# Documentation of 2018 Emissions fr om Electric Generating Units

in the Eastern United States for MANE-VU’s Regional Haze Modeling

Final Report

16 August 2009

Prepared for:

Mid-Atlantic / Northeast Vi sibility Union (MANE-VU)

Prepared by:

Alpine Geophysics, LLC

387 Pollard Mine Road Burnsville, NC 28714

Mid-Atlantic Regional Air Mana gement Association (MARAMA)

8900 LaSalle Road

Suite 636
Towson, MD 21286

# 08/16/09

ACKNOWLEDGEMENTS

Alpine Geophysics, LLC (Alpine), was under cont ract with the Mid-Atlantic Regional Air Management Association (MARAMA) to assist in the revision, analysis, and adjustment of

emission inventories and associated factors necessa ry to provide data and inputs required for air quality analyses and state implem entation plan (SIP) submittals.

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

Work Order #7 of the MANE-VU Future Year Em issions 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 Ag ency under assistance agreement XA 97318101.

MARAMA further revised Section 5 of the documen t after receipt of Mr. Stella’s final work product. MARAMA provided text and tables to reflect emissions inve ntory analysis that continued after the expiration of Alpine’s cont ract. MARAMA also revised the document in response to comments received from stakeholde rs on the April 28, 2008 Revised Final Draft.

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 an alytical and editorial contributions to the preparation of this document, and Patrick Davis of MARAMA and Dr. John Graham of

NESCAUM contributed to em issions inventory data an alysis 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.

# TABLE OF CONTENTS

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

1.1 P URPOSE 

# .............................................................................................................................. 1

1.2 B ACKGROUND ON 

# E MISSIONS 

# P ROJECTIONS 

# ........................................................................ 1

1.3 F ACTORS 

# C AUSING 

# V ARIATION IN 

# EGU E MISSIONS 

# F ORECASTS 

# ......................................... 2

2 PREPARATION OF EGU FORECASTS........................................................................... 4

2.1 D ECISION TO 

# U SE THE 

# IPM M ODEL 

# ..................................................................................... 4

2.2 T HE 

# I NTEGRATED 

# P LANNING 

# M ODEL 

# (IPM)........................................................................ 5

2.3 U.S. EPA U SE OF 

# IPM......................................................................................................... 5

2.3.1 EPA’s Base Ca se 2004 ........................................................................................................... ...............5

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

2.3.3 EPA’s CAIR Modeling Limitations................................................................................................ ........7


# 2.4 RPO U SE OF 

# IPM

– P HASE 

# I ................................................................................................ 8

2.5 RPO U SE OF 

# IPM

– P HASE 

# II ............................................................................................... 8

2.6 S TATE 

# R ESULTS



# – P HASE 

# II................................................................................................ 10

2.7 MANE-VU S PONSORED 

# CAIR P LUS 

# IPM M ODELING 

# ...................................................... 10

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

3.1 U SE OF 

# SMOKE E MISSIONS 

# P ROCESSING 

# M ODEL 

# ............................................................. 12

3.2 P REPARING 

# IPM O UTPUT FOR 

# U SE IN 

# SMOKE M ODEL 

# ..................................................... 12

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


# 3.3 S TATE 

# R ESULTS



# – P HASE 

# II A UGMENTED 

# .......................................................................... 18

3.4 NIF TO 

# IDA ....................................................................................................................... 18

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

4.1 E MISSION 

# C ONTROL 

# M ODIFICATIONS WITHIN 

# VISTAS, MRPO, AND 

# CENRAP............... 20

4.2 E MISSION 

# F ACTOR AND 

# C ONTROL 

# M ODIFICATIONS FOR 

# VISTAS E MISSION 

# S OURCES 

# ..... 20

4.3 E MISSION 

# I NVENTORY 

# R EPLACEMENT BY 

# W ESTERN 

# S TATES 

# ............................................. 21

4.4 E LIMINATING 

# D OUBLE 

# C OUNTING OF 

# EGU U NITS 

# ............................................................ 21

4.5 P RELIMINARY 

# R ESULTS FROM 

# P HASE 

# II A DDITIONAL 

# M ODIFICATIONS 

# ............................. 22

4.6 R EVISED 

# R ESULTS



# – VISTAS B ASE 

# G2 A DJUSTMENT 

# ....................................................... 24

5 ADDITIONAL ADJUSTMENTS BY NORT HEASTERN STATES AND MODELERS

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

5.1 I NTRODUCTION 

# .................................................................................................................. 26

5.2 B EST 

# A VAILABLE 

# R ETROFIT 

# T ECHNOLOGY 

# (BART) ......................................................... 26

5.3 MANE-VU S TATE 

# M ODIFICATIONS OF 

# IPM R ESULTS 

# ...................................................... 26

5.4 MANE-VU EGU S TRATEGY 

# ............................................................................................. 27

5.5 I MPLEMENTATION OF 

# MANE-VU C ONTROL 

# S TRATEGY FOR 

# K EY 

# EGU S 

# .......................... 28

5.6 S TATE 

# R ESULTS



# – N ORTHEASTERN 

# S TATE 

# A DJUSTMENTS 

# ................................................. 30

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

7 REFERENCES .................................................................................................................... 3 4

# LIST OF TABLES

Table 1. State Level Fuel Use and Emissi on Summary; 2018 VISTASII_PC_1f.xls. 11

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

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

# . 16

Table 4. EPA-approved condensable PM em ission 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 VI STASII_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 SO 2 

# 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 Po ints Contributing to Visibility Impairment in MANE-VU in 2002 37

# 1 1 INTRODUCTION

1.1 Purpose

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 pa rt, 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 a nd are often considered for controls to meet air quality objectives. Theref ore, it is especially important to accurately represent and document EGU emissions and associated characteristics in a regulatory modeling application.

This report describes the development of futu re year EGU emission estimates for use in MidAtlantic/Northeast Visibility Union (MANE-VU) 2018 regional haze modeling.

This document synthesizes information from severa l documents that already describe parts of the process of preparing emissions estimates and pr ovides information not ye t included in other documents. It covers the following major steps in that process: preparat ion of the inter-Regional Planning Organization (RPO) Integr ated Planning Model® (IPM) runs commonly referred to as the Visibility Improvement State and Tribal As sociation of the Southe ast (VISTAS) IPM runs, the post-processing of those runs to create Sparse Matrix Operator Kernel Emissions (SMOKE) input files, the modification of those files to refl ect 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 a bout preparing EGU forecasts and related work by the U.S. Environmental Protection Agency (EPA).

1.2 Background on Emissions Projections

Emission projections for point sources depend on changes in source level activity, the emission

factors or installed controls. Th e approach taken to project poin t source emissions depends on the level of detail necessary in the projection. Chan ges in point source emissions are accounted for by a combination of growth, control, and retiremen t 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. Retirem ent (and replacement of these sources with new sources) must be considered be cause regulations affecting new sources may differ from those affecting existing sources.

The projection year control factor accounts fo r both changes in emission factors due to technology improvements and new levels of contro l required by regulations. The control factor accounts for three variables: regul ation control, rule effectiven ess, and rule penetration.

Control factors are closely linked to the type of emission process (identified by Source Classification Code (SCC)) and s econdarily to the type of i ndustry identified by Standard Industrial Classification (SIC). Point source projections should account for Federal, State, and local regulations affec ting these categories.

# 2 A complicating factor is the requirement for emission offsets in nonatt ainment 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 reduc tions to selected source categories to account for the emission growth which must be offset; or 3) selecting the indivi dual sources, based on a cost analysis, from which offsets are likely to come.

1.3 Factors Causing Variation in EGU Emissions Forecasts

There are various sources of uncertainty in esti mating future EGU emissi ons. These include the relative prices of various fuels (especially coal, oil, and natura l gas), predictions of which plants will be shut down, the size, type, and location of new plants, the total demand for electricity, and requirements imposed by state-specific or plant- specific regulations or orders. Emissions

estimates based on the methods described in th is report represent the MANE-VU states’ best

effort to forecast future emissions in light of these and other uncertainties.

When projecting Electricity Generating Unit (E GU) emissions in the Eastern United States, emission trading should be considered. There are three general appr oaches to performing projections while accounting for su ch trading schemes. The first option is to optimize control

levels across the domain based on the cost of alte rnative 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 c onsidered 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 historica lly 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 so me or all of the year. Fuel switching is often done in cases where sources aver age their emissions to meet federal mandates. Fuel switching may also be used as a seasonal compliance strate gy (e.g., switching from residual fuel oil to natural gas in order to reduce NOx em issions during the ozone season. The variation in emissions over the course of the year caused by fuels switching must be calculated properly in projections.

Repowering is another example of a planned change in emission rates which should be considered. In this case, the unit may be switchi ng entirely from coal to natural gas or may be completing a major modification whic h 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 poi nt 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 s ources in that area will increase.

# 3 As can be seen from the discussion above, an y 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 Re gional Planning Organizations (RPOs) in the

Eastern U.S. made a decision to utilize cons istent forecasting methods for EGU emissions, as they are one of the most significant contributor s 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. Each RPO ends up using somewhat different estimates, as discussed below, but there was a great deal of cooperation and data sharing throughout the

process.

# 4 2 PREPARATION OF EGU FORECASTS

2.1 Decision to Use the IPM Model

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 resu lted in part from a study of EGU forecast methods prepared by E.H. Pechan and Associ ates, Inc. (Pechan) for the Midwest Regional Planning Organization (MRPO) (Pechan, 2004) , which recommended IPM as a viable methodology. Although IPM results were availabl e from work conducted by EPA to support their rulemaking for the Clean Air Interstate Ru le (CAIR), the RPOs c oncluded that certain model inputs needed to be revise d. Thus, the RPOs decided to work together to hire contractors to conduct new IPM modeling and to post-process th e IPM results. This section describes the

recommendation to use IPM.

The Lake Michigan Air Direct ors Consortium (LADCO) sought contractor assistance in reviewing emissions inventory growth for ex isting 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 intere st were sulfur dioxide (SO 2 

# ), oxides of nitrogen (NO

x 

# ), particulate matter (PM), ammonia (NH 3 

# ), and mercury (Hg). Projecti on years of interest included 2009 (the approximate time for ozone and PM 2.5 

# attainment) and 2018 (a longer term regional haze planning horizon). The geographic area of inte rest was the eastern half of the United States (to capture the trading issues affecting the Midwest States).

