

Fuel Use (TBtu) Emissions (Tons)
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2.7 MANE-VU Sponsored CAIR Plus IPM Modeling 

Using the IPM Phase II RPO modeling platform MANE-VU contracted with ICF to evaluate the 

impact of both tightening the SO2 and NOx CAIR caps and to expand the CAIR region to include 

the electricity generating sector in additional states the Eastern United States. As part of this 

analysis, ICF developed a new Base Case that implemented EPA’s CAIR, CAMR and CAVR 

policies and a Policy Case with lower SO2 and NOx CAIR caps in an extended region. The new 

Base Case was developed for comparison to the Policy Case. The model assumptions and data 

used in this analysis are somewhat different than those in the RPO Phase II analysis and are 

described in Section B of the project report (ICF, 2007). Neither the base or policy cases from 

the CAIR Plus project were used in subsequent SIP modeling. 
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3 POST PROCESSING OF IPM OUTPUT 

3.1 Use of SMOKE Emissions Processing Model 

On behalf of MANE-VU, NESCAUM modelers used an emissions processing model to prepare 

data produced by the IPM model for use in air quality and visibility modeling. The Sparse 

Matrix Operator Kernel Emissions (SMOKE) Modeling System is an emissions processing 

system designed to create gridded, speciated, hourly emissions for input into a variety of air 

quality models, such as EPA’s Community Multi-Scale Air Quality (CMAQ) model and 

Regional Modeling System for Aerosols and Deposition (REMSAD) (Houyoux, et. al., 2000). 

SMOKE supports area, biogenic, mobile (both onroad and nonroad), and point source emissions 

processing for criteria, particulate, and toxic pollutants. For biogenic emissions modeling, 

SMOKE uses the Biogenic Emission Inventory System, version 2.3 (BEIS2) and version 3.09 

and 3.12 (BEIS3). SMOKE is also integrated with the onroad emissions model MOBILE6. 

The sparse matrix approach used throughout SMOKE permits rapid and flexible processing of 

emissions data. Flexible processing comes from splitting the processing steps of inventory 

growth, controls, chemical speciation, temporal allocation, and spatial allocation into 

independent steps whenever possible. The results from these steps are merged together in the 

final stage of processing using vector-matrix multiplication. It allows individual steps (such as 

adding a new control strategy, or processing for a different grid) to be performed and merged 

without having to redo all of the other processing steps. Individual emission scenarios were 

simulated for MANE-VU using the SMOKE Modeling System. 

The Northeast States for Coordinated Air Use Management (NESCAUM), on behalf of MANEVU and its participating States, conducted regional air quality simulations for calendar year 2002 

and several future periods (NESCAUM, 2008). This work was directed at satisfying a number of 

goals under the Haze State Implementation Plan (SIP), including a contribution assessment, a 

pollution apportionment for 2018, and the evaluation of visibility benefits of control measures 

being considered for achieving reasonable progress goals and establishing a long-term emissions 

management strategy for MANE-VU Class I areas. The modeling tools utilized for these 

analyses include the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5), SMOKE, 

CMAQ and REMSAD, and incorporate tagging features that allow for the tracking of individual 

source regions or measures. These tools have been evaluated and found to perform adequately 

relative to U.S. EPA modeling guidance. 

As described below, in order for NESCAUM to process the Electric Generating Unit (EGU) 

emissions generated by the Integrated Planning Model® (IPM) procedures noted above, a series 

of intermediate steps were required to get the activity and emission data into the appropriate 

format for SMOKE processing. 

3.2 Preparing IPM Output for Use in SMOKE Model 

IPM can produce projections at the regional, state, plant, or unit level. Data must be parsed to 

provide the unit level information required for chemical transport modeling. Parsing involves 
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13 

developing detailed unit level information from the model’s projections at the model plant level. 

ICF parsed the VISTASII_PC_1f data for use by the RPOs. 

Further post-processing of IPM parsed output is needed to prepare the files for use by the 

SMOKE emissions processing model. The following sections describe the intermediate steps 

necessary to make these conversions. The first step is the augmentation of the IPM parsed output 

files to include additional unit level characteristics and pollutant estimates necessary for one 

atmosphere modeling. This step converts the IPM parsed data files into EPA’s National Emission 

Inventory Input Format (NIF). The second step is the additional conversion of these NIF files 

into the Inventory Data Analyzer (IDA) format required by the SMOKE emissions processor. 

3.2.1 IPM to NIF 

After running IPM, ICF provided an initial spreadsheet file containing unit-level records for 

both: 

(1) “existing” units (those currently in operation during the modeled base year) and 

(2) committed/planned or new generic aggregates (new generic units expected to come 

online or identified as needed to meet electric generation demand in a geographic 

area). 

IPM parsed file records include unit and fuel type data; existing, retrofit (for SO2 and NOx), and 

separate NOx control information; annual SO2 and NOx emissions and heat input; summer season 

(May-September) NOx and heat input; July day NOx and heat input; coal heat input by coal type; 

nameplate capacity megawatt (MW), and State FIPS codes (Federal Information Processing 

codes used to identify geographic areas). Existing units also had county FIPS code, a unique 

plant identifier (ORISPL) and unit ID (also called boiler ID) (BLRID); generic units did not have 

these data. 

The processing of IPM parsed data to NIF format included estimating emissions not generated by 

IPM and adding control efficiencies, stack parameters, latitude-longitude coordinates, and State 

identifiers (plant ID, point ID, stack ID, process ID) from a series of lookup tables or by 

matching to individual units as configured in base year 2002 emission files (Pechan, 2005). 

Additionally, new generic units created by IPM were sited in a county and given appropriate IDs. 

This processing is described in more detail below. 

Generic Units: The new generic units and associated data were prepared by transforming the 

generic aggregates into units similar in size and fuel to existing units in terms of the available 

data. Generic aggregates were split into smaller generic units based on their unit types and 

capacity. Each generic unit was provided a dummy ORIS unique plant and boiler ID, and were 

given a county FIPS code based on an algorithm that sited each generic unit by assigning a sister 

plant that is in a county based on its attainment/nonattainment status. Within a State, existing 

plants (in county then ORIS plant code order) in attainment counties were used first as sister sites 

to new generic units (to obtain county location), followed by existing plants in PM 

nonattainment counties, followed by existing plants in 8-hour ozone nonattainment counties. No 

States identified counties that should not be considered when siting new generic units, so this 

process was identical to the one used for EPA IPM post-processing under CAIR. 
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14 

SCCs were assigned to existing units using unit/fuel/firing/bottom type data. SCCs were 

assigned to generic units using unit and fuel type information. Latitude-longitude coordinates 

were assigned, first using the EPA-provided data files, secondly using an in-house contractor 

developed latitude-longitude file, and lastly using county centroids. These additional location 

files were only used when the data were not provided in the original 2002 base year files. Stack 

parameters were then assigned to each unit, first using the EPA-provided data files, secondly 

using an in-house stack parameter file based on previous EIA-767 data, and lastly using an EPA 

June 2003 SCC-based default stack parameter file. These data were only used when the data 

were not provided in the 2002 base year files. 

IPM does not calculate emissions for all pollutants necessary for regional haze modeling. 

Therefore additional data were required to estimate VOC, CO, filterable primary PM10 and 

PM2.5, PM condensable, and NH3 emissions. Thus, ash and sulfur contents were assigned by first 

using 2002 EIA-767 values for existing units or SCC-based defaults; filterable PM10 and PM2.5

efficiencies were obtained from the 2002 EGU NEI that were based on 2002 EIA-767 control 

data and the PM Calculator program (a default of 99.2 percent is used for coal units if 

necessary); fuel use was back calculated from the given heat input and a default SCC-based heat 

content; and emission factors were obtained from an EPA-approved emission factor file based on 

AP-42 emission factors. Table 2 presents the SCC-based default heat content and stack 

parameters used when actual data were not available. Table 3 (worksheet sccemfac100704 from 

MRPOpostprocdatafiles.xls, Pechan 2005) reflects emission factors used to develop emission 

estimates of CO, VOC, filterable PM, and NH3. 
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Table 2. SCC Default Heat Content and Stack Parameters from IPM to NIF Conversion. 

Heat Content Height Diameter Temp Velocity

SCC Fuel (Btu/SCC Unit) (ft) (ft) (degrees F) (ft/s)

10100201 Bituminous Coal 23.4286 603.2 19.8 281.2 76.5

10100202 Bituminous Coal 23.4286 509.7 14.6 226.0 62.0

10100203 Bituminous Coal 23.4286 491.6 16.6 278.4 80.5

10100204 Bituminous Coal 23.4286 225.0 0.6 67.2 2.4

10100211 Bituminous Coal 23.4286 0.0 0.0 0.0 0.0

10100212 Bituminous Coal 23.4286 445.6 17.4 275.2 77.6

10100217 Bituminous Coal 23.4286 399.3 10.8 245.6 40.1

10100221 Subbituminous Coal 17.8870 983.0 22.8 350.0 110.0

10100222 Subbituminous Coal 17.8870 468.5 16.0 254.7 65.6

10100223 Subbituminous Coal 17.8870 446.8 15.9 308.0 93.6

10100224 Subbituminous Coal 17.8870 255.5 10.0 251.3 15.3

10100226 Subbituminous Coal 17.8870 495.8 18.9 259.2 91.2

10100238 Subbituminous Coal 17.8870 600.0 22.5 315.0 78.0

10100301 Lignite Coal 12.9149 427.5 22.3 232.8 74.2

10100302 Lignite Coal 12.9149 483.5 21.0 229.4 92.4

10100303 Lignite Coal 12.9149 462.0 21.7 271.3 72.5

10100317 Lignite Coal 12.9149 326.7 12.3 326.7 74.7

10100601 Natural Gas 1023.8846 263.9 10.3 236.0 46.9

10100801 Coke 27.4376 371.3 5.5 122.4 20.4

10102018 Waste Coal 12.0929 0.0 0.0 0.0 0.0

20100201 Natural Gas 1023.8846 62.0 10.0 585.3 61.3

20100301 Gasified Coal 1023.8846 62.0 10.0 585.3 61.3

Stack Parameters
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Table 3. EPA-Approved Emission Factor File for CO, VOC, filterable PM, and NH3. 

