# Contributions to Regional Haze in

the Northeast and Mid-Atlantic

United States

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

Prepared by NESCAUM For the Mid-Atlantic/Northeast Visibility Union (MA NE-VU)



# August 2006 Figure III-21 2002 Annual Average PM 2.5 , Sulfate, Nitrate and Total Carbon for MANE-VU based on IMPROVE and STN data. Mass data are supple mented by the FRM

network

CONTRIBUTION OF RPO TO PM SO4 IN CLASS I AREAS

0 10 20 30 40 50 60 70 80 90 100

Brigantine, NJ Acadia, ME Lye Brook, VT Shenandoah,

VAPercent MANE-VU LADCO VISTAS CenRAP Esurface Contribution to PM Sulfate in Brigantine, NJ

CT

0.5% LA

0.3% MS

0.3%

MO

0.9% AR

0.2% ME

0.1%

IA

0.6% MN

0.5% TX

0.7%

MA

1.0% SC

0.9%

WI

1.3% AL

1.2% GA

1.6% NH

0.3% DC

0.0% RI

0.0% VT

0.0%

DE

2.0%

TN

1.6% IL

2.2% KY

2.2%
NC

2.9% MI

2.9% NJ

3.5% IN

3.9% VA

4.0% MD

4.0% WV

4.1% NY

4.3% OH

9.2% PA

11.2%

Other US Elevated

Emission

1.9% Surface Emission +

All Canada & Mexico

Emission

30% AIRS vs. CMAQ in PA,MD,DE,DC,VA,WV,NC during August 9~16, 2002

0 25 50 75 100

-75 -50 -25 0 25 50 75

Fractional Bias (%)Fractional Error (%) . . O3 hourly

O3 daytime hourly

O3 diurnal max

O3 daily mean

15%/35%

30%/50%

60%/75%

### Members of NESCAUM Board

Arthur Marin, Executive Director

NESCAUM

Anne Gobin, Bureau Chief Connecticut Department of Environmental Protection, Bureau of Air Management

James P. Brooks, Bureau Director Maine Department of Environmental Protection, Burea u of Air Quality

Barbara Kwetz, Director Massachusetts Department of Environmental Protectio n, Bureau of Waste Prevention

Robert Scott, Director New Hampshire Department of Environmental Services, Air Resources Division

William O’Sullivan, Director New Jersey Department of Environmental Protection, Office of Air Quality Management

David Shaw, Director New York Department of Environmental Conservation, Division of Air Recourses

Stephen Majkut, Chief Rhode Island Department of Environmental Management , Office of Air Resources

Richard A. Valentinetti, Director Vermont Department of Environmental Conservation, A ir Pollution Control Divisionii 

# Contributions to Regional Haze in

the Northeast and Mid-Atlantic

United States

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

Prepared by
NESCAUM



# August 2006iii



# Contributions to Regional Haze in the Northeast

and Mid-Atlantic United States Mid-Atlantic/Northeast Visibility Union (MANE-VU)

Contribution Assessment

Project Director

Gary Kleiman, NESCAUM

Program Officer

Marcia Spink, U.S. EPA

Editors

John Graham, NESCAUM Gary Kleiman, NESCAUM Marika Tatsutani, NESCAUM Paul Miller, NESCAUM

P r i n c i p a l C o n t r i bu t o r s ( li s t e d a l p h a b e t i c a l l y b y c h a p t er )

Chapter 1: Gary Kleiman, NESCAUM

Chapter 2: Gary Kleiman, NESCAUM

Chapter 3: George Allen, John Graham, Alan Leston, NESCAUM

Chapter 4: Michael Borucke, John Graham, NESCAUM, Serpil Kayin,

MARAMA, Jung-Hun Woo, NESCAUM

Chapter 5: Serpil Kayin, MARAMA Iyad Kheirbek, Gary Kleiman,

NESCAUM, Rich Poirot, VT DEC.

Chapter 6: John Graham, Shan He, Emily Savelli, Ju ng-Hun Woo,

NESCAUM

Chapter 7: Mark Garrison, ERM, Dan Riley, Paul Wish inski, VT DEC

Chapter 8: All of the above

Chapter 9: Gary Kleiman, NESCAUMiv



## Acknowledgments

NESCAUM gratefully acknowledges the support of the United States Environmental Protection Agency. Funding for this report was prov ided through the MidAtlantic/Northeast Visibility Union (MANE-VU) Regio nal Planning Organization (Grant No. X-983799-01-0) to the Ozone Transport Commissio n.

This report is the result of a joint effort by staf f of the MANE-VU and its member states.

NESCAUM also thanks the following individuals and o rganizations for providing comments on this report:

Tom Downs, Maine Department of Environmental Protec tion

VISTAS

MOG

Dominion Power

AMC Ann McWilliams, EPA Region 1

Printed: October 2006vvi 

### TABLE OF CONTENTS

Acknowledgments....................................

................................................... ....................... iv

Units, Symbols, Acronyms ...........................

................................................... .................. xi

Executive Summary..................................

................................................... .................... xiv

1. Introduction....................................

................................................... ......................... 1-1

2. Conceptual Model of Regional Haze in the MANE-VU Region .............................. 2-1 2.1. Visibility Effects of Particulate Matter (PM).

................................................... 2-1 2.2. Chemical Composition of Particulate Matter in MANE-VU ........................... 2-1 2.3. Geographic Considerations and Attribution of P M/Haze Contributors............ 2-2

2.4. Seasonal differences..........................

................................................... ............. 2-5 2.5. Implications for control strategies ...........

................................................... ...... 2-7 2.6. Summary.......................................

................................................... ............... 2-10

3. Overview of Monitoring Results ..................

................................................... .......... 3-1

3.1. Baseline Conditions ...........................

................................................... ............ 3-1 3.1.1. Preview of revised IMPROVE Algorithm for aer osol extinction............... 3-5 3.2. 2002 Monitoring Data..........................

................................................... .......... 3-9

3.2.1. Sulfate .....................................

................................................... ................. 3-9

3.2.2. Southwest-Northeast Gradient................ .................................................. 3 -10 3.2.3. Seasonality .................................

................................................... ............ 3-12

3.2.4. Seasonal Mechanisms .........................

................................................... ... 3-13

3.3. RAIN data .....................................

................................................... ............... 3-15

4. Haze-Associated Pollutant Emissions.............

................................................... ........ 4-1 4.1. Emissions Inventory Characteristics...........

................................................... ... 4-1 4.1.1. Sulfur Dioxide (SO 2 ).................................................. ................................. 4-1 4.1.2. Volatile Organic Compounds (VOC) ............ ............................................. 4-2 4.1.3. Oxides of Nitrogen (NO X ) .................................................. ........................ 4-5 4.1.4. Primary Particulate Matter (PM 10 and PM 2.5 )............................................. 4-7 4.1.5. Ammonia Emissions (NH 3 ) .................................................. ...................... 4-9

4.2. Contribution Assessments Based on Emissions In ventories .......................... 4-12 4.2.1. Sulfur Dioxide Emissions Divided by Distance ....................................... 4-12 4.2.2. Emissions times Upwind Probability.......... .............................................. 4-17

5. Data Analysis Techniques........................

................................................... ............... 5-1 5.1. Trajectory Analysis...........................

................................................... ............. 5-1 5.1.1. Incremental Probability.....................

................................................... ....... 5-2 5.1.2. Clustered Back-Trajectories.................

................................................... .... 5-3 5.1.3. Cluster-Weighted Probability ................

................................................... .. 5-4 5.2. Source Apportionment Models and Ensemble Traje ctory Analysis of Source Apportionment Results ..............................

................................................... ............... 5-5 5.3. Trajectory Model Evaluation and Future Work... ............................................. 5-7vii

6. Chemical Transport Models.......................

................................................... ............. 6-1 6.1. Chemical Transport Model (CTM) platforms – Ove rview............................... 6-1 6.2. Preliminary Results...........................

................................................... ............. 6-3

7. Lagrangian Dispersion Models....................

................................................... ........... 7-1

7.1. Platform Overview.............................

................................................... ............ 7-2 7.2. CALPUFF Modeling Results for Individual Source s....................................... 7-2 7.3. CALPUFF Modeling Results Overview............. .............................................. 7-3

7.4. CALPUFF Results for Ranked State Sulfate Contr ibutions............................. 7-3 7.5. Future work and potential uses of CALPUFF resu lts for BART determinations..

.................................................. ................................................... .................... 7-11

8. Synthesis of Results Using Different Source Asse ssment Techniques ..................... 8-1

8.1. Ranked Contribution...........................

................................................... ........... 8-1

9. Conclusion ......................................

................................................... ........................ 9-1

Appendix A: Application of Trajectory Analysis Meth ods to Sulfate Source Attribution

Studies in the Northeast U.S.

Appendix B: Source Attribution by Receptor-Based Me thods

Appendix C: Chemical Transport Model Results for Su lfate Source Attribution Studies in

the Northeast U.S.

Appendix D: Development of Parallel CALPUFF Dispers ion Modeling Platforms for

Sulfate Source Attribution Studies in the Northeast U.S.viii FIGURES

Figure 2-1. Summer time at Mt Washington ...........

................................................... ..... 2-7 Figure 2-2. Wintertime in Boston ...................

................................................... .............. 2-7 Figure 3-1. Comparison of Old and New Algorithms fo r Baseline Worst Days............. 3-7 Figure 3-2. Comparison of Old and New Algorithms fo r Baseline Worst Days............. 3-8 Figure 3-3. New Jersey Urban Area Compared to an Up wind Background Site ............ 3-9 Figure 3-4. MANE-VU FRM PM 2.5 statistics along a southwest to northeast axis.... ... 3-10 Figure 3-5. 2002 Seasonal average SO4 based on IMPR OVE and STN data............... 3-11 Figure 3-6. 2002 Annual average PM 2.5 , sulfate, nitrate and total carbon for MANE-VU based on IMPROVE and STN data. Mass data are supple mented by the FRM

network............................................

................................................... ................. 3-11 Figure 3-7. Moving 60-day average of fine aerosol m ass concentrations based on longterm data from two northeastern cities.............

................................................... 3-12 Figure 3-8. 30-day average fine aerosol mass concen trations from eight northeastern

cities .............................................

................................................... .................... 3-12 Figure 3-9. Mean hourly fine aerosol concentrations during the summer season ......... 3-14 Figure 3-10. Mean hourly fine aerosol concentration s during the winter season.......... 3-14 Figure 3-11. 2-Hour Reconstructed scattering at Aca dia, Maine using semi-continuous

SO 4 and OC data for the third quarter of 2004 ......... .......................................... 3-19 Figure 4-1. State Level Sulfur Dioxide Emissions ...

................................................... .... 4-3 Figure 4-2. SO 2 (Bar graph: Percentage fraction of four source cat egories, Circle: Annual

emissions amount in 10 6 tons per year) .................................... ............................ 4-3 Figure 4-3. VOC (Bar graph: Percentage fraction of four source categories, Circle:

Annual emissions amount in 10 6 tons per year).................................... ................ 4-4 Figure 4-4. State Level Nitrogen Oxides Emissions..

................................................... ... 4-6 Figure 4-5. NO X (Bar graph: Percentage fraction of four source cat egories, Circle:

Annual emissions amount in 10 6 tons per year).................................... ................ 4-6 Figure 4-6. State Level Primary PM 10 Emissions .......................................... .................. 4-8 Figure 4-7. State Level Primary PM 2.5 Emissions.......................................... .................. 4-8 Figure 4-8. Primary PM 10 (Bar graph: Percentage fraction of four source cate gories,

Circle: Annual emissions amount in 10 6 tons per year).................................... .. 4-10 Figure 4-9. Primary PM 2.5 (Bar graph: Percentage fraction of four source cat egories, Circle: Annual emissions amount in 106 tons per yea r) ..................................... 4-10 Figure 4-10. State Level Ammonia Emissions .........

................................................... .. 4-11 Figure 4-11. NH 3 (Bar graph: Percentage fraction of four source cat egories, Circle:

Annual emissions amount in 10 6 tons per year).................................... .............. 4-12 Figure 4-12. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on emissions divided by distance (Q/d) result s......................................... 4-16 Figure 4-13. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I receptors based on emissions divided by distance (Q/d) result s......................................... 4-16 Figure 4-14. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on emissions times upwind probability (E x UP ) results.......................... 4-19 Figure 4-15. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I receptors based on emissions times upwind probability (E x UP ) results.......................... 4-19ix

Figure 5-1. Incremental Probability (Top 10% Sulfat e) at Acadia, Brigantine and Lye

Brook 2000-2004 ....................................

................................................... ........... 5-3 Figure 5-2. Incremental Probability (Bottom 10% Sul fate) at Acadia, Brigantine and Lye

Brook 2000-2004 ....................................

................................................... ........... 5-3 Figure 5-3. Proximity based cluster with the highes t associated sulfate value for three sites in the MANE-VU region, Acadia (sulf=3.19 μ g/m 3 ), Brigantine (sulf=6.79 μ g/m 3 ), and Lye Brook (sulf=3.92 μ g/m 3 ) .......................................... 5-4 Figure 5-4. Cluster Weighted Probability at Acadia, Brigantine and Lye Brook 2000-

2004...............................................

................................................... ..................... 5-5 Figure 5-5. Sulfate characteristics of “secondary s ulfate” (coal) sources identified at

eastern sites ......................................

................................................... .................. 5-6 Figure 5-6. Incremental Probabilities for "Secondar y Sulfate" (Coal) Sources in Eastern

U.S................................................

................................................... ...................... 5-7 Figure 5-7. Comparison of probability fields for ob served sulfate, “sulfate” source profiles for seven eastern sites and reconstructed deciviews................................ 5-7 Figure 5-8. Comparison of IP contours generated by ATAD and HYSPLIT (both EDAS and FNL) for sulfate, nickel and selenium at Lye Br ook...................................... 5-8 Figure 5-9. ATAD Transport Layer Depth (TLD) by mon th. Color indicates the length of time prior to arriving at the receptor. ............

................................................... ..... 5-9 Figure 6-1. Modeling domains used in NESCAUM air qu ality modeling studies. ......... 6-2 Figure 6-2(a) and (b): CMAQ Integrated SIP Modelin g Platform simulation results for 2002, 2009 and 2018 relative to Uniform Progress Go als calculated according to current USEPA guidance .............................

................................................... ...... 6-3 Figure 6-3. REMSAD modeling tagging schemes. ...... .................................................. 6 -5 Figure 6-4. 2002 Eastern states’ contribution to an nual PM sulfate in Acadia, ME ....... 6-6 Figure 6-5. 2002 Eastern states’ contribution to an nual PM sulfate in Brigantine, NJ ... 6-6 Figure 6-6. 2002 Eastern states’ contribution to an nual PM sulfate in Lye Brook, VT .. 6-7 Figure 6-7. 2002 Eastern states’ contribution to an nual PM sulfate in Shenandoah, VA6-7 Figure 6-8. Comparison of Sulfate Extinctions on 20 % Worst Visibility Days.............. 6-8 Figure 7-1. CALPUFF modeling domain utilized by MAN E-VU .................................. 7-1 Figure 7-2. Correlation between MM5-based source co ntributions (Maryland/ERM) and NWS/rawindsonde-based source contributions (VT DEC) for common EGUs modeled at four receptor sites in or near MANE-VU.. ......................................... 7-4 Figure 7-3a. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on observation-based (VT) CALPUFF results.... ........................................ 7-9 Figure 7-4a. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on MM5-based (MD) CALPUFF results............ ...................................... 7-10 Figure 8-1(a-d). Comparison of normalized (percent contribution) results using different techniques for ranking state contributions to sulfa te levels at the MANE-VU Class

I sites ............................................

