Technical Support Document for the Modeling Portions ofthe District Of Columbia’s

Regional Haze State Implementation Plan (SIP)

Entitled “District of Columbia Regional Haze

State Implementation Plan (SIP)

October 2011”

TSD Prepared November, 2011

Todd A. Ellsworth

Office of Air Monitoring and Analysis, 3AP40

U.S. Environmental Protection Agency, Region 3

1650 Arch Street

Philadelphia, Pennsylvania 19103

ZZ2—

Reviewed by Andrew Hass, Acting Associate Director,

Office of Air Monitoring and Analysis (3AP40)

/1/7 //(

Date Signed

1
October 2011”

Purpose of the Technical Support Document

This Technical Support Document (TSD) describes thç Environmental Protection

Agency’s (EPA’s) evaluation ofthe modeling portions ofthe District of Columbia’s

(D.C.) State Implementation Plan (SIP) revision entitled “District of Columbia Regional

Haze

State Implementation Plan (SIP) Revised September 2011”. This SIP revision will

hereafter be referred to as the D.C. Haze SIP.

The purpose ofthis TSD is to provide more detailed information than can be contained in

the official notice published in the Federal Register. Readers who need more informationthan we provide in this TSD or want to review the modeling in more detail should read

the above referenced D.C. Haze SIP.

The Regulatory FrameworkIn section 169A(a)(1) of the 1977 Amendments to the CAA, Congress created a program

for protecting visibility in the nation’s national parks and wilderness areas. This sectionof the CAA establishes as a national goal the “prevention of any future, and the

remedying of any existing, impairment of visibility in mandatory Class I Federal areas 1

which impairment results from manmade air pollution.” On December 2, 1980, EPA

promulgated regulations to address visibility impairment in Class I areas that is

“reasonably attributable” to a single source or small group ofsources, i.e., “reasonablyattributable visibility impairment” (RAVI). See 45 FR 80084. These regulations

represented the first phase in addressing visibility impairment. EPA deferred action on

regional haze that emanates from a variety ofsources until monitoring, modeling and

scientific knowledge about the relationships between pollutants and visibility impairment

were improved.

‘Areas designated as mandatory Class I Federal areas consist of national parks exceeding 6000 acres,

wilderness areas and national memorial parks exceeding 5000 acres, and all international parks that were in

existence on August 7, 1977 (42 U.S.C. 7472(a)). In accordance with section 169A of the CAA, EPA, inconsultation with the Department of Interior, promulgated a list of 156 areas where visibility is identified as

an important value (see 44 FR 69122, November 30, 1979). The extent of a mandatory Class I area includessubsequent changes in boundaries, such as park expansions (42 U.S.C. 7472(a)). Although states and tribes

may designate as Class I additional areas which they consider to have visibility as an important value, therequirements of the visibility program set forth in section 1 69A ofthe CAA apply only to “mandatory

Class I Federal areas.” Each mandatory Class I Federal area is the responsibility of a “Federal Land

Manager” (FLM). (42 U.S.C. 7602(i))

2
The RHR addressed the combined visibility effects of various pollution sources over a

wide geographic region. 40 CFR 51.308(b) requires states to submit the first

implementation plan addressing regional haze visibility impairment no later than

December 17, 2007. Consequently, all 50 states, including those without Class I areas,

Washington, D.C., and the Virgin Islands, are required to submit Regional Haze SIPs 2.

The USEPA designated five Regional Planning Organizations (RPOs) to assist with the

coordination and cooperation needed to address the visibility issue. The Mid-Atlantic

Region Air Management Association (MARAMA), the Northeast States for Coordinated

Air Use Management (NESCAUM) and the Ozone Transport Commission (OTC)

established the Mid-Atlantic/Northeast Visibility Union (MANE-VU) 8regional planning

organization to coordinate efforts to address visibility impairment at seven Class I areas

located in the Mid-Atlantic and Northeast corridor: Acadia National Park, ME;Brigantine Wilderness, NJ; Great Gulf Wilderness, NH; Lye Brook Wilderness, VT;

Moosehorn Wilderness, ME; Presidential Range — Dry River Wilderness, NH; and

Roosevelt Campobello International Park, New Brunswick.

!‘

2Albuquerque/Bernalillo County in New Mexico must also submit a regional haze SIP to completely satisfy

the requirements of section 11 O(a)(2)(D) of the CAA for the entire State of New Mexico under the New

Mexico Air Quality Control Act (section 74-2-4).