SCC FUEL COEF VOCEF PM10EF PM25EF NH3EF PMFLAG

10100201 BIT 0.5000 0.0400 2.6000 1.4800 0.030 A

10100202 BIT 0.5000 0.0600 2.3000 0.6000 0.030 A

10100203 BIT 0.5000 0.1100 0.2600 0.1100 0.030 A

10100204 BIT 5.0000 0.0500 13.2000 4.6000 0.030

10100211 BIT 0.5000 0.0400 2.6000 1.4800 0.030 A

10100212 BIT 0.5000 0.0600 2.3000 0.6000 0.030 A

10100217 BIT 18.0000 0.0500 12.4000 1.3640 0.030

10100221 SUB 0.5000 0.0400 2.6000 1.4800 0.030 A

10100222 SUB 0.5000 0.0600 2.3000 0.6000 0.030 A

10100223 SUB 0.5000 0.1100 0.2600 0.1100 0.030 A

10100224 SUB 5.0000 0.0500 13.2000 4.6000 0.030

10100226 SUB 0.5000 0.0600 2.3000 0.6000 0.030 A

10100238 SUB 18.0000 0.0500 16.1000 4.2000 0.030

10100301 LIG 0.2500 0.0700 1.8170 0.5214 0.030 A

10100302 LIG 0.6000 0.0700 2.3000 0.6600 0.030 A

10100303 LIG 0.6000 0.0700 0.8710 0.3690 0.030 A

10100317 LIG 0.1500 0.0300 12.0000 1.4000 0.030

10100601 NG 84.0000 5.5000 1.9000 1.9000 3.200

10100801 PC 0.6000 0.0700 7.9000 4.5000 0.397 A

10102018 WC 0.1500 0.0300 12.0000 1.4000 0.030

20100201 NG 83.8628 2.1477 1.9380 1.9380 6.560

20100301 IGCC 34.6500 2.2050 11.5500 11.5500 6.560

Notes:

1. SCCs beginning with 101002 (coal), 101003 (coal), 101008 (coke), or 101020 (waste coal), 

emission factors in LB/TON; SCCs beginning with 101006 (natural gas), 201002 (natural gas), 

or 201003 (IGCC), emission factors are in LB/E6FT3.

2. If PMFLAG = 'A', then multiply ash content with PM emission factor.

Source: Table derived from worksheet sccemfac100704 from MRPOpostprocdatafiles.xls, Pechan 2005. 

Condensable PM: To estimate total primary PM emissions, additional calculations were 

conducted to derive condensable PM emissions from these sources. In MANE VU and VISTAS 

PM condensable emissions were calculated based on factors derived from AP-42 defaults. In 

MRPO no condensable emissions were estimated or included in the inventory. (Janssen, 2008) 

Table 4 (worksheet pmcdef from MRPOpostprocdatafiles.xls, Pechan 2005) shows these PM 

condensable emission factors and SCC assignments. 
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Table 4. EPA-Approved Condensable PM Emission Factor Assignment. 

SCC PMCDEF (LB/E6BTU)

10100201, 10100202, 10100203, 10100211, 10100212, 10100221, 10100222, 

10100223, 10100226, 10100301, 10100302, 10100303 0.02002

10100201, 10100202, 10100203, 10100211, 10100212, 10100221, 10100222, 

10100223, 10100226, 10100301, 10100302, 10100303 1 (0.1 * sulfur content - 0.03)3

10100204, 10100224 0.0400

10100217, 10100238, 10100317, 10102018 0.0100

10100601 0.0057

10100801 0.0100

20100201, 20100301 0.0047

Notes:

1. If the emission factor is less than 0.01, then it is set equal to 0.01.

2. AND there is either an SO2 FGD or a PM scrubber (for MRPO post-processing); or AND there is an SO2 wet FGD 

(for EPA post-processing).

3. AND there is any PM control other than a scrubber and there is no SO2 control (for MRPO post-processing); or 

AND there is any control other than an SO2 wet FGD (for EPA post-processing).

Source: Table derived from worksheet pmcdef from MRPOpostprocdatafiles.xls, Pechan 2005. 

Additional Pollutants: As noted above, in processing IPM parsed data to convert it to NIF 

format, emissions of additional pollutants were estimated. Emissions for 28 temporal-pollutant 

combinations were estimated since there are seven pollutants (VOC, CO, primary PM10 and 

PM2.5, NH3, SO2 and NOx) and four temporal periods (annual, summer season, winter season, 

July day). 

Crosswalk Match to 2002 Inventory: The final step in the IPM to NIF conversion process was to 

match the IPM unit IDs with the identifiers in the base year 2002 inventory for existing EGUs. A 

crosswalk file was used to obtain FIPS State and county, plant ID (within State and county), and 

point ID. If the FIPS State and county, plant ID and point ID were in the 2002 base year NIF 

tables, then the process ID and stack ID were obtained from the NIF; otherwise, defaults, 

described above, were used. 

The post-processed files were then provided in NIF 3.0 format. Two sets of tables were 

developed : “NIF files” for IPM units that had a crosswalk match and were in the 2002 base year 

inventory, and “NoNIF files” for IPM units that were not in the 2002 base year inventory (which 

included existing units with or without a crosswalk match as well as generic units). Two special 

cases relating to the crosswalk match were handled as follows: 

1. One-to-many match: At a given plant, if one IPM boiler ID was matched to more 

than one point ID, the boiler data were put on the first point ID records; records from 

the other point IDs were deleted from the relevant tables. 

2. Many-to-one match: At a given plant, if more than one IPM boiler ID was matched 

to one point ID, all the boilers’ emissions (tons), throughput (really heat input in 

MMBtu), and capacity (MW) were summed (“summed boiler”) and put on that point 
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18 

ID’s records in the relevant tables. The values for stack parameters and latitudelongitude values were those from the first record summed. 

3.3 State Results – Phase II Augmented 

Summarizing the results of the estimation of additional pollutants, Table 5 presents additional 

pollutant augmented State level emission results from the 2018 Inter-RPO CAIR Case IPM v. 

2.1.9 fossil-only parsed file (VISTASII_PC_1f with pollutant augmentation; found in modeling 

file ida_egu_18_basef_2453605.txt from VISTAS BaseF). A comparison of RPO totals for SO2

and NOx shows that these are the same as presented in Table 1. 

3.4 NIF to IDA 

The main purpose of the SMOKE conversion task was to convert EGU emission inventories 

provided in NIF format into the IDA format required by the SMOKE model for the criteria 

pollutants VOC, NOx, CO, SO2, PM10, PM2.5, and NH3. Annual and seasonal emissions were 

taken directly from the NIF structured inventories with no alternate temporal calculations 

performed (e.g., estimate seasonal emissions from annual or annual from seasonal). The temporal 

allocation module of the SMOKE emissions processor was intended to be used to further define 

temporal distribution of these emissions. 

No quality assurance (QA) related to the reported values in the NIF files was conducted (e.g., it 

was assumed that reported emission levels were correct) and therefore the QA focus was to 

maintain the integrity of the mass files in the conversion to IDA. 

Each set of NIF structured data had a unique set of relational tables necessary to maintain the 

information required in each source sector based on its reporting requirements. Conversion 

scripts to read the information from each of these relational data sets and convert them to the 

IDA structures required by this task were implemented by Alpine (Alpine, 2006). Prior to and 

after the conversion from NIF to IDA, a list of emission summary reports was developed to 

check that the emissions input into the conversion process were the same as output into the IDA 

formatted files. 
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19 

Table 5. State Level Emission Summary; 2018 VISTASII_PC_1f with Pollutant Augmentation. 

Modeling file ida_egu_18_basef_2453605.txt from VISTAS Base F. (fossil-only) 

State RPO NOx SO2 VOC CO PM-10 PM-2.5 NH3

Connecticut MANE-VU 3,418 6,697 145 9,837 959 927 341

Delaware MANE-VU 12,341 35,442 117 1,183 2,950 2,438 76

District Of Columbia MANE-VU 103 83 5 154 104 99 12

Maine MANE-VU 1,827 5,436 53 4,057 296 279 139

Maryland MANE-VU 14,709 28,065 575 11,831 8,253 6,433 435

Massachusetts MANE-VU 18,157 17,486 484 13,860 3,918 3,233 1,059

New Hampshire MANE-VU 3,089 7,469 73 1,697 2,268 2,156 124

New Jersey MANE-VU 13,636 32,495 352 7,611 4,017 3,515 564

New York MANE-VU 24,376 51,445 758 22,242 11,031 9,343 1,472

Pennsylvania MANE-VU 82,881 135,946 1,920 41,445 31,580 23,756 1,790

Rhode Island MANE-VU 576 55 42 1,627 157 156 127

Vermont MANE-VU 105 35 3 117 26 25 9

MANE-VU Total 175,218 320,651 4,528 115,659 65,558 52,360 6,148

Alabama VISTAS 41,714 190,029 1,599 27,888 20,401 15,936 2,009

Florida VISTAS 56,506 139,526 2,027 58,982 24,804 18,403 3,948

Georgia VISTAS 56,180 178,196 1,940 33,040 25,929 19,087 2,374

Kentucky VISTAS 64,099 229,596 1,623 17,103 24,659 18,813 782

Mississippi VISTAS 8,895 27,226 511 12,228 7,270 4,358 918

North Carolina VISTAS 57,774 102,217 1,232 14,386 31,797 26,551 847

South Carolina VISTAS 46,318 118,584 932 11,263 26,740 22,629 793

Tennessee VISTAS 29,873 112,343 922 7,391 15,008 12,988 449

Virginia VISTAS 43,144 80,602 863 16,482 19,652 17,300 881

West Virginia VISTAS 51,208 124,464 1,447 12,946 23,538 16,968 721

VISTAS Total 455,711 1,302,784 13,096 211,709 219,798 173,034 13,722

Illinois MRPO 71,233 241,136 2,229 17,868 32,650 30,132 1,152

Indiana MRPO 95,376 376,864 2,105 19,416 35,082 27,835 1,274

Michigan MRPO 98,685 398,562 1,623 17,522 38,902 34,276 1,091

Ohio MRPO 83,129 215,501 2,254 23,832 42,754 33,323 1,773

Wisconsin MRPO 45,701 155,369 1,101 11,901 15,629 14,246 626

MRPO Total 394,124 1,387,432 9,312 90,539 165,016 139,813 5,915

Arkansas CENRAP 33,097 82,605 696 11,429 3,897 3,326 814

Iowa CENRAP 51,119 147,305 770 8,759 10,033 8,615 569

Kansas CENRAP 83,333 81,486 798 7,203 8,520 6,807 461

Louisiana CENRAP 30,432 74,263 660 11,043 3,966 3,590 919

Minnesota CENRAP 41,029 85,847 674 5,563 8,162 7,034 343

Missouri CENRAP 77,660 280,887 1,579 13,165 18,456 16,769 800

Nebraska CENRAP 50,781 73,629 450 3,590 2,296 1,915 217

Oklahoma CENRAP 76,048 113,680 1,008 28,182 5,561 4,840 1,355

Texas CENRAP 153,837 339,433 4,988 102,583 38,952 31,631 6,424

CENRAP Total 597,336 1,279,135 11,622 191,518 99,842 84,528 11,902

Arizona WRAP 81,858 60,640 1,170 29,037 11,515 9,644 2,189

California WRAP 23,767 5,447 1,496 56,188 5,442 5,337 4,402

Colorado WRAP 70,171 87,163 667 12,139 4,751 4,166 609

Idaho WRAP 718 0 36 1,398 113 113 109

Montana WRAP 38,504 22,066 326 3,035 7,217 4,636 193

Nevada WRAP 47,404 31,172 479 9,862 5,244 4,315 750

New Mexico WRAP 74,010 52,916 554 5,991 13,435 7,637 388

North Dakota WRAP 71,711 108,645 784 9,937 5,670 4,757 324

Oregon WRAP 11,330 10,034 276 9,322 1,311 1,305 722

South Dakota WRAP 14,574 12,085 110 536 362 297 33

Utah WRAP 60,782 37,819 423 3,523 6,459 4,881 211

Washington WRAP 26,379 12,236 451 11,848 3,780 3,192 898

Wyoming WRAP 81,182 40,265 678 5,672 8,537 7,116 341

WRAP Total 602,389 480,488 7,449 158,487 73,834 57,395 11,170

National Total 2,224,778 4,770,490 46,007 767,912 624,049 507,129 48,857

IPM Generated Augmented Pollutants

Annual Emissions (Tons)
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4 MODIFICATIONS BY OTHER REGIONS 