................................................... ...................... 8-7 Figure 8-2. Estimated RPO contributions to sulfate concentrations at Class I areas using different assessment techniques ....................

................................................... ... 8-10 Figure 8-3. Ranked contributions of states to ambie nt sulfate concentrations at Acadia National Park, Maine. ..............................

................................................... ........ 8-11 Figure 8-4. Ranked contributions of states to ambie nt sulfate concentrations at Brigantine Wilderness Area, New Jersey. .......................

................................................... .. 8-12x TABLES

Table 3-1. Fine mass and percent contribution for 2 0% worst days................................ 3-2 Table 3-2. Particle extinction and percent contribu tion for 20% worst days................... 3-2 Table 3-3. Natural background and baseline calculat ions for select Class I areas .......... 3-2 Table 3-4. Percent particle B ext reduction needed to meet uniform progress ......... ......... 3-3 Table 3-5. Mass reductions required on 20% worst da ys based on extinction estimates in

Table 3-4 ..........................................

................................................... .................. 3-4 Table 3-6. Estimated Mass Reduction on an Average D ay ............................................. 3-5 Table 3-7. Aerosol extinction by specie for 20% wor st days .......................................... 3- 6 Table 3-8. Aerosol extinction by specie for 20% bes t days............................................. 3-6

Table 4-1. 2002 SO 2 CALPUFF-scaled Emissions over Distance Impact ( μ g/m 3 )....... 4-15

Table 4-2. 2002 SO 2 Upwind Probability (percent contribution)......... ......................... 4-18 Table 7-1. CALPUFF Overall Modeling Summary ........ ................................................ 7-3 Table 7-2a. Sulfate Ion Impacts by State (Annual Av erage)........................................... 7- 5 Table 8-1. Annual Average Sulfate Impact from REMSA D (%).................................... 8-2 Table 8-2. Annual Average Sulfate Impact from Q/D ( %).............................................. 8-3 Table 8-3. Annual Average Sulfate Impact from CALPU FF (NWS Observations) (%)8-4 Table 8-4. Annual Average Sulfate Impact from CALPU FF (MM5) (%) ...................... 8-5 Table 8-5. Annual Average Sulfate Impact from perce nt time upwind method (%)....... 8-6 Table 8-6. Ranked Contributing States to Acadia Sul fate ............................................... 8-9xi



## Units, Symbols, Acronyms

Acronyms

AGL – Above Ground Level ATAD – Atmospheric Transport and

Diffusion Model ARL – Air Resources Laboratory (NOAA)

BART – Best Available Retrofit Technology BEIS – Biogenic Emission Inventory System BRAVO - Big Bend Regional Aerosol and Visibility Observational study

CAIR – Clean Air Interstate Rule CALMET – Meteorological model for developing input data for CALPUFF CALPUFF – Lagrangian dispersion model developed by EarthTech, Inc. CAMNET – Northeast Visibility Camera

Network

CASTNet – Clean Air States and Trends

Network

CEMS – Continuous Emissions Monitoring System CENRAP – Central Regional Air Planning

Association CFR – Code of Federal Regulations CMAQ – Community Multi-scale Air Quality Model

CMB – Chemical Mass Balance CMU – Carnegie Mellon University CTM – Chemical Transport Model CWP – Clustered Weighted Probability EDAS – Eta Data Assimilation System

EFIG – USEPA Emission Factor and Inventory Group EGU – Electricity Generating Unit EMAD – Emissions, Monitoring and Analysis Division

ERM – Environmental Resources Management, Inc. FASTNET – Fast Aerosol Sensing and Tools for Natural aErosol Tracking FNL – FiNaL run of the Global Data Assimilation System

FRM – Federal Reference Method GIS – Geographic Information System IMPROVE – Interagency Monitoring of

Protected Visual Environments IP – Incremental Probability

HAPS – Hazardous Air Pollutants HYSPLIT – Hybrid Single-Particle Langrangian Integrated Trajectory

model

MANE-VU – Mid-Atlantic/Northeast Visibility Union MARAMA – Mid Atlantic Region Air Management Association MDE – Maryland Department of the

Environment MDNR – Maryland Department of Natural

Resources

MM5 – Fifth Generation Mesoscale Model

MOBILE – Mobile Source Emission Factor Model (USEPA) MWRPO – Midwest Regional Planning Organization NAAQS – National Ambient Air Quality

Standards

NARSTO – North American Research Strategy for Tropospheric Ozone NCAR – National Center for Atmospheric

Research NEI – National Emissions Inventory

NESCAUM – Northeast States for Coordinated Air Use Management NET – National Emissions Trends (EPA)

NOAA – National Oceanic and Atmospheric Administration

NRC – National Research Council NTI – National Toxics Inventory

NWS – National Weather Service OAQPS – USEPA Office of Air Quality Planning and Standardsxii

OAR – USEPA Office of Air and

Radiation OTC – Ozone Transport Commission PCA – Principle Component Analysis

PM – Particulate Matter

PMF – Positive Matrix Factorization

PSCF – Potential Source Contribution

Function

RAIN – Real Time Aerosol Intensive

Network REMSAD – Regulatory Modeling System for Aerosols and Deposition RH – Relative Humidity RPO – Regional Planning Organization RTA – Residence Time Analysis SIP – State Implementation Plan SMOKE – Sparse Matrix Operator Kernel

Emissions model STN – Speciation Trends Network TLD – Transport Layer Depth TSC – Technical Support Committee UMD – University of Maryland UNMIX – Mathematical receptor model

used for source attribution studies

USEPA – United States Environmental Protection Agency

USFS – United States Forest Service

USFWS – United States Fish and Wildlife

Service

USNPS – United States National Park

Service VISTAS - Visibility Improvement State

and Tribal Association of the Southeast VT DEC – Vermont Department of

Environmental Conservation WRAP-Western Regional Air Partnership

Chemical Species

BC – Black Carbon

CM – coarse mass

CO – carbon monoxide

EC – elemental carbon HC – hydrocarbons

H 2 SO 4 – sulfuric acid

HNO 3 – nitric acid

NO X – oxides of nitrogen (NO 2 and NO)

NO – nitric oxide

NO 2 – nitrogen dioxide

NO 3 - – nitrate

NH 3 – ammonia

NH 4 – ammonium NH 4 HSO 4 – ammonium bisulfate (NH 4 ) 2 SO 4 – ammonium sulfate (NH 4 NO 3 ) – ammonium nitrate

O 3 – ozone OC – organic carbon OMC – organic mass from carbon

PM 2.5 – particle matter up to 2.5 μ m in size

PM 10 – particle matter up to 10 μ m in size

Se – selenium SOA – secondary organic aerosol

SO 2 – sulfur dioxide

SO 4 2- – sulfate VOC – volatile organic compounds

Symbols

b ext – light extinction coefficient (Mm -1 )

C i – constant for wind sector i

d – distance E x UP – emissions times upwind probability f(RH) – relative humidity adjustment

factor I – impact Q – annual emissions

R 2 – correlation coefficientxiii

Units Length

m – meter

μ m – micrometer (0.000001m; 10 -6 m) km – kilometer (1,000 x m; 10 3 m) Mm – Megameter (1,000,000 x m; 10 6 m)

Area

m 2 – square meter
km 2 – square kilometer

Volume

L – liter

m 3 – cubic meter

Concentration

μ g/m 3 – micrograms per cubic meter ng/m 3 – nanograms per cubic meter ppb – parts per billion ppm – parts per million

Scattering Efficiency

m 2 /g – square meters per gram

Visibility

dv – deciviewxiv



## Executive Summary

Regional haze State Implementation Plans (SIPs) due in December 2007 must include a contribution assessment and pollution app ortionment analysis as part of the long-term emissions management strategy for meeting visibility improvement objectives in Class I areas subject to USEPA’s 1999 Regional H aze Rule. The MidAtlantic/Northeast Visibility Union (MANE-VU) Techn ical Support Committee (TSC) has adopted a weight-of-evidence approach as a firs t step toward meeting these obligations and in an effort to better understand t he causes of visibility impairment at Class I areas within the MANE-VU region. The weigh t-of-evidence approach relies on several independent methods for assessing the contr ibution of different emissions sources and geographic source regions to regional haze in t he northeastern and mid-Atlantic portions of the United States.

The preliminary findings described in this report d raw from the considerable body of work that has already been developed concerning the nature and extent of visibility impairment in the MANE-VU region. This work has pro duced a conceptual model of regional haze in which sulfate emerges as the most important single constituent of hazeforming fine particle pollution and the principle c ause of visibility impairment across the region. Sulfate alone accounts for anywhere from on e-half to two-thirds of total fine particle mass on the 20 percent haziest days at MAN E-VU Class I sites. Even on the 20 percent clearest days, sulfate generally accounts f or the largest fraction (40 percent or more) of total fine particle mass in the region. S ulfate has an even larger effect when one considers the differential visibility impacts of di fferent particle constituents. It typically accounts for 70–82 percent of estimated particle-in duced light extinction at northeastern

and mid-Atlantic Class I sites.

While substantial visibility impairment is common a cross the region, it is most severe in the southern and western portions of MANE -VU that are closest to large power plant sources of sulfur dioxide (SO 2 ) emissions located in the Ohio River and Tennessee Valleys. Summertime visibility is driven almost ex clusively by the presence or absence of regional sulfate, whereas wintertime visibility depends on a combination of regional

and local influences coupled with local meteorological conditions (inversions) that can lead to the concentrated build-up of emissions from local sources.

These findings suggest that an effective emissions management approach would rely heavily on broad-based regional SO 2 control efforts in the eastern United States aimed at reducing summertime fine particulate matte r (PM 2.5 ) concentrations. MANEVU is investigating additional measures to reduce i n-region emissions of SO 2 and organic carbon (OC), which is typically the next most impor tant contributor to overall fine particle mass throughout the region. Nearby SO 2 reductions can help reduce wintertime PM concentrations, while OC reductions can help red uce total PM concentrations yearround. For areas with high wintertime PM levels, s trategies aimed at reducing ambient levels of nitrogen oxides (NO X ) may also be effective.

Available monitoring data provide strong evidence t hat regional SO 2 reductions have yielded, and will continue to yield, reduction s in ambient secondary sulfate levels with subsequent reductions in regional haze and ass ociated light extinction. They indicate that reductions in anthropogenic primary particle e missions will also result in visibilityxv

improvements, but that these will not have a zone o f influence as large as those of the secondary aerosols.

Given the dominant role of sulfate in the formation of regional haze in the Northeast and Mid-Atlantic region — and the likelih ood that SO 2 reductions will therefore need to play a central role in achieving near-term visibility improvements — this report focuses on early efforts to assess the regional sulfate contribution to ambient fine particle levels experienced at the (primarily rural) MANE-VU Class I areas. The primary objective of this report is to identify and describe the suite of analytical tools and techniques that are presently available for: (1) un derstanding the causes of sulfate-driven visibility impairment at Class I areas in MANE-VU a nd nearby regions, as well as the

relative contribution of various emissions sources and geographic source regions; and (2) describe how these tools and techniques will be app lied in future MANE-VU SIP work.

The analytical and assessment tools discussed in th is report include Eulerian (grid-based) source models, Lagrangian (air parcel- based) source dispersion models, as well as a variety of data analysis techniques that include source apportionment models, back trajectory calculations, and the use of monito ring and inventory data. A range of methodological approaches characterize these tools, which Table ES-1 summarizes. The tools rely on different data sources and entail var ying degrees of sophistication and uncertainty. Thus, it is important to emphasize tha t these methods have been extensively reviewed, updated, and refined over the past year t o ensure that the highest quality results are now available for the SIP development process. The overall coherence and consistency of results that emerges from applicatio n of these tools and techniques suggest that what is known about the causes of sulfate poll ution in the MANE-VU region is sufficiently robust to provide a useful and appropr iate basis for design of future control programs and for consultations between different re gional organizations charged with planning for compliance with the Regional Haze Rule

. Figure ES-1 provides one illustration of the high d egree of correspondence in the results. The figure shows rankings of state contrib utions to sulfate mass at Brigantine Wilderness Area in New Jersey derived from several of the techniques listed in Table ES1. 1 There is substantial consistency across a variety of analysis methods using techniques based on disparate chemical, meteorological and phy sical principles. Taken together, these findings create a strong weight-of-evidence c ase for the preliminary identification of the most significant contributors to visibility impairment in the MANE-VU Class I

areas.

Similar results for other sites demonstrate that hi ghly simplified, empirical approaches for identifying source contributions are consistent with more sophisticated approaches. Therefore, a firm basis exists for add ressing contributions to regional transport of sulfate, and the range of variability between these techniques suggests the precision of these estimates.

1 As described in Chapter 8, REMSAD is the only analysis platform used to quantify “out of domain” contributions to sulfate. Thus, the REMSAD calculated con tribution for the “out of domain” sources (17% at Brigantine, NJ) was used to calculate the percent contributio n shown in Figure ES-1 for all other

methods.xvi We have further aggregated these results by regiona l planning organization (RPO) using state-by-state sulfate mass contributions (in μ g/m 3 ) derived by the REMSAD, CALPUFF, emissions/distance, and emissions times (× ) upwind probability methods. 2
Figure ES-2 shows these results in terms of their a bsolute contribution (displayed within the bars shown in the graphic) and in terms of thei r proportional contribution relative to

other RPOs. 1

Table ES-1. Summary of technical approaches for att ributing state contributions to

observed sulfate in MANE-VU Class I areas.

Analytical technique Approach Emissions/distance Empirical Incremental probability Lagrangian trajectory techn ique Cluster-weighted probability Lagrangian trajectory technique Emissions × upwind probability Empirical/trajectory hybrid Source apportionment approaches Receptor model/traj ectory hybrid REMSAD tagged species Eulerian source model CALPUFF with MM5-based meteorology Lagrangian sourc e dispersion model CALPUFF with observation-based meteorology Lagrangi an source dispersion model

2 See Chapter 4 for an explanation of how the emissions di vided by distance technique is expressed as a sulfate mass concentration and the associated assumptions for th e emissions × upwind probability method. Figure ES-1. Comparison results using different tec hniques for ranking state contributions (in units of percent of in-domain con tribution) to sulfate levels at

Brigantine Wilderness Area, New Jersey.

0 2 4 6 8 10 12 14 16 18OTHERPAOHWVMDCANAVANCNYNJINKYGADEILMITNCENRSCMAALWICTNHMEDCMSRI REMSAD

Q/D
CALPUFF (VT)
CALPUFF (MD)

%UWxvii Notwithstanding small differences in precisely whic h states were included within each assessment technique, estimates obtained from averaging over the five quantitative assessment techniques indicate that MANE-VU states account for about 25-30 percent of the sulfate in the Acadia, Brigantine, and Lye Broo k Class I areas. The Midwest RPO (MWRPO) and Visibility Improvement State and Tribal Association of the Southeast (VISTAS) states each account for about 15 percent o f the total sulfate contribution at Acadia and about 25 percent each at Brigantine and Lye Brook. The Central states Regional Air Partnership (CENRAP) states, Canada, a nd an “out of domain” contribution

add the remainder. 3 Although variation exists across estimates of con tributions for different sites and using different techniques, the overall pattern is generally consistent.