Figure 1. Geographical Areas of Regional Planning Organizations

RéñaI Planning Organizations

3
The District of Columbia is a member of the Mid-Atlantic / Northeast Visibility Union

(MANE-VU) RPO. Members of MANE —VU are listed in Table 1.

Table 1 MANE-VU Members

Connecticut Pennsylvania

Delaware Penobscot Nation

District of Rhode Island

Columbia

Maine St. Regis Mohawk Tribe

Maryland Vermont

Massachusetts U.S. Environmental Protection

Agency*

New Hampshire U.S. National Park Service*

New Jersey U.S. Fish and Wildlife Service*

New York U.S. Forest Service*

*No1voting members

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p

ary 

mittees are the Technical Support Committee (TSC) charged with assessing

the nature and magnitude of the regional haze problem within MANE-VU, interpreting

the results oftechnical work, and report on such work to the MANE-VU Board. In

addition to the formal working committees, there are also three standing working groups

of the TSC. They are broken down by topic area: Emissions Inventory, Modeling, and

Monitoring/Data Analysis Workgroups.

Introduction to the District of Columbia’s Regional Haze State

Implementation Plan EPA promulgated a rule to address regional haze on July 1,

1999 (see 64 FR 35713), the RHR. The RHR addressed the combined visibility effects of

various pollution sources over a wide geographic region. 40 CFR 51.308(b) requires

states to submit the first implementation plan addressing regional haze visibility

impairment no later than December 17, 2007.

This SIP was developed based on consultations and work-products ofthe MidAtlantic/Northeast Visibility Union (MANE-VU) Regional Planning Organization (RPO). Itencompasses 1) monitoring strategies for evaluating visibility impacts, 2) baselines and - trends, 3) long-term strategies (LTS), 4) how D.C. meets its fair share ofthe “reasonable

progress goals” (RPG) towards reducing visibility impairment in Class I areas, and 5) Best

Available Retrofit Technology (BART). MANE-VU states agreed upon a 2 percent sulfate attribution to a Class I area in order for an upwind state to meet the definition of“significantly contributing” to visibility impairment for that Class I area. The study results

presented in Table 8.1 ofthe D.C. Haze SIP show that D.C. emission sources do not

significantly contribute to visibility impairment in any Class 1 area. Therefore, this SIPfocuses on how the D.C. control measures will help other MANE-VU states improve

visibility.

D.C. believes their Visibility SIP demonstrates that D.C. has met its BART, RPG and LTSobligations for the first visibility impairment planning period through existing D.C./Federal

regulations and on-the-books/on-the-way federal emission controls. In addition to extensive

consultation with the MANE-VU states, D.C. has consulted with Federal Land Managers

(FLMs) responsible for the Class I areas, and the EPA in the development of this SIP.

What Are The Components Of A Modeled Regional Haze

Demonstration?Modeling Process Overview

The goal ofthe regional haze program is to return to natural conditions by 2064, and

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The New York State Department of Environmental Conservation (NYSDEC) was the

lead agency for coordinating and running the modeling platform used by the MAVE-VURPO. Modeling centers responsible for running the platform included the NYSDEC, the

University ofMaryland at College Park (UMD), the Northeast States for Coordinated Air

Use Management (NESCAUM), the New Jersey Department of EnvironmentalProtection (NJ DEP), and the Virginia Department of Environmental Quality (VA DEQ).

Each modeling center was responsible for installing the modeling platform, conducting

diagnostic tests and completing a benchmark run to ensure accurate results.

The MANE-VU RPO used the Community Multi-scale Air Quality model (CMAQ)

version 4.5.1 as its photochemical grid model. The model uses simulations of chemicalreactions, emission ofPM2.5 and PM2.5precursors and a sophisticated meteorological

model (The Pennsylvania State University/National Center for Atmospheric Research

Mesoscale Meteorological Model (MM5)) to produce speciated PM2.5concentrations overthe eastern United States. The meteorological data used in the meteorological model was

for the 2002 base year. The photochemical grid model was run with the base year

meteorology and base year emissions to determine ifthe model performance was

satisfactory. Once the model performance was determined to be adequate, PM 2.5concentrations were modeled by running the model with projected emissions for 2018

and the original 2002 meteorology. The meteorology was held constant so that the results

of changing the emissions would not be influenced by changing meteorology.