4.1 Emission Control Modifications within VISTAS, MRPO, and CENRAP 

State and local agencies and invited stakeholders from VISTAS, MRPO, and CENRAP reviewed 

the results of the Inter-RPO Phase II set of IPM runs. These stakeholders primarily reviewed and 

commented on the IPM results with respect to IPM decisions on NOx post-combustion controls 

and SO2 scrubbers and provided additional information on when and where new SO2 and NOx

controls were planned to come online based on the best available data from state rules, 

enforcement agreements, compliance plans, permits, and discussions/commitments from 

individual companies. They also reviewed the IPM results to verify that known and existing 

controls and emission rates were properly reflected in the IPM runs. After considering 

comments, adjustments to the IPM results were made to specific units using any new information 

they had as part of the permitting process or other contact with the industry that indicated which 

units would install controls as a result of CAIR and when these new controls would come on-line 

(MACTEC, 2007; MRPO 2006; ENVIRON 2007). 

As described in the following section, some entities specified changes to the controls assigned by 

IPM to reflect their best estimates of emission control levels. These changes typically involved 

either 1) adding selective catalytic reduction (SCR) or scrubber controls to units where IPM did 

not predict SCR or scrubber controls, or 2) removing IPM-assigned SCR or scrubber controls at 

units where the commenting entity indicated there were no firm plans for controls at those units. 

At this point in the process MANE-VU decided not to make any changes to the northeastern state 

IPM output regardless of state knowledge of discrepancies with actual conditions. MANE-VU 

determined that IPM provided a reasonable estimate of the impact of the CAIR cap and trade 

program consistent with methods used by EPA, and planners were concerned that adjustments 

would not reflect the allocation of ALL allowed emissions under CAIR. 

In MANE-VU’s final modeling, many of the changes made by the other RPOs were included, 

but due to the timing of the release of revised data, the location with respect to the modeling 

domain, and need to progress with modeling, MANE-VU did not incorporate changes reflected 

in the final CENRAP EGU files. 

4.2 Emission Factor and Control Modifications for VISTAS Emission Sources 

VISTAS reviewed the PM and NH3 emissions from its States’ EGUs provided after the original 

IPM to NIF conversation conducted for the RPOs and identified significantly higher emissions in 

2009/2018 than in 2002. VISTAS determined this conversion used a set of PM and NH3

emission factors that were “the most recent EPA approved uncontrolled emission factors” for 

estimating 2009/2018 EGU emissions but were most likely not the same emission factors used 

by States for estimating these emissions in 2002. Thus, the emission increase from 2002 to 

2009/2018 was simply an artifact of the change in emission factors, not anything to do with 

changes in activity or control technology application. During this review, VISTAS additionally 

identified an inconsistent use of SCCs for determining emission factors between the base and 

future years. 
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21 

Documentation (Alpine, 2005a, b) indicates that VISTAS adjusted the 2002 base year emissions 

inventory to account for these discrepancies in base year and future year PM and NH3 emission 

factor use. Using the latest “EPA-approved” uncontrolled emission factors by SCC, Alpine 

utilized data collected under EPA’s Consolidated Emissions Reporting Rule (CERR) or data 

reported by VISTAS. Alpine used reported annual heat input, fuel throughput, heat, ash, and 

sulfur content to estimate annual uncontrolled emissions for units identified as output by IPM. 

This step was conducted for non-CEM pollutants (CO, VOC, PM, and NH3) only. For PM 

emissions, the condensable component of emissions was calculated and added to the resulting 

PM primary estimations. The resulting emissions were then adjusted by any control efficiency 

factors reported in the CERR or VISTAS data collection effort. The second adjustment was to 

the future year inventories. Alpine updated the SCCs in the future year inventory to assign the 

same base year SCC. Using the same methods as described for the 2002 revisions, those nonIPM generated pollutants were estimated using IPM predicted fuel characteristics and base year 

2002 SCC assignments. 

In addition to the changes to the emission factor assignments, SCC, and IPM-assigned controls, 

VISTAS also specified other changes to the IPM results or converted IPM to NIF files. 

Comments on changes in stack parameters from the 2002 inventory were implemented in the 

converted files for the 2018 inventory. Changes to stack parameters were also made in cases 

where new controls were scheduled to be installed. In cases where an emission unit was 

projected to have an SO2 scrubber by 2018, some States were able to provide revised stack 

parameters for some units based on design features for the new control system. Other units 

projected to install scrubbers by 2018 were not far enough along in the design process to have 

specific design details. For those units, VISTAS made the following assumptions: 1) the 

scrubber is a wet scrubber; 2) keep the current stack height the same; 3) keep the current flow 

rate the same, and 4) change the stack exit temperature to 169 degrees F (this is the virtual 

temperature derived from a wet temperature of 130 degrees F) (MACTEC, 2007). VISTAS 

determined that exit temperature (wet) of 130 degrees F +/- 5 degrees F is representative of 

different size units and wet scrubber technology. 

4.3 Emission Inventory Replacement within WRAP Domain 

During the development of their EGU emission forecast, the western states RPO (WRAP) 

conducted an exercise where IPM was not used to prepare emission estimates from EGU 

sources. Using capacity factor adjustments and emission control assumptions, WRAP developed 

a forecast of EGU emissions based on its initial 2002 base year inventory (ERG, 2006). This 

revised forecast was used by many of the RPOs and replaced the emissions generated for the 

domain by IPM. This change by WRAP is reflected in the difference in State emission totals 

between Tables 5 and 6. As WRAP is outside the MANE VU modeling domain, this change was 

not reflected in MANE-VU modeling. MANE-VU did not change its boundary conditions to 

reflect this change. 

4.4 Eliminating Double Counting of EGU Units 

An additional set of procedures was used by MANE-VU and VISTAS to avoid double counting 

of EGU emissions in the 2018 point source inventory (MACTEC, 2006, 2007). Since each 
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22 

RPO’s 2002 emissions inventory file contained both EGUs and non-EGU point sources, and 

EGU emissions were projected using IPM, it was necessary to split the 2002 point source file 

into two components. The first component contained those emission units accounted for in the 

IPM forecasts. The second component contained all other point sources not accounted for in 

IPM. 

As described in the previous section, 2018 NIF files for EGUs were prepared from the IPM 

parsed files. All IPM matched units were initially removed from the 2018 point source inventory 

to create the non-EGU inventory (which was projected to 2018 using non-EGU growth and 

control factors). This was done on a unit-by-unit basis based on a cross-reference table that 

matched IPM emission unit identifiers (ORISPL plant code and BLRID emission unit code) to 

NIF emission unit identifiers (FIPSST state code, FIPSCNTY county code, State Plant ID, State 

Point ID). When there was a match between the IPM ORISPL/BLRID and the emission unit ID, 

the unit was assigned to the EGU inventory; all other emission units were assigned to the nonEGU inventory. 

If an emission unit was contained in the NIF files created from the IPM output, the 

corresponding unit was removed from the initial 2018 point source inventory. For VISTAS, the 

NIF 2018 EGU files from the IPM parsed files were then merged with the non-EGU 2018 files to 

create a complete 2018 point source scenario. 

Next, several ad-hoc QA/QC queries were done to verify that there was no double-counting of 

emissions in the EGU and non-EGU inventories: 

• The IPM parsed files were reviewed to identify EGUs accounted for in IPM. This list of 

emission units was compared to the non-EGU inventory derived from the IPM-NIF crossreference table to verify that units accounted for in IPM were not double-counted in the 

non-EGU inventory. As a result of this comparison, a few adjustments were made in the 

cross-reference table to add emission units for plants to ensure these units accounted for 

in IPM were moved to the EGU inventory. 

• The non-EGU inventory was further reviewed to identify remaining emission units with 

an Standard Industrial Classification (SIC) code of “4911 Electrical Services” or Source 

Classification Code of “1-01-xxx-xx External Combustion Boiler, Electric Generation”. 

The list of sources meeting these selection criteria were compared to the IPM parsed file 

to ensure that these units were not double-counted. 

• VISTAS invited various stakeholder groups to review the 2018 point source inventory to 

verify whether there was any double counting of EGU emissions. In some instances, 

corrections were provided where an emission unit was double counted. 

4.5 Preliminary Results from Phase II Additional Modifications 

Table 6 summarizes the Base G emissions inventory for EGUs, presenting State level emission 

results from the 2018 Inter-RPO CAIR Case IPM v. 2.1.9 parsed file modified by VISTAS, 
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23 

MRPO, and WRAP per the methods noted in the above sections. Note that no changes occurred 

to the MANE-VU state emissions as a result of these changes. 

Table 6. State Level Emission Summary; 2018 VISTAS Base G Modeling file 

ptinv_egu_2018_11sep2006.txt. Based on 2018 VISTASII_PC_1f (fossil-only) with adjustments 

from VISTAS, MRPO, and WRAP. 