This 2004 Pechan report provided a detailed eval uation of three EGU growth modeling methods

of interest to the LADCO States for consid eration in developing its own approach. These evaluations addressed the following at tributes of each modeling approach:

• Description of primary an alytical 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 include d the Integrated Planning Model ® 

# (IPM), the National Energy Modeling System (NEMS), and the Elect ric Power Market Model (EPMM).

# 5 Based on the conclusions and summ ary of the report (P echan, 2004), the four participating RPOs (MANE-VU, MRPO, VISTAS, and the Central Re gional 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) a nd used to support public and private sector clients. This model is a proprietary, multi-re gional, dynamic, deterministic linear programming model of the U.S. electric power se ctor. It provides forecasts of least-cost capaci ty expansion, electricity dispatch, and em ission control strategies for meeting energy demand and environmental, transmission, dispatc h, and reliability constr aints. It can be us ed to evaluate the cost and emissions impacts of propose d policies to limit emissions of SO 2 

# , NO

x 

# , carbon dioxide (CO 2 

# ), and Hg from the electric po wer sector. The IPM model was a key analytical tool used by EPA in developing the Clean Air Interstate Rule (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 emi ssion control options encompassing a broad array of re trofit technologies along with emission reductions through fuel switching, changes in capacity mix and electricity dispatch strategies; a nd (3) its capability to model a variety of environmental market mech anisms, such as emissions caps, allowances, trading, and banking. The model’s ability to capt ure the dynamics of th e allowance market and its provision of a wide range of emissions reduction options ar e particularly important for assessing the impact of multi-emissions e nvironmental 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 Colu mbia. The next two sections describe EPA modeling results available to the RPOs early in the planning process. Then

Section 2.3.3 reviews the limitati ons of the EPA results that led the RPOs to conduct additional IPM modeling.

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 emissi on laws and regulations whose provi sions 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 Ac t Amendments of 1990 (CAAA), but whose provisions have not yet been finalize d, 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 pr ovisions of CAIR, the primary federal regulatory measure for achieving the National Ambient Air Quality Standards (NAAQS)

# 6 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 par ticles of diameter 2.5 micrometers or less, i.e., PM 2.5 

# ). EPA Base Case 2004 was used to evaluate policy alternatives which ultimately resulted in CAIR. The final CA IR was issued on March 10, 2005. EPA Base Case 2004 includes measures to implement ozone and particulate matte r standards to the extent that some of the state regulati ons 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 ma tter standards are not captured in the base

case.

• Mercury Regulations on Elect ric 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) st andards that were scheduled to be promulgated by December 15, 2004, but, pending litigation, were superseded by CAMR. Consequently, this base case did 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 establ ished 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 wilderne ss areas], which impairment results from manmade air pollu tion.” The regulations required states to submit revised SIPs that (1) establish goals that provi de for reasonable progr ess 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 n ecessarily an accurate re flection of what would actually occur, this assumption ensured that th e base case was policy neutral with respect to future environmental policies.

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 em ission reductions from large el ectricity generating units.

While EPA believed that the modeling it init ially performed for the January 2004 proposal provided a reasonable estimate of the impact of requiring highly cost-effective emission reductions from electricity generati ng units, it did not exactly m odel the proposed control region.

For both SO 2 

# and NO

x 

# , EPA used modeling assumptions that differed slightly from the January 2004 CAIR proposal. For SO 2 

# in particular, EPA modeled th e program assuming a cap on

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

# 7 done at that time provided a reasonable approxim ation of the impacts of the original CAIR, because 92 percent of the SO 2 

# emissions in the 48 contiguous Stat es occur in the 28 States that were covered by the proposal, EPA completed additional analysis. This additional analysis examined the effect of covering the geographi c region proposed in the January 30, 2004 proposal using the NO

x 

# emissions cap and a close approximation of the SO 2 

# 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 SO 2 

# and NO

x 

# , 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 assu mptions for future fuel prices and electricity demand growth (EPA, 2004). In addition, EPA’s modeling using IPM did not take into account the potential for advancements in the capabilities of pollution control technologies for SO 2 

# and NO

x 

# removal as

well as reductions in their costs over time.

Retirement Ratios: EPA issued a CAIR supplemental notice of proposed rulemaking that proposed two alterna tives for how the SO 2 

# 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 SO 2 

# 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 SO 2 

# emissions from the power se ctor 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

SO 2 

# from over-compliance that would result from this ratio. Therefore, in this modeling, EPA analyzed slightly greater SO 2 

# emission reductions th an required by the pr oposal. 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.

BART: The EPA did not incorporate any best ach ievable retrofit technology (BART) modeling in this analysis. BART would achieve reduction s in non-CAIR States and had the potential to mitigate leakage issues.

Demand Response: EPA’s 2004 CAIR case includes a dema nd response to incr eased natural gas prices but not electricity prices. In the model, increased gas pr ices would prompt the public to curtail their use of gas and en courage 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 gene ration and emissions to

some extent.

# 8 State Rules: Only some State-adopted rules were inco rporated into EPA’s modeling framework.

A list of the State Mul ti-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 r un 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 pow er 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. 1



# In August 2004, MRPO contracted with Pechan to post-process the VISTAS’ IPM outputs to provide the (National Emission I nventory Input Format) NIF forma tted emission files needed for the regional inventory. The IPM ou tput files were delivered by ICF to VISTAS in November 2004 and the post-processed data files were de livered 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 In ter-RPO runs. The Phase I runs were ultimately not used in RPO modeling of regi onal haze, as further revisions to the inputs were necessary once the final version of CAIR was adopted.

2.5 RPO Use of IPM – Phase II

On March 10, 2005, EPA issued the final CAIR . A consortium of RPOs, (MANE-VU, VISTAS, MRPO, and CENRAP) conducted a nother 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 commen ts. Several conference ca lls were conducted in the spring of 2005 among the participating RP Os to discuss and provide comments on IPM assumptions related to six main topics: power system operation, generating resources, emission control technologies, set-up parameters, financia l assumptions, and fuel assumptions. Based on these discussions, VISTAS sponsored a new se t of IPM runs to reflect the final CAIR

1 

## “Parsing” results refers to allocating emissions estimates to individual units. Results may be analyzed in aggregate form but must be parsed in order to be used in air quality modeling. (See Section 3.2)

# 9 requirements as well as certain changes to IPM assu mptions that were agreed to by the RPOs. ICF performed the following four runs using IP M 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 supp ly curves adjusted for the U.S. Energy

Information Administration’s most recent Annual Energy Outlook (AEO 2005) reference case price and volume relati onships (VISTASII_BC_2Y).

 Strategy Case with EPA 2.1.9 coal, gas and oi l price assumptions (VISTASII_PC_1f).

 Strategy Case with EPA 2.1.9 coal and ga s supply curves adjusted for AEO 2005 reference case price and volume relationships (VISTASII_PC_2C).

The above runs were parsed for 2009 and 2018 r un years. The output taken from the Strategy Case with EPA 2.1.9 coal, gas, and oil price assu mptions (VISTASII_PC_1f) is also referred to

as the Inter-RPO CAIR Case IPM 2.1.9 or RPO 2.1.9 IPM and is the basis for discussion in the remainder of this report. That run was also parsed for 2012. The RP O 2.1.9 IPM parsed results were post-processed for 2009, 2012, 2018, as described in Section 3.2.1

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 electric ity demand projections were used. (U.S. EPA runs were adjusted to reflect reduced demand due to vol untary 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 ga s 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 aggr egated coal supply regions were comparable to AEO 2005. Coal grades and supply re gions 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.

 The cost and performance of new units we re 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 foss il 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 north east state regulations were incorporated.

 Northeast Renewable Portfolio Standards (RPS) were modeled based on the Regional Greenhouse Gas Initiative analys is. 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.

# 10  Selective Catalytic Reduction (SCR) and Sc rubber 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 SO 2 

# 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 strategy case scenarios (VISTAS II_PC_1f and VISTAS II_PC_2c). 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). No te that IPM produces only NO

x 

# and SO 2 

# emissions estimates.

2.7 MANE-VU Sponsored CAIR Plus IPM Modeling

Using the IPM Phase II RPO modeling platform M ANE-VU contracted with ICF to evaluate the impact of both tightening the SO 2 

# and NO

x 

# CAIR caps and expanding the CAIR region to include the electricity generating sector in additional states the Eastern Un ited States. As part of this analysis, ICF developed a new Base Case th at implemented EPA’s CAIR, CAMR and CAVR policies and a Policy Case with lower SO 2 

# and NO

x 

# 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 (IC F, 2007). Neither the base or policy cases from the CAIR Plus project were us ed in subsequent SIP modeling.

# 11 Table 1. State Level Fuel Use and Emission Summa ry; 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)

# 12 3 POST PROCESSING OF IPM OUTPUT

3.1 Use of SMOKE Emissions Processing Model

On behalf of MANE-VU, modelers from Th e Northeast States for Coordinated Air Use Management (NESCAUM) used an emissions pro cessing model to prepar e data produced by the IPM model for use in air quality and visibility modeli ng. The Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System is an em issions 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 proc essing for criteria, particulate, and toxic pollutants. For biogeni c 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 onr oad emissions model MOBILE6.

The sparse matrix approach used throughout SMOKE permits rapid and flexible processing of emissions data. Flexible processing comes fr om splitting the processing steps of inventory growth, controls, chemical speciation, tempor al allocation, and spa tial allocation into independent steps whenever possible. The results from these steps are merged together in the final stage of processing using vector-matrix mu ltiplication. It allows in dividual 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 processi ng steps. Individual emission scenarios were simulated for MANE-VU using the SMOKE Modeling System.

NESCAUM, on behalf of MANE-VU and its particip ating 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 u nder the Haze State Implem entation Plan (SIP), including a contribution assessment, a polluti on 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 manageme nt strategy for MANE-VU Class I areas. The modeling tools utilized for these analyses in clude the Fifth-Genera tion NCAR / Penn State

Mesoscale Model ( 

# MM5), SMOKE, CMAQ and REMSAD, and in corporate tagging features that allow for the tracking of individua l source regions or measures. These tools have been evaluated and found to perform adequately relati ve to U.S. EPA modeling guidance.