The EPA modeling guidance 3recommends that modeling be used to develop relative

response factors for each of 6 components of particulate matter between a base period

(2000-2004) and a future 5-year period which will be reviewed in 2018. PM components

used for regional haze-related applications are:

-mass associated with sulfates;

-mass associated with nitrates;

-mass associated with organic carbon;

-mass associated with elemental carbon;-mass associated with fine soil (i.e., crustal material);

-mass associated with coarse particulate matter (i.e., PMio - PM2.5).

Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals

for Ozone, PM2.5, and Regional Haze, EPA -454/B-07-002, April 2007

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p

utu

e co

ce

t

at

o

s for each component on 

best

and

“worst” days. Future component concentrations are then inserted into the equation

relating light extinction to concentrations of particulate matter. The resulting estimates

for future light extinction on “best” and “worst” days are compared with observations

made during the base period to assess the future year visibility improvement and

demonstrate reasonable progress by 2018 in meeting the ultimate goal of natural visibilityconditions by 2064.

Steps Required in Modeling Future Year Visibility Improvement

The modeling guidance lists nine steps for preparing modeling to demonstrate reasonable

progress toward visibility improvement goals.

1. Develop a conceptual description ofthe problem to be addressed.

2. Select an appropriate model to support the demonstration.

3. Select appropriate meteorological time periods to model.

4. Choose an appropriate area to model with appropriate horizontal/vertical resolution

and establish the initial and boundary conditions that are suitable for the application.

5. Generate meteorological inputs to the air quality model.

6. Generate emissions inputs to the air quality model.

7. Run the air quality model with base case emissions and evaluate the performance.

Perform diagnostic tests to improve the model, as necessary.

8. Perform future year modeling (including additional control strategies, if necessary) and

use the results to calculate future year visibility and visibility improvement.

How Did The District Of Columbia Address All Of The

Components a Modeled Demonstration Of Future Year Visibility

Improvement?

The D.C. Haze SIP addresses each ofthe required elements of a modeling analysis used

to predict visibility improvement that is expected by 2018.

Conceptual Description of the Problem

A conceptual model describes how weather patterns affect the formation and transport of

PM25,accounting for emissions and photochemistry.

The conceptual model for the D.C. Haze SIP is described in Appendix 0 ofthe D.C.Haze SIP in a document entitled: The Nature ofthe Fine Particle and Regional Haze Air

Quality Problems in the Mid-Atlantic Northeast Visibility Union(MANE-VU) Region. A

Conceptual Description (NESCA UM), November 2006. This document was prepared by

Northeast States for Coordinated Air Use Management (NESCAUM) for use by the OTC

7
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Episodes

of high summertime sulfate concentrations are consistent with stagnant meteorological

flow conditions upwind of the Philadelphia-Wilmington, PA-NJ-DE PM2.5NAA and the

accumulation of airborne sulfate (via atmospheric oxidation of S02) followed by longrange transport ofsulfur emissions from industrialized areas within and outside the area.

National assessments have indicated that in the winter, sulfate levels in urban areas arehigher than background sulfate levels across the eastern U.S., indicating that the local

urban contribution to wintertime sulfate levels is significant relative to the regional

sulfate contribution from long-range transport. A network analysis for the winter of 2002suggests that the local enhancement ofsulfate in urban areas ofthe MANE-VU region

ranges from 25 to 40% and that the long-range transport component of PM2.5 sulfate is

still the dominant contributor in most eastern cities.

In the winter, urban organic carbon (OC) and sulfate each account for about a third ofthe

overall PM2.5 mass concentration observed in Philadelphia and New York City areas.

Nitrate also makes a significant contribution to urban PM2.5 levels observed in the

northeastern United States during the winter months. Wintertime concentrations of OC

and nitrate in urban areas can be twice the average regional concentrations of these

pollutants, indicating the importance of local source contributions. This is likely becausewinter conditions are more conducive to the formation of local inversion layers which

prevent vertical mixing. Under these conditions, emissions from tailpipe, industrial and

other local sources become concentrated near the Earth’s surface, adding to background

pollution levels associated with regionally transported emissions.

In summary, the presence of fine particulate matter in ambient air significantly obscures

visibility during most parts ofthe year at sites across the MANE-VU region. Particle

pollution generally, and its sulfate component specifically, constitute the principle driver

for regional visibility impacts. While the broad region experiences visibility impairment,

it is most severe in the southern and western portions of MANE-VU that are closest tolarge power plant S02 sources in the Ohio River and Tennessee Valleys.