Shenandoah National Park, Virginia, which is a VIST AS Class I area, has a

somewhat reversed order of relative contributions. There, VISTAS and MWRPO states account for roughly 30 percent of overall sulfate e ach, with MANE-VU states contributing roughly 15-20 percent and CENRAP state s, Canada and “out of domain” accounting for the remainder.

3 Note here that the contribution representing out of domai n sources was – in all cases – derived solely by the REMSAD platform and that this value has been applied to the other analysis techniques to provide a consistent estimate of the total contributions to sulfate po llution at each site. Figure ES-2. Estimated RPO contributions to sulfate concentrations at Class I areas

using different assessment techniques

0.73

0.65 1.03

0.58 1.20 0.89 1.37 1.07 0.68 0.56 0.83 0.59 0.81 0.56 0.83 0.96 0.24

0.40 0.60

0.49 0.63

0.61 0.94 0.58 0.46 0.57 0.77
0.52 1.05 0.94 1.22 0.88 0.18 0.26 0.39

0.45 0.77 0.87 1.04 0.84 0.29 0.36 0.51

0.42 1.36 1.11 1.49 1.25

0.02 0.05 0.09 0.05 0.04 0.07 0.10 0.05

0.04 0.07 0.12

0.05 0.06 0.08 0.09

0.07

0.18 0.19 0.29 0.35 0.24 0.08 0.11 0.20

0.27 0.12 0.17
0.28 0.19 0.08 0.11 0.26

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%REMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAINxviii Other qualitative analysis methods have been develo ped that reinforce the findings shown above. These include trajectory met hods and source apportionment techniques. These receptor-based methods provide c ompelling support for the more quantitative attribution methods discussed previous ly. Figure ES-3 (left panel) shows the source region associated with a “coal combustion/se condary sulfate” source profile observed at Brigantine Wilderness in New Jersey and (right panel) the predominant meteorological pathways associated with the highest sulfate observations at Brigantine. The meteorological transport regime most common dur ing high sulfate observations (shown on the right) directly connects the most lik ely source region with the receptor site (shown on the left), which reinforces the large qua ntitative contributions of source states determined for the Brigantine receptor in Chapter 8

. Finally, we note that while sulfate is the most imp ortant particle constituent for designing near-term control strategies, reductions in other local and distant pollutant emissions are important. Additional measures will be necessary in the long term to address public health impacts of ambient fine parti cle concentrations and to achieve longterm regional haze goals to restore pristine visibi lity conditions year-round in the nation’s Class I wilderness areas. This is especially true during winter months, when planners need to give particular consideration to reducing u rban and mobile sources of NO X and

OC as well as sources of SO 2

.

Figure ES-3. Geographic regions associated with “co al combustion/secondary sulfate” sources (left) and sulfate transport (right) for Br igantine Wilderness Area, NJ.

Note: This figure is the consistency of interpretation betw een the “coal-combustion/secondary sulfate” source region and receptor site shown in the left hand panel being directly connected by the predominant meteorological transport pathway on high obs erved sulfate days at Brigantine, shown in the right hand panel.xixTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 1-1



## 1. INTRODUCTION

The 1999 Regional Haze Rule (hereafter, the Haze Ru le) 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 tho se states or regions that may be influencing specially protected federal lands known as Federal Class I areas. 4 These states or regions would then be subject to the cons ultation provisions of the Haze Rule. The Haze Rule also requires a “pollution apportionm ent” analysis as part of the long-term emissions management strategy for each site.

In 2004, Congress harmonized the timeline for SIP s ubmissions, including SIPs for meeting federal fine particulate matter (PM 2.5 ) and regional haze requirements. 5 One effect of this change is that the “regional plannin g SIP” or “committal SIP” — originally due one year after PM designations — will now be du e along with all other SIP products in late 2007 or early 2008.

The Haze Rule originally would have applied a very low threshold test to determine whether a state would be part of a region al planning process, As a result of the congressional harmonization, however, the requireme nt for a contribution assessment is now, in effect, part of the “pollution apportionmen t” analysis used to determine which sources must be included in a long-term emissions m anagement strategy. This is subject to a somewhat higher threshold of evidence since it forms the basis for judging whether long-term strategies are adequately addressing the causes of haze in protected areas.

To adequately determine the degree to which specifi c geographic regions or areas are contributing to visibility impairment at MANE-V U Class I areas, the MANE-VU Technical Support Committee (TSC) has adopted a wei ght-of-evidence approach that relies on several independent methods of attributio n. These include Eulerian (grid-based) source models, Lagrangian (air pollution-based) sou rce dispersion models, and a variety of data analysis techniques that include source app ortionment models, back trajectory calculations, and the use of monitoring and invento ry data.

4 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 par ks 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 Gul f Wilderness, New Hampshire; Lye Brook Wilderness, Vermont; Moosehorn Wilderness (within the Mo osehorn National Wildlife Refuge), Maine; Presidential Range – Dry River Wilderness, New Hampshire; an d Roosevelt Campobello International Park, New Brunswick.

5 In the Omnibus Appropriations Act of 2004 [Consolidat ed Appropriations Act for Fiscal Year 2004, Pub. L. 108–199, January 23, 2004], Congress harmonized both designations and regional haze SIP deadlines. EPA promulgated PM 2.5 designations for all areas of each state on December 17, 2004. T he Omnibus Appropriations Act provides that regional haze SIPs for each state as a whole are then due not later than three years after promulgation of the PM 2.5 designations. Thus, all components of the regional haze SIPs are now due no later than December 17, 2007 (three years after th e USEPA issued the official designations). The USEPA has suggested informally that they will accept Regio nal Haze SIPs in April 2008 when PM 2.5 SIPs are due.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 1-2

While we already know much about visibility impairm ent and its causes in the MANE-VU region (see NESCAUM, 2001; NESCAUM, 2002), significant gaps in understanding remain with respect to the organic co mponent of fine particulate pollution. While we expect continuing research activities to s ubstantially benefit future SIP efforts,

the MANE-VU members have determined that sufficient information exists to design effective emission control strategies to meet visib ility goals through 2018.

Reducing sulfur emissions offers particular leverag e for achieving near-term visibility goals. It is the sulfate fraction of ai rborne fine particle matter that dominates light extinction on the 20 percent worst visibility days in the Northeast and Mid-Atlantic region. This is important because improving visibil ity on the 20 percent worst days is a near-term regulatory objective under the Regional H aze rule. In addition, many tools are available for assessing sulfate contributions. The refore, this document focuses to a large extent on assessing sources and source regions for the sulfate fraction of haze-causing particles.

To lay a foundation for the analyses described in l ater chapters of this report, Chapter 2 provides a conceptual model of visibility impairment in the eastern United States. Chapter 3 presents a summary of available monitoring data and observations that we use to support the conceptual model and to valid ate models and data analyses. In fact, measured data — far from being used merely to suppo rt modeling analyses — serve as the primary basis for several of the receptor techn iques presented in later chapters. There is thus no substitute for a robust monitoring netwo rk to understand the causes of fine particle pollution and visibility impairment.

Later chapters reinforce the notions introduced in Chapters 2 and 3 in using emission inventories (Chapter 4), receptor-based ap proaches including the use of back trajectories, trajectory clustering techniques and source apportionment models (Chapter 5), Eulerian chemical transport models (Chapter 6), and Lagrangian dispersion models (Chapter 7). We synthesize and interpret these vari ous techniques in Chapter 8 and present conclusions in Chapter 9. We discuss techn ical aspects of the analyses in several of these later chapters in greater detail in a seri es of appendices.

As a general matter throughout this report, the foc us is on assessing the contribution of all sources within broad geographic al areas (i.e., whole states) whose combined emissions are likely to contribute to regi onal haze. As cited in Watson (2002), the National Research Council (NRC) has concluded t hat:

(1) “...a program that focuses solely on determining the contribution of individual emission sources to visibility impairmen t is doomed to failure. Instead, strategies should be adopted that consider many sources simultaneously on a regional basis, although assess ment of the effect of individual sources will remain important in some si tuations;” (2) “...there are (and will probably continue to be) considerable uncertainties in ascertaining a precise relationship between individ ual sources and the spatial pattern of regional haze;” and (3) “...the best appro ach for evaluating emission sources is a nested progression from simpl er and more direct models to more complex and detailed methods” (Watso n, 2002).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 1-3

Watson (2002) goes on to point out that, “Part of t he modeling conundrum is the focus of modeling efforts on demonstrating attainme nt rather than gaining a better understanding of the situation. Although USEPA emph asizes the construction of a conceptual model and evaluation of the weight of ev idence in its introduction, the modeling details contained in the guidance are busi ness as usual: seeking a quantitative comparison of present and future design values with a numerical goal.”

Consistent with the NRC’s admonition and USEPA’s st ated desire to incorporate weight-of-evidence approaches to improve conceptual models, MANE-VU has attempted wherever possible to incorporate qualitative analys es in sensible ways so as to increase confidence in its quantitative estimates of the con tribution of various emissions sources and source regions to regional haze.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 1-4



### References

NESCAUM, Regional Haze and Visibility Impairment in the Nort heast and Mid-Atlantic

United States , Northeast States for Coordinated Air Use Manageme nt, Boston, MA, January, 2001.

NESCAUM, Updated Visibility Statistics for the MANE-VU Regio n Technical Memorandum #1, Northeast States for Coordinated Air Use Managemen t, Boston, MA February, 2002.

Watson, J., “Visibility: Science and Regulation,” JAWMA 52:628-713, 2002.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-1



## 2. CONCEPTUAL MODEL OF REGIONAL HAZE IN THE

MANE-VU REGION

Developing a conceptual model of regional haze requ ires combining experience and atmospheric-science expertise with multiple dat a sources and analysis techniques. This includes measured data on ambient pollutant co ncentrations as well as emission inventory and meteorological data, chemical transpo rt modeling, and observationally based models (NARSTO, 2003). Here, we begin with a conceptual model based on the existing scientific literature concerning fine part icles and their effect on visibility. This includes numerous review articles and reports on th e subject. Most past assessments of fine particle pollution and visibility impairment h ave tended to be national in scope. For purposes of this discussion, we have selectively re viewed the literature in order to present a distinctly Eastern focus.

Because the uncertainties involved in any particula r method of analysis are usually large or ill-defined, it is preferable to d evelop visibility and fine particle management strategies with inputs from multiple ana lyses using multiple approaches. The MANE-VU TSC has adopted this approach, which le ads to the diversity of data analyses and model results that follow. Later chap ters of this report use original contributions and analyses developed by MANE-VU res earchers to bolster and support the concepts presented in these introductory chapte rs. MANE-VU has combined the outputs and integrated them into a final conceptual model that explains the formation and transport mechanisms for fine particulate matter in the eastern United States.



### 2.1. Visibility Effects of Particulate Matter (PM)

Visibility impairment in the eastern United States is largely due to the presence of light-absorbing and light-scattering fine particles in the atmosphere. The USEPA has identified visibility impairment as the best unders tood of all environmental effects of air pollution (Watson, 2002). A long-established physi cal and chemical theory relates the interaction of particles and gases in the atmospher e with the transmission of visual information along a sight path from object to obser ver.
Visibility-impairing particle-light interactions ar e sensitive to the chemical composition of the particles involved, and also dep end strongly on ambient relative humidity. Secondary particles, which form in the at mosphere through chemical reactions, tend to fall within a size range that is most effec tive at scattering visible light (NARSTO, 2003). These particles are generally smaller than one micrometer (μm) or one onemillionth of a meter. The particles that contribut e most to visibility impairment also are a

concern under the health-based National Ambient Air Quality Standard (NAAQS) for fine particulate matter, defined as including all p articles with an aerodynamic diameter less than 2.5 μm (PM 2.5 ).



### 2.2. Chemical Composition of Particulate Matter in MANE- VU

Sulfate alone accounts for anywhere from one-half t o two-thirds of total fine particle mass on the 20 percent haziest days at all MANE-VU Class I sites. Even on the 20 percent clearest days, sulfate generally account s for the largest fraction (40 percent or more) of total fine particle mass in the region (NE SCAUM, 2001). Sulfate accounts for aTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-2

major fraction of PM 2.5 , not only in the Northeast but across the eastern United States (NARSTO, 2003).

After sulfate, organic carbon (OC) consistently acc ounts for the next largest fraction of total fine particle mass. Its contribut ion typically ranges from 20 to 30 percent of total fine particle mass on the haziest days. Th e fact that the contribution from organic carbon can be as high as 40 percent at the more rur al sites on the 20 percent clearest days is likely indicative of the role played by organic emissions from vegetation (so-called “biogenic hydrocarbons” (HC)). Relative contributio ns to overall fine particle mass from nitrate (NO 3 ), elemental carbon, and fine soil are all smaller (typically under 10 percent), but the relative ordering among the three species v aries with location. Nitrate plays a noticeably more important role at urban sites compa red to northeastern and mid-Atlantic Class I locations, perhaps reflecting a greater con tribution from vehicles and other urban pollution sources (NESCAUM, 2001).

Almost all particle sulfate originates from sulfur dioxide (SO 2 ) oxidation and typically associates with ammonium (NH 4 ) in the form of ammonium sulfate ((NH 4 ) 2 SO 4 ), 95 percent of SO 2 emissions are from anthropogenic sources (primaril y from fossil fuel combustion), while the majority of ammonium comes from agricultural activities and, to a lesser extent, from transporta tion sources in some areas (NARSTO, 2003).

Two major chemical pathways produce sulfate from SO 2 in the atmosphere. In the gas phase, production of sulfate involves the o xidation of SO 2 to sulfuric acid (H 2 SO 4 ), ammonium bisulfate (NH 4 HSO 4 ), or ammonium sulfate, depending on the availability of ammonia (NH 3 ). In the presence of small wet particles (typical ly much, much smaller than rain drops or even fog), a highly efficient aqueous phase process can

oxidize SO 2 to sulfate extremely quickly (~10 percent per hour)

. Not only is sulfate the dominant contributor to fin e particle mass in the region, it accounts for anywhere from 60 percent to almost 80 percent of the difference between fine particle concentrations on the clearest and ha ziest days at northeastern and midAtlantic Class I sites. Notably, at urban locations such as Washington, DC, sulfate accounts for only about 40 percent of the differenc e in average fine particle concentrations for the 20 percent most versus least visibility impaired days (NESCAUM, 2001). We discuss this further in the next section of this chapter.

Some of the dominant components of total fine parti cle mass have an even larger effect when considering the differential visibility impacts of different particle species. Sulfate typically accounts for over 70 percent of e stimated particle-induced light

extinction at northeastern and mid-Atlantic Class I sites. Organic carbon continues to be the second most important contributor to particle-i nduced light extinction at rural sites on the most impaired days, but slips to third behind n itrate in Washington, DC (NESCAUM, 2001).



### 2.3. Geographic Considerations and Attribution of PM/Haz e Contributors

In the East, an accumulation of particle pollution often results in hazy conditions extending over thousands of square kilometers (km 2 ) (NARSTO, 2003). SubstantialTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-3

visibility impairment is a frequent occurrence in e ven the most remote and pristine areas of the Northeast and Mid-Atlantic region (NESCAUM, 2001).

Both annual average and maximum daily fine particle concentrations are highest near heavily industrialized areas and population ce nters. Not surprisingly, given the direct connection between fine particle pollution and haze , the same pattern emerges when one compares measures of light extinction on the most a nd least visibility impaired days at parks and wilderness areas subject to the Haze Rule in the Northeast and Mid-Atlantic region (NESCAUM, 2001).