As described below, in order for NESCAUM to process the EGU emissions generated by the

IPM ® 

# procedures noted above, a series of intermedia te steps were required to get the activity and emission data into the appropria te format for SMOKE processing.

3.2 Preparing IPM Output for Use in SMOKE Model

IPM can produce projections at the regional, state, pl ant, or unit level. Data must be parsed to provide the unit level information required for chemical transport modeling. Parsing involves developing detailed unit level info rmation from the model’s projecti ons at the model plant level. ICF parsed the VISTASII_PC_1f data for use by the RPOs.

# 13 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 st ep 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 additiona l conversion of these NIF files into the Inventory Data Analy zer (IDA) format required by th e SMOKE emissions processor.

3.2.1 IPM to NIF

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

both: (1) “existing” units (those currently in ope ration during the modele d base year) and (2) committed/planned or new generic aggregat es (new generic units expected to come

online or identified as needed to meet el ectric generation dema nd in a geographic area).

IPM parsed file reco rds include unit and fuel type data; existing, re trofit (for SO 2 

# and NO

x 

# ), and separate NO

x 

# control information; annual SO 2 

# and NO

x 

# emissions and heat input; summer season (May-September) NO

x 

# and heat input; July day NO

x 

# and heat input; coal heat input by coal type; nameplate capacity megawatt (MW), and State FI PS codes (Federal Information Processing codes used to identify geographic areas). Exis ting units also had county FIPS codes, a unique plant identifier (ORISPL) and unit ID (also called boiler ID) (BLRID); gene ric units did not have

these data.

The processing of IPM parsed data to NIF format included estimating emissions of pollutants not generated by IPM and adding control efficien cies, 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 y ear 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 sma ller generic units based on their unit types and capacity. Each generic unit was provided a dummy ORIS unique plant and boiler ID, and was given a county FIPS code based on an algorithm that sited each gene ric 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 co de order) in attainment counties were used first as sister sites to new generic units (to obt ain 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.

# 14 SCCs were assigned to existing units using unit/fuel/firing/bottom t ype data. SCCs were assigned to generic units using unit and fuel type information. Latitude-longitude coordinates were assigned, first using the EP A-provided data files, secondly using an in-house contractor developed latitude-longitude file, and lastly using county cent roids. These addi tional location files were only used when the da ta 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 pa rameter file based on previous EI A-767 data, and las tly using an EPA June 2003 SCC-based default stack parameter file . These data were only used when the data were not provided in th e 2002 base year files.

IPM does not calculate emissions for all pollu tants necessary for regional haze modeling. Therefore additional data were required to estimate VOC, CO, filterable primary PM 10 

# and

PM 2.5 

# , PM condensable, and NH 3 

# emissions. Thus, ash and sulfur contents were assigned by first using 2002 EIA-767 values for existing units or SCC-based defaults; filterable PM 10 

# and PM 2.5 

# efficiencies were obtained from the 2002 EGU NEI that were based on 2002 EIA-767 control data and the PM Calculator pr ogram (a default of 99.2 percen t is used for coal units if necessary); fuel use was back calculated from th e 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 th e SCC-based default heat content and stack parameters used when actual data were not av ailable. Table 3 (worksheet sccemfac100704 from MRPOpostprocdatafiles.xls, Pech an 2005) reflects emission fact ors used to develop emission estimates of CO, VOC, filterable PM, and NH 3



# .

# 15 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

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



# .

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 sccemfac 100704 from MRPOpostprocdatafiles.xls, Pechan 2005.



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

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

PM condensable emissions were calculated base d 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 fr om MRPOpostprocdatafiles.xls, Pechan 2005) shows these PM condensable emission factors and SCC assignments.

# 17 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 0.0200 2



## 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 SO 2 

## FGD or a PM scrubber (for MRPO post-processing); or AND there is an SO 2 

## wet FGD
(for EPA post-processing).

3. AND there is any PM control other than a scrubber and there is no SO 2 

## control (for MRPO post-processing); or
AND there is any control other than an SO 2 

## wet FGD (for EPA post-processing).

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



# Additional Pollutants : As noted above, in pr ocessing 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 PM 10 

# and

PM 2.5 

# , NH 3 

# , SO 2 

# and NO

x 

# ) and four temporal periods (ann ual, summer season, winter season, and 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 obt ain 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 w ithout 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 delete d 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

# 18ID’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 (VIS TASII_PC_1f with pollutant a ugmentation; found in modeling

file ida_egu_18_basef_2453605.txt from VISTAS BaseF). A co mparison of RPO totals for SO 2 

# and NO

x 

# 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, NO

x 

# , CO, SO 2 

# , PM 10 

# , PM 2.5 

# , and NH 3 

# . Annual and seasonal emissions were taken directly from the NIF structured invent ories 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 us ed to further define temporal distribution of these emissions.

No quality assurance (QA) related to the reporte d 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 thes e relational data sets and convert them to the IDA structures required by this task were impl emented by Alpine (Alpine, 2006). Prior to and after the conversion from NIF to IDA, a list of emission summary repor ts was developed to check that the emissions input in to the conversion process were th e same as output into the IDA

formatted files.

# 19 Table 5. State Level Emission Summary; 2018 VIST ASII_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 Augmented Pollutants IPM Generated Annual Emissions (Tons)

# 20 4 MODIFICATIONS BY OTHER REGIONS

4.1 Emission Control Modifications wi thin VISTAS, MRPO, and CENRAP

State and local agencies and invited stakeholde rs 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 NO

x 

# post-combustion controls

and SO 2 

# scrubbers and provided additional information on when and where new SO 2 

# and NO

x 

# 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 th e IPM results to verify that known and existing

controls and emission rates we re properly reflected in the IPM runs. After considering comments, those RPOs adjusted the IPM results for specific units using new information they had as part of the permitting process or other co ntact with the industry th at indicated which units

would install controls as a re sult 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 cataly tic reduction (SCR) or scrubber controls to units where IPM did not predict SCR or scrubber controls, or 2) re moving IPM-assigned SCR or scrubber controls at units where the commenting entity indicated there we re 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 di screpancies with actual conditions. MANE-VU determined that IPM provided a reasonable estima te 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 cha nges 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, M ANE-VU did not incorpor ate changes reflected

in the final CENRAP EGU files.

4.2 Emission Factor and Control Modifica tions for VISTAS Emission Sources

VISTAS reviewed the PM and NH 3 

# emissions from its States’ EGUs provided after the original

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

2009/2018 than in 2002. VISTAS determined th is conversion used a set of PM and NH 3 

# 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 applic ation. During this review, VISTAS additionally

identified an inconsistent use of SCCs for de termining emission factors between the base and future years.

# 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 NH 3 

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

utilized data collected under EPA’s Consolidat ed Emissions Reporting Rule (CERR) or data reported by VISTAS. Alpine us ed reported annual heat input, fuel throughput, heat, ash, and

sulfur content to estimate annual uncontrolled 2002 emissions for units identified as having corresponding output from IPM. This step wa s conducted for non-CEM pollutants (CO, VOC, PM, and NH 3 

# ) only. For PM emissions, the condensable component of emissions was calculated and added to the resulting PM primary estima tions. The resulting 2002 emissions were then adjusted by any control efficiency factors repo rted in the CERR or VISTAS data collection effort. The second adjustment was to the future y ear inventories. Alpine updated the SCCs in the future year inventory to assign the same SCC used in the base year. Using the same methods as described for the 2002 revisions, those non-IPM generated pollutants were estimated using IPM predicted fuel characteristics a nd base year 2002 SCC assignments.

In addition to the changes to the emission factor assignments, SCC, and IPM-assigned controls, VISTAS also specified other ch anges to the IPM re sults 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 in stalled. In cases where an emission unit was projected to have an SO 2 

# scrubber by 2018, some Stat es were able to pr ovide revised stack parameters for some units based on design f eatures for the new control system. Other units projected to install sc rubbers by 2018 were not far enough along in the design process to have specific design details. For those units, VIST AS made the following assumptions: 1) the scrubber is a wet scrubber; 2) ke ep the current stack he ight 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 temperatur e of 130 degrees F) (M ACTEC, 2007). VISTAS determined that exit temperatur e (wet) of 130 degrees F +/- 5 degrees F is representative of

different size units and wet scrubber technology.

4.3 Emission Inventory Replacement by Western States

During the development of their EGU emission fo recast, the Western Regional Air Partnership (WRAP) conducted an exercise where IPM was not used to pr epare emission estimates from EGU sources. Using capacity factor adjustme nts 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 some of the other 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 sour ce inventory (MACTEC, 2006, 2007). Since each

# 22 RPO’s 2002 emissions inventory file containe d 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 contai ned those emission units accounted for in the IPM forecasts. The second component contained al l 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 wa s 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 th e IPM ORISPL/BLRID a nd the emission unit ID, the unit was assigned to the EGU inventory; al l 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 initia l 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 a nd non-EGU inventories:

 The IPM parsed files were reviewed to identif y 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 account ed for in IPM were not double-counted in the non-EGU inventory. As a result of this compar ison, a few adjustments were made in the

cross-reference table to add emission units fo r 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 Classi fication (SIC) code of “4911 El ectrical Services” or Source

Classification Code of “1-01-xxx-xx External Combustion Boiler, Electric Generation”. The list of sources meeting thes e selection criteria were compar ed to the IPM parsed file

to ensure that these units were not double-counted.

 VISTAS invited various stakeh older groups to review the 20 18 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, pr esenting State level emission

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

# 23 MRPO, and WRAP per the methods noted in the a bove sections. Note th at 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)

# 24 4.6 Revised Results – VISTAS Base G2 Adjustment

VISTAS further refined their future predicti ons 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 IP M v. 2.1.9 parsed file modified by VISTAS.