State RPO NOx SO2 VOC CO PM-10 PM-2.5 NH3

Connecticut MANE-VU 3,418 6,697 145 9,836 959 927 341

Delaware MANE-VU 12,341 35,442 117 1,183 2,950 2,438 76

District Of Columbia MANE-VU 103 83 5 154 104 99 12

Maine MANE-VU 1,827 5,436 53 4,057 296 279 139

Maryland MANE-VU 14,709 28,065 575 11,831 8,253 6,433 435

Massachusetts MANE-VU 18,157 17,486 484 13,860 3,917 3,233 1,059

New Hampshire MANE-VU 3,089 7,469 73 1,697 2,268 2,156 124

New Jersey MANE-VU 13,636 32,495 352 7,611 4,017 3,515 564

New York MANE-VU 24,376 51,445 758 22,242 11,031 9,343 1,471

Pennsylvania MANE-VU 82,881 135,946 1,919 41,446 31,580 23,756 1,790

Rhode Island MANE-VU 576 55 42 1,627 157 156 127

Vermont MANE-VU 105 35 3 117 26 25 9

MANE-VU Total 175,219 320,651 4,528 115,660 65,558 52,360 6,148

Alabama VISTAS 62,860 135,782 1,620 21,611 7,385 4,380 1,033

Florida VISTAS 56,827 133,037 1,857 42,573 9,287 6,288 2,665

Georgia VISTAS 69,308 226,477 1,805 35,584 18,217 11,319 1,676

Kentucky VISTAS 59,740 211,225 1,344 12,125 6,194 4,067 436

Mississippi VISTAS 10,455 15,143 1,055 11,822 7,007 6,853 545

North Carolina VISTAS 56,526 96,402 1,147 16,376 32,676 26,014 608

South Carolina VISTAS 50,068 87,202 860 13,078 28,110 24,454 578

Tennessee VISTAS 30,008 112,353 886 7,126 15,861 13,321 241

Virginia VISTAS 60,615 109,391 921 14,017 13,505 11,757 553

West Virginia VISTAS 51,177 115,322 1,382 11,896 6,344 3,643 177

VISTAS Total 507,583 1,242,334 12,877 186,205 144,586 112,094 8,513

Illinois MRPO 71,233 241,136 2,229 17,868 32,649 30,132 1,152

Indiana MRPO 95,376 351,858 2,105 19,416 35,081 27,835 1,274

Michigan MRPO 78,605 288,006 1,623 17,521 38,902 34,276 1,091

Ohio MRPO 83,129 215,501 2,254 23,832 42,753 33,322 1,772

Wisconsin MRPO 45,701 155,369 1,101 11,901 15,629 14,246 626

MRPO Total 374,044 1,251,871 9,311 90,539 165,015 139,812 5,915

Arkansas CENRAP 33,097 82,605 696 11,429 3,897 3,326 814

Iowa CENRAP 51,119 147,305 770 8,758 10,033 8,615 569

Kansas CENRAP 83,333 81,486 798 7,203 8,520 6,807 461

Louisiana CENRAP 30,432 74,263 660 11,043 3,966 3,590 919

Minnesota CENRAP 41,029 85,847 674 5,563 8,162 7,035 343

Missouri CENRAP 77,660 280,887 1,579 13,165 18,456 16,769 799

Nebraska CENRAP 50,781 73,629 450 3,590 2,296 1,914 217

Oklahoma CENRAP 76,048 113,680 1,008 28,182 5,561 4,840 1,355

Texas CENRAP 153,837 339,433 4,988 102,581 38,952 31,630 6,424

CENRAP Total 597,336 1,279,135 11,622 191,515 99,842 84,527 11,901

Arizona WRAP 59,774 55,941 724 17,806 2,811 634 630

California WRAP 17,537 1,528 2,558 31,173 1,219 1,059 0

Colorado WRAP 77,113 60,914 1,465 18,939 3,138 307 537

Idaho WRAP 2,236 1,683 50 3,283 335 87 0

Montana WRAP 44,733 31,303 565 11,818 1,796 247 13

Nevada WRAP 54,300 22,118 1,570 10,598 4,230 768 903

New Mexico WRAP 32,925 17,796 695 10,976 794 627 43

North Dakota WRAP 82,741 152,828 909 13,647 3,958 2,645 383

Oregon WRAP 15,742 15,096 474 5,753 1,288 323 219

South Dakota WRAP 17,681 13,522 118 689 247 217 52

Utah WRAP 76,136 41,394 597 17,150 4,637 2,000 1,350

Washington WRAP 16,884 7,011 249 4,008 1,474 1,027 12

Wyoming WRAP 104,142 96,745 1,147 18,871 10,445 7,411 404

WRAP Total 601,942 517,879 11,122 164,711 36,371 17,353 4,547

National Total 2,256,124 4,611,869 49,460 748,629 511,371 406,146 37,024

Annual Emissions (Tons)
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24 

4.6 Revised Results – VISTAS Base G2 Adjustment 

VISTAS further refined their future predictions based on further state input. The resulting 

modeling file was called the Base G2 inventory. Table 7 presents State level emission results 

from the Base G2 2018 Inter-RPO CAIR Case IPM v. 2.1.9 parsed file modified by VISTAS. 

Some states specified changes to the controls assigned by IPM to reflect their best estimates of 

emission control levels. These changes typically involved either 1) adding selective catalytic 

reduction (SCR) or scrubber controls to units where IPM did not predict SCR or scrubber 

controls, or 2) removing IPM-assigned SCR or scrubber controls at units where the commenting 

entity indicated their were no firm plans for controls at those units. These changes were based 

on those states’ best available information about where and when emissions controls were 

expected to be installed, as well as information concerning IPM-predicted plant closures that 

were deemed unlikely to occur. In comparing Table 7 with Table 6, it can be seen that the 

changes included in the Base G2 inventory were requested by the states of Florida, Georgia, and 

North Carolina. 

Note that no changes were made at this time by the MANE-VU states. The net effect of these 

changes was to reduce emissions of SO2 relative to either Table 5 or Table 6. 
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25 

Table 7. State Level Emission Summary; 2018 VISTAS Base G2 Modeling file 

egu_18_vistas_g2_20feb2007.txt. Based on 2018 VISTASII_PC_1f (fossil-only) with 

adjustments from VISTAS, MRPO, and WRAP. 

State RPO NOx SO2 VOC CO PM-10 PM-2.5 NH3

Connecticut MANE-VU 3,418 6,697 145 9,836 959 927 341

Delaware MANE-VU 12,341 35,442 117 1,183 2,950 2,438 76

District Of Columbia MANE-VU 103 83 5 154 104 99 12

Maine MANE-VU 1,827 5,436 53 4,057 296 279 139

Maryland MANE-VU 14,709 28,065 575 11,831 8,253 6,433 435

Massachusetts MANE-VU 18,157 17,486 484 13,860 3,917 3,233 1,059

New Hampshire MANE-VU 3,089 7,469 73 1,697 2,268 2,156 124

New Jersey MANE-VU 13,636 32,495 352 7,611 4,017 3,515 564

New York MANE-VU 24,376 51,445 758 22,242 11,031 9,343 1,471

Pennsylvania MANE-VU 82,881 135,946 1,919 41,446 31,580 23,756 1,790

Rhode Island MANE-VU 576 55 42 1,627 157 156 127

Vermont MANE-VU 105 35 3 117 26 25 9

MANE-VU Total 175,219 320,651 4,528 115,660 65,558 52,360 6,148

Alabama VISTAS 62,860 135,782 1,620 21,611 7,385 4,380 1,033

Florida VISTAS 58,341 139,200 1,904 42,947 9,355 6,331 2,665

Georgia VISTAS 69,308 75,051 1,805 35,584 18,217 11,319 1,676

Kentucky VISTAS 59,740 211,225 1,344 12,125 6,194 4,067 436

Mississippi VISTAS 10,455 15,143 1,055 11,822 7,007 6,853 545

North Carolina VISTAS 56,526 102,680 1,147 16,376 32,676 26,014 608

South Carolina VISTAS 50,068 87,202 860 13,078 28,110 24,454 578

Tennessee VISTAS 30,008 112,353 886 7,126 15,861 13,321 241

Virginia VISTAS 60,615 109,391 921 14,017 13,505 11,757 553

West Virginia VISTAS 51,177 105,932 1,382 11,896 6,344 3,643 177

VISTAS Total 509,098 1,093,959 12,923 186,579 144,654 112,137 8,513

Illinois MRPO 71,233 241,136 2,229 17,868 32,649 30,132 1,152

Indiana MRPO 95,376 351,858 2,105 19,416 35,081 27,835 1,274

Michigan MRPO 78,605 288,006 1,623 17,521 38,902 34,276 1,091

Ohio MRPO 83,129 215,501 2,254 23,832 42,753 33,322 1,772

Wisconsin MRPO 45,701 155,369 1,101 11,901 15,629 14,246 626

MRPO Total 374,044 1,251,871 9,311 90,539 165,015 139,812 5,915

Arkansas CENRAP 33,097 82,605 696 11,429 3,897 3,326 814

Iowa CENRAP 51,119 147,305 770 8,758 10,033 8,615 569

Kansas CENRAP 83,333 81,486 798 7,203 8,520 6,807 461

Louisiana CENRAP 30,432 74,263 660 11,043 3,966 3,590 919

Minnesota CENRAP 41,029 85,847 674 5,563 8,162 7,035 343

Missouri CENRAP 77,660 280,887 1,579 13,165 18,456 16,769 799

Nebraska CENRAP 50,781 73,629 450 3,590 2,296 1,914 217

Oklahoma CENRAP 76,048 113,680 1,008 28,182 5,561 4,840 1,355

Texas CENRAP 153,837 339,433 4,988 102,581 38,952 31,630 6,424

CENRAP Total 597,336 1,279,135 11,622 191,515 99,842 84,527 11,901

Arizona WRAP 59,774 55,941 724 17,806 2,811 634 630

California WRAP 17,537 1,528 2,558 31,173 1,219 1,059 0

Colorado WRAP 77,113 60,914 1,465 18,939 3,138 307 537

Idaho WRAP 2,236 1,683 50 3,283 335 87 0

Montana WRAP 44,733 31,303 565 11,818 1,796 247 13

Nevada WRAP 54,300 22,118 1,570 10,598 4,230 768 903

New Mexico WRAP 32,925 17,796 695 10,976 794 627 43

North Dakota WRAP 82,741 152,828 909 13,647 3,958 2,645 383

Oregon WRAP 15,742 15,096 474 5,753 1,288 323 219

South Dakota WRAP 17,681 13,522 118 689 247 217 52

Utah WRAP 76,136 41,394 597 17,150 4,637 2,000 1,350

Washington WRAP 16,884 7,011 249 4,008 1,474 1,027 12

Wyoming WRAP 104,142 96,745 1,147 18,871 10,445 7,411 404

WRAP Total 601,942 517,879 11,122 164,711 36,371 17,353 4,547

National Total 2,257,639 4,463,494 49,506 749,003 511,439 406,189 37,024

Annual Emissions (Tons)
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26 

5 ADDITIONAL ADJUSTMENTS BY NORTHEASTERN STATES AND MODELERS 

FOR REGIONAL HAZE SIP MODELING 

5.1 Introduction 

MANE VU used the G2 inventory as the basis for further adjustments to incorporate MANE-VU 

state changes and also to represent the MANE VU control strategy for key EGUs. These 

modifications resulted in a) SO2 emissions reductions at one MANE-VU EGU source subject to 

Best Available Retrofit Technology (BART) requirements, 2) emissions increases in MANE-VU 

to reflect states’ best estimates that some sources predicted by IPM to be closed would continue 

to operate and information about where and when emission controls would or would not be 

installed, 3) SO2 emissions reductions at key EGUs (or alternative facilities) to reflect the 

MANE-VU EGU strategy, and 4) increases in SO2 emissions to estimate the effect of emissions 

trading under the CAIR program. Each of these is explained below. 