Contributions to fine particle mass concentrations at rural locations include longrange pollutant transport as well as non-anthropoge nic background contributions. Urban areas generally show mean PM 2.5 levels exceeding those at nearby rural sites. In t he Northeast, this difference implies that local urban contributions are roughly 25 percent of the annual mean urban concentrations, with regional aerosol contributing the remaining, and larger, portion (NARSTO, 2003).

This rural versus urban difference in typical conce ntrations also emerges in a source apportionment analysis of fine particle poll ution in Philadelphia (Chapter 10, NARSTO, 2003) using two different mathematical mode ls, UNMIX and Positive Matrix Factorization (PMF). (We describe these models in greater detail in Chapter 5 and Appendix B.) This analysis provides additional ins ight concerning sources of fine particle pollution in urban areas of the densely po pulated coastal corridor between Washington D.C. and New England. Specifically, this analysis found the following apportionment of PM 2.5 mass in the study area:

• Local SO 2 and sulfate: ~ 10 percent

• Regional sulfate: ~ 50 percent

• Residual oil: 4-8 percent

• Soil: 6–7 percent

• Motor vehicles: 25–30 percent

The analysis does not account for biogenic sources, which most likely are embedded in the motor vehicle fraction (NARSTO, 200 3). The Philadelphia study suggests that both local pollution from near-by sou rces and transported “regional” pollution from distant sources contribute to the hi gh sulfate concentrations observed in urban locations along the East Coast on an annual a verage basis. Summertime sulfate and organic carbon are strongly regional in eastern North America. Typically 75–95 percent of the urban sulfate concentrations and 60– 75 percent of the urban OC concentrations arise from cumulative region-wide co ntributions (NARSTO, 2003).

While these statistics provide some preliminary con text for attributing responsibility for the region’s particulate matter and visibility problems, they say nothing about the relative efficiency of a state’s or regio n’s emissions in causing or contributing to the problem. It is clear that distance from the emissions source matters. Local, nearby sources are exceedingly important and sources wi thin about 200 kilometers (km) are much more efficient (on a per ton emitted basis) at producing pollution impacts at eastern

Class I sites such as Shenandoah National Park than emissions sources farther away (USNPS, 2003). In general, the “reach” of sulfate air pollution resulting from SO 2Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-4

emissions is longest (650-950 km). The reach of amm onia emissions or reduced nitrogen relative to nutrient deposition is the shortest (ar ound 400 km), while oxides of nitrogen and sulfur — in terms of their impacts with respect to acidic deposition — have a reach between 550–650 km and 600–700 km, respectively (US NPS, 2003).

Monitoring evidence indicates that non-urban visibi lity impairment in eastern North America is predominantly due to sulfate parti cles, with organic particles generally second in importance (NARSTO, 2003). This makes se nse, given the “long reach” of

SO 2 emissions once they are chemically transformed int o sulfate and given the ubiquitous

nature of OC sources in the East.

The poorest visibility conditions occur in highly i ndustrialized areas encompassing and adjacent to the Ohio and Tennessee River Valleys. These areas feature large coal-burning power stations, steel mi lls, and other large emissions sources. Average visibility conditions are also poor in the highly populated and industrialized mid-Atlantic seaboard but improve gradually northea st of New York City (Watson, 2002).

A review of source apportionment and ensemble traje ctory analyses conducted by USEPA (2003) found that all back trajectory analyse s for Eastern sites associated sulfate with the Ohio River Valley area. Studies also frequ ently associated other types of industrial pollutants with known source areas. Seve ral studies in the USEPA review noted transport across the Canadian border, specifi cally sulfates from the midwestern United States into Canada, and smelter emissions fr om Canada into the northeastern

United States.

A recent, comprehensive analysis of air quality pro blems at Shenandoah National Park conducted by the U.S. National Park Service (U SNPS, 2003) focused on contributions to particulate pollution and visibili ty impairment south of the MANE-VU region. In descending order of importance, the Nat ional Park Service analysis determined that Ohio, Virginia, West Virginia, Penn sylvania, and Kentucky comprise the top five of thirteen key states contributing to amb ient sulfate concentrations and haze impacts at the park. West Virginia, Ohio, Virginia, Pennsylvania, and Kentucky comprise the top five contributing states with respect to su lfur deposition impacts at the park. Finally, Virginia, West Virginia, Ohio, Pennsylvani a, and North Carolina were found to be the top five states contributing to deposition i mpacts from oxidized nitrogen at the park (USNPS, 2003).

In summary, the National Park Service found that em ission sources located within a 200 kilometer (125 mile) radius of Shenandoah cau se greater visibility and acidic deposition impacts at the park, on a per ton basis, than do more distant emissions sources (USNPS, 2003). When mapping deposition and concent ration patterns for all three pollutants using contour lines, the resulting geogr aphic pattern shows a definite eastward tilt in the area of highest impact. This is the re sult of prevailing wind patterns, which tend to transport most airborne pollutants in an ar c from the north-northeast to the east. 6
The Park Service found, for example, that emissions originating in the Ohio River Valley end up three times farther to the east than to the west (USNPS, 2003).

6 The prevailing winds are eastward to northeast. This leads to greater pollution transport to the eastnortheast relative to other directions.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-5

We note that several MANE-VU states may themselves be contributing to fine particle mass concentrations observed at Shenandoah . According to the Park Service analysis, sources in Pennsylvania contribute on the order of 10 percent of observed ambient sulfate mass at the park, while sources in Maryland, New York and Delaware contribute 3.5, 1.7 and 0.5 percent respectively (U SNPS, 2003).



### 2.4. Seasonal differences

Eastern and western coastal regions of the United S tates and Canada show marked seasonality in the concentration and composition of fine particle pollution, while central interior regions do not (NARSTO, 2003). While the MANE-VU domain extends inland as far as the Pennsylvania and Ohio border, the maj ority of Class I areas in MANE-VU cluster along the East Coast and thus typically sho w strong seasonal influences.

Maximum PM 2.5 concentrations occur during the summer over most o f the Northeast,

with observed summer values for rural areas in the region, on average, twice those of winter. Winter nitrate concentrations, however, ar e generally higher than those observed in summer and, as mentioned above, urban concentrat ions typically exceed rural concentrations year-round. In addition, local mobi le source carbon grows in importance during wintertime. Hence, in some large urban area s such as Philadelphia and New York City, peak concentrations of PM 2.5 can occur in winter.

The conceptual models that explain elevated regiona l PM 2.5 peak concentrations in the summer differ significantly from models that explain the largely urban peaks observed during winter. On average, summertime conc entrations of sulfate in the

northeastern United States are more than twice that of the next most important fine particle constituent, OC, and more than four times the combined concentration of nitrate and black carbon (BC) constituents (NARSTO, 2003). Episodes of high summertime sulfate concentrations are consistent with stagnant meteorological flow conditions and the accumulation of airborne sulfate (via atmospheric o xidation of SO 2 ) through long-range transport of sulfur emissions from industrialized a reas within and outside the region.

National assessments (NARSTO, 2003) have indicated that in the winter, sulfate levels in urban areas are almost twice as high as b ackground sulfate levels across the eastern U.S., indicating that the local urban contr ibution to wintertime sulfate levels is comparable in magnitude to the regional sulfate con tribution from long-range transport. MANE-VU’s network analysis for the winter of 2002 s uggests that the local

enhancement of sulfate in urban areas of the OTR is somewhat less with ranges from 25 to 40% and that the long range transport component of PM sulfate is still the dominant

contributor in most eastern cities.

In the winter, urban OC and sulfate each account fo r about a third of the overall

PM 2.5 mass concentration observed in Philadelphia and Ne w York City. Nitrate also makes a significant contribution to urban PM 2.5 levels observed in the northeastern United States during the winter months. Wintertime concentrations of OC, sulfate, and

NO 3 in urban areas can be twice the average regional c oncentrations of these pollutants, indicating the importance of local source contribut ions (NARSTO, 2003). This is likely

because winter conditions are more conducive to the formation of local inversion layers that prevent vertical mixing. Under these conditio ns, emissions from tailpipe, industrialTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-6

and other local sources become concentrated near th e Earth’s surface, adding to background pollution levels associated with regiona lly transported emissions.

It is worth noting that while sulfate plays a signi ficant role in episodes of elevated particle pollution during summer and winter months, the processes by which sulfate forms may vary seasonally. Nearly every source app ortionment study reviewed by USEPA (2003) identified secondary sulfate originati ng from coal combustion sources as the largest or one of the largest contributors to o verall fine particle mass in the region. It often accounted for more than 50 percent of PM 2.5 mass at some locations during some seasons. In a few cases, source apportionment studi es identified a known local source of sulfate, but most assessments (in conjunction with back trajectory analysis) have pointed to coal-fired power plants in the Midwest as an imp ortant source for regional sulfate. Studies with multiple years of data have also tende d to identify a distinguishable chemical “signature” for winter versus summer sourc es of sulfate, with the summer version typically accounting for a greater share of overall fine particle mass. Researchers have speculated that the two profiles represent two extremes in the chemical transformation processes that occur in the atmosphe re between the source regions where emissions are released and downwind receptor sites. We note that while coal combustion

is often referred to as the “sulfate source” becaus e of the dominance of its sulfate contribution, coal combustion is usually the single largest source of selenium (Se) and other heavy metal trace elements (USEPA, 2003).

Visually, hazy summer days in the Northeast can app ear quite different from hazy winter days. The milky, uniform visibility impairme nt shown in Figure 2-1 is typical of summertime regional haze events in the Northeast. D uring the winter, by comparison, reduced convection and the frequent occurrence of s hallow inversion layers often creates a layered haze with a brownish tinge, as shown in F igure 2-2. This visual difference suggests seasonal variation in the relative contrib ution of different gaseous and particle constituents during the summer versus winter months (NESCAUM, 2001). Rural and inland areas tend not to experience these layered h aze episodes as frequently due to the

lack of local emission sources in most rural areas (valleys with high wood smoke contributions are an exception).

Overall (regional) differences in summer versus win ter particle mass concentrations and corresponding visibility impairm ent (as measured by light extinction) are largely driven by seasonal variation in sulfate mass concentrations. This is because winter meteorological conditions are less conducive to the oxidation of sulfate from SO 2 (as borne out by the previously cited source apport ionment studies). In addition, seasonal differences in long-range transport patterns from u pwind SO 2 source regions may be a

factor.

The greater presence of nitrate during the cold sea son is a consequence of the chemical properties of ammonium nitrate. Ammonia bo nds more weakly to nitrate than it does to sulfate, and ammonium nitrate tends to diss ociate at higher temperatures. Consequently, ammonium nitrate becomes more stable at lower temperatures and hence contributes more to overall light extinction during the winter months (NESCAUM, 2001).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-7



### 2.5. Implications for control strategies

A 2003 assessment of fine particulate matter by NAR STO 7 notes that, “[c]urrent air-quality management approaches focusing on reduc tions of emissions of SO 2 , NO X, and VOCs are anticipated to be effective first step s towards reducing PM 2.5 across North America, noting that in parts of California and som e eastern urban areas VOC (volatile organic compounds) emissions could be important to nitrate formation.”

This conclusion seems to be well supported by the h istorical record, which documents a pronounced decline in particulate sulfa te concentrations across the eastern United States during the 1990s. The timing of this observed decline suggests that this is

linked to reductions in SO 2 emissions resulting from controls implemented unde r the federal Acid Rain Program beginning in the early to mid 1990s. From 1989 to 1998, SO 2

7 NARSTO was formerly an acronym for the "North American Resear ch Strategy for Tropospheric Ozone." More recently, the term NARSTO became simply a wordmark signif ying a tri-national, public-private partnership for dealing with multiple features of troposph eric pollution, including ozone and suspended particulate matter. For more information on NARSTO see http://www.cgenv.com/Narsto/

.

Figure 2-1. Summer time at Mt Washington
Clean Day Typical Haze Event

Figure 2-2. Wintertime in Boston
Clean Day Typical Haze EventTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-8

emissions in the eastern half of the country — that is, including all states within a region defined by the western borders of Minnesota and Lou isiana — declined by about 25 percent. This decline in SO 2 emissions correlated with a decline of about 40 pe rcent in average SO 2 and sulfate concentrations, as measured at Clean A ir States and Trend Networks (CASTNet) monitoring sites in the same reg ion over the same time period. In fact, at prevailing levels of atmospheric SO 2 loading, the magnitudes of the emissions and concentration changes were not statistically differ ent. This finding suggests that regional

reductions in SO 2 emissions have produced near-proportional reductio ns of particulate sulfate in the eastern United States (NARSTO, 2003) . Reductions since 1990 in precursor SO 2 emissions are likely also responsible for a contin ued decline in median

sulfate concentrations in the northeastern United S tates. Nevertheless, the fact that episodes of high ambient sulfate concentrations (wi th peak levels well above the regional median or average) continue to occur, especially du ring the summertime when regional transport from the Ohio River Valley is also at its peak, suggests that further reductions in regional and local SO 2 emissions would provide significant further air qua lity and visibility benefits (NARSTO, 2003).

For urban areas of the northeastern and southeaster n United States, an effective emissions management approach may be to combine reg ional SO 2 control efforts aimed at reducing summertime PM 2.5 concentrations with local SO 2 and OC control efforts.

Local SO 2 reductions would help reduce wintertime PM concent rations, while OC reductions can help reduce overall PM concentration s year-round. For areas with high wintertime PM levels, strategies that involve NO X reductions may also be effective (NARSTO, 2003).

Further support for this general approach may be fo und in a review of several studies by Watson (2002) that concluded SO 2 emission reductions have in most cases been accompanied by statistically significant reduc tions in ambient sulfate concentrations. One study (Husar and Wilson, 1993) shows that regionally averaged light extinction closely tracks regionally averaged SO 2 emissions for the eastern United States from 1940 through the mid-1980s. Another study by M alm et al. (2002) shows that regionally averaged emissions and ambient concentra tions decreased together from 1988 through 1999 over a broad region encompassing the s tates of Connecticut, Delaware, Illinois, Indiana, Kentucky, Maine, Massachusetts, Maryland, Michigan, New Hampshire, New Jersey, New York, Ohio, Pennsylvania , Rhode Island, Vermont, Virginia, Wisconsin, and West Virginia (Watson, 200 2).

These studies and available data from the IMPROVE ( Interagency Monitoring of Protected Visual Environment) monitoring network pr ovide strong evidence that regional

SO 2 reductions have yielded, and will continue to yiel d, reductions in ambient secondary sulfate levels with subsequent reductions in region al haze and associated light extinction. They indicate that reductions in anthropogenic prim ary particle emissions will also result in visibility improvements, but that these will not have a zone of influence as large as those of the secondary aerosols (Watson, 2002).

Watson (2002) notes that during the 65 years in whi ch the regional haze program aims to reach its final visibility goals, several o pportunities to revise this basic control approach will arise through the decadal SIP cycle. This enables new scientific results to continue to exert a positive influence as states im plement new regulatory controlTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-9

programs for SO 2 , NO X and VOCs, and as ambient concentrations of these p ollutants change relative to each other and relative to ambie nt ammonia levels. As these relationships between species change, atmospheric c hemistry may dictate a revised control approach to those previously described. Fu rther research on these issues should be a priority for supporting 2018 SIP submissions. They include the possibility that:

• Reduction of sulfate in a fully neutralized atmosph ere (excess ammonia) could encourage ammonium nitrate formation.