Some states specified changes to the controls as signed by IPM to reflect their best estimates of emission control levels. These changes typically involved either 1) addi ng selective catalytic reduction (SCR) or scrubber controls to units where IPM did not predict SCR or scrubber controls, or 2) removing IPM-as signed SCR or scrubber controls at units where the commenting entity indicated their were no firm plans for cont rols at those units. These changes were based

on those states’ best available information a bout where and when emissions controls were expected to be installed, as well as informati on concerning IPM-predicte d 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 i nventory were requested by the st ates 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 SO 2 

# relative to either Table 5 or Table 6.

# 25 Table 7. State Level Emission Summary; 2018 VISTAS Base G2 Modeling file egu_18_vistas_g2_20feb2007.txt. Based on 2018 VI STASII_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)

# 26 5 ADDITIONAL ADJUSTMENTS BY NORTHE ASTERN 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) SO 2 

# emissions reductions at one M ANE-VU EGU source subject to

Best Available Retrofit Technol ogy (BART) requirements, 2) em issions increases in MANE-VU

to reflect states’ best estimates that some sour ces predicted by IPM to be closed would continue to operate and information about where and wh en emission controls would or would not be installed, 3) SO 2 

# emissions reductions at key EGUs (or alternative faciliti es) to reflect the MANE-VU EGU strategy, and 4) increases in SO 2 

# emissions to estimate the predicted 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 anticip ated 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 eigh t units would likely be controlled under BART alone. State-provided potential control technologies and levels of control for these sources we re 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 Modifica tions of IPM Results

Previously, during development of the Base G a nd 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 SO 2 

# controls were planne d to come online. Modifications were based on state rules, enforcement agreements, compliance plans, permits, and commitments from individual companies. St ates 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 predicte d by IPM to close were very unlikely to cease operation.

The net effect of these adjustments was an increase in SO 2 

# emissions in the MANE-VU region as

a whole. In Delaware SO 2 

# 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 (B ase F). Emissions of SO 2 

# in other MANE-VU states increased. No changes were made in emissions of other pollutants.

# 27 5.4 MANE-VU EGU Strategy

MANE-VU states have recognized that SO 2 

# 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 SO 2 

# 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 SO 2 

# 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 ba se year, 2002. These three ar eas are Acadia, Brigantine and Lye Brook. Details of the modeling are pr ovided in Appendix D of the Contribution Assessment. (NESCAUM, 2006a) Appendix D of the Contribution Assessment includes a model performance evaluation, including compar isons of CALPUFF results from two different modeling groups using different me teorological drivers, as well as comparisons to other models

and to ambient data. The overall CALPUFF results were similar and performance was acceptable.

The CALPUFF modeling results were sorted to identify the indivi dual stacks causing the highest

24-hr concentrations at each of the three Class I areas. Impacts were not summed for all stacks at a facility. The 100 top stacks for each Cla ss I area are listed in Tables 10 and 20 from Appendix D “Dispersion Model Technique s” of the Contribution Assessment.

The two modeling centers us ed 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 cent ers also used different meteorological data—one

used data from the MM5 model and the other us ed National Weather Se rvice observation-based meteorology.

There are differences between results from th e 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 identifie d 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 EG U 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 m odeled, only providing a CEMS Identification number.

# 28 MARAMA used information contained in IPM i nput files to match 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 ar ea to pursue 90 percen t 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 inco rporated State comments gathered during the inter-RPO consultation process in estimating the imp act of this strategy on visibility at Class I areas. This process is desc ribed 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 vi sibility, MANE-VU asked states to pursue a 90 percent reduction in SO 2 

# emissions from the 167 EGU stacks identified as described in Section 5.4 and listed in Appendix A. MARAMA gather ed information from MANE-VU, MRPO, and VISTAS states and regional sta ff to obtain information about antic ipated 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 ve rify that known and existing c ontrols and emission rates were properly reflected in the IPM r uns. In addition, commenters noted that some units predicted by

IPM to be shutdown would not shutdown.

Adjustments to the IPM results were made to sp ecific units using informa tion states had obtained as part of the permitting process or other contac t with the industry that indicated which units

would install controls as a re sult of CAIR and when these new controls would come on-line (Koerber, 2007; VISTAS 2007). In general, th e changes at specific E GUs provided by VISTAS reflected their Base G2 inventory, and, as di scussed with MRPO, the changes NESCAUM made

to emissions from sources in the MRPO were consistent with source s 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 Virg inia (Chesapeake and Clinch River). .

NESCAUM determined the desired emissions leve ls 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 so urces. In each region, predicted 2018 emissions exceeded the target level. Therefore, emi ssions reductions from other EGU sources were considered in order to meet the target emissi ons reductions for the region (both within MANEVU and in other RPOs). This resulted in a net decrease in emissions in all three affected RPOs.

Emissions of SO 2 

# 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.

# 29 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 tr ading, MANE-VU decided that emi ssions 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 emi ssions from states subject to the CAIR cap and trade program. For MANE-VU, 75,809 tons we re 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 rema ining 440,541 tons added b ack were allocated to

VISTAS and MRPO based on the fraction of their contribution to the total SO 2 

# 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 Regi onal Planning Organizations (RPO s). VISTAS used Base G2, MANE-VU used the March 2008 Modeling Inventory, and MRPO used IPM 3.0..

Table 8. Comparison of Regional SO 2 

# Emissions Estimates for 2018 (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 re gions together, but to modify the locations of the emissions to better reflect the states’ estimates and to achieve

# 30 reductions at the 167 stacks identified as importa nt contributors to regi onal haze at MANE-VU 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 SO 2 

# emissions predicted by the RPO 2.1.9 IPM run but greater than emissions in the G2 i nventory 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 I nventory for EGUs.

All future EGU emissions estimates involve un certainty. MANE-VU be lieves 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 tabl e 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. Appendix A provi des details for the top 167 EGU stacks.

# 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)

# 32 Files used in preparing Table 9 include fo r 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

# 33 6 EGU PREPARATION TIMELINE

The following section provides a ch ronological review of the events and milest ones 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 pa rameters 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 NEED S 3.0 for next round of EPA modeling

 EPA releases IPM 2006 revised projections

 RPOs identify potential control measures and est imate benefits for meeting reasonable progress goals

 Additional RPO control and modeling file adjustments to Phase II runs

2007

 RPOs analyze cost and other factors as sociated 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 consultatio n regarding potential control measures

 MANE-VU states agree to pursue several control measures

 RPOs begin regional modeling to asses s visibility impacts of controls

2008

 RPOs model to determine progress goals for regional haze SIP

# 34 7 REFERENCES

Alpine Geophysics, LLC, 2005a

. EGU Emission Factors and Em ission Factor Assignment

, technical memorandum prepared for VIST AS EGU Special Interest Workgroup, 2005.

Alpine Geophysics, LLC, 2005b

. Emission Factor/Rate Developm ent for IPM Post-Processing

, technical memorandum prepared for VI STAS EGU Special Interest Workgroup.

Alpine Geophysics, LLC, 2006

. Future Year Emissions Inventor ies, 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 (J anuary 2005) – Technical Memorandum , prepared for Lake Michigan Air Directors Consortium, 2005.

ENVIRON, 2007. Technical Support Document for CE NRAP 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 Cl ean Air Interstate Rule Us ing 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. Sp arse 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 Genera ting 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.

# 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 fr om 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 Ai r 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-con ceptual-model.pdf/, Northeast States for Coordinated Air Use Management, Boston, MA, 2006b.

NESCAUM, 2008a

. MANE-VU Modeling for Reasonable Pr ogress 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 fr om Mark Janssen 3/28/2008.

# 36

# 37 Appendix A

TOP ELECTRIC GENERATING EMISSI ON 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 2002 impact on visibilit y impairment were identified by CALPUFF modeling conducted by two modeling centers. 2 

# Many of these stacks have a regional impact and therefore significantly impact more than one Cla ss I Area. When the “Top Impacting” stacks for each of the three areas are aggregated into a single group there are 167 individual “Top Impacting” stacks identified. Fi gure A-1 indicates the location of the 167 stacks, and the tables following the map provides identif ying information, emissions us ed in the CALPUFF modeling, and predicted impacts.

The following information may be found in the listed columns of Table A-1:

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 meteorologi cal 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 term s 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 te rms of its predicted sulfate ion annual impact on Lye Brook in 2002 using National Weather Service data.

10. MM5 2002 SO 2 

# Tons per Year: Emissions calcula ted from CEMS data and used by modelers who used the MM5 generated meteorological data

11. VTDEC 2002 SO 2 

# Tons per Year: Emissions calcu lated from CEMS data and used by modelers who used the national weathe r 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/ga s fired electric generating units

15. State Name—table is in alphabetical order by state

16. State Code

2 

## For more information and detailed modeling results, see Appendix D: Source Dispersion Model Methods, in

NESCAUM 2006a.

# 38 Note that this list was created using 2002 emissions. By 2018 some of the units using these

stacks will have emission controls installed or be repowered or s hut down. This list represents

the EGU stacks that had the la rgest individual impacts on baseline visibility in 2002 at three

MANE-VU Class 1 areas.

Table A-2 presents predicted 2018 emissions estimate s for the same list of key stacks. Column 6 of Table A-2 gives the 2018 emissions predic ted by the IPM run VISTAS_PC_1f. Column 7

lists the revised 2018 emissions used in MANE-VU’s March 2008 modeling. Column 8 provides the basis for revising the emissions. See Section 5.5 of this report for more

information.