5.2 Best Available Retrofit Technology (BART) 

To assess the impacts of the implementation of the BART provisions of the Regional Haze Rule, 

NESCAUM included estimated reductions anticipated for BART-eligible facilities not covered 

by CAIR in the MANE-VU region in the 2018 CMAQ modeling analysis. A survey of state staff 

indicated that eight units would likely be controlled under BART alone. State-provided potential 

control technologies and levels of control for these sources were incorporated into the 2018 

emission inventory projections used in MANE-VU’s March 2008 modeling run (NESCAUM, 

2008b). The eight BART-eligible units included one EGU point source, which is located in 

Maine (Wyman Station).

5.3 MANE-VU State Modifications of IPM Results 

Previously, during development of the Base G and Base G2 inventories, MANE-VU states had 

relied on the RPO IPM model results (Base F) without revisions. In 2007, the MANE-VU states 

decided that they should revise the estimates, as other RPOs had done, to reflect their best 

estimates of future source operations and controls. State and regional staff reviewed and revised 

the IPM results with respect to when and where new SO2 controls were planned to come online. 

Modifications were based on state rules, enforcement agreements, compliance plans, permits, 

and commitments from individual companies. States reviewed the IPM results to verify that 

known and existing controls and emission rates were properly reflected in the IPM results. In 

addition, states noted that some units predicted by IPM to close were very unlikely to cease 

operation. 

The net effect of these adjustments was an increase in SO2 emissions in the MANE-VU region as 

a whole. In Delaware SO2 emissions decreased due to controls on a major source. Emissions in 

Connecticut, the District of Columbia, Rhode Island, and Vermont remained the same as 

predicted by RPO IPM 2.1.9 (Base F). Emissions of SO2 in other MANE-VU states increased. 

No changes were made in emissions of other pollutants. 
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27 

5.4 MANE-VU EGU Strategy 

MANE-VU states have recognized that SO2 emissions from power plants are the single largest 

contributing sector to visibility impairment in the Northeast’s Class I areas. Sulfate formed 

through atmospheric processes from SO2 emissions are responsible for over half the mass and 

approximately 70-80 percent of the extinction on the worst visibility days (NESCAUM, 2006a, 

and b). The emissions from power plants dominate the SO2 inventory. 

A modeling analysis was conducted to identify those EGUs with the greatest impact on visibility 

in MANE-VU. As part of the MANE VU Contribution Assessment, two MANE-VU modeling 

centers undertook CALPUFF modeling to identify the top 100 stacks that impacted three of the 

MANE VU Class I areas in the base year, 2002. These three areas are Acadia, Brigantine and 

Lye Brook. Details of the modeling are provided in Appendix D of the Contribution 

Assessment. (NESCAUM, 2006a) The 100 top stacks for each Class I area are listed in Tables 

10 and 20 from Appendix D “Dispersion Model Techniques” of the Contribution Assessment. 

The two modeling centers used 2002 U.S. EPA Continuous Emission Monitoring System 

(CEMS) data reported by the power companies, which is stack based rather than emission unit 

based. A power plant may have several stacks. Each stack may vent emissions from one or 

more units at the plant. The two modeling centers also used different meteorological data—one 

used data from the MM5 model and the other used National Weather Service observation-based 

meteorology. 

There are differences between results from the two centers because of the differences in 

meteorological input data and also because of rounding when summing annual emissions. As a 

result the MM5-based modeling identified some stacks as being in the top 100 impacting a 

MANE-VU Class I area that were not identified by the observation-based modeling, and vice 

versa. For purposes of identifying key stacks, all stacks on either list were included. 

MARAMA combined the lists of the top 100 EGU stacks in Tables 10 and 20 from Appendix D 

of the Contribution Assessment and eliminated both duplications and stacks that were outside the 

MANE-VU consultation area. (The consultation area includes states contributing at least 2% of 

the sulfate monitored at MANE-VU Class I areas in 2002.) This process resulted in 167 unique 

stacks impacting one or more of the three MANE-VU Class I areas. The use of stacks rather 

than units or facilities was chosen as more consistent with the results of the modeling presented 

in the Contribution Assessment. The Contribution Assessment Appendix D tables did not 

identify the units or facilities that were modeled, only providing a CEMS Identification number. 

MARAMA used information contained in IPM input files to match up the plant name and type 

where the stack was located. The resulting list of 167 stacks is found in Appendix A of this 

report. 

MANE-VU asked states in the consultation area to pursue 90 percent control on all units 

emitting from those stacks by 2018. MANE-VU recognized that this level of control may not be 

feasible in all cases. NESCAUM modelers incorporated State comments gathered during the 
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28 

inter-RPO consultation process in estimating the impact of this strategy on visibility at Class I 

areas. This process is described below in Section 5.5. 

5.5 Implementation of MANE-VU Control Strategy for Key EGUs 

As part of the MANE-VU strategy to improve visibility, MANE-VU asked states to pursue a 90 

percent reduction in SO2 emissions from the 167 EGU stacks identified as described in Section 

5.4 and listed in Appendix A. MARAMA gathered information from MANE-VU, MRPO, and 

VISTAS states and regional staff to obtain information about anticipated emissions changes. 

State and local agencies and individual stakeholders from MANE-VU, MRPO and VISTAS 

reviewed and revised the IPM results with respect to controls planned to come online. They also 

reviewed the IPM results to verify that known and existing controls and emission rates were 

properly reflected in the IPM runs. In addition, commenters noted that some units predicted by 

IPM to be shutdown would not shutdown. 

Adjustments to the IPM results were made to specific units using information states had obtained 

as part of the permitting process or other contact with the industry that indicated which units 

would install controls as a result of CAIR and when these new controls would come on-line 

(Koerber, 2007; VISTAS 2007). In general, the changes at specific EGUs provided by VISTAS 

reflected their Base G2 inventory, and, as discussed with MRPO, the changes NESCAUM made 

to emissions from sources in the MRPO were consistent with sources where controls were 

predicted in EPA’s IPM 3.0 run for 2018, since MRPO modeling relied on IPM 3.0. In addition 

to the 167 stacks, MANE-VU incorporated further corrections to source emissions as requested 

by VISTAS states at the following locations: North Carolina (Cliffside), South Carolina 

(Jefferies), Kentucky (Spurlock), and Virginia (Chesapeake and Clinch River). . 

NESCAUM determined the desired emissions levels for the 167 key stacks based on a 90 percent 

reduction in continuous emissions monitoring data from 2002. This established a target 

emissions level for the region from those stacks. NESCAUM compared these levels with the 

information provided by the states for those sources. In each region, predicted 2018 emissions 

exceeded the target level. Therefore, emissions reductions from other sources were considered 

in order to meet the target emissions reductions for the region.(both within MANE-VU and in 

other RPOs). This resulted in a net decrease in emissions in all three affected RPOs. Emissions 

of SO2 would have decreased by over 14,000 tons per year in MANE-VU, over 304,000 tons per 

year in the Midwest, and over 197,000 tons per year in the VISTAS region. 

However, MANE-VU planners recognized that CAIR allows emissions trading, and that 

reductions at one unit could be offset increases at another unit within the CAIR region. Because 

most states do not restrict trading, MANE-VU decided that emissions should be increased to 

represent the implementation of the strategy for the 167 stacks within the limits of the CAIR 

program. Therefore, NESCAUM increased the emissions from states subject to the CAIR cap 

and trade program. For MANE-VU, 75,809 tons were added back, leaving total regional 

emissions from the MANE-VU region greater than the original Inter-RPO IPM-based estimate 

but consistent with state projections. The remaining 440,541 tons added back were allocated to 
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29 

VISTAS and MRPO based on the fraction of their contribution to the total SO2 emissions. The 

additional emissions correspond to an increase of 20.5 percent, with a total of 223,856 tons 

added to MRPO and 216,685 added to VISTAS. 

Table 8 shows the emissions difference between the results of two IPM runs and the modeling 

inventories used by three Regional Planning Organizations (RPOs). VISTAS used Base G2, 

MANE-VU used the March 2008 Modeling Inventory, and MRPO used IPM 3.0.. 

Table 8. Comparison of Regional SO2 Emissions Estimates. 

(1000 tons per year) 

 MANE-VU MRPO VISTAS TOTAL 

RPO 2.1.9 (VISTASII_PC_1f) (fossil only) 321 1,387 1,303 3,011 

Reductions made by 

VISTAS and MRPO (Base G2) 0 -136 -209 -344 

Net additional changes made 

by MANE-VU 66 24 222 311 

MANE-VU March 2008 Modeling 

Inventory (fossil only) 387 1,276 1,316 2,978 

MANE-VU minus RPO 2.1.9 

(negative numbers mean 

MANE-VU's modeling inventory 

was less than RPO 2.1.9) 66 -112 13 -33 

EPA 3.0 (fossil only) 421 1,328 1,458 3,207 

RPO 2.1.9 minus EPA 3.0 

(negative number means RPO 

2.1.9 was less than EPA 3.0) -100 59 -155 -196 

MANE-VU 3/08 minus EPA 3.0 

(negative numbers mean 

MANE-VU's modeling inventory 

was less than EPA 3.0) -34 -53 -142 -229 

The intent of the MANE-VU modelers’ final EGU emissions adjustments was to retain the same 

level of emissions as predicted by the RPO CAIR IPM run for the three regions together, but to 

modify the locations of the emissions to better reflect the states’ estimates and to achieve 

reductions at the 167 stacks identifie++d as important contributors to regional haze at MANEVU Class I areas. As shown in Table 8, above, the MANE-VU adjustments resulted in total 

emissions from the three regions being less than the SO2 emissions predicted by the RPO 2.1.9 

IPM run but greater than emissions in the G2 inventory used by VISTAS modelers. In both the 

MANE-VU and VISTAS regions, the MANE-VU Modeling Inventory is greater than the 

VISTAS/Inter-RPO IPM run and in MRPO it is smaller. Results from IPM 3.0 also are provided 

for comparison, and are uniformly greater than the MANE-VU Modeling Inventory for EGUs. 
DRAFT—DO NOT CITE OR QUOTE—4/28/08

30 

All future EGU emissions estimates involve uncertainty. MANE-VU believes its process of 

adding back emissions resulted in a reasonable, conservative estimate of the implementation of 

the MANE-VU request for a 90% reduction at key EGU facilities. 