The presence or absence of regional sulfate almost exclusively drives summer visibilityimpairment, whereas winter visibility depends on a combination ofregional and local

influences coupled with local meteorological conditions (inversions) that lead to theconcentrated build-up of pollution.

Sulfate is the key particle constituent from the standpoint of designing control strategies

to improve visibility conditions in the northeastern United States. Significant further

8
)

Modeling Platforms

Currently two regional-scale air quality models have been evaluated and used by

NESCAUM to perform air quality simulations. These are the Community Multi-scale

Air Quality modeling system (CMAQ; Byun and Ching, 1999) and the Regional

Modeling System for Aerosols and Deposition (REMSAD; SAl, 2002). CMAQ was

developed by USEPA, while REMSAD was developed by ICF Consulting/Systems

Applications International (ICF/SAI) with USEPA support. CMAQ has undergone

extensive community development and peer review (Amar et al., 2005) and has been

successfully used in a number ofregional air quality studies (Bell and Ellis, 2003;

Hogrefe et al., 2004; Jimenez and Baldasano, 2004; Mao and Talbot, 2003; Mebust et al.,2003). REMSAD has also has been peer reviewed (Seigneur et al., 1999) and used by

USEPA for regulatory applications (www.epa. gov/otaq/regs!hd2007/frm/r00028 .pdf and

www .epa gov ‘clearskies’air quality tech.html) to study ambient concentrations and

deposition ofsulfate and other PM species.

Five modeling centers, worked collectively to maximize efficiency of computing

resources in MANE-VU for SIP modeling. These centers include NY DEC, NJ

DEP/Rutgers, VA DEQ, UMD, and NESCAUM.

Meteorological Time Periods Used in the Modeling

All of 2002, which represents the baseline period from 2000 to 2004, was included in

CMAQ modeling. 2002 was divided into five periods. UMD is responsible for modeling

the period from January 1 to February 28; NJ DEP/Rutgers are responsible for the period

from March ito May 14; NY DEC is responsible for the period from May 15 to

September 30; VA DEQ is responsible for the period from October 1 to October 31; and

NESCAUM is responsible for the period from November 1 to December 31. Each period

uses a 15 day spin up run to minimize the impact ofthe default initial concentration

fields. Each group performs CMAQ simulations on its period for a series ofscenarios

including 2002 Base Case, 2009 Base Case, 2018 Base Case, 2009 Control Case, and

2018 Control Case. All scenarios adopt the same meteorological field (2002) and

boundary conditions, varying only emission inputs. To ensure consistency between

modeling groups, a benchmark test was conducted by each group.

Meteorological Data Used in the Air Quality Model

The MANE-VU states decided to use a prognostic meteorological model that provides

life-like meteorological inputs to the photochemical grid model. The Pennsylvania State

University/National Center for Atmospheric Research Mesoscale Meteorological Model

9
south directions, respectively. A 12-km inner domain was selected to better characterizeair quality in MANE-VU and surrounding RPO regions. This domain covers the eastern

region, which includes the northeastern, central, and southeastern U.S., as well as

southeastern Canada. It extends from 660W-94in longitude and 290N50in

latitude with 172 X 172 grid cells (Figure 3).

I

Vertical resolution is the number of layers and the size of each layer in the model. The

layers in the photochemical grid model were set up to be compatible with the model that

produced weather conditions for the photochemical grid model. The vertical resolution

used in the modeling exercise followed EPA’s modeling guidance and thereforeadequately represents the atmosphere where PM2.5is emitted, forms and is transported.

Figure 3. Modeling domains used in MANE-VU air quality modeling studies with

CMAQ. Outer (blue) domain grid is 36 km and inner (red) domain is 12 km grid.The gridlines are shown at 180 km intervals (5 x 5 36 km cells/15 x 15 12 km cells).

102

IL 4.z

Eastern Modeling Domain (12km grid)

RPO National Domain (36km grid) -fr

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y

uture year inventories were developed for the years

2009, 2012 and 2018 based on the 2002 base year. These future year emission inventories

include emissions growth due to projected increases in economic activity as well as the

emissions reductions due to the implementation of control measures.

The 2002 emissions were first generated by the individual states in the MANE-VU area.