Figure A-1 . Top 167 US Electric Generating Facil ity Stacks Affecting MANE-VU Class I

Areas in 2002.39

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 State Name State Code1 D005935 593 90 542,138 2,136 1 EDGE MOOR O/G Steam Delaware 102 D005941 594 95 3,742 2 INDIAN RIVER Coal Steam Delaware 103 D005942 594 74 3,760 2 INDIAN RIVER Coal Steam Delaware 104 D005943 594 84 44 4,686 4,682 2 INDIAN RIVER Coal Steam Delaware 105 D005944 594 69 21 7,390 7,384 2 INDIAN RIVER Coal Steam Delaware 106 D007031LR 703 79 86 75 38,520 38,486 3 BOWEN Coal Steam Georgia 137 D007032LR 703 72 89 61 68 37,289 37,256 3 BOWEN Coal Steam Georgia 138 D007033LR 703 71 99 74 64 63 94 43,067 43,029 3 BOWEN Coal Steam Georgia 139 D007034LR 703 69 95 86 58 60 89 41,010 40,974 3 BOWEN Coal Steam Georgia 1310 D00709C02 709 84 75 89 71 47,591 47,5494 HARLLEE BRANCH Coal Steam Georgia 1311 D00861C01 861 28 96 65 46 62 42,355 42,318 5 COFFEEN Coal Steam Illinois 1712 D010011 1001 53 28,876 28,851 6 CAYUGA Coal Steam Indiana 1813 D010012 1001 95 46 68 26,016 25,992 6 CAYUGA Coal Steam Indiana 1814 D00983C01 983 52 19,922 7 CLIFTY CREEK Coal Steam Indiana 1815 D00983C02 983 54 18,131 7 CLIFTY CREEK Coal Steam Indiana 1816 D0099070 990 55 10070 37 29,801 29,774 8 ELMERW STOUT O/G Steam Indiana 1817 D06113C03 6113 30 48 14 43 22 41 71,18271,119 9 GIBSON Coal Steam Indiana 1818 D06113C04 6113 44 70 97 83 73 83 27,84827,823 9 GIBSON Coal Steam Indiana 1819 D01008C01 1008 73 10047 24,109 24,087 10 R GALLAGHER Coal Steam Indiana 1820 D01008C02 1008 98 55 23,849 23,828 10 R GALLAGHER Coal Steam Indiana 1821 D06166C02 6166 62 44 30 81 33 57 51,708 51,663 11 ROCKPORT Coal Steam Indiana 1822 D00988C03 988 77 15,946 12 TANNERS CREEK Coal Steam Indiana 1823 D00988U4 988 14 29 52 34 7 19 45,062 45,022 12 TANNERS CREEK Coal Steam Indiana 1824 D01010C05 1010 43 32 12 28 31 17 60,747 60,693 13 WABASH RIVER Coal Steam Indiana 1825 D067054 6705 34 60 34 44 73 40,118 40,082 14 WARRICK Coal Steam Indiana 1826 D06705C02 6705 92 75 96 27,895 14 WARRICK Coal Steam Indiana 1827 D01353C02 1353 38 30 15 26 85 29 41,545 41,508 15 BIG SANDY Coal Steam Kentucky 21Table A-1.2002 Data for Key EGU Stacks40

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 State Name State Code28 D01384CS1 1384 22 58 21,837 21,817 16 COOPER Coal Steam Kentucky 2129 D01355C03 1355 21 51 99 68 52 38,104 38,070 17 E W BROWN Coal Steam Kentucky 2130 D060182 6018 8339 12,083 18 EAST BEND Coal Steam Kentucky 2131 D01356C02 1356 93 71 88 50 59 25,646 25,623 19 GHENT Coal Steam Kentucky 2132 D060411 6041 61 18,375 20 H L SPURLOCK Coal Steam Kentucky 2133 D060412 6041 53 91 98 20,491 20,47320 H L SPURLOCK Coal Steam Kentucky 2134 D013644 1364 81 7,185 21 MILL CREEK Coal Steam Kentucky 2135 D013782 1378 87 20,245 22 PARADISE Coal Steam Kentucky 2136 D013783 1378 76 10011 84 55 42 46,701 46,660 22 PARADISE Coal Steam Kentucky 2137 D015074 1507 781,170 23 WILLIAM F WYMAN O/G Steam Maine 2338 D006021 602 90 38 10020,014 19,996 24 BRANDON SHORES Coal Steam Maryland 2439 D006022 602 99 29 99 19,280 19,263 24 BRANDON SHORES Coal Steam Maryland 2440 D015521 1552 63 17,782 17,767 25 C P CRANE Coal Steam Maryland 2441 D015522 1552 68 14,274 14,262 25 C P CRANE Coal Steam Maryland 2442 D01571CE2 1571 42 47 1 4 20 28 48,566 48,522 26 CHALK POINT Coal Steam Maryland 2443 D01572C23 1572 73 79 47 45 69 32 32,188 32,159 27 DICKERSON Coal Steam Maryland 2444 D015543 1554 7710,084 10,075 28 HERBERT A WAGNER O/G Steam Maryland 2445 D015731 1573 67 50 16 12 56 38 36,823 36,79029 MORGANTOWN Coal Steam Maryland 2446 D015732 1573 59 53 10 13 51 39 30,788 30,76129 MORGANTOWN Coal Steam Maryland 2447 D016191 1619 37 80 9,252 9,244 30 BRAYTON POINT Coal Steam Massachusetts 2548 D016192 1619 35 66 8,889 8,881 30 BRAYTON POINT Coal Steam Massachusetts 2549 D016193 1619 4 14 65 56 79 19,325 19,308 30 BRAYTON POINT Coal Steam Massachusetts 2550 D015991 1599 5 3665 13,014 13,00231 CANAL O/G Steam Massachusetts 2551 D015992 1599 7 2774 8,980 8,971 31 CANAL O/G Steam Massachusetts 2552 D016061 1606 48 5,249 32 MOUNT TOM Coal Steam Massachusetts 2553 D016261 1626 85 3,430 33 SALEM HARBOR Coal Steam Massachusetts 2554 D016263 1626 91 78 4,971 4,966 33 SALEM HARBOR Coal Steam Massachusetts 2541

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 State Name State Code55 D016264 1626 32 252,880 2,878 33 SALEM HARBOR O/G Steam Massachusetts 2556 D016138 1613 94 4,376 34 SOMERSET Coal Steam Massachusetts 2557 D01702C09 1702 96 4,565 35 DAN E KARN Coal Steam Michigan 2658 D01733C12 1733 49 24 80 80 45 22 46,08146,040 36 MONROE Coal Steam Michigan 2659 D01733C34 1733 27 26 76 26 27 39,36239,327 36 MONROE Coal Steam Michigan 2660 D017437 1743 91 15,805 37 ST CLAIR Coal Steam Michigan 2661 D017459A 1745 76 61 18,341 18,324 38 TRENTON CHANNEL Coal Steam Michigan 2662 D023641 2364 2 57 9,356 9,348 39MERRIMACK Coal Steam New Hampshire 3363 D023642 2364 1 17 99 28 87 19,453 19,43539 MERRIMACK Coal Steam New Hampshire 3364 D080021 8002 45 745,033 5,028 40 NEWINGTON O/G Steam New Hampshire 3365 D023781 2378 81 2 15 9,747 9,738 41 B L ENGLAND Coal Steam New Jersey 3466 D024032 2403 63 97 25 5040 44 18,785 18,768 42 HUDSON O/G Steam New Jersey 3467 D024081 2408 95 8,076 43 MERCER Coal Steam New Jersey 3468 D024082 2408 60 5,675 43 MERCER Coal Steam New Jersey 3469 D02549C01 2549 64 41 42 72 25,343 25,320 44 C R HUNTLEY Coal Steam New York 3670 D02549C02 2549 99 12,317 44 C R HUNTLEY Coal Steam New York 3671 D024804 248071 7,720 45 DANSKAMMER O/G Steam New York 3672 D02554C03 2554 33 51 62 27 51 30,151 30,125 46 DUNKIRK Coal Steam New York 3673 D02526C03 2526 78 14,929 47 WESTOVER Coal Steam New York 3674 D025276 2527 80 12,650 48 GREENIDGE Coal Steam New York 3675 D025163 2516 967,359 49 NORTHPORT O/G Steam New York 3676 D025945 2594 76 1,747 50 OSWEGO O/G Steam New York 3677 D02642CS2 2642 91 14,086 51 ROCHESTER 7 Coal Steam New York 3678 D080061 8006 93 3,817 52 ROSETON O/G Steam New York 3679 D080062 8006 88 2,840 52 ROSETON O/G Steam New York 3680 D080421 8042 13 12 18 5 10 34 57,820 57,769 53 BELEWS CREEK Coal Steam North Carolina 3781 D080422 8042 23 15 32 10 15 49 45,296 45,256 53 BELEWS CREEK Coal Steam North Carolina 3782 D027215 2721 98 45 87 39 97 85 19,145 19,128 54CLIFFSIDE Coal Steam North Carolina 3783 D027133 2713 61 14,460 55 L V SUTTON Coal Steam North Carolina 3742

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 State Name State Code84 D027093 2709 97 9,390 56 LEE Coal Steam North Carolina 3785 D027273 2727 10040 48 75 84 26,329 26,305 57 MARSHALLCoal Steam North Carolina 3786 D027274 2727 89 39 83 51 66 82 27,308 27,284 57MARSHALL Coal Steam North Carolina 3787 D06250C05 6250 60 59 35 37 27,395 27,371 58 MAYO Coal Steam North Carolina 3788 D027121 2712 59 12,031 12,02059 ROXBORO Coal Steam North Carolina 3789 D027122 2712 82 41 54 23 94 29,337 29,31059 ROXBORO Coal Steam North Carolina 3790 D02712C03 2712 56 37 57 24 21 78 30,776 30,74959 ROXBORO Coal Steam North Carolina 3791 D02712C04 2712 88 72 47 47 22,962 22,94159 ROXBORO Coal Steam North Carolina 3792 D0283612 2836 55 20 48 8929 35 41,432 41,39560 AVON LAKE Coal Steam Ohio 3993 D028281 2828 29 9 31 30 24 8 37,307 37,274 61 CARDINAL Coal Steam Ohio 3994 D028282 2828 56 20,598 20,580 61 CARDINAL Coal Steam Ohio 3995 D028283 2828 80 15,372 61 CARDINAL Coal Steam Ohio 3996 D028404 2840 3 1 6 22 3 87,801 87,72462 CONESVILLE Coal Steam Ohio 3997 D02840C02 2840 84 73 81 63 22,79122,771 62 CONESVILLE Coal Steam Ohio 3998 D028375 2837 86 5635 70 35,970 35,938 63 EASTLAKE Coal Steam Ohio 3999 D081021 8102 23 71 59 95 18,207 18,191 64 GEN J M GAVIN Coal Steam Ohio 39100 D081022 8102 78 12,333 12,322 64 GEN J M GAVIN Coal Steam Ohio 39101 D028501 2850 36 67 39 53 45 30,798 30,771 65 J M STUART Coal Steam Ohio 39102 D028502 2850 24 65 40 49 98 46 28,698 28,673 65 J M STUART Coal Steam Ohio 39103 D028503 2850 26 72 62 27,968 27,944 65 J M STUART Coal Steam Ohio 39104 D028504 2850 20 77 45 52 88 54 27,343 27,319 65 J M STUART Coal Steam Ohio 39105 D060312 6031 67 77 90 19,517 19,500 66 KILLEN STATION Coal Steam Ohio 39106 D02876C01 2876 40 7 3 9 30 10 72,593 72,529 67 KYGER CREEK Coal Steam Ohio 39107 D028327 2832 65 28 59 22 48 20 46,991 46,950 68 MIAMI FORT Coal Steam Ohio 39108 D02832C06 2832 60 43 64 23,694 23,673 68 MIAMI FORT Coal Steam Ohio 39109 D028725 2872 74 92 78 90 36 30,079 30,05269 MUSKINGUM RIVER Coal Steam Ohio 39110 D02872C04 2872 6 19 13 6 19 15 83,134 83,060 69 MUSKINGUM RIVER Coal Steam Ohio 39111 D02864C01 2864 70 56 61 63 49 24 35,193 35,162 70 R E BURGER Coal Steam Ohio 3943