5.6 State Results – Northeastern State Adjustments 

Table 9 presents State level emission results as modified by the Northeastern States per the 

methods noted in the above sections. This table summarizes the input data used in the MANEVU 2018 March 2008 Modeling run as documented in NESCAUM’s 2018 Visibility Projections

report dated March 2008. 
DRAFT—DO NOT CITE OR QUOTE—4/28/08

31 

Table 9. State Level 2018 Emission Summary; March 2008 MANE-VU EGU Modeling 

Inventory. (See next page for file names.) 

State RPO NOx SO2 VOC CO PM-10 PM-2.5 NH3

Connecticut MANE-VU 3,418 6,697 145 9,836 959 927 341

Delaware MANE-VU 12,341 10,941 117 1,183 2,950 2,438 76

District Of Columbia MANE-VU 103 83 5 154 104 99 12

Maine MANE-VU 1,827 6,806 53 4,057 296 279 139

Maryland MANE-VU 14,709 43,764 575 11,831 8,253 6,433 435

Massachusetts MANE-VU 18,157 45,941 484 13,860 3,917 3,233 1,059

New Hampshire MANE-VU 3,089 10,766 73 1,697 2,268 2,156 124

New Jersey MANE-VU 13,636 15,918 352 7,611 4,017 3,515 564

New York MANE-VU 24,376 74,587 758 22,242 11,031 9,343 1,471

Pennsylvania MANE-VU 82,881 170,992 1,919 41,446 31,580 23,756 1,790

Rhode Island MANE-VU 576 55 42 1,627 157 156 127

Vermont MANE-VU 105 35 3 117 26 25 9

MANE-VU Total 175,219 386,584 4,528 115,660 65,558 52,360 6,148

Alabama VISTAS 62,860 163,567 1,620 21,611 7,385 4,380 1,033

Florida VISTAS 58,341 167,685 1,903 42,946 9,355 6,330 2,665

Georgia VISTAS 69,308 90,408 1,805 35,584 18,217 11,319 1,676

Kentucky VISTAS 59,740 255,559 1,344 12,125 6,194 4,067 436

Mississippi VISTAS 10,455 18,241 1,055 11,822 7,007 6,853 545

North Carolina VISTAS 56,526 126,042 1,147 16,376 32,676 26,014 608

South Carolina VISTAS 50,068 105,436 860 13,078 28,110 24,454 578

Tennessee VISTAS 30,008 135,344 886 7,126 15,861 13,320 241

Virginia VISTAS 60,615 125,849 921 14,017 13,505 11,757 553

West Virginia VISTAS 51,177 127,609 1,382 11,896 6,344 3,643 177

VISTAS Total 509,098 1,315,740 12,922 186,579 144,653 112,137 8,512

Illinois MRPO 71,233 208,832 2,229 17,868 32,649 30,132 1,152

Indiana MRPO 95,376 403,473 2,105 19,416 35,081 27,835 1,274

Michigan MRPO 78,605 213,066 1,623 17,521 38,902 34,276 1,091

Ohio MRPO 83,129 353,293 2,254 23,832 42,753 33,322 1,772

Wisconsin MRPO 45,701 96,934 1,101 11,901 15,629 14,246 626

MRPO Total 374,044 1,275,598 9,311 90,539 165,015 139,812 5,915

Arkansas CENRAP 33,097 82,605 696 11,429 3,897 3,326 814

Iowa CENRAP 51,119 147,305 770 8,758 10,033 8,615 569

Kansas CENRAP 83,333 81,486 798 7,203 8,520 6,807 461

Louisiana CENRAP 30,432 74,263 660 11,043 3,966 3,590 919

Minnesota CENRAP 41,029 85,847 674 5,563 8,162 7,035 343

Missouri CENRAP 77,660 280,887 1,579 13,165 18,456 16,769 799

Nebraska CENRAP 50,781 73,629 450 3,590 2,296 1,914 217

Oklahoma CENRAP 76,048 113,680 1,008 28,182 5,561 4,840 1,355

Texas CENRAP 153,837 339,433 4,988 102,581 38,952 31,630 6,424

CENRAP Total 597,336 1,279,135 11,622 191,515 99,842 84,527 11,901

Arizona WRAP 59,774 55,941 724 17,806 2,811 634 630

California WRAP 17,537 1,528 2,558 31,173 1,219 1,059 0

Colorado WRAP 77,113 60,914 1,465 18,939 3,138 307 537

Idaho WRAP 2,236 1,683 50 3,283 335 87 0

Montana WRAP 44,733 31,303 565 11,818 1,796 247 13

Nevada WRAP 54,300 22,118 1,570 10,598 4,230 768 903

New Mexico WRAP 32,925 17,796 695 10,976 794 627 43

North Dakota WRAP 82,741 152,828 909 13,647 3,958 2,645 383

Oregon WRAP 15,742 15,096 474 5,753 1,288 323 219

South Dakota WRAP 17,681 13,522 118 689 247 217 52

Utah WRAP 76,136 41,394 597 17,150 4,637 2,000 1,350

Washington WRAP 16,884 7,011 249 4,008 1,474 1,027 12

Wyoming WRAP 104,142 96,745 1,147 18,871 10,445 7,411 404

WRAP Total 601,942 517,879 11,122 164,711 36,371 17,353 4,547

National Total 2,257,639 4,774,936 49,505 749,003 511,439 406,188 37,023

Annual Emissions (Tons)
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32 

Files used in preparing Table 9 include for CENRAP and WRAP, the VISTAS Base G2 

Modeling file (egu_18_vistas_g2_20feb2007.txt.), and the following additional files: 

MANE-VU: 

EGU2018_MANEVUv3_nonSO2.ida 

EGU2018_MANEVU_SO2_non167plus.ida 

EGU2018_MANEVU_SO2_167plus.ida 

VISTAS: 

EGU2018_VISTASG2_SO2_non167plus_CAIR

addback.ida 

EGU2018_VISTASG2_SO2_167plus_CAIRadd

back.ida 

EGU2018_VISTASG2_nonSO2.ida 

MRPO: 

EGU2018_MWRPO_SO2_167plus_CAIRaddback.

ida 

EGU2018_MWRPO_SO2_non167p_non65_CAIR

addback.ida 

EGU2018_MWRPO_SO2_65_CAIRaddback.ida 

EGU2018_MWRPO_nonSO2.ida 
DRAFT—DO NOT CITE OR QUOTE—4/28/08

33 

6 EGU PREPARATION TIMELINE 

The following section provides a chronological review of the events and milestones that occurred 

during the preparation of EGU emission forecasts in support of regional haze SIP preparation. 

2004 

• VISTAS/MRPO sponsor first IPM 2.1.6 runs for 2018 (Phase I) 

• Phase I (VISTAS_CAIR_2) results released 

2005 

• RPOs move to IPM 2.1.9 (Phase II) 

• Revisions to NEEDS input file and global parameters submitted by RPOs for revised runs 

• Phase II (VISTAS_II_PC_1f) results released 

• IPM parsed to NIF and NIF to SMOKE IDA format conversion occurs 

• Initial RPO adjustments and modifications of IPM results 

• RPOs share IPM 2.1.9 inputs and configuration from Phase II with EPA 

• EPA releases IPM 2.1.9 results of CAIR/CAMR modeling 

2006 

• Additional RPO control and modeling file adjustments to Phase II runs 

• RPOs simulate 2018 forecast year to support regional haze SIP submittals 

• RPOs work with EPA to configure NEEDS 3.0 for next round of EPA modeling 

• EPA releases IPM 2006 revised projections 

• RPOs identify potential control measures and estimate benefits for meeting reasonable progress 

goals 

• Additional RPO control and modeling file adjustments to Phase II runs 

2007 

• RPOs analyze cost and other factors associated with potential control measures 

• RPOs coordinate with EPA on inputs and runs of IPM 3.0 

• EPA releases IPM 3.0 results of revised CAIR/CAMR/CAVR modeling 

• Interstate and inter-regional consultation regarding potential control measures 

• MANE-VU states agree to pursue several control measures 

• RPOs begin regional modeling to assess visibility impacts of controls 

2008 

• RPOs model to determine progress goals for regional haze SIP 

• States finalize regional haze SIPs 
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34 

7 REFERENCES 

Alpine Geophysics, LLC, 2005a. EGU Emission Factors and Emission Factor Assignment, 

technical memorandum prepared for VISTAS EGU Special Interest Workgroup, 2005.

Alpine Geophysics, LLC, 2005b. Emission Factor/Rate Development for IPM Post-Processing, 

technical memorandum prepared for VISTAS EGU Special Interest Workgroup.

Alpine Geophysics, LLC, 2006. Future Year Emissions Inventories, Draft Work Assignment #1, 

Technical Memorandum - Task 1, prepared for MARAMA, 2006. 

E.H. Pechan & Associates, Inc., 2004. Electricity Generating Unit (EGU) Growth Modeling 

Method Task 2 Evaluation – Final Report, prepared for Lake Michigan Air Directors 

Consortium, 2004. 

E.H. Pechan & Associates, Inc., 2005. LADCO IPM Model Parsed File Post-Processing 

Methodology and File Preparation (January 2005) – Technical Memorandum, prepared for Lake 

Michigan Air Directors Consortium, 2005. 

ENVIRON, 2007. Technical Support Document for CENRAP Emissions and Air Quality 

Modeling to Support Regional Haze State Implementation Plans, prepared for Central Regional 

Air Planning Association. 

EPA, 2004. Analysis in Support of the Clean Air Interstate Rule Using the Integrated Planning 

Model, Technical Support Document for the Final Clean Air Interstate Rule, 2004. 

EPA, 2005a. Modeling of Control Costs, Emissions, and Control Retrofits for Cost Effectiveness 

and Feasibility Analyses, Technical Support Document for the Final Clean Air Interstate Rule, 

2005. 

EPA, 2005b. Standalone Documentation for EPA Base Case 2004 (V.2.1.9) Using the Integrated 

Planning Model, EPA 430-R-05-011. Available at http://www.epa.gov/airmarkets/progsregs/epaipm/past-modeling.html#version2004. 

ERG, 2006. WRAP Point and Area Source Emissions Projections for the 2018 Base Case 

Inventory, Version 1, prepared for Emission Inventory Workgroup, 2006. 

Houyoux, M.; Vukovich, J.; Brandmeyer, J., 2000. Sparse Matrix Operator Kernal Emissions 

Modeling System (SMOKE) User Manual, Version 1.1.2 Draft, MCNC-North Carolina 

Supercomuting Center Environmental Programs. 

ICF Resources, LLC., 2005. Future Year Electricity Generating Sector Emission Inventory 

Development Using the Integrated Planning Model (IPM®) in Support of Fine Particulate Mass 

and Visibility Modeling in the VISTAS and Midwest RPO Regions, prepared for VISTAS, 2005. 
DRAFT—DO NOT CITE OR QUOTE—4/28/08

35 

ICF Resources, LLC., 2007. Comparison of CAIR and CAIR Plus Proposal using the 

Integrated Planning Model (IPM®), prepared for MARAMA. 