• Ever greater emissions reductions could be required to produce a given level of improvement in ambient pollutant concentra tions because of nonlinearities in the atmospheric formation of sulfate

.

• Changes in ambient conditions favoring the aqueous oxidation of sulfate (this pathway largely accounts for the non-linearit y noted above) may have implications for future emissions control prog rams. Causes of changing ambient conditions could include, for exam ple, climate change.

West et al. (1999) examine a scenario for the easte rn United States where PM 2.5 mass decreases linearly with ammonium sulfate until the latter is fully neutralized by

ammonia. Further reductions would free ammonia for combination with gaseous nitric acid that, in turn, would slightly increase PM 2.5 until all of the nitric acid is neutralized. At that point, further sulfate reductions would onc e again be reflected in lower PM 2.5 mass. This is an extreme case that is more relevant to source areas (e.g., Ohio) where nitric acid (HNO 3 ) is more abundant than in areas with lower emissio ns (e.g., Vermont) (Watson, 2002).

In most situations with non-neutralized sulfate (ty pical of the eastern United States), ammonia is a limiting agent for the format ion of nitrate but will not make any difference until sulfate is reduced to the point wh ere it is completely neutralized. At that point, identifying large sources of ammonia emissio ns will be important. This point is likely to be many years in the future, however (Wat son, 2002).

Based on analyses using the Community Multi-Scale A ir Quality (CMAQ) model, the aqueous phase production of sulfate in the Nort heast appears to be very oxidant limited and hence non-linear. Thus, conditions that are conducive to a dominance of the gas-phase production pathway drive the summer peaks in ambient sulfate levels. Nonetheless, the expected reduction in ambient sulf ate levels resulting from a given

reduction in SO 2 emissions is less than proportional overall due to the non-linearity introduced by the aqueous pathway for sulfate forma tion (NARSTO, 2003). These nonlinearity effects are more pronounced for haze than for sulfate deposition, especially at higher sulfate air concentrations (USNPS, 2003).

Finally, we note that because visibility in the cle arest areas is sensitive to even minute increases in particle concentrations, strate gies to preserve visibility on the clearest days may require stringent limits on emissions grow th. In this context, even the dilute emissions from distant sources can be important (NA RSTO, 2003).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-10



### 2.6. Summary

The presence of fine particulate matter in ambient air significantly obscures visibility during most parts of the year at sites a cross the MANE-VU region. Particle pollution generally, and its sulfate component spec ifically, constitute the principle driver for regional visibility impacts. While the broad r egion experiences visibility impairment, it is most severe in the southern and western porti ons of MANE-VU that are closest to large power plant SO 2 sources in the Ohio River and Tennessee Valleys.

The presence or absence of regional sulfate almost exclusively drives summer visibility impairment, whereas winter visibility de pends on a combination of regional and local influences coupled with local meteorological conditions (inversions) that lead to the concentrated build-up of pollution.

Sulfate is the key particle constituent from the st andpoint of designing control strategies to improve visibility conditions in the northeastern United States. Significant

further reductions in ambient sulfate levels are ac hievable, though they will require more than proportional reductions in SO 2 emissions.

Long-range pollutant transport and local pollutant emissions are important, especially along the eastern seaboard, so one must also look beyond the achievement of further sulfate reductions. During the winter month s, in particular, consideration also needs to be given to reducing urban sources of SO 2 , as well as NO X and OC (NARSTO, 2003).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-11



### References

Husar, R.B. and Wilson, W.E., “Haze and Sulfur Emis sion Trends in the Eastern United States,” Environ. Sci. Technol. 27 (1), 12-16, 1993.

NARSTO, Particulate Matter Science for Policy Makers: A NARSTO Assessme nt , EPRI 1007735, February, 2003.

NESCAUM, Regional Haze and Visibility Impairment in the Nort heast and Mid-Atlantic

United States , Northeast States for Coordinated Air Use Manageme nt, Boston, MA, January, 2001.

Malm, W. C., Schichtel, B. A., Ames, R.B., and Gebh art, K.A., “A 10-year spatial and temporal trend of sulfate across the United States, ” J. Geophys. Res

. 107(D22):4627

, doi:10.1029/2002JD002107, 2002.

USEPA, COMPILATION OF EXISTING STUDIES ON SOURCE APPORTION MENT

FOR PM

2.5 , Second Draft , Emissions, Monitoring, and Analysis Division, Off ice of Air Quality Planning and Standards, Research Triangle P ark, NC, August, 2003.

USNPS, Assessment of Air Quality and Related Values in She nandoah National Park

, Technical Report NPS/NERCHAL/NRTR-03/090, U.S. Depa rtment of the Interior, National Park Service, Northeast Region, Philadelph ia, Pennsylvania, May, 2003.

Watson, J., “Visibility: Science and Regulation,” JAWMA 52:628-713, 2002.

West, J.J., Ansari, A.S., and Pandis, S.N., “Margin al PM 2.5 : Nonlinear Aerosol Mass Response to Sulfate Reductions in the Eastern Unite d States,” JAWMA 49:1415-1424,

1999.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 2-12Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-1



## 3. OVERVIEW OF MONITORING RESULTS

SIP developers use monitoring data in three importa nt ways to support regional haze SIP activities. Section 3.1 presents measurem ents from the IMPROVE network needed in establishing SIP requirements. Following USEPA guidance (USEPA, 2003a; USEPA, 2003b), we use these data to preview the uni form progress goals that SIP developers must consider for each Class I area.

Section 3.2 reviews a recent NESCAUM report (NESCAU M, 2004b) to demonstrate how available monitoring data support a nd validate the conceptual model presented in Chapter 2.

Section 3.3 presents early results from the MANE-VU Real-Time Aerosol Intensive Network (RAIN). These suggest some of th e ways MANE-VU is preparing to extend and improve understanding of visibility issu es across the region. We anticipate this aspect of the MANE-VU monitoring strategy to b e critical for future status reports and SIP updates.



### 3.1. Baseline Conditions

The Haze Rule requires states and tribes to submit plans that include calculations

of current and estimated baseline and natural visib ility conditions. They will use monitoring data from the IMPROVE program as the bas is for these calculations. Table 3-1 presents the five-year average 8 of the 20 percent worst day mass concentrations in six

Class I areas. Five of these areas are in MANE-VU and one (Shenandoah) is nearby but located in a neighboring regional planning organiza tion (RPO) region. 9 Table 3-2 gives the corresponding worst day contributions to partic le extinction for the six Class I areas. Each of these tables show the relative percent cont ribution for all six Class I sites. Sulfate and organic carbon dominate the fine mass, with sulfate even more important to particle extinction.

To guide the states in calculating baseline values of reconstructed extinction and for estimating natural visibility conditions, USEPA released two documents in the fall of 2003 outlining recommended procedures (USEPA 2003a; USEPA 2003b). These proposed methods were used, along with the data in Table 3-1 and Table 3-2 to create Table 3-3, which provides detail on the 20 percent worst conditions for the six Class 1

areas.
The first column of data in the Table 3-3 gives the default natural background levels for the worst visibility days at these six s ites. Although debate continues with regard to some assumptions underlying the USEPA def ault approach for estimating natural background visibility conditions, MANE-VU h as decided to use this approach, at least initially, for 2008 SIP planning purposes (NE SCAUM, 2004a). The second column shows the baseline visibility conditions on the 20 percent worst visibility days. These

values are based on IMPROVE data from the official five-year baseline period (2000-

8 Great Gulf calculations are based on four years of data (2001-2 004).

9 Note that values presented for Shenandoah, a Class I area in t he Visibility Improvement State and Tribal Association of the Southeast (VISTAS) region, are for com parative purposes only. VISTAS will determine uniform rates of progress for areas within its region.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-2

2004). Using these baseline and natural background estimates, we derive the uniform rate of progress shown in the third column. 10 The final column displays the interim 2018 progress goal based on 14 years of improvement at t he uniform rate.

Table 3-1. Fine mass and percent contribution for 2 0% worst days



### 20% Worst-day fine mass ( μ g/m 3 

### ) / % contribution to fine mass

Site SO 4 NO 3 OC EC Soil

Acadia 6.3 / 60% 0.8 / 8% 2.5 / 23% 0.4 / 4% 0.5 / 5% Brigantine 11.5 / 59% 1.8 / 9% 4.5 / 23% 0.7 / 4% 1 .0 / 5%

Great Gulf 7.3 / 63% 0.3 / 3% 2.9 / 25% 0.4 / 3% 0. 6 / 5% Lye Brook 8.5 / 62% 1.1 / 8% 3.0 / 22% 0.5 / 3% 0.6 / 5%

Moosehorn 5.7 / 58% 0.7 / 7% 2.6 / 27% 0.4 / 4% 0.4 / 4%

Shenandoah 13.2 / 72% 0.7 / 4% 3.3 / 18% 0.6 / 3% 0.7 / 4%

Table 3-2. Particle extinction and percent contribu tion for 20% worst days


### 20% Worst-day particle extinction (Mm -1 

### ) / % contribution to extinction

Site SO 4 NO 3 OC EC Soil CM

Acadia 66.0 / 73% 8.1 / 9% 10.1 / 11% 4.4 / 5% 0.5 / 1% 1.8 / 2% Brigantine 106.2 / 69% 16.1 / 10% 18.3 / 12% 7.1 / 5% 1.0 / 1% 5.2 / 4%

Great Gulf 66.5 / 76% 3.0 / 3% 10.6 / 13% 3.8 / 4% 0.5 / 1% 2.9 / 3% Lye Brook 76.7 / 73% 9.3 / 9% 12.1 / 11% 4.7 / 5% 0 .7 / 1% 1.8 / 2%

Moosehorn 56.1 / 70% 6.3 / 8% 10.5/ 13% 4.4 / 5% 0. 4 / 0% 2.1 / 3%

Shenandoah 132.5 / 82% 5.8 / 4% 13.2 / 8% 5.7 / 4% 0.8 / 0% 2. 6 / 2%

Table 3-3. Natural background and baseline calculat ions for select Class I areas

Site Natural

Background (dv) Baseline

2000-04 (dv) Uniform Rate

(dv/year) Interim Progress

Goal 2018 (dv)

Acadia 11.45 22.34 0.18 19.80 Brigantine 11.28 27.60 0.27 23.97

Great Gulf 11.30 22.25 0.18 19.69 Lye Brook 11.25 23.70 0.21 20.80

Moosehorn 11.36 21.18 0.16 18.89

Shenandoah 11.27 27.88 0.28 24.00

The regional haze rule calls for steady improvement of visibility on the 20 percent worst visibility days. States are to consider this uniform rate of progress, and if

reasonable measures can be identified to meet or ex ceed this rate while ensuring no degradation of visibility on the best days, then it should be adopted as a Federal Class I

10 We calculate the rate of progress as (baseline – natural backgroun d)/60 to yield the annual deciview (dv) improvement needed to reach natural background conditions in 2064, starting from the 2004 baseline.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-3

area’s reasonable progress goal. A number of instructive analyses ar e presented below using each area’s uniform progress goal as an examp le, but these should not be interpreted as constituting MANE-VU recommendations on reasonable progress goals.

As a practical means of analyzing uniform progress goals, we have examined the components of observed fine particle pollution that substantially contribute to visibility degradation. This analysis shows that certain spec ies dominate the extinction budget while others play virtually no role on the worst ha ze days.

As demonstrated in Table 3-2, the inorganic constit uents of fine particles (sulfates and nitrates) are the dominant contributors to visi bility impairment, accounting for about 80 percent of total particle extinction. Within th e MANE-VU sites, the relative split between these two components is about eight to one sulfate to nitrate (at Shenandoah, the average 20 percent worst day contribution of sulfat es is even more dominant). Carbonaceous components account for the bulk of the remaining particle extinction, ranging from 12 to nearly 20 percent, mostly in the form of organic carbon. The remaining components add little to the extinction b udget on the worst days, with a few percent attributable to coarse mass and around a ha lf percent from fine soil.

One approach to designing control strategies for a chieving reasonable progress goals is to reduce all components of PM 2.5 in equal proportion. Achieving the 2018 uniform progress goals (expressed in Mm -1 in the second column of Table 3-4) requires between a 29 and 36 percent reduction in each compo nent of the six haze components of fine particle extinction if their relative percent contributions to the current worst baseline conditions are kept constant (see the third column of Table 3-4). Given the dominant role of sulfate and nitrate, however, and the difficulty in obtaining 29 to 36 percent reductions in some of the other categories such as soil or cou rse mass, sulfate- and nitrate-based control programs are likely to offer more reasonabl e emission reduction opportunities.

11 We derive the information in this table from the resul ts of Table 3-3. First, we converted the baseline and interim goal levels from dv to Mm -1 units, thus avoiding the logarithmic nature embedded int o the deciview calculations. The first column of the table gives the d ifference between baseline and interim goal. The ratio of this difference to the baseline yields the unif orm rate of reduction tabulated in the second column. We generate the paired species reduction percentages by us ing the wet and dry aerosol extinction
coefficients. We determine f(RH) values by dividing the fiv e-year B ext average by the dry extinction coefficient, giving a weighted average value of the f(RH) dur ing the worst 20% of days. Similarly, in Table 3-5, we calculate mass values using the relative contributio ns of the species to be reduced and their wet and dry efficiencies. Table 3-4. Percent particle B ext reduction needed to meet uniform progress 11

Site Particle Extinction

Decrease (Mm -1 ) Uniform

Reduction (%) Sulfate/Nitrate

Reduction (%) OC/EC

Reduction (%)

Acadia 27.7 31 38 194 Brigantine 55.3 36 46 218

Great Gulf 30.6 33 42 195 Lye Brook 35.4 34 41 210

Moosehorn 23.4 29 38 158

Shenandoah 57.1 36 42 303Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-4

The fourth column of Table 3-4 displays the results if a sulfate and nitrate focused control approach were taken to meet uniform progres s goals. For these two inorganic species, a greater reduction would be necessary on the 20 percent worst days if the other four components showed no change relative to baseli ne levels. The last column shows that the contribution of the carbonaceous species i s too small to meet the entire required 2018 progress goal on its own (i.e. the percent red uction is greater than 100) if a carbononly control approach were attempted.

Since it is easier to understand the implications o f requisite mass reductions, rather than extinction, Table 3-5 tabulates the cor responding mass changes required for meeting uniform progress goals on the 20 percent wo rst days. On an absolute mass basis, the changes across sites are more varied than they are when viewed from a percentage change perspective. That in part is a function of the relative pollution levels at each site, in addition to the logarithmic nature of the decivi ew (dv). This table (along with Table 3-6) can aid planners to gauge the potential impact that meeting uniform progress goals under the Regional Haze program will have on region al fine particle mass levels.