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 State Name State Code112 D07253C01 7253 89 58 57 33 30,977 30,949 71 RICHARD GORSUCH Ohio 39113 D028665 2866 82 53 19,796 19,779 72 W H SAMMIS Coal Steam Ohio 39114 D028667 2866 57 16 42 41 41 16 33,601 33,572 72 W H SAMMIS Coal Steam Ohio 39115 D02866C01 2866 97 54 93 96 92 30 24,649 24,627 72 W H SAMMIS Coal Steam Ohio 39116 D02866C02 2866 69 92 50 26,022 25,999 72 W H SAMMIS Coal Steam Ohio 39117 D02866M6A 2866 85 58 19,564 19,546 72 W H SAMMIS Coal Steam Ohio 39118 D060191 6019 93 72 60 21,496 73 W H ZIMMER Coal Steam Ohio 39119 D028306 2830 46 38 70 40 12 69 30,466 30,439 74 WALTER C BECKJORD Coal Steam Ohio 39120 D031782 3178 77 63 81 16,484 16,469 75 ARMSTRONG Coal Steam Pennsylvania 42121 D031403 3140 31 34 9 46 18 18 38,801 38,767 76 BRUNNER ISLAND Coal Steam Pennsylvania 42122 D03140C12 3140 52 46 49 69 25 23 29,736 29,709 76 BRUNNER ISLAND Coal Steam Pennsylvania 42123 D082261 8226 25 21 33 42 36 9 40,268 40,232 77 CHESWICK Coal Steam Pennsylvania 42124 D03179C01 3179 16 10 5 8 5 4 79,635 79,565 78HATFIELD'S FERRY Coal Steam Pennsylvania 42125 D031221 3122 11 6 26 38 17 14 45,754 45,71479 HOMER CITY Coal Steam Pennsylvania 42126 D031222 3122 9 4 37 92 13 11 55,216 55,16779 HOMER CITY Coal Steam Pennsylvania 42127 D031361 3136 8 2 4 14 6 1 87,434 87,357 80 KEYSTONE Coal Steam Pennsylvania 42128 D031362 3136 18 3 8 19 8 2 62,847 62,791 80 KEYSTONE Coal Steam Pennsylvania 42129 D03148C12 3148 71 84 17,214 81 MARTINS CREEK Coal Steam Pennsylvania 42130 D031491 3149 19 8 35 7 1 6 60,242 60,188 82 MONTOUR Coal Steam Pennsylvania 42131 D031492 3149 15 5 21 20 3 5 50,276 50,232 82 MONTOUR Coal Steam Pennsylvania 42132 D031131 3113 82 9,674 83 PORTLAND Coal Steam Pennsylvania 42133 D031132 3113 36 93 14,294 83 PORTLAND Coal Steam Pennsylvania 42134 D03131CS1 3131 54 31 79 32 65 22,344 22,324 84 SHAWVILLE Coal Steam Pennsylvania 42135 D033193 3319 10011,045 85 JEFFERIES O/GSteam South Carolina 45136 D033194 3319 90 8711,838 85 JEFFERIES O/GSteam South Carolina 45137 D03297WT1 3297 68 61 17,671 86 WATEREE Coal Steam South Carolina 45138 D03297WT2 3297 83 73 17,199 86 WATEREE Coal Steam South Carolina 45139 D03298WL1 3298 35 94 37 25,170 25,14887 WILLIAMS Coal Steam South Carolina 4544