Koerber, 2007. Personal and electronic communication from Mark Janssen to Julie McDill, 

MARAMA and Andy Bodnarik, New Hampshire DEP. 

MACTEC, 2006. Development of Emission Projections for 2009, 2012, and 2018 for NonEGU 

Point, Area, and Nonroad Sources in the MANE-VU Region, prepared for MARAMA. 

MACTEC, 2007. Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission 

Inventories for VISTAS, prepared for VISTAS, 2007. 

MRPO, 2006. Personal and electronic communication from Mark Janssen to Gregory Stella, 

Alpine Geophysics, LLC., 2006. 

NESCAUM, 2006a. Contributions to Regional Haze in the Northeast and Mid-Atlantic U.S., 

Northeast States for Coordinated Air Use Management, Boston, MA, 2006a. 

NESCAUM, 2006b. The Nature of the Fine Particle and Regional Haze Air Quality Problems in 

the MANE-VU Region: A Conceptual Description, Available at: 

http://www.nescaum.org/documents/2006-1102-pm-conceptual-model.pdf/, Northeast States for 

Coordinated Air Use Management, Boston, MA, 2006b. 

NESCAUM, 2008a. MANE-VU Modeling for Reasonable Progress Goals Model performance 

evaluation, pollution apportionment, and control measure benefits, prepared for MANE-VU, 

2008a. 

NESCAUM, 2008b. 2018 Visibility Projections, prepared for MANE-VU. 

VISTAS, 2007. Electronic communication from Doris McLeod 11/7/2007. 

Janssen, 2008. Personal communication from Mark Janssen 3/28/2008. 
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36 

Appendix A 

TOP ELECTRIC GENERATING EMISSION POINTS CONTRIBUTING TO 

VISIBILITY IMPAIRMENT IN MANE-VU IN 2002 

For each of three MANE-VU Class I Areas the 100 Electric Generating Unit (EGU) stacks with 

the most significant impact on visibility impairment were identified by Calpuff modeling 

conducted by two modeling centers.1

 Many of these stacks have a regional impact and therefore 

significantly impact more than one Class I Area. When the “Top Impacting” stacks are 

aggregated into a single group there are 167 individual “Top Impacting” stacks identified. The 

map on the following page indicates the location of the 167 stacks, and the table following the 

map provides identifying information, emissions used in the Calpuff modeling, and predicted 

impacts. The following information may be found in the listed columns of the following table: 

1. Row Number (1 through 167) 

2. CEMS Unit ID: an arbitrary number identifying the CEMS unit 

3. ORIS ID: a standard identification number associated with each unit 

4. Acadia MM5: The rank of this source based on its predicted sulfate ion annual 

impact on Acadia in 2002 using meteorological data from the MM5 model. (A blank 

in columns 4, 5, 6, 7, 8, or 9 indicates this source was not among the top 100 for this 

Class I area as predicted by the indicated model.) 

5. Acadia VTDEC: The rank of this source in terms of its predicted sulfate ion annual 

impact on Acadia in 2002 using National Weather Service data. 

6. Brig MM5: The rank of this source in terms of its predicted sulfate ion annual impact 

on Brigantine in 2002 using meteorological data from the MM5 model. 

7. Brig VTDEC: The rank of this source in terms of its predicted sulfate ion annual 

impact on Brigantine in 2002 using National Weather Service data. 

8. Lye MM5: The rank of this source in terms of its predicted sulfate ion annual impact 

on Lye Brook in 2002 using meteorological data from the MM5 model. 

9. Lye VTDEC: The rank of this source in terms of its predicted sulfate ion annual 

impact on Lye Brook in 2002 using National Weather Service data. 

10. MM5 2002 SO2 Tons per Year: Emissions calculated from CEMS data and used by 

modelers who used the MM5 generated meteorological data 

11. VTDEC 2002 SO2 Tons per Year: Emissions calculated from CEMS data and used 

by modelers who used the national weather service generated meteorological data 

12. Plant Number (1 through 105): The 167 stacks are located at 105 plants. 

13. Plant Name—table is in alphabetical order by plant within each state 

14. Plant Type: coal fired or oil/gas fired electric generating units 

15. State Name—table is in alphabetical order by state 

16. State Code 

 

1

 For more information and detailed modeling results, see Appendix D: Source Dispersion Model Methods, in 

NESCAUM 2006a. 
DRAFT—DO NOT CITE OR QUOTE—4/28/08

37 

Figure A-1. Top 167 US Electric Generating Facility Stacks Affecting MANE-VU Class I 

Areas in 2002. 
DRAFT—DO NOT 

38 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

1 D005935 593 90 54 2,138 2,136 1 EDGE MOOR O/G Steam 2 D005941 594 95 3,742 2 INDIAN RIVER Coal Steam 3 D005942 594 74 3,760 2 INDIAN RIVER Coal Steam 4 D005943 594 84 44 4,686 4,682 2 INDIAN RIVER Coal Steam 5 D005944 594 69 21 7,390 7,384 2 INDIAN RIVER Coal Steam 6 D007031LR 703 79 86 75 38,520 38,486 3 BOWEN Coal Steam 7 D007032LR 703 72 89 61 68 37,289 37,256 3 BOWEN Coal Steam 8 D007033LR 703 71 99 74 64 63 94 43,067 43,029 3 BOWEN Coal Steam 9 D007034LR 703 69 95 86 58 60 89 41,010 40,974 3 BOWEN Coal Steam 

10 D00709C02 709 84 75 89 71 47,591 47,549 4 HARLLEE BRANCH Coal Steam 11 D00861C01 861 28 96 65 46 62 42,355 42,318 5 COFFEEN Coal Steam 12 D010011 1001 53 28,876 28,851 6 CAYUGA Coal Steam 13 D010012 1001 95 46 68 26,016 25,992 6 CAYUGA Coal Steam 14 D00983C01 983 52 19,922 7 CLIFTY CREEK Coal Steam 15 D00983C02 983 54 18,131 7 CLIFTY CREEK Coal Steam 16 D0099070 990 55 10

0 

70 37 29,801 29,774 8 ELMER W STOUT O/G Steam 

17 D06113C03 6113 30 48 14 43 22 41 71,182 71,119 9 GIBSON Coal Steam 18 D06113C04 6113 44 70 97 83 73 83 27,848 27,823 9 GIBSON Coal Steam 19 D01008C01 1008 73 10

0 

47 24,109 24,087 10 R GALLAGHER Coal Steam 

20 D01008C02 1008 98 55 23,849 23,828 10 R GALLAGHER Coal Steam 21 D06166C02 6166 62 44 30 81 33 57 51,708 51,663 11 ROCKPORT Coal Steam 22 D00988C03 988 77 15,946 12 TANNERS CREEK Coal Steam 23 D00988U4 988 14 29 52 34 7 19 45,062 45,022 12 TANNERS CREEK Coal Steam 24 D01010C05 1010 43 32 12 28 31 17 60,747 60,693 13 WABASH RIVER Coal Steam 25 D067054 6705 34 60 34 44 73 40,118 40,082 14 WARRICK Coal Steam 26 D06705C02 6705 92 75 96 27,895 14 WARRICK Coal Steam 27 D01353C02 1353 38 30 15 26 85 29 41,545 41,508 15 BIG SANDY Coal Steam 
DRAFT—DO NOT 

39 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

28 D01384CS1 1384 22 58 21,837 21,817 16 COOPER Coal Steam 29 D01355C03 1355 21 51 99 68 52 38,104 38,070 17 E W BROWN Coal Steam 30 D060182 6018 83 39 12,083 18 EAST BEND Coal Steam 31 D01356C02 1356 93 71 88 50 59 25,646 25,623 19 GHENT Coal Steam 32 D060411 6041 61 18,375 20 H L SPURLOCK Coal Steam 33 D060412 6041 53 91 98 20,491 20,473 20 H L SPURLOCK Coal Steam 34 D013644 1364 81 7,185 21 MILL CREEK Coal Steam 35 D013782 1378 87 20,245 22 PARADISE Coal Steam 36 D013783 1378 76 10

0 

11 84 55 42 46,701 46,660 22 PARADISE Coal Steam 

37 D015074 1507 78 1,170 23 WILLIAM F WYMAN O/G Steam 38 D006021 602 90 38 10

0 

20,014 19,996 24 BRANDON SHORES Coal Steam 

39 D006022 602 99 29 99 19,280 19,263 24 BRANDON SHORES Coal Steam 40 D015521 1552 63 17,782 17,767 25 C P CRANE Coal Steam 41 D015522 1552 68 14,274 14,262 25 C P CRANE Coal Steam 42 D01571CE2 1571 42 47 1 4 20 28 48,566 48,522 26 CHALK POINT Coal Steam 43 D01572C23 1572 73 79 47 45 69 32 32,188 32,159 27 DICKERSON Coal Steam 44 D015543 1554 77 10,084 10,075 28 HERBERT A WAGNER O/G Steam 45 D015731 1573 67 50 16 12 56 38 36,823 36,790 29 MORGANTOWN Coal Steam 46 D015732 1573 59 53 10 13 51 39 30,788 30,761 29 MORGANTOWN Coal Steam 47 D016191 1619 37 80 9,252 9,244 30 BRAYTON POINT Coal Steam 48 D016192 1619 35 66 8,889 8,881 30 BRAYTON POINT Coal Steam 49 D016193 1619 4 14 65 56 79 19,325 19,308 30 BRAYTON POINT Coal Steam 50 D015991 1599 5 36 65 13,014 13,002 31 CANAL O/G Steam 51 D015992 1599 7 27 74 8,980 8,971 31 CANAL O/G Steam 52 D016061 1606 48 5,249 32 MOUNT TOM Coal Steam 53 D016261 1626 85 3,430 33 SALEM HARBOR Coal Steam 54 D016263 1626 91 78 4,971 4,966 33 SALEM HARBOR Coal Steam 
DRAFT—DO NOT 