Table 3-6 provides an estimate of mass decreases th at might be expected on an average day. It assumes using either a uniform rate of change in all species, or a uniform rate of change in the sulfate and nitrate component of fine particulate, to achieve the progress toward the 2018 goals, respectively. Thes e values are likely a lower bound to the annual average change at Class I areas anticipa ted from current conditions to 2018 as they are based on the assumption that on the best d ays, no change occurs and the percent reduction on the middle days is half of what is pre dicted on the worst. 12

12 We derived the values tabulated in Figure 3-6 as follows : We multiplied half of the percentage change expected on the worst 20% of days by the average mass concentrat ion of each species for the middle 20% of days. Note that if we apply a 25% reduction on the clean er remaining quintile and 75% reduction on the dirtier remaining quintile, the annual average reduction wou ld presumably be greater than that on the middle days given the skew in the distribution of all day s. For example, in the inorganic-only case at Acadia, the average of the worst 20% change and best 20% is (2.69 + 0)/2 or 1.35 μg/m 3 , which is nearly four times greater than the middle day. Further, given t he large reduction on the worst days, it is reasonable to expect some small improvement on the best days. Table 3-5. Mass reductions required on 20% worst days based on extinction

estimates in Table 3-4



### 20% Worst Day Mass Reduction ( μ g/m 3 

### )

Uniform Percent Change All Species Only Inorganic Only

Carbonaceous

Site SO 4 NO 3 OC EC SO 4 NO 3 OC EC

Acadia 1.95 0.25 0.76 0.13 2.38 0.31 4.80 0.85 Brigantine 4.14 0.65 1.64 0.26 5.22 0.82 9.92 1.56

Great Gulf 2.42 0.11 0.97 0.13 3.06 0.14 5.74 0.76 Lye Brook 2.85 0.36 1.02 0.16 3.49 0.44 6.36 1.00

Moosehorn 1.68 0.20 0.77 0.13 2.14 0.26 4.12 0.69

Shenandoah 4.78 0.24 1.19 0.21 5.57 0.28 9.94 1.74Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-5

Table 3-6. Estimated Mass Reduction on an Average D ay 

### Estimated Average Day Mass Reduction ( μ g/m 3 

### )
Uniform Percent Change All Species Only Inorganic

Site SO 4 NO 3 OC EC SO 4 NO 3

Acadia 0.25 0.05 0.16 0.02 0.31 0.06 Brigantine 0.80 0.19 0.38 0.08 1.01 0.25

Great Gulf 0.28 0.04 0.19 0.03 0.36 0.05 Lye Brook 0.29 0.07 0.17 0.03 0.36 0.09

Moosehorn 0.25 0.05 0.19 0.03 0.32 0.06

Shenandoah 0.79 0.24 0.28 0.05 0.92 0.28



### 3.1.1. Preview of revised IMPROVE Algorithm for aerosol ex tinction

Recently, the IMPROVE Steering Committee accepted a n alternative approach for calculating visibility metrics based on measure d aerosol concentrations. The new algorithm improves the correspondence between the r econstructed extinction and directly measured light scattering at the extremes of the vi sibility range. These extremes form the basis for determining the uniform progress “glide p ath.”

The new equation revises or adds to the original ve rsion. The most significant changes include:

• revision of the dry aerosol extinction coefficients for sulfate, nitrate and organic carbon,

• splitting sulfate, nitrate and organic mass into sm all and large size fractions based on total species mass,

• revised f(RH) curves for inorganic species,

• inclusion of sea salt mass and associated f(RH) gro wth factor,

• use of a site-specific Rayleigh scattering term, and

• revision of the organic mass multiplier.

The VIEWS website provides the revised dataset for all IMPROVE data, allowing the calculation of the baseline period with the new algorithm. Natural background calculation methods that mirror many of the changes adopted as an alternative for baseline calculations have been suggested; however, none have been formally adopted by the IMPROVE Steering Committee at this time.

As a first step toward assessing the implications o f the algorithm revisions, we compare the baseline visibility levels from the old and new approaches. The new calculation approach results in between one and two deciview increase in the 20 percent worst visibility conditions during the baseline per iod for the six sites considered. Extinction changes are observed for all components, with increases ranging from 6 to 42 percent depending on species. The greatest overall percentage change occurs for organicTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-6

carbon and the least for fine soil. Changes in the baseline 20 percent best days were much less with the absolute contribution of a compo nent to visibility degradation increasing in some cases and decreasing in others. On average, the values decrease by

0.1 deciview. Table 3-7 and Table 3-8 summarize th e species-specific changes for worst and best days’ aerosol extinction.

Table 3-7. Aerosol extinction by specie for 20% worst days


### 20% worst-day particle extinction (Mm -1 

### ) New Algorithm / Old Algorithm

Site SO 4 NO 3 OC EC Soil Coarse Salt

Acadia 76.4 / 66 8.6 / 8.1 12.5 / 10.1 4.8 / 4.4 0.6 / 0.5 2.1 / 1.8 1.4 / 0 Brigantine 134.2 / 106.2 18.1 / 16.1 25.9 / 18.3 7.9 / 7.1 1.0 / 1.0 6.5 / 5.2 0.7 / 0

Great Gulf 79.6 / 66.5 3.4 / 3.0 14.8 / 10.6 4.3 / 3.8 0.6 / 0.5 3.1 / 2.9 0.1 / 0 Lye Brook 94.4 / 76.7 10 / 9.3 17.1 / 12.1 5.3 / 4.7 0.7 / 0.7 2.1 / 1.8 0.1 / 0

Moosehorn 64 / 56.1 7 / 6.3 13.4 / 10.5 5.1 / 4.4 0.4 / 0.4 2.5 / 2.1 1.1 / 0

Shenandoah 169.6 / 132.5 7.9 / 5.8 18.2 / 13.2 6.5 / 5.7 0.8 / 0.8 3.0 / 2.6 0.1 / 0

Table 3-8. Aerosol extinction by specie for 20% best days


### 20% best-day particle extinction (Mm -1 

### ) New Algorithm / Old Algorithm

Site SO 4 NO 3 OC EC Soil Coarse Salt

Acadia 6.8 / 7.4 1.1 / 1.2 2.3 / 2.4 0.9 / 0.9 0.1 / 0.1 0.7 / 0.7 0.4 / 0 Brigantine 5.7 / 6.2 1.0 / 1.1 2.0 / 2.1 0.9 / 0.9 0.1 / 0.1 0.9 / 0.7 0.2 / 0

Great Gulf 5.7 / 6.2 1.0 / 1.1 2.0 / 2.1 0.9 / 0.9 0.1 / 0.1 0.9 / 0.7 0.2 / 0 Lye Brook 4.5 / 5.0 1.2 / 1.2 1.3 / 1.4 0.6 / 0.6 0 .1 / 0.1 0.5 / 0.5 0.0 / 0

Moosehorn 6.8 / 7.3 1.0 / 1.2 3.1 / 3.1 1.0 / 1.0 0 .1 / 0.1 1.1 / 1.1 0.3 / 0

Shenandoah 11.4 / 12.8 4.2 / 4.4 2.9 / 3.0 1.6 / 1.6 0.2 / 0.2 1.1 / 1.1 0.1 / 0

Figure 3-1and Figure 3-2 graphically compare the ol d and new algorithm for six sites. The left-hand side of the figures presents the old contribution of aerosol extinction while the right-hand side shows the new calculation s. Relatively small differences are apparent, with slight relative decreases in sulfate contribution offset by small increases in nitrate, organic carbon and the addition of sea sal t.

The potential impact of these changes on the unifor m rate of progress slope cannot be determined at this time, since revisions in natural background calculations remain incomplete. A preliminary assessment, howev er, suggests that natural background estimates for MANE-VU may increase by ab out 10 percent. This translates to a change of just over one deciview. This estima te combined with the average increase

of 1.5 deciview in baseline conditions would not li kely change the slope of the uniform progress curve in any significant way. Nonetheless , the actual mass reductions required could change given the logarithmic nature of the ha ze index, where marginal mass changes are larger at higher deciview levels. It i s not a straightforward exercise to estimate the potential effect of such changes given the increased complexity of the new algorithm relative to the old equation.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-7

Figure 3-1. Comparison of Old and New Algorithms fo r Baseline Worst Days

Acadia 20% Worst

Extinction (90.9 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Great Gulf 20% Worst

Extinction (87.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse Brigantine 20% Worst

Extinction (154.0 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse Acadia 20% Worst

Extinction (106.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Brigantine 20% Worst

Extinction (194.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Great Gulf 20% Worst

Extinction (106 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSaltTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-8

Figure 3-2. Comparison of Old and New Algorithms fo r Baseline Worst Days

Moosehorn 20% Worst

Extinction (79.9 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse LyeBrook 20% Worst

Extinction (105.2 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Shenandoah 20% Worst

Extinction (160.6 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse LyeBrook 20% Worst

Extinction (129.7 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Moosehorn 20% Worst

Extinction (93.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Shenandoah 20% Worst

Extinction (206.1 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSaltTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-9



### 3.2. 2002 Monitoring Data

The recent MANE-VU report “2002 Year in Review” (NE SCAUM, 2004b) provides a comprehensive review of monitoring data available to support SIP development in the MANE-VU region, including data o n fine particle composition, as well as temporal and spatial distributions. The dat a in this study support the conceptual model in several important ways. They show that: ( 1) the single largest component of fine particle mass is sulfate; (2) the largest sulf ate-generating emissions sources that affect the MANE-VU region lie to the south and west of the region; (3) fine particle concentrations are bi-modal with peaks in the summe r and winter; and (4) summer and winter peak concentrations are generally caused by different chemical and physical processes in the atmosphere (i.e., summer peaks are strongly related to regional sulfate transport whereas winter peaks result from the sum of regionally-generated sulfate and locally generated sulfate, as well as organics and nitrate that build up during local stagnation events).



### 3.2.1. Sulfate

Data from several monitoring programs indicate that sulfate (on an annual basis) is the single largest component of fine particle ma ss in the MANE-VU region. Figure 3-3 displays sample data from two Speciation Trends Net work (STN) sites in New Jersey. This shows that sulfate accounts for roughly half o f fine particle mass on an annual average basis at background sites and about a third at the urban site. During summer, sulfate comprises over half the fine particle mass at rural background sites and two-fifths of fine particle mass at the urban site. When cons idering the different light-extinguishing properties of various fine particle constituents, s ulfate is responsible for an even greater fraction of visibility impairment. It accounts for between three-quarters and four-fifths of overall light extinction on the 20 percent worst- v isibility days (Table 3-2).

Figure 3-3. New Jersey Urban Area Compared to an Up wind Background Site

2002 Urban-Rural PM 2.5

0 5 10 15 20 25NJBGNJBGNJBGNJBGNJBGMass (ug/m3) Other PM2.5
Soil EC_STN OCM_STN
AmmNO3
AmmSO4

annual winter spring summer fallTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-10



### 3.2.2. Southwest-Northeast Gradient

Figure 3-4 shows that PM 2.5 mass declines fairly steadily along a southwest to

northeast transect of MANE-VU. This decline is con sistent with the existence of large fine particle emissions sources (both primary and s econdary) to the south and west of the MANE-VU region.

This trend in PM 2.5 mass is primarily due to a marked southwest-to-nor theast gradient in ambient sulfate concentrations during t hree seasons of the year as illustrated in Figure 3-5. Wintertime concentrations, by contras t, are far more uniform across the entire region. Figure 3-6 shows that on an annual b asis, both total PM and sulfate mass are highest in the southwestern portions of MANE-VU (note the different scales for each pollutant). High concentrations of nitrate and org anic particle constituents, which play a role in localized wintertime PM episodes, tend to be clustered along the northeastern urban corridor and in other large urban centers.

Sulfate is a secondary pollutant, meaning that it fo rms in the atmosphere from precursor emissions. The formation of sulfate from SO 2 emissions requires time in an oxidizing environment. Therefore, it is likely that a substantial portion of the sulfate observed in the MANE-VU region is from sulfur emitt ed from south and west of the region. Modeled meteorological (trajectory) data p resented in Chapter 5 support this conclusion by showing that the dominant wind direct ion over the MANE-VU region during periods of high sulfate concentrations is fr om the southwest.

Figure 3-4. MANE-VU FRM PM 2.5 statistics along a southwest to northeast axis

0 5 10 15 20 25 30 35

PA MD DE NJ NY CT MA NH VT RI MEug/m3 0.95%

Mean

Median

5%Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-11

Figure 3-5. 2002 Seasonal average SO4 based on IMPR OVE and STN data

Figure 3-6. 2002 Annual average PM 2.5 , sulfate, nitrate and total carbon for MANE-VU based on IMPROVE and STN data. Mass data are supple mented by the FRM network.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-12



### 3.2.3. Seasonality

In general, fine particle concentrations in MANE-VU are highest during the warmest (summer) months but also exhibit a secondar y peak during the coldest (winter) months. This bimodal seasonal distribution of peak values is readily apparent in Figure 3-7. The figure shows the smoothed 60-day running average of fine particle mass concentrations using continuous monitoring data fro m two northeastern cities over a period of several years.

Figure 3-7. Moving 60-day average of fine aerosol m ass concentrations based on long-term

data from two northeastern cities

Figure 3-8. 30-day average fine aerosol mass concen trations from eight northeastern citiesTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-13

Although the patterns exhibited by these monitoring data include occasional anomalies (as in the summer of 2000), summer peak c oncentrations in both cities of Figure 3-7 are generally much higher than the surrou nding winter peaks. Figure 3-8 also demonstrates this bimodal pattern. Though slightly more difficult to discern in just a single year’s worth of data, a “W” pattern does eme rge at almost all sites across the region during 2002 with the winter peak somewhat lo wer than the summer peak at most sites. Urban monitors in Wilmington, Delaware and New Haven, Connecticut have wintertime peak values approaching those of summer.



### 3.2.4. Seasonal Mechanisms

In the summertime, MANE-VU sites repeatedly experie nce sulfate events due to transport from regions to the south and west. Duri ng such events, rural and urban sites throughout the MANE-VU region record high (i.e., >1 5 μg/m 3 ) daily average PM 2.5 concentrations. Meteorological conditions during t he summer frequently allow for summer “stagnation” events when very low wind speed s and warm temperatures allow pollution levels to build in an air mass as it is s lowly transported across the continent. During these events, atmospheric ventilation is poo r and local emission sources add to the burden of transported pollution with the result tha t concentrations throughout the region (both rural and urban) are relatively uniform. Gen erally there are enough of these events

to drive the difference between urban and rural sit es down to less than 1 μg/m 3 during the warm or hot months of the year. As a result, conce ntrations of fine particles aloft will often be higher than at ground-level during the sum mertime, especially at rural monitoring sites. Thus, when atmospheric “mixing” occurs during summer 13 mornings (primarily 7 to 11 a.m.), fine particle concentrati ons at ground-level can actually increase (see Hartford, CT or Camden, NJ in Figure 3-9).

During the wintertime, strong inversions frequently trap local emissions overnight and during the early morning, resulting in elevated urban concentrations. These

inversions occur when the earth’s surface loses the rmal energy by radiating it into the atmosphere (especially on clear nights). The resul t is a cold, stable layer of air near the ground. At sunrise, local emissions (both mobile a nd stationary) begin increasing in strength and build-up in the stable ground layer (w hich may extend only 100 meters or less above-ground). Increasing solar radiation duri ng the period between 10 a.m. and noon typically breaks this cycle by warming the gro und layer so that it can rise and mix with air aloft. Because the air aloft during winte rtime is typically less polluted than the surface layer, this mixing tends to reduce ground-l evel particle concentrations (see Figure 3-10). This diurnal cycle generally drives wintert ime particle concentrations, although the occasional persistent temperature inversion can have the effect of trapping and concentrating local emissions over a period of seve ral days, thereby producing a significant wintertime pollution episode.