MARAMA then coordinated and quality assured the 2002 inventory data. The 2002

emissions from non-MANE-VU areas within the modeling domain were obtained from

other Regional Planning Organizations for their corresponding areas. These Regional

Planning Organizations included the Visibility Improvement State and Tribal Associationofthe Southeast (VISTAS), the Midwest Regional Planning Organization and the Central

Regional Air Planning Association.

Version 3 ofthe 2002 base year emission inventory was used in the regional modeling

exercise. Technical support documentation for the MANE-VU 2002 base inventory is

presented in Appendix D ofthe D.C. Haze SIP. This document explains the data sources,

methods, and results for preparing this version ofthe 2002 base year criteria air pollutant

and ammonia emissions inventory. Documentation for the future year estimations of

EGUs is presented in Appendix F ofthe D.C. Haze SIP. Documentation for the future

year estimations ofthe remaining source sectors (non-EGU sectors) is presented inAppendix E ofthe D.C. Haze SIP.

Model Performance Evaluation

NESCAUM evaluated the 2002 annual 12 km resolution meteorological fields generated by

MM5 using ENVIRON’s METSTAT program. Model results ofsurface wind speed, wind

direction, temperature, and humidity were paired with measurements from EPA’s Clean Air

Status and Trends Network (CASTNET) and National Center for Atmospheric Research’s

Techniques Data Laboratory (TDL) network by hour and by location and then statistically

compared. Based on this statistical comparison between model prediction and data from the

two networks for wind speed, wind direction, temperature, and humidity, MM5 performs

well. An acceptable small bias, high index of agreement and strong correlation with

CASTNET and TDL data are shown. Since MM5 uses TDL data for nudging, the model

predictions are in better agreement with TDL data than with CASTNET data. MM5 performs

better in Midwest and Northeast than Southeastern US.

CMAQ modeling was conducted for year 2002 by cooperative modeling efforts from

NYDEC, U’D, NJDEP, Rutgers, VADEP, and NESCAUM. CMAQ Performance for PM

2.5 species and visibility was examined based on this CMAQ run on a 12 km resolution

domain. Measurements from IMPROVE and STN networks were paired with model

predictions by location and time for evaluation. The goal and the criteria for PM2.s evaluation

11
fine soil has the majority ofpoints outside the criteria curve, but there were some sites still

within the goal. For the MANE-VU region, CMAQ performs best for PM25 sulfate, followed

by PM2.5, EC, nitrate, OC, and then fine soil. Regional haze modeling also requires CMAQ

performance evaluation for aerosol extinction coefficient (Bext) and the haze index. Modeled

daily aerosol extinction at each improve site was calculated following the IMPROVE formula

with modeled daily PM2.5 species concentration and relative humidity factors from

IMPROVE. The approach used natural background visibility estimates and the haze indexfollowing EPA Guidance. The modeled Bext showed a near 1:1 linear relationship (slope of0.78 and r of 0.46) with IMPROVE observed Bext. The regression excluded three points from

July 7, 2002; the monitors were directly impacted by Canadian fires whose emissions were

not modeled.

Uniform Rate of Progress

The key difference between SIPs from States with Class I areas and those States without

Class I areas, but may have sources that impact visibility on Class I areas, is thecalculation ofthe baseline and natural visibility for their Class I areas and the

determination of uniform rate ofprogress goals - expressed in deciviews - that providefor reasonable progress towards achieving natural visibility by 2064. It is the Class I

states responsibility assess these calculations. The Class I States must also consult with

those States, which may reasonably be anticipated to cause or contribute to visibility

impairment in their Class I areas (40 CFR 5 1.308 (d)(1)(i-vi)).

The baseline visibility conditions are calculated for the baseline period between 2002 and

2004. The average impairment for the 20 most and 20 least impaired days are determined

for each calendar year and compiled into the average ofthree annual averages (40 CFR51.308 (d)(2)(i)). The natural visibility conditions are determined for the same baseline

period with the most and least impaired days determined by available monitoring data or

an appropriate data analysis technique (40 CFR 5 1.308 (d)(iii-iv)).

U.S. Environmental Protection Agency (EPA) released guidance on June 7, 2007 to use

in setting reasonable progress goals. The goals must provide improvement in visibility for

the most impaired days, and ensure no degradation in visibility for the least impaired

days over the State Implementation Plan (SIP) period. The State must also provide an

assessment ofthe number of years it would take to attain natural visibility condition ifimprovement continues at the rate represented by the reasonable progress goal. Figure 4

illustrates an example of how Uniform Rate of Progress is calculated.