40 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

55 D016264 1626 32 25 2,880 2,878 33 SALEM HARBOR O/G Steam 56 D016138 1613 94 4,376 34 SOMERSET Coal Steam 57 D01702C09 1702 96 4,565 35 DAN E KARN Coal Steam 58 D01733C12 1733 49 24 80 80 45 22 46,081 46,040 36 MONROE Coal Steam 59 D01733C34 1733 27 26 76 26 27 39,362 39,327 36 MONROE Coal Steam 60 D017437 1743 91 15,805 37 ST CLAIR Coal Steam 61 D017459A 1745 76 61 18,341 18,324 38 TRENTON CHANNEL Coal Steam 62 D023641 2364 2 57 9,356 9,348 39 MERRIMACK Coal Steam 63 D023642 2364 1 17 99 28 87 19,453 19,435 39 MERRIMACK Coal Steam 64 D080021 8002 45 74 5,033 5,028 40 NEWINGTON O/G Steam 65 D023781 2378 81 2 15 9,747 9,738 41 B L ENGLAND Coal Steam 66 D024032 2403 63 97 25 50 40 44 18,785 18,768 42 HUDSON O/G Steam 67 D024081 2408 95 8,076 43 MERCER Coal Steam 68 D024082 2408 60 5,675 43 MERCER Coal Steam 69 D02549C01 2549 64 41 42 72 25,343 25,320 44 C R HUNTLEY Coal Steam 70 D02549C02 2549 99 12,317 44 C R HUNTLEY Coal Steam 71 D024804 2480 71 7,720 45 DANSKAMMER O/G Steam 72 D02554C03 2554 33 51 62 27 51 30,151 30,125 46 DUNKIRK Coal Steam 73 D02526C03 2526 78 14,929 47 WESTOVER Coal Steam 74 D025276 2527 80 12,650 48 GREENIDGE Coal Steam 75 D025163 2516 96 7,359 49 NORTHPORT O/G Steam 76 D025945 2594 76 1,747 50 OSWEGO O/G Steam 77 D02642CS2 2642 91 14,086 51 ROCHESTER 7 Coal Steam 78 D080061 8006 93 3,817 52 ROSETON O/G Steam 79 D080062 8006 88 2,840 52 ROSETON O/G Steam 80 D080421 8042 13 12 18 5 10 34 57,820 57,769 53 BELEWS CREEK Coal Steam 81 D080422 8042 23 15 32 10 15 49 45,296 45,256 53 BELEWS CREEK Coal Steam 82 D027215 2721 98 45 87 39 97 85 19,145 19,128 54 CLIFFSIDE Coal Steam 83 D027133 2713 61 14,460 55 L V SUTTON Coal Steam 
DRAFT—DO NOT 

41 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

84 D027093 2709 97 9,390 56 LEE Coal Steam 85 D027273 2727 10

0 

40 48 75 84 26,329 26,305 57 MARSHALL Coal Steam 

86 D027274 2727 89 39 83 51 66 82 27,308 27,284 57 MARSHALL Coal Steam 87 D06250C05 6250 60 59 35 37 27,395 27,371 58 MAYO Coal Steam 88 D027121 2712 59 12,031 12,020 59 ROXBORO Coal Steam 89 D027122 2712 82 41 54 23 94 29,337 29,310 59 ROXBORO Coal Steam 90 D02712C03 2712 56 37 57 24 21 78 30,776 30,749 59 ROXBORO Coal Steam 91 D02712C04 2712 88 72 47 47 22,962 22,941 59 ROXBORO Coal Steam 92 D0283612 2836 55 20 48 89 29 35 41,432 41,395 60 AVON LAKE Coal Steam 93 D028281 2828 29 9 31 30 24 8 37,307 37,274 61 CARDINAL Coal Steam 94 D028282 2828 56 20,598 20,580 61 CARDINAL Coal Steam 95 D028283 2828 80 15,372 61 CARDINAL Coal Steam 96 D028404 2840 3 1 6 2 2 3 87,801 87,724 62 CONESVILLE Coal Steam 97 D02840C02 2840 84 73 81 63 22,791 22,771 62 CONESVILLE Coal Steam 98 D028375 2837 86 56 35 70 35,970 35,938 63 EASTLAKE Coal Steam 99 D081021 8102 23 71 59 95 18,207 18,191 64 GEN J M GAVIN Coal Steam 

100 D081022 8102 78 12,333 12,322 64 GEN J M GAVIN Coal Steam 101 D028501 2850 36 67 39 53 45 30,798 30,771 65 J M STUART Coal Steam 102 D028502 2850 24 65 40 49 98 46 28,698 28,673 65 J M STUART Coal Steam 103 D028503 2850 26 72 62 27,968 27,944 65 J M STUART Coal Steam 104 D028504 2850 20 77 45 52 88 54 27,343 27,319 65 J M STUART Coal Steam 105 D060312 6031 67 77 90 19,517 19,500 66 KILLEN STATION Coal Steam 106 D02876C01 2876 40 7 3 9 30 10 72,593 72,529 67 KYGER CREEK Coal Steam 107 D028327 2832 65 28 59 22 48 20 46,991 46,950 68 MIAMI FORT Coal Steam 108 D02832C06 2832 60 43 64 23,694 23,673 68 MIAMI FORT Coal Steam 109 D028725 2872 74 92 78 90 36 30,079 30,052 69 MUSKINGUM RIVER Coal Steam 110 D02872C04 2872 6 19 13 6 19 15 83,134 83,060 69 MUSKINGUM RIVER Coal Steam 111 D02864C01 2864 70 56 61 63 49 24 35,193 35,162 70 R E BURGER Coal Steam 
DRAFT—DO NOT 

42 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

112 D07253C01 7253 89 58 57 33 30,977 30,949 71 RICHARD GORSUCH 113 D028665 2866 82 53 19,796 19,779 72 W H SAMMIS Coal Steam 114 D028667 2866 57 16 42 41 41 16 33,601 33,572 72 W H SAMMIS Coal Steam 115 D02866C01 2866 97 54 93 96 92 30 24,649 24,627 72 W H SAMMIS Coal Steam 116 D02866C02 2866 69 92 50 26,022 25,999 72 W H SAMMIS Coal Steam 117 D02866M6A 2866 85 58 19,564 19,546 72 W H SAMMIS Coal Steam 118 D060191 6019 93 72 60 21,496 73 W H ZIMMER Coal Steam 119 D028306 2830 46 38 70 40 12 69 30,466 30,439 74 WALTER C BECKJORD Coal Steam 120 D031782 3178 77 63 81 16,484 16,469 75 ARMSTRONG Coal Steam 121 D031403 3140 31 34 9 46 18 18 38,801 38,767 76 BRUNNER ISLAND Coal Steam 122 D03140C12 3140 52 46 49 69 25 23 29,736 29,709 76 BRUNNER ISLAND Coal Steam 123 D082261 8226 25 21 33 42 36 9 40,268 40,232 77 CHESWICK Coal Steam 124 D03179C01 3179 16 10 5 8 5 4 79,635 79,565 78 HATFIELD'S FERRY Coal Steam 125 D031221 3122 11 6 26 38 17 14 45,754 45,714 79 HOMER CITY Coal Steam 126 D031222 3122 9 4 37 92 13 11 55,216 55,167 79 HOMER CITY Coal Steam 127 D031361 3136 8 2 4 14 6 1 87,434 87,357 80 KEYSTONE Coal Steam 128 D031362 3136 18 3 8 19 8 2 62,847 62,791 80 KEYSTONE Coal Steam 129 D03148C12 3148 71 84 17,214 81 MARTINS CREEK Coal Steam 130 D031491 3149 19 8 35 7 1 6 60,242 60,188 82 MONTOUR Coal Steam 131 D031492 3149 15 5 21 20 3 5 50,276 50,232 82 MONTOUR Coal Steam 132 D031131 3113 82 9,674 83 PORTLAND Coal Steam 133 D031132 3113 36 93 14,294 83 PORTLAND Coal Steam 134 D03131CS1 3131 54 31 79 32 65 22,344 22,324 84 SHAWVILLE Coal Steam 135 D033193 3319 10

0 

 11,045 85 JEFFERIES O/G Steam 

136 D033194 3319 90 87 11,838 85 JEFFERIES O/G Steam 137 D03297WT1 3297 68 61 17,671 86 WATEREE Coal Steam 138 D03297WT2 3297 83 73 17,199 86 WATEREE Coal Steam 139 D03298WL1 3298 35 94 37 25,170 25,148 87 WILLIAMS Coal Steam 
DRAFT—DO NOT 

43 

Row number 

CEMS Unit 

ORIS ID 

Acadia MM5 

Acadia VTDEC 

Brig MM5 

Brig VTDEC 

Lye MM5 

Lye VTDEC 

MM5 2002 S02 TPY 

VTDEC 2002 SO2 

TPY 

Plant Name 

Plant Type 

140 D062491 6249 58 82 17,920 88 WINYAH Coal Steam 141 D03403C34 3403 85 20,314 89 GALLATIN Coal Steam 142 D03405C34 3405 39 19,368 90 JOHN SEVIER Coal Steam 143 D03406C10 3406 10 11 27 33 4 43 104,523 104,431 91 JOHNSONVILLE Coal Steam 144 D03407C15 3407 64 87 66 67 76 37,308 37,274 92 KINGSTON Coal Steam 145 D03407C69 3407 48 98 91 82 91 38,645 38,611 92 KINGSTON Coal Steam 146 D038033 3803 55 9,493 93 CHESAPEAKE Coal Steam 147 D038034 3803 94 16 10,806 93 CHESAPEAKE Coal Steam 148 D037974 3797 90 9,293 94 CHESTERFIELD Coal Steam 149 D037975 3797 88 44 27 86 19,620 19,602 94 CHESTERFIELD Coal Steam 150 D037976 3797 66 18 7 3 34 66 40,570 40,534 94 CHESTERFIELD Coal Steam 151 D03775C02 3775 47 16,674 95 CLINCH RIVER Coal Steam 152 D038093 3809 52 64 29 10,477 10,468 96 YORKTOWN Coal Steam 153 D03809CS0 3809 96 43 19 17 62 21,219 21,201 96 YORKTOWN Coal Steam 154 D039423 3942 79 10,126 97 ALBRIGHT Coal Steam 155 D039431 3943 51 23 20 32 16 13 42,385 42,348 97 FORT MARTIN Coal Steam 156 D039432 3943 50 22 22 31 14 12 45,850 45,809 97 FORT MARTIN Coal Steam 157 D039353 3935 41 33 28 11 64 26 42,212 42,174 98 JOHN E AMOS Coal Steam 158 D03935C02 3935 17 42 43 1 11 21 63,066 63,010 98 JOHN E AMOS Coal Steam 159 D03947C03 3947 86 62 55 57 25 38,575 38,541 99 KAMMER Coal Steam 160 D03936C02 3936 98 15,480 15,467 100 KANAWHA RIVER Coal Steam 161 D03948C02 3948 58 13 17 36 9 7 55,405 55,356 101 MITCHELL Coal Steam 162 D062641 6264 75 49 50 18 77 40 42,757 42,719 102 MOUNTAINEER Coal Steam 163 D03954CS0 3954 68 24 25 23 67 20,130 20,112 103 MT STORM Coal Steam 164 D0393851 3938 79 97 12,948 12,936 104 PHILIP SPORN Coal Steam 165 D03938C04 3938 94 26,451 26,427 104 PHILIP SPORN Coal Steam 166 D060041 6004 66 83 31 21,581 21,562 105 PLEASANTS Coal Steam 167 D060042 6004 88 92 20,550 20,532 105 PLEASANTS Coal Steam