13 Here we define summer as May, June, July and August.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-14

Figure 3-9. Mean hourly fine aerosol concentrations during the summer season

Fine Aerosol Diurnal Pattern at 18 MANE-VU Sites

0 5 10 15 20 25 30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23ug/m3

Hartford, CT New Haven, CT Waterbury, CT Wilmington, DE Lewiston, ME Portland, ME Bangor, ME Camden, NJ
Newark, NJ Trenton, NJ Elizabeth, NJ NY City, NY Buffalo, NY Rochester, NY Pittsburgh, PA Burlington, VT Rutland, VT Boston, MA

Figure 3-10. Mean hourly fine aerosol concentration s during the winter season

Fine Aerosol Diurnal Pattern at 18 MANE-VU Sites

0 5 10 15 20 25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23ug/m3

Hartford, CT New Haven, CT Waterbury, CT Wilmington, DE Lewiston, ME Portland, ME Bangor, ME Camden, NJ
Newark, NJ Trenton, NJ Elizabeth, NJ NY City, NY Buffalo, NY Rochester, NY Pittsburgh, PA Burlington, VT Rutland, VT Boston, MATools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-15

Rural areas experience the same temperature inversi ons but have relatively fewer

local emissions sources so that wintertime concentr ations in rural locations tend to be lower than those in nearby urban areas. Medium and long-range fine particle transport events do occur during the winter but to a far less er extent than in the summertime. In sum, it is the interplay between local and distant sources together with seasonal meteorological conditions that drives the observed 3–4 μ g/m 3 wintertime rural versus

urban difference in PM concentrations.



### 3.3. RAIN data

Routine monitoring networks operated by USEPA, the Na tional Park Service or state monitoring agencies collected much of the mon itoring data shown so far. We anticipate that these data will continue to provide crucial information on the nature and extent of visibility impairment across the region. In addition, MANE-VU is also developing a network of enhanced monitoring sites c apable of providing continuous data on the concentration, composition, and visibility i mpacts of fine particles. These data will be critical for understanding the more complex issues associated with organic carbon as well as any tradeoffs between sulfate and nitrat e control. This Rural Aerosol Intensive Network (RAIN), which was first deployed in 2004, i s therefore likely to play a prominent role in future visibility control program s and in the development of regional

haze SIPs due in 2018.

NESCAUM coordinates the RAIN effort as a cooperative effort of the MANEVU member state air agencies. The network covers t he region from western Maryland (near large sulfur sources in the Ohio River Valley ) through northwestern Connecticut to

Acadia National Park in Maine. The initial network consists of these three rural, moderate elevation (700 to 2,500 feet) sites in a s outhwest to northeast line, all with detailed PM and visibility related measurements. Th e network design includes highly time resolved (1-2 hour) aerosol mass, composition, and optical property measurements. These provide enhanced insight into regional aeroso l generation and source characterization, which are factors that drive shor t term visibility, and aerosol model performance and evaluation. In addition to these t hree sites, as of 2006 the NYDEC/SUNY-Albany intensive measurement site at Pinnacl e State Park (Addison, NY, seven miles southwest of Corning, NY, and seven mil es north of the Pennsylvania border) has most of the RAIN parameters and methods other than visibility; efforts are underway to bring that site into the RAIN program ( to ensure consistent method operation) and to add visibility measurements.

The RAIN sites use the Sunset Laboratory Model 3 fie ld carbon analyzer and the new Thermo Environmental Model 5020 sulfate analyze r. This is the first use of these methods in routine, ongoing state-run networks. Co mbined with other more routine measurements such as IMPROVE aerosol, NGN-2 (wet) ne phelometers, continuous

PM 2.5 , trace SO 2 , ozone, meteorology, and automated digital visibil ity cameras (CAMNET), these methods make up the core RAIN monit oring lineup. Some of the

RAIN sites will have additional related measurement s, including “true” trace CO, NO X , dry scattering (NGN-3a nephelometer), and other mea surements. An Air and WasteTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-16

Management Association conference proceedings paper provides more information on the design of the network and examples of data from the summer of 2004. 14

A longer term goal of RAIN is to enhance the networ k with other measurements and sites in future years. A National Weather Servi ce ASOS visibility sensor at a RAIN site would allow the large network of existing ASOS d ata to be “tethered” to visibility measurements we understand well. Strong aerosol ac idity, nitric acid, and ammonia are

measurements that would be desirable on either an i ntegrated or real-time basis. There

are no continuous nitrate measurements in RAIN at t his time because available methods suitable for routine deployment in state networks a re not yet sufficiently robust. 15 Lack of continuous nitrate data is not a significant iss ue for this analysis since nitrate is not (yet) a major visibility factor at these rural site s. We expect that most of the continuous

method data from RAIN to be available in real-time to web data resources like VIEWS, FASTNET and AIRNowTech by the end of 2006.

Measurements similar to those in RAIN done towards the west and south borders of the MANE-VU domain (Ohio and Virginia for exampl e) would greatly enhance our understanding of the impact of the large sulfur sou rce region in and around the Ohio River Valley on regional visibility. We encourage agencies and RPOs in those areas to develop intensive sites to complement the RAIN data

. As an initial test of the RAIN network, we examined visibility and related particle information for the third quarter of 2004 to determ ine how well the data from one (or both) of two recently installed semi-continuous mon itors could reproduce the visibility data reported by existing NGN-2a nephelometers. Th e relevant data came from two monitors of interest: the Thermo Model 5020 (for su lfate) and the Sunset Labs (Model 3) semi-continuous analyzer for elemental and organic carbon. In addition, a Rotronic sensor (Model MP-101A, with active aspiration) measu red relative humidity (RH) data on-site in order to supply a correction factor - f( RH) - for estimating the light scattering associated with various fine particle constituents.

Because ammonium sulfate is the major component of haze-producing particulate pollution in the northeastern United States, we exam ined sulfate data first. The Thermo Model 5020 reports sulfate and the IMPROVE algorithm for calculating visibility parameters assumes that all sulfate is in the form of ammonium sulfate. During high

sulfate events in the rural Northeast this is not a lways the case, although it is still a reasonable first assumption.

The Thermo sulfate method has been shown to consist ently under-report sulfate

relative to IMPROVE sulfate measurements at the RAIN sites, but not at some other

sites. Since the correlation with IMPROVE sulfate is high at all RAIN sites, the hourly

RAIN sulfate data can be corrected to be “IMPROVE”-l ike with reasonable confidence.

A RAIN technical memorandum describes this issue in more detail. 16 For the Acadia sulfate data used here, the daily correlation coeff icient (R 2 ) between IMPROVE and

14 http://www.nescaum.org/documents/allen-awma_haze-rain-paper-oct -2004_proceedings.pdf/

15 See the EPA method evaluation report at http://www.epa.gov/ttn/amtic/semicontin.html for more

information.

16 “Rural Aerosol Intensive Network (RAIN) Preliminary Dat a Analysis,” available at: http://www.nescaum.org/documents/2006-05-memo8-rain.pdf/Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-17

Thermo sulfate is 0.95 (based on third and fourth q uarter 2004 data). A correction factor of 1.30 is applied to the Thermo sulfate data based on the linear regression of IMPROVE and Thermo sulfate 24-hour samples for the third an d fourth quarters of 2004 data; this

correction makes the Thermo sulfate data consistent with the IMPROVE sulfate data.

We need three types of data to relate direct measur es of atmospheric light scattering to a re-constructed or calculated estima te of light scattering based on observed sulfate levels: (1) direct measurements of light sc attering (via nephelometer); (2) sulfate measurements; and (3) relative humidity measurement s. The three RAIN sites in the

northeastern United States measure each of these var iables. Of these sites, however, only

the McFarland Hill site at Acadia National Park in Ma ine is within a Class I area. Therefore, we selected data from the McFarland Hill site for the preliminary analysis we

describe below.

Given the highly non-linear relationship between re lative humidity and ammonium sulfate particle size and the limitations of relative humidity (RH) sensor accuracy at very high values of RH, we excluded fro m this analysis data collected when relative humidity was equal to or greater than 95 p ercent. Of the 2,208 hourly observations recorded from June 1 through September 30, this relative humidity

‘exclusion’ removed 525 hours. Data for an additio nal 92 hours were not available due to missing measurements from either the sulfate monito r or the nephelometer. We excluded a further 35 hours due to flagged nephelometer perf ormance (such flags could be triggered by excess noise or rate-of-change in the signal). This left 1,556 hourly observation pairs for the third quarter, equivalent to a data capture rate of 70 percent - still a substantial sample given the nature of the emerging technology employed at the

RAIN sites.

We multiplied sulfate concentrations from the Therm o 5020 by 1.37 to convert them to a mass equivalent for ammonium sulfate (thi s is the same factor IMPROVE uses). This new variable (SULFATE) is the strongest driver of light extinction in the Northeast because of the extreme size-dependent nat ure of ammonium sulfate light scattering, which in turn is highly (and very non-l inearly) dependent on atmospheric relative humidity. Next, we converted the hourly R H values to a relative humidity function “f(RH)” by using a conversion table adopte d by IMPROVE. 17 Then we applied a “dry specific scattering” coefficient of “3” 18 to the hourly SULFATE values. The final equation is shown below:

Reconstructed Sulfate Scattering = 3 * f(RH) * (SUL FATE)

When we compared this reconstructed estimate of hou rly light scattering to the IMPROVE NGN-2a nephelometer data (via a least-square s linear regression), we

obtained an R 2 of 0.888. When two apparent outlier hours are rem oved (both of which occurred during periods when relative humidity was over 87 percent and changing rapidly) the regression slope is 0.846, the interce pt is -5, and R 2 increases to 0.942. This

17 See: http://vista.cira.colostate.edu/improve/Tools/humidity_correct ion.htm ; this is the original f(RH) table, not the new one.

18 Described at http://vista.cira.colostate.edu/improve/Tools/ReconBext/recon Bext.htmTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-18

implies that sulfate alone is responsible for appro ximately 85 percent of the light scattering (and visibility degradation) for this pe riod of measurement.

Because elemental carbon absorbs light much more st rongly than it scatters light, we added only the “light-scattering carbon” (OC) de tected by the Sunset Model 3 to this reconstruction. The IMPROVE program uses the follow ing equation to describe the impact of light-scattering carbon:

Reconstructed Carbon Scattering = 4 * f org (RH) * [OMC]

where the dry scattering coefficient of this carbon fraction is set at “4,” the relative humidity factor is set at unity (due to the weak hy groscopicity of organic carbon), and OMC represents “organic mass by carbon.” The IMPROV E Steering Committee has recently adopted 1.8 as an alternative organic mass multiplier (rather than 1.4) for calculating OMC values for use in reconstructed ext inction as described in section 3.1. We have also used 1.8 for the analysis presented be low.

Because the RAIN sites collect carbon data over two -hour periods, we averaged the McFarland Hill sulfate (Thermo-5020), scattering (NGN-2) and RH (Rotronic) hourly data into two-hour, whole number blocks in order to bring the data from Sunset Labs into the reconstruction equation. In addition, we subtr acted a “filter blank” value for the

Sunset OC data of 0.5 μ g/m 3 (empirically derived from user experience of the M odel 3) from the OC data prior to their use in the reconstr uction calculation (OMC = (Sunset OC – 0.5) x 1.8). See Figure 3-11 for results of these reconstructed estimates of visibility using both sulfate and carbon measurements.

As indicated by Figure 3-11, adding the organic carb on data to the sulfate data significantly improves the agreement between recons tructed estimates of aerosol scattering and direct visibility measurements at th e McFarland Hill site. Specifically, it appears that these two components of the ambient ae rosol generally explain about 94 percent of the observed scattering at Acadia during the summer, with a very high

correlation coefficient even at 2-hour intervals. This is excellent agreement considering that scattering from nitrate and crustal aerosol co mponents is not included in this

reconstruction.

These data demonstrate that the highly time-resolve d nature of RAIN data is invaluable in examining short-term variations (i.e. , on the order of days to weeks) in haze production and transport. The sulfate, carbon and other monitoring capabilities emerging from the RAIN project will provide another valuable tool to state and tribal authorities in seeking to understand the sources of regional haze and to craft effective control strategies. A more detailed analysis of RAIN data is available in a recently released

MANE-VU technical memorandum. 19

19 “Rural Aerosol Intensive Network (RAIN) Preliminary Dat a Analysis,” available at: http://www.nescaum.org/documents/2006-05-memo8-rain.pdf/Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-19

Figure 3-11. 2-Hour Reconstructed scattering at Aca dia, Maine using semicontinuous SO 4 and OC data for the third quarter of 2004Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 3-20



### References

NESCAUM, “Natural Background Visibility Conditions: Considerations and Proposed Approach to the Calculation of Natural Background V isibility Conditions at MANE-VU Class I Areas,” Northeast States for Coordinated Air Use Management, Boston, MA,

June 2004a.

NESCAUM, “2002: A Year in Review,” Northeast States f or Coordinated Air Use Management, Boston, MA, December, 2004b.

USEPA, “Guidance for Tracking Progress under the Regio nal Haze Rule,” EPA-454/B03-004 September 2003a.

USEPA, “Guidance for Estimating Natural Visibility Co nditions under the Regional Haze Program,” EPA-454/B-03-005 September 2003b.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-1



## 4. HAZE-ASSOCIATED POLLUTANT EMISSIONS

This chapter explores the origin and quantity of ha ze-forming pollutants emitted

in the eastern and the mid-Atlantic United States. It also describes the procedures used to prepare emissions inventory data for use in chemica l transport models (Chapter 6 describes in greater detail the models themselves).

The pollutants that affect fine particle formation, and thus contribute to regional haze, are sulfur oxides (SO X ), nitrogen oxides (NO X ), volatile organic compounds (VOC), ammonia (NH 3 ), and particles with an aerodynamic diameter less than or equal to 10 and 2.5 μm (i.e., primary PM 10 and PM 2.5 ). The emissions dataset illustrated below is the 2002 MANE-VU Version 2 regional haze emissions inventory. The emission inventories include carbon monoxide (CO), but we do not consider that pollutant here as it does not contribute to regional haze. The MANE- VU regional haze emissions inventory version 3.0, released in April 2006, has superseded version 2 for modeling purposes. This inventory update was developed thro ugh the Mid-Atlantic Regional Air Management Association (MARAMA) for the MANE-VU RPO. The comparative observations among recent emission inventories pres ented here (the 1996 USEPA NET and 1999 NEI) would hold true were version 3.0 subs tituted for version 2.0. 20

The first section of this chapter describes emissio n characteristics by pollutant and source type (e.g., point, area, and mobile). The s econd section describes on-going efforts to process emissions inventory data in support of a ir quality modeling. The final section provides source apportionment estimates for several MANE-VU Class 1 areas based on

2002 SO 2 inventory data.



### 4.1. Emissions Inventory Characteristics

4.1.1. Sulfur Dioxide (SO 2 

### )

SO 2 is the primary precursor pollutant for sulfate par ticles. Sulfate particles commonly account for more than 50 percent of partic le-related light extinction at northeastern Class I areas on the clearest days and for as much as or more than 80 percent

20 EPA's Emission Factor and Inventory Group (EFIG) (USE PA/OAR (Office of Air and Radiation)/OAQPS (Office of Air Quality Planning and Standa rds)/EMAD (Emissions, Monitoring and Analysis Division) prepares a national database of air emissio ns information with input from numerous state and local air agencies, from tribes, and from industry. This database contains information on stationary and mobile sources that emit criteria air pollutan ts and their precursors, as well as hazardous air pollutants (HAPs). The database includes estimates of annual emissions, by source, of air pollutants in each area of the country on an annual basis. The NEI includes emission estimates for all 50 states, the District of Columbia, Puerto Rico, and the Virgin Island s. Emission estimates for individual point or major sources (facilities), as well as county level estimates for area, mobile and other sources, are available currently for years 1985 through 1999 for criteria pollut ants, and for years 1996 and 1999 for HAPs. Data from the NEI help support air dispersion modeling, regi onal strategy development, setting regulation, air toxics risk assessment, and tracking trends in emissions over time. For emission inventories prior to 1999, the National Emission Trends (NET) database maintained crit eria pollutant emission estimates and the National Toxics Inventory (NTI) database maintained HAP emi ssion estimates. Beginning with 1999, the NEI began preparing criteria and HAP emissions data in a mor e integrated fashion to take the place of the

NET and the NTI.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-2

on the haziest days. Hence, SO 2 emissions are an obvious target of opportunity for reducing regional haze in the eastern United States. Combustion of coal and, to a substantially lesser extent, of certain petroleum p roducts accounts for most anthropogenic

SO 2 emissions. In fact, in 1998 a single source categ ory — coal-burning power plants — was responsible for two-thirds of total SO 2 emissions nationwide (NESCAUM, 2001a).