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Uniform Rate al8dV l4yr of Progress 60 yr

— 4.2 dv Over First

Planning Penod

Natural

Conditions—*,

10 —, . , , , , . .

2004 2024 2034 2044 2054 2064

Year

Reasonable Progress Goals

Since there are no Class I areas within the District of Columbia, the District is not

required to establish reasonable progress goals for any Class I areas. Under 40 CFR

Section 51.308(d)(1)(iv), States with Class I areas must consult with other States as they

develop goals for their Class I areas. As a member of MANE-VU, the District worked incooperation with MANE-VU Class I states as those states established reasonable progress

goals for their Class I areas. MANE-VU also sponsored meetings and conference calls to

facilitate consultation with states in the VISTAS RPO, including Virginia and West

Virginia.

Modeled Visibility Projections for 2018

The CMAQ air quality model was used to simulate base period emissions and future

emissions. The modeling results for the base year period (2002) and the year

representing the end of the first planning period (2018) are used to develop relative

response factors (RRF) for each component of particulate matter identifie&previously in

this TSD. The relative response factors are multiplied by the measured species

concentration data during the base period (for the measured 20% best and worst days).

This results in daily future year species concentrations data. The projected concentrations

are then used to derive daily visibility in deciviews and are averaged across all best and

worst days to create the projected future visibility. The results of this procedure are

plotted along with the uniform progress glide slope in Figure 3-1 through Figure 3-7 in

Appendix I of the D.C. Haze SIP.

2014

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ct of Columbia.

Summary of Photochentical Grid Modeling Results

In summary, the photochemical grid modeling, presented in the D.C. Haze SIP, followEPA’s modeling guidance and is acceptable to EPA. All sites included in the MANE-VU

analysis are projected to meet or exceed the uniform rate of progress goal for 2018 on the

20 percent worst days. In addition, no site anticipates increases visibility impairment

relative to the baseline on the 20 percent best days.

Contribution Assessment

The 1999 Regional Haze Rule requires States and Tribes to submit State Implementation

Plans (SIPs) to the U.S. Environmental Protection Agency (USEPA) for approval by

January 2008 at the latest. The haze SIPs must include a “contribution assessment” to

identify those states or regions that may be influencing specially protected federal lands

known as Federal Class I areas. 4These states or regions would then be subject to the

consultation provisions ofthe Haze Rule. The Haze Rule also requires a “pollutionapportionment” analysis as part ofthe long-term emissions management strategy for each

site.

As described in the Conceptual Description portion ofthis TSD, sulfate alone accounts

for anywhere from one-halfto two-thirds of total fine particle mass on the 20 percent

haziest days at MANE-VU Class I sites. As a result ofthe dominant role ofsulfate in the

formation ofregional haze in the Northeast and Mid-Atlantic region, MANE-VUconcluded that an effective emissions management approach would rely heavily on

broad-based regional S02 control efforts in the eastern United States.

Area of Influence for MANE-VU Class I Areas

The key difference between SIPs from States with Class I areas and States without ClassI areas is the calculation of the baseline and natural visibility for their Class I areas, and

the determination ofreasonable progress goals. Class I States calculate baseline visibility

conditions for the period between 2002 and 2004. The average impairment for the mostand least impaired days are determined for each calendar year and compiled into the

Class I designation applies to national parks exceeding 6,000 acres, wilderness areas and national

memorial parks exceeding 5,000 acres, and all international parks that were in existence prior to 1977. In

the MANE-VU area, this includes: Acadia National Park, Maine; Brigantine Wilderness (within the Edwin

B. Forsythe National Wildlife Refuge), New Jersey; Great Gulf Wilderness, New Hampshire; Lye Brook

Wilderness, Vermont; Moosehorn Wilderness (within the Moosehorn National Wildlife Refuge), Maine;Presidential Range — Dry River Wilderness, New Hampshire; and Roosevelt Campobello International

Park, New Brunswick.

14
e 

C

ass I areas located in the Mid

Atlantic and Northeast. The District of

Columbia does not have a Class I area located within its borders. As a result, the Rule

requires D.C., in consultation with MANE-VU and others, to identify where its emissions

are most likely to influence visibility in Class I areas. In order to identify states whose

emissions are most likely to influence visibility in MANE-VU Class I areas, MANE-VU

prepared the Contributions to Regional Haze in the Northeast and Mid-Atlantic United

States (Contribution Assessment). The full report can be found in Appendix K oftheD.C. Haze SIP.