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 State Name State Code140 D062491 6249 58 82 17,920 88 WINYAH Coal Steam South Carolina 45141 D03403C34 3403 85 20,314 89 GALLATIN Coal Steam Tennessee 47142 D03405C34 3405 39 19,368 90 JOHN SEVIER Coal Steam Tennessee 47143 D03406C10 3406 10 11 27 33 4 43 104,523 104,431 91 JOHNSONVILLE Coal Steam Tennessee 47144 D03407C15 3407 64 87 66 67 76 37,308 37,274 92 KINGSTON Coal Steam Tennessee 47145 D03407C69 3407 48 98 91 82 91 38,645 38,611 92 KINGSTON Coal Steam Tennessee 47146 D038033 3803 55 9,493 93 CHESAPEAKE Coal Steam Virginia 51147 D038034 3803 94 16 10,806 93 CHESAPEAKE Coal Steam Virginia 51148 D037974 3797 90 9,293 94 CHESTERFIELD Coal Steam Virginia 51149 D037975 3797 88 44 27 86 19,620 19,602 94 CHESTERFIELD Coal Steam Virginia 51150 D037976 3797 66 18 7 3 34 66 40,570 40,534 94 CHESTERFIELD Coal Steam Virginia 51151 D03775C02 3775 47 16,674 95 CLINCH RIVER Coal Steam Virginia 51152 D038093 3809 52 64 29 10,477 10,468 96 YORKTOWN Coal Steam Virginia 51153 D03809CS0 3809 96 43 19 17 62 21,219 21,201 96 YORKTOWN Coal Steam Virginia 51154 D039423 3942 79 10,126 97 ALBRIGHT Coal Steam West Virginia 54155 D039431 3943 51 23 20 32 16 13 42,385 42,348 97 FORT MARTIN Coal Steam West Virginia 54156 D039432 3943 50 22 22 31 14 12 45,850 45,809 97 FORT MARTIN Coal Steam West Virginia 54157 D039353 3935 41 33 28 11 64 26 42,212 42,174 98JOHN E AMOS Coal Steam West Virginia 54158 D03935C02 3935 17 42 43 1 11 21 63,066 63,010 98JOHN E AMOS Coal Steam West Virginia 54159 D03947C03 3947 86 62 55 57 25 38,575 38,541 99 KAMMER Coal Steam West Virginia 54160 D03936C02 3936 98 15,480 15,467 100KANAWHA RIVER Coal Steam West Virginia 54161 D03948C02 3948 58 13 17 36 9 7 55,405 55,356 101 MITCHELL Coal Steam West Virginia 54162 D062641 6264 75 49 50 18 77 40 42,757 42,719 102MOUNTAINEER Coal Steam West Virginia 54163 D03954CS0 3954 68 24 25 23 67 20,130 20,112 103 MT STORM Coal Steam West Virginia 54164 D0393851 3938 79 97 12,948 12,936 104PHILIP SPORN Coal Steam West Virginia 54165 D03938C04 3938 94 26,451 26,427 104PHILIP SPORN Coal Steam West Virginia 54166 D060041 6004 66 83 31 21,581 21,562 105 PLEASANTS Coal Steam West Virginia 54167 D060042 6004 88 92 20,550 20,532 105 PLEASANTS Coal Steam West Virginia 5445Table A-2.Predicted 2018 SO2Emissions from Key EGU StacksStateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)DelawareEdge Moor5935O/GSteam0.01,406.0State CommentsDelawareIndian River5941CoalSteam4,289.20.0State CommentsDelawareIndian River5942CoalSteam4,538.90.0State CommentsDelawareIndian River5943CoalSteam764.11,759.0State CommentsDelwareIndian River5944CoalSteam19,665.83,657.0State CommentsGeorgiaBowen7031BLRCoalSteam4,830.82,909.7VISTAS_2018G2GeorgiaBowen7032BLRCoalSteam4,864.72,930.1VISTAS_2018G2GeorgiaBowen7033BLRCoalSteam6,111.33,681.0VISTAS_2018G2GeorgiaBowen7034BLRCoalSteam6,294.33,791.1VISTAS_2018G2GeorgiaHarllee Branch7093CoalSteam1,158.11,395.1VISTAS_2018G2GeorgiaHarllee Branch7094CoalSteam1,132.61,364.4VISTAS_2018G2IllinoisCoffeen86101CoalSteam3,314.64,761.8IPM 3.0IllinoisCoffeen86102CoalSteam5,444.37,365.8IPM 3.0IllinoisAlcoa AllowanceManagement Inc(WARRICK)67051CoalSteam0.010,013.5IPM 3.046StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)IllinoisAlcoa AllowanceManagement Inc(WARRICK)67052CoalSteam0.09,604.4IPM 3.0IllinoisAlcoa AllowanceManagement Inc(WARRICK)67054CoalSteam628.14,091.5IPM 3.0IndianaCayuga10011CoalSteam4,660.76,298.8IPM 3.0IndianaCayuga10012CoalSteam4,501.36,405.7IPM 3.0IndianaClifty Creek9831CoalSteam944.32,594.3IPM 3.0IndianaClifty Creek9832CoalSteam7,682.72,528.4IPM 3.0IndianaClifty Creek9833CoalSteam7,636.72,513.2IPM 3.0IndianaClifty Creek9834CoalSteam7,604.82,502.7IPM 3.0IndianaClifty Creek9835CoalSteam7,440.82,448.8IPM 3.0IndianaClifty Creek9836CoalSteam903.42,889.3IPM 3.0IndianaGibson61131CoalSteam5,640.17,994.7IPM 3.0IndianaGibson61132CoalSteam5,735.68,039.2IPM 3.0IndianaGibson61133CoalSteam5,784.97,875.5IPM 3.0IndianaGibson61134CoalSteam8,032.413,116.6IPM 3.047StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)IndianaHarding StreetStation (EW Stout)99070CoalSteam1,845.95,476.9IPM 3.0IndianaR Gallagher10081CoalSteam5,383.23,571.1IPM 3.0IndianaR Gallagher10082CoalSteam5,284.73,505.7IPM 3.0IndianaR Gallagher10083CoalSteam5,309.23,522.0IPM 3.0IndianaR Gallagher10084CoalSteam5,383.23,571.1IPM 3.0IndianaRockport6166MB1CoalSteam32,349.815,531.2IPM 3.0IndianaRockport6166MB2CoalSteam32,660.415,680.3IPM 3.0IndianaTanners Creek988U1CoalSteam5,222.06,756.8IPM 3.0IndianaTanners Creek988U2CoalSteam3,770.16,562.1IPM 3.0IndianaTanners Creek988U3CoalSteam5,289.89,211.3IPM 3.0IndianaTanners Creek988U4CoalSteam4,507.612,433.5Mark JanssenIPM 3.0IndianaWabash River10102CoalSteam3,037.15,827.8Mark JanssenIPM 3.0IndianaWabash River10103CoalSteam3,071.15,397.1Mark JanssenIPM 3.0IndianaWabash River10104CoalSteam3,071.15,640.3Mark JanssenIPM 3.0IndianaWabash River10105CoalSteam3,528.15,954.8Mark JanssenIPM 3.048StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)IndianaWabash River10106CoalSteam3,039.64,192.7Mark JanssenIPM 3.0KentuckyBig Sandy1353BSU1CoalSteam675.7814.0VISTAS_2018G2KentuckyBig Sandy1353BSU2CoalSteam4,203.45,063.5VISTAS_2018G2KentuckyCooper13841CoalSteam4,400.55,301.0VISTAS_2018G2KentuckyCooper13842CoalSteam596.5718.6VISTAS_2018G2KentuckyE W Brown13552CoalSteam748.0901.1VISTAS_2018G2KentuckyE W Brown13553CoalSteam1,767.02,128.6VISTAS_2018G2KentuckyEast Bend60182CoalSteam2,221.62,676.2VISTAS_2018G2KentuckyGhent13563CoalSteam5,104.16,148.5VISTAS_2018G2KentuckyGhent13564CoalSteam4,976.75,995.1VISTAS_2018G2KentuckyH L Spurlock60411CoalSteam767.6924.7VISTAS_2018G2KentuckyH L Spurlock60412CoalSteam4,871.55,868.4VISTAS_2018G2KentuckyMill Creek13644CoalSteam12,822.615,446.5VISTAS_2018G2KentuckyParadise13782CoalSteam7,940.59,565.4VISTAS_2018G2KentuckyParadise13783CoalSteam22,538.527,150.6VISTAS_2018G249StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)MaineWilliam FWyman15074O/GSteam0.01162.5manevu_2002v3MarylandBrandon Shores6021CoalSteam3,799.85,392.0State CommentsMarylandBrandon Shores6022CoalSteam3,673.35,627.0State CommentsMarylandC P Crane15521CoalSteam900.01,532.0State CommentsMarylandC P Crane15522CoalSteam875.21,646.0State CommentsMarylandChalk Point15711CoalSteam1,506.22,606.0State CommentsMarylandChalk Point15712CoalSteam1,510.52,733.0State CommentsMarylandDickerson15721CoalSteam797.41,238.0State CommentsMarylandDickerson15722CoalSteam867.21,355.0State CommentsMarylandDickerson15723CoalSteam768.71,285.0State CommentsMarylandHerbert AWagner15543CoalSteam1,551.11,239.0State CommentsMarylandMorgantown15731CoalSteam3,037.14,646.0State CommentsMarylandMorgantown15732CoalSteam2,987.24,679.0State CommentsMassachusettsBrayton Point16191CoalSteam1,924.7925.2State CommentsMassachusettsBrayton Point16192CoalSteam1,875.6888.9State Comments50StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)MassachusettsBrayton Point16193CoalSteam4,775.04,775.0State CommentsMassachusettsCanal15991O/GSteam0.013066.0manevu_2002v3MassachusettsCanal15992O/GSteam0.08948.3manevu_2002v3MassachusettsMount Tom16061CoalSteam242.6242.6State CommentsMassachusettsSalem Harbor16261CoalSteam3,421.53,421.5IPM 2.1.9MassachusettsSalem Harbor16263CoalSteam405.2405.2IPM 2.1.9MassachusettsSalem Harbor16264O/GSteam0.02,897.0manevu_2002v3MassachusettsSomerset16138CoalSteam2,372.82,372.8IPM 2.1.9MichiganDan E Karn17023O/GSteam?0.03,358.52002 CEMS dataMichiganDan E Karn17024O/GSteam?0.02,169.22002 CEMS dataMichiganMonroe17331CoalSteam27,485.919,089.8Mark JanssenIPM 3.0MichiganMonroe17332CoalSteam27,806.619,530.4Mark JanssenIPM 3.0MichiganMonroe17333CoalSteam28,043.92,035.8Mark JanssenIPM 3.0MichiganMonroe17334CoalSteam30,584.31,987.6Mark JanssenIPM 3.051StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)MichiganSt. Clair17437CoalSteam17,052.211,925.8Mark JanssenIPM 3.0MichiganTrenton Channel17459ACoalSteam17,832.613,868.9Mark JanssenIPM 3.0NewHampshireMerrimack23641CoalSteam1,894.61,894.6State CommentsNewHampshireMerrimack23642CoalSteam1,091.11,091.1State CommentsNewHampshireNewington80021O/GSteam0.03,297.0State CommentsNew JerseyB L England23781CoalSteam5,201.3520.1State CommentsNew JerseyHudson24032CoalSteam10,958.41,095.8State CommentsNew JerseyMercer24081CoalSteam1,921.01,921.0State CommentsNew JerseyMercer24082CoalSteam1,921.01,921.0State CommentsNew YorkC R Huntley254963CoalSteam0.00.0NOT-IN_manevu_2002v3New YorkC R Huntley254964CoalSteam3,602.10.0IPM 2.1.9New YorkC R Huntley254965CoalSteam0.00.0NOT-IN_manevu_2002v3New YorkC R Huntley254966CoalSteam0.07,085.0manevu_2002v3New YorkC R Huntley254967CoalSteam633.00.0State CommentsNew YorkC R Huntley254968CoalSteam633.50.0State Comments52StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)New YorkDanskammer24804CoalSteam763.5763.5State CommentsNew YorkDunkirk25543CoalSteam667.40.0State CommentsNew YorkDunkirk25544CoalSteam647.47,085.0State CommentsNew YorkGoudey252611CoalSteam1,921.11,921.1State CommentsNew YorkGoudey252612CoalSteam1,921.11,921.1State CommentsNew YorkGoudey252613CoalSteam3,856.13,856.1IPM 2.1.9New YorkGreenidge25276CoalSteam390.1605.0State CommentsNew YorkNorthport25163O/GSteam0.07,359.02002 CEMS dataNew YorkOswego25945O/GSteam0.01,747.02002 VTDECdataNew YorkRochester 726423CoalSteam2,800.40.0State CommentsNew YorkRochester 726424CoalSteam3,231.3200.0State CommentsNew YorkRoseton80061O/GSteam0.03,817.02002 VTDECdataNew YorkRoseton80062O/GSteam0.02,840.02002 