Based on that work, MANE-VU concluded that it was appropriate to define an “Area ofInfluence” (AOl) including all of the states participating in MANE-VU pjjother states

outside MANE-VU for which modeling indicated they contributed at least two percent

(2%) ofthe sulfate ion in MANE-VU Class I areas in 2002. The Visibility Improvement

State and Tribal Association ofthe Southeast (VISTAS) 5also conducted an AOl analysis,

which used a level of one percent (1%) to assess whether an upwind state significantly

contributed.

The primary contribution assessment tool used in the D.C. Haze SIP was the Regional

Modeling System for Aerosols and Deposition (REMSAD) (SAl, 2002). A significant

feature ofthe REMSAD work used to evaluate regional contributions is that NESCAUM

reprocessed the S02 emission data from each state to take advantage ofREMSAD’s

tagging capabilities. Thus, all SO2emissions included in the model for the eastern half of

the country were tagged according to state of origin, and emissions from Canada and the

boundary conditions were also tagged. This allowed for a rough estimation ofthe total

contribution from elevated point sources in each state to simulated sulfate concentrationsat eastern receptor sites. Using identical emission and meteorological inputs to those

prepared for the CMAQ SIP modeling platform, described earlier in this TSD, REMSAD

was used to simulate the annual average impact of each state’s SO 2emission sources onthe sulfate fraction of PM over the northeastern United States. A more in-depth 2.5

description of the REMSAD modeling used for contribution assessment can be found in

Appendix K ofthe D.C. Haze SIP.

The REMSAD contribution assessment modeling contained in the D.C. Haze SIP

conforms to EPA modeling requirements and is acceptable to EPA.

VISTAS is comprised ofthe following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North

Carolina, South Carolina, Tennessee, Virginia, West Virginia, the Eastern Band of Cherokee Indians, and

Knox County, TN

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VU states with Class I areas for contributing to meet the reasonable progress goals for2018.

With respect to sulfate, the Contribution Assessment estimated emissions from withinMANE-VU in 2002 were responsible for about 25-30 percent of the sulfate at Class I areas

located within and nearby to the MANE-VU region (see Chapter 8 of Appendix K ofthe

D.C. Haze SIP). The contribution ofsulfate at these Class I areas from other regions, Canada,

and outside the modeling domain were also significant.

As described in Table 8.1 ofthe D.C. Haze SIP, the District emissions do not

significantly contribute to visibility degradation in any Class I area.

BART

On July 6, 2005 (70 FR 39104) EPA finalized 40 CFR 51 — Regional Haze Regulations

and Guidelines for Best Available Retrofit Technology (BART) Determinationsaddressing the issues from the Circuit Court decisions. The BART requirements were

most recently updated on October 5, 2006. BART is defined as an emission limitation

based on the degree ofreduction achievable through the application ofthe best system of

continuous emission reduction for each pollutant, which is emitted by a BART-eligiblesource. The changes to the rule included how the States would identify the best system ofcontinuous emission control technology and by which States can consider an individualfacility’s contribution to regional haze when determining to require controls, and what

the level of control should be met. The rule changes also clarified the requirements

associated with demonstrating how emissions trading or alternative programs may be

used as an alternative to applying Best Available Retrofit Technology (BART)

Requirements.

Congress defined sources potentially subject to BART - as major stationary sources,

including reconstructed sources; from one of 26 identified source categories whichincluded utility and industrial boilers, and large industrial plants such as pulp mills,

refineries and smelters; which have the potential to emit 250 tons per year or more of any

air pollutant, and which were placed in operation between August 1962 and August 1977.

[CAA 169A (b)(2)(A) & (g)(7)].

District of Columbia Sources Subject to BARTBenning Road Generating Station (BRGS)

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Summary of EPA

s Technical Findings

The technical analyses and modeling used to assess uniform rate of progress and to

support the LTS were successfully developed consistent with EPA’s interim and final

modeling guidance. All MANE-VU sites are projected to meet or exceed the uniform

rate of progress goal for 2018 on the 20 percent worst days. In addition, no site

anticipates increases visibility impairment relative to the baseline on the 20 percent best

days. EPA finds the MANE-VU technical modeling analyses presented in the D.C. Haze

SIP to be acceptable because the models that were used were applied according to EPA

modeling guidance.

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