VTDECdataNorth CarolinaBelews Creek80421CoalSteam2,535.53,054.3VISTAS_2018G2North CarolinaBelews Creek80422CoalSteam3,217.53,875.9VISTAS_2018G253StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)North CarolinaCliffside27215CoalSteam1,951.72,351.1VISTAS_2018G2North CarolinaL V Sutton27133CoalSteam1,036.51,248.6VISTAS_2018G2North CarolinaLee27093CoalSteam697.68,403.5VISTAS_2018G2North CarolinaMarshall27273CoalSteam2,242.92,701.9VISTAS_2018G2North CarolinaMarshall27274CoalSteam2,207.82,659.6VISTAS_2018G2North CarolinaMayo62501ACoalSteam953.91,149.1VISTAS_2018G2North CarolinaMayo62501BCoalSteam953.41,148.5VISTAS_2018G2North CarolinaRoxboro27121CoalSteam998.71,203.1VISTAS_2018G2North CarolinaRoxboro27122CoalSteam2,438.02,936.9VISTAS_2018G2North CarolinaRoxboro27123ACoalSteam1,071.11,290.3VISTAS_2018G2North CarolinaRoxboro27123BCoalSteam1,071.11,290.3VISTAS_2018G2North CarolinaRoxboro27124ACoalSteam1,253.01,509.4VISTAS_2018G2North CarolinaRoxboro27124BCoalSteam1,253.01,509.4VISTAS_2018G2OhioAvon Lake283612CoalSteam5,809.53,201.9Mark JanssenIPM 3.0OhioCardinal28281CoalSteam2,417.16,964.7Mark JanssenIPM 3.054StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)OhioCardinal28282CoalSteam5,320.97,356.2Mark JanssenIPM 3.0OhioCardinal28283CoalSteam5,366.88,505.4Mark JanssenIPM 3.0OhioConesville28401CoalSteam582.1701.2IPM 2.1.9OhioConesville28402CoalSteam592.8714.1IPM 2.1.9OhioConesville28404CoalSteam7,333.96,616.6Mark JanssenIPM 3.0OhioEastlake28375CoalSteam5,113.713,883.0Mark JanssenIPM 3.0OhioGen J M Gavin81021CoalSteam6,479.28,584.7Mark JanssenIPM 3.0OhioGen J M Gavin81022CoalSteam6,464.98,565.7Mark JanssenIPM 3.0OhioJ M Stuart28501CoalSteam4,810.15,629.2Mark JanssenIPM 3.0OhioJ M Stuart28502CoalSteam4,718.15,497.4Mark JanssenIPM 3.0OhioJ M Stuart28503CoalSteam4,815.95,615.2Mark JanssenIPM 3.0OhioJ M Stuart28504CoalSteam4,828.25,637.7Mark JanssenIPM 3.0OhioKillen Station60312CoalSteam4,879.05,823.1Mark JanssenIPM 3.0OhioKyger Creek28761CoalSteam942.91,521.3Mark JanssenIPM 3.0OhioKyger Creek28762CoalSteam953.81,539.0Mark JanssenIPM 3.055StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)OhioKyger Creek28763CoalSteam954.81,540.6Mark JanssenIPM 3.0OhioKyger Creek28764CoalSteam964.01,555.4Mark JanssenIPM 3.0OhioKyger Creek28765CoalSteam951.01,534.5Mark JanssenIPM 3.0OhioMiami Fort28325-1CoalSteam1,502.51,035.5Mark JanssenIPM 3.0OhioMiami Fort28325-2CoalSteam1,502.51,035.5Mark JanssenIPM 3.0OhioMiami Fort28326CoalSteam6,285.13,790.2Mark JanssenIPM 3.0OhioMiami Fort28327CoalSteam2,209.24,771.7Mark JanssenIPM 3.0OhioMuskingumRiver28721CoalSteam883.87,366.3Mark JanssenIPM 3.0OhioMuskingumRiver28722CoalSteam865.47,213.6Mark JanssenIPM 3.0OhioMuskingumRiver28723CoalSteam913.49,149.2Mark JanssenIPM 3.0OhioMuskingumRiver28724CoalSteam848.08,494.4Mark JanssenIPM 3.0OhioMuskingumRiver28725CoalSteam5,033.75,430.2Mark JanssenIPM 3.0OhioR E Burger28641CoalSteam0.00.02002 CEMS dataOhioR E Burger28642CoalSteam0.00.02002 CEMS dataOhioR E Burger28643CoalSteam0.00.02002 CEMS data56StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)OhioR E Burger28644CoalSteam0.00.02002 CEMS dataOhioR E Burger28645CoalSteam462.22,096.3Mark JanssenIPM 3.0OhioR E Burger28646CoalSteam1,491.52,096.3Mark JanssenIPM 3.0OhioR E Burger28647CoalSteam1,317.71,528.3Mark JanssenIPM 3.0OhioR E Burger28648CoalSteam790.31,615.6Mark JanssenIPM 3.0OhioRichardGorsuch72531CoalSteam1,806.36,938.7IPM 3.0OhioRichardGorsuch72532CoalSteam1,436.98,117.3IPM 3.0OhioRichardGorsuch72533CoalSteam1,440.96,867.5IPM 3.0OhioRichardGorsuch72534CoalSteam1,801.46,520.9IPM 3.0OhioW H Sammis28661CoalSteam836.04,510.1Mark JanssenIPM 3.0OhioW H Sammis28662CoalSteam841.54,557.2Mark JanssenIPM 3.0OhioW H Sammis28663CoalSteam862.54,713.0Mark JanssenIPM 3.0OhioW H Sammis28664CoalSteam828.64,417.3Mark JanssenIPM 3.0OhioW H Sammis28665CoalSteam1,357.27,004.0Mark JanssenIPM 3.0OhioW H Sammis28666ACoalSteam3,544.33,217.1Mark JanssenIPM 3.057StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)OhioW H Sammis28667CoalSteam3,446.43,245.3Mark JanssenIPM 3.0OhioWalter CBeckjord28306CoalSteam1,790.19,715.7Mark JanssenIPM 3.0OhioW H Zimmer60191CoalSteam4,658.17,552.8Mark JanssenIPM 3.0PennsylvaniaArmstrong31782CoalSteam779.11,674.1State CommentsPennsylvaniaBrunner Island31401CoalSteam1,072.8599.0IPM 2.1.9PennsylvaniaBrunner Island31402CoalSteam1,206.8884.3IPM 2.1.9PennsylvaniaBrunner Island31403CoalSteam2,289.61,963.3IPM 2.1.9PennsylvaniaCheswick82261CoalSteam4,636.92,011.6IPM 2.1.9PennsylvaniaHatfields Ferry31791CoalSteam2,215.02,784.8IPM 2.1.9PennsylvaniaHatfields Ferry31792CoalSteam2,220.12,650.8IPM 2.1.9PennsylvaniaHomer City31221CoalSteam3,502.42,288.0State CommentsPennsylvaniaHomer City31222CoalSteam3,246.12,767.8State CommentsPennsylvaniaKeystone31361CoalSteam4,763.74,385.7State CommentsPennsylvaniaKeystone31362CoalSteam4,679.73,145.3State CommentsPennsylvaniaMartins Creek31481CoalSteam1,331.70.0IPM 2.1.958StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)PennsylvaniaMartins Creek31482CoalSteam1,198.10.0IPM 2.1.9PennsylvaniaMontour31491CoalSteam3,958.53,050.5State CommentsPennsylvaniaMontour31492CoalSteam3,993.42,522.4State CommentsPennsylvaniaPortland31131CoalSteam589.99,740.2State CommentsPennsylvaniaPortland31132CoalSteam830.214,568.8State CommentsPennsylvaniaShawville31311CoalSteam444.07,144.7State CommentsSouth CarolinaJefferies33193CoalSteam1,261.98,144.7VISTAS_2018G2South CarolinaJefferies33194CoalSteam1,233.67,962.0VISTAS_2018G2South CarolinaWateree3297WAT1CoalSteam15,017.81,809.1VISTAS_2018G2South CarolinaWateree3297WAT2CoalSteam875.21,054.3VISTAS_2018G2South CarolinaWilliams3298WIL1CoalSteam1,279.91,541.8VISTAS_2018G2South CarolinaWinyah62491CoalSteam680.9820.2VISTAS_2018G2TennesseeGallatin34033CoalSteam579.8698.4VISTAS_2018G2TennesseeGallatin34034CoalSteam573.1690.4VISTAS_2018G2TennesseeJohn Sevier34053CoalSteam451.4543.8VISTAS_2018G259StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)TennesseeJohn Sevier34054CoalSteam463.8558.7VISTAS_2018G2TennesseeJohnsonville34061CoalSteam5,030.46,059.8VISTAS_2018G2TennesseeJohnsonville34062CoalSteam4,729.25,696.9VISTAS_2018G2TennesseeJohnsonville34063CoalSteam4,559.15,492.0VISTAS_2018G2TennesseeJohnsonville34064CoalSteam4,722.85,689.3VISTAS_2018G2TennesseeJohnsonville34065CoalSteam4,576.65,513.1VISTAS_2018G2TennesseeJohnsonville34066CoalSteam4,540.05,469.0VISTAS_2018G2TennesseeJohnsonville34067CoalSteam6,287.67,574.2VISTAS_2018G2TennesseeJohnsonville34068CoalSteam5,584.76,727.4VISTAS_2018G2TennesseeJohnsonville34069CoalSteam5,474.96,595.2VISTAS_2018G2TennesseeJohnsonville340610CoalSteam4,869.15,865.5VISTAS_2018G2TennesseeKingston34071CoalSteam371.9448.0VISTAS_2018G2TennesseeKingston34072CoalSteam371.9448.0VISTAS_2018G2TennesseeKingston34073CoalSteam371.9448.0VISTAS_2018G2TennesseeKingston34074CoalSteam371.9448.0VISTAS_2018G260StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)TennesseeKingston34075CoalSteam486.8586.4VISTAS_2018G2TennesseeKingston34076CoalSteam486.8586.4VISTAS_2018G2TennesseeKingston34077CoalSteam486.8586.4VISTAS_2018G2TennesseeKingston34078CoalSteam486.8586.4VISTAS_2018G2TennesseeKingston34079CoalSteam602.3725.5VISTAS_2018G2VirginiaChesapeake38033CoalSteam691.53,332.0VISTAS_2018G2VirginiaChesapeake38034CoalSteam1,038.85,005.2VISTAS_2018G2VirginiaChesterfield37974CoalSteam743.8896.0VISTAS_2018G2VirginiaChesterfield37975CoalSteam1,560.61,879.9VISTAS_2018G2VirginiaChesterfield37976CoalSteam3,633.34,376.8VISTAS_2018G2VirginiaClinch River37751CoalSteam539.06,542.3VISTAS_2018G2VirginiaClinch River37752CoalSteam545.36,618.2VISTAS_2018G2VirginiaYorktown38091CoalSteam6,169.6743.3VISTAS_2018G2VirginiaYorktown38092CoalSteam6,521.3785.4VISTAS_2018G2VirginiaYorktown38093CoalSteam0.03,342.8VISTAS_2018G261StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)West VirginiaAlbright39423CoalSteam660.1795.2VISTAS_2018G2West VirginiaFort Martin39431CoalSteam4,922.15,929.3VISTAS_2018G2West VirginiaFort Martin39432CoalSteam4,890.05,890.6VISTAS_2018G2West VirginiaJohn E Amos39351CoalSteam6,612.77,965.8VISTAS_2018G2West VirginiaJohn E Amos39352CoalSteam6,693.98,063.7VISTAS_2018G2West VirginiaJohn E Amos39353CoalSteam10,821.013,035.3VISTAS_2018G2West VirginiaKammer39471CoalSteam951.11,145.8VISTAS_2018G2West VirginiaKammer39472CoalSteam949.21,143.4VISTAS_2018G2West VirginiaKammer39473CoalSteam948.71,142.8VISTAS_2018G2West VirginiaKanawha River39361CoalSteam902.71,087.5VISTAS_2018G2West VirginiaKanawha River39362CoalSteam900.31,084.5VISTAS_2018G2West VirginiaMitchell39481CoalSteam7,646.29,210.8VISTAS_2018G2West VirginiaMitchell39482CoalSteam7,581.99,133.4VISTAS_2018G2West VirginiaMountaineer62641CoalSteam11,433.513,773.1VISTAS_2018G2West VirginiaMount Storm39541CoalSteam5,318.33,843.9VISTAS_2018G262StateFacility NameORISIDUnitIDUnitTypeORIGINAL2018 (1)SO2TPYREVISED2018 (2)SO2TPYBASIS FORCHANGE (3)West VirginiaMount Storm39542CoalSteam5,318.33,843.9VISTAS_2018G2West VirginiaPhilip Sporn393811CoalSteam668.1804.8VISTAS_2018G2West VirginiaPhilip Sporn393821CoalSteam647.6780.1VISTAS_2018G2West VirginiaPhilip Sporn393831CoalSteam643.0774.6VISTAS_2018G2West VirginiaPhilip Sporn393841CoalSteam633.2762.8VISTAS_2018G2West VirginiaPhilip Sporn393851CoalSteam1,942.62,340.1VISTAS_2018G2West VirginiaPleasants60041CoalSteam6,334.01,898.0VISTAS_2018G2West VirginiaPleasants60042CoalSteam6,164.71,847.3VISTAS_2018G2WisconsinPleasant Prairie61701CoalSteam1,735.21,769.6IPM 3.0WisconsinPleasant Prairie61702CoalSteam1,748.11,782.9IPM 3.0