Figure 4-1 shows SO 2 emissions trends in the MANE-VU states extracted f rom the NEI for the years 1996, 1999, and the 2002 MANE -VU inventory (USEPA, 2005; MARAMA, 2004). Most of the states (with the except ion of Maryland) show declines in year 2002 annual SO 2 emissions as compared to 1996 emissions. Some of th e states show an increase in 1999 followed by a decline in 2002 a nd others show consistent declines throughout the entire period. The upward trend in emissions after 1996 probably reflects electricity demand growth during the late 1990s com bined with the availability of banked emissions allowances from initial over-compliance w ith control requirements in Phase 1 of the USEPA Acid Rain Program. This led to relativel y low market prices for allowances later in the decade, which encouraged ut ilities to purchase allowances rather than implement new controls as electricity output e xpanded. The observed decline in the

2002 SO 2 emissions inventory reflects implementation of the second phase of the USEPA Acid Rain Program, which in 2000 further reduced all owable emissions and extended emissions limits to more power plants. Figure 4-2 s hows the percent contribution from different source categories to overall, annual 2002 SO 2 emissions in the MANE-VU states. The chart shows that point sources dominat e SO 2 emissions, which primarily consist of stationary combustion sources for genera ting electricity, industrial energy, and heat. Smaller stationary combustion sources called “ area sources” (primarily commercial and residential heating) are another important sour ce category in the MANE-VU states. By contrast, on-road and non-road mobile sources ma ke only a relatively small

contribution to overall SO 2 emissions in the region (NESCAUM, 2001a).



### 4.1.2. Volatile Organic Compounds (VOC)

Existing emission inventories generally refer to “v olatile organic compounds” (VOCs) for hydrocarbons whose volatility in the atm osphere makes them particularly important from the standpoint of ozone formation. Fr om a regional haze perspective, we are concerned less with the volatile organic gases emitted directly to the atmosphere and more with the secondary organic aerosol (SOA) that t he VOCs form after condensation and oxidation processes. Thus the VOC inventory cat egory is of interest primarily from the organic carbon perspective of PM 2.5 . After sulfate, organic carbon generally accounts for the next largest share of fine particle mass an d particle-related light extinction at northeastern Class I sites. The term organic carbo n encompasses a large number and variety of chemical compounds that may come directl y from emission sources as a part of primary PM or may form in the atmosphere as secondar y pollutants. The organic carbon present at Class I sites almost certainly includes a mix of species, including pollutants originating from anthropogenic (i.e., manmade) sour ces as well as biogenic hydrocarbons emitted by vegetation. Recent efforts to reduce man made organic carbon emissions have been undertaken primarily to address summertime ozo ne formation in urban centers. Future efforts to further reduce organic carbon emis sions may be driven by programs that address fine particles and visibility.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-3

Figure 4-1. State Level Sulfur Dioxide Emissions

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Connecticut

De l aware District of Columbia Main

e

M a ry land

M assachu s et t s

Ne w Ha m p s h ire

Ne w J e rs e y New Y or k

Pe nn s y l v a n ia

Rh o d e Is l and

V e r mo ntmillion t/y NEI 1996

NEI 1999

MV 2002 v.2

Figure 4-2. SO 2 (Bar graph: Percentage fraction of four source cat egories, Circle:

Annual emissions amount in 10 6 tons per year)

0% 25% 50% 75% 100%

Connecticut

Delaware

D istr i c t of Columbia M

a i ne Mar y land

Mas sa ch use t t s New H a m p sh i r e New Jers e y New York

P enns y lvania Rhode Island Vermont 0.0 0.2 0.4 0.6 0.8 1.0 1.2million t/y

AREA NONROAD ONROAD POINT EmissionTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-4

Understanding the transport dynamics and source reg ions for organic carbon in northeastern Class I areas is likely to be more com plex than for sulfate. This is partly because of the large number and variety of OC speci es, the fact that their transport characteristics vary widely, and the fact that a gi ven species may undergo numerous complex chemical reactions in the atmosphere. Thus , the organic carbon contribution to visibility impairment at most Class I sites in the East is likely to include manmade pollution transported from a distance, manmade poll ution from nearby sources, and biogenic emissions, especially terpenes from conife rous forests.

As shown in Figure 4-3, the VOC inventory is dominat ed by mobile and area

sources. On-road mobile sources of VOCs include ex haust emissions from gasoline passenger vehicles and diesel-powered heavy-duty ve hicles as well as evaporative emissions from transportation fuels. VOC emissions may also originate from a variety of area sources (including solvents, architectural coa tings, and dry cleaners) as well as from some point sources (e.g., industrial facilities and petroleum refineries).

Biogenic VOCs may play an important role within the rural settings typical of Class I sites. The oxidation of hydrocarbon molecu les containing seven or more carbon atoms is generally the most significant pathway for the formation of light-scattering organic aerosol particles (Odum et al., 1997). Smal ler reactive hydrocarbons that may contribute significantly to urban smog (ozone) are less likely to play a role in organic aerosol formation, though we note that high ozone l evels can have an indirect effect on visibility by promoting the oxidation of other avai lable hydrocarbons, including biogenic

Figure 4-3. VOC (Bar graph: Percentage fraction of four source categories, Circle:

Annual emissions amount in 10 6 tons per year)

0% 25% 50% 75% 100%

Co n n e c t i c u t
De lawa r e

Di strict o f C o l u mbia Maine

M a ryla n d

Ma s sach u s e tts New H a mpsh i re

Ne w J e rs e y New Yor k

P ennsy l v a n i a

Rh o d e I s l a n d

V ermontmillion t/y

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

AREA NONROAD ONROAD POINT EmissionTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-5

emissions (NESCAUM, January 2001). In short, we nee d further work to characterize the organic carbon contribution to regional haze in the Northeast and Mid-Atlantic states and to develop emissions inventories that will be o f greater value for visibility planning

purposes.



### 4.1.3. Oxides of Nitrogen (NO X 

### )

NO X emissions contribute directly to visibility impair ment in the eastern U.S. by forming light-scattering nitrate particles. Nitrat e generally accounts for a substantially smaller fraction of fine particle mass and related light extinction than sulfate and organic carbon at northeastern Class I sites. Notably, nit rate may play a more important role at urban sites and in the wintertime. In addition, NO X may have an indirect effect on summertime visibility by virtue of its role in the formation of ozone, which in turn promotes the formation of secondary organic aerosol s (NESCAUM 2001a).

Figure 4-4 shows NO X emissions in the MANE-VU region at the state level

. Since 1980, nationwide emissions of NO X from all sources have shown little change. In fact, emissions increased by 2 percent between 1989 and 1998 (USEPA, 2000a). This increase is most likely due to industrial sources a nd the transportation sector, as power plant combustion sources have implemented modest em issions reductions during the same time period. Most states in the MANE-VU region experienced declining NO X emissions from 1996 through 2002, except Massachuse tts, Maryland, New York, and Rhode Island, which show an increase in NO X emissions in 1999 before declining to

levels below 1996 emissions in 2002.

Power plants and mobile sources generally dominate s tate and national NO X emissions inventories. Nationally, power plants ac count for more than one-quarter of all

NO X emissions, amounting to over six million tons. Th e electric sector plays an even larger role, however, in parts of the industrial Mi dwest where high NO X emissions have a particularly significant power plant contribution. By contrast, mobile sources dominate

the NO X inventories for more urbanized Mid-Atlantic and Ne w England states to a far greater extent, as shown in Figure 4-5. In these st ates, on-road mobile sources — a category that mainly includes highway vehicles — re present the most significant NO X source category. Emissions from non-road (i.e., of f-highway) mobile sources, primarily diesel-fired engines, also represent a substantial fraction of the inventory. While there are

fewer uncertainties associated with available NO X estimates than in the case of other key haze-related pollutants — including primary fine pa rticle and ammonia emissions — further efforts could improve current inventories i n a number of areas (NESCAUM, 2001a).

In particular, better information on the contributi on of area and non-highway mobile sources may be of most interest in the conte xt of regional haze planning. First, available emission estimation methodologies are wea ker for these types of sources than for the large stationary combustion sources. Moreov er, because SO 2 and NO X emissions must mix with ammonia to participate in secondary p article formation, emissions that occur over large areas at the surface may be more e fficient in secondary fine particulate

formation than concentrated emissions from isolated tall stacks (Duyzer, 1994).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-6

Figure 4-4. State Level Nitrogen Oxides Emissions



### 0.0 0.2 0.4 0.6 0.8 1.0 1.2

C o n n e c ticut

D e l awar e

D i s tr ic t o f C o l u m b i a Maine Ma r yl a n d Massachusetts New Hamp s h ir e

N ew J e r sey New Yor k Pennsylvani a

R hod e I sl a nd Vermontmillion t/y NEI 1996

NEI 1999

MV 2002 v.2

Figure 4-5. NO X (Bar graph: Percentage fraction of four source cat egories, Circle:

Annual emissions amount in 10 6 tons per year)

0% 25% 50% 75% 100%

Co n ne c t i cut

D el a ware

D i str ict of C ol umbi a Maine

Maryla nd

M assachu s etts

New H ampshire

Ne w Jersey

N ew Y ork Pennsy l vania Rhode Island

V er mo ntmillion t/y

0.0 0.3 0.6 0.9

AREA NONROAD ONROAD POINT EmissionTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-7



### 4.1.4. Primary Particulate Matter (PM 10 

### and PM 2.5 

### )

Directly-emitted or “primary” particles (as distinc t from secondary particles that form in the atmosphere through chemical reactions i nvolving precursor pollutants like

SO 2 and NO X ) can also contribute to regional haze. For regulato ry purposes, we make a distinction between particles with an aerodynamic d iameter less than or equal to 10 micrometers and smaller particles with an aerodynam ic diameter less than or equal to 2.5 micrometers (i.e., primary PM 10 and PM 2.5 , respectively).

Figure 4-6 and Figure 4-7 show PM 10 and PM 2.5 emissions for the MANE-VU states for the years 1996, 1999, and 2002. Note th at for PM 10 the inventory values are drawn from the 2002 NEI. Most states show a steady decline in annual PM 10 emissions over this time period. By contrast, emission trend s for primary PM 2.5 are more variable.

Crustal sources are significant contributors of pri mary PM emissions. This category includes fugitive dust emissions from cons truction activities, paved and unpaved roads, and agricultural tilling. Typically, monito rs estimate PM 10 emissions from these types of sources by measuring the horizontal flux o f particulate mass at a fixed downwind sampling location within perhaps 10 meters of a roa d or field. Comparisons between estimated emission rates for fine particles using t hese types of measurement techniques

and observed concentrations of crustal matter in th e ambient air at downwind receptor sites suggest that physical or chemical processes r emove a significant fraction of crustal material relatively quickly. As a result, it rarely entrains into layers of the atmosphere where it can transport to downwind receptor locatio ns. Because of this discrepancy

between estimated emissions and observed ambient co ncentrations, modelers typically

reduce estimates of total PM 2.5 emissions from all crustal sources by applying a fa ctor of 0.15 to 0.25 before including in modeling analyses.

From a regional haze perspective, crustal material g enerally does not play a major role. On the 20 percent best-visibility days durin g the baseline period (2000-2004), it accounted for six to eleven percent of particle-rel ated light extinction at MANE-VU Class 1 sites. On the 20 percent worst-visibility d ays, however, crustal material generally plays a much smaller role relative to other haze-fo rming pollutants, ranging from two to three percent. Moreover, the crustal fraction incl udes material of natural origin (such as soil or sea salt) that is not targeted under the Ha ze Rule. Of course, the crustal fraction can be influenced by certain human activities, such as construction, agricultural practices, and road maintenance (including wintertime salting) — thus, to the extent that these types of activities are found to affect visibility at nor theastern Class I sites, control measures targeted at crustal material may prove beneficial.

Experience from the western United States, where the crustal component has generally played a more significant role in driving overall particulate levels, may be helpful to the extent that it is relevant in the ea stern context. In addition, a few areas in the Northeast, such as New Haven, Connecticut and Pr esque Isle, Maine, have some experience with the control of dust and road-salt a s a result of regulatory obligations stemming from their past non-attainment status with respect to the NAAQS for PM 10

.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-8

Figure 4-6. State Level Primary PM 10 Emissions

0.0 0.2 0.4 0.6 0.8

Connecticut

Delaware

Dist ric t of Colum bia M ai ne

M aryla n d

M as s achu set t s New Hampshire

New J er sey

N ew Y or k

P enns ylv ania Rhod e Islan d Verm on tmillion t/y NEI 1996

NEI 1999

NEI 2002

Figure 4-7. State Level Primary PM 2.5 Emissions

0.0 0.1 0.2 0.3

Connectic u t Delaware

Dis t ric t o f Co l umbia M

a in e

M a r y lan d Massach u s e tts

Ne w H a m p s h ire

N e w Jer s ey

N e w York

P ennsy l vani a Rhode Island
Ve

r m o ntmillion t/y NEI 1996

NEI 1999

MV 2002 v.2Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-9

Current emissions inventories for the entire MANE-V U area indicate residential wood combustion represents 25 percent of primary fi ne particulate emissions in the region. This implies that rural sources can play a n important role in addition to the contribution from the region’s many highly populate d urban areas. An important consideration in this regard is that residential wo od combustion occurs primarily in the winter months, while managed or prescribed burning activities occur largely in other seasons. The latter category includes agricultural field-burning activities, prescribed burning of forested areas and other burning activit ies such as construction waste burning. Limiting burning to times when favorable meteorolog ical conditions can efficiently disperse resulting emissions can manage many of the se types of sources.

Figure 4-8 and Figure 4-9 show that area and mobile s ources dominate primary

PM emissions. (The NEI inventory categorizes resident ial wood combustion and some other combustion sources as area sources.) The rel ative contribution of point sources is larger in the primary PM 2.5 inventory than in the primary PM 10 inventory since the crustal component (which consists mainly of larger or “coar se-mode” particles) contributes mostly to overall PM 10 levels. At the same time, pollution control equipm ent commonly installed at large point sources is usually more ef ficient at capturing coarse-mode particles.



### 4.1.5. Ammonia Emissions (NH 3 

### )

Knowledge of ammonia emission sources will be neces sary in developing effective regional haze reduction strategies becaus e of the importance of ammonium sulfate and ammonium nitrate in determining overall fine particle mass and light scattering. According to 1998 estimates, livestock agriculture and fertilizer use accounted for approximately 86 percent of all ammon ia emissions to the atmosphere (USEPA, 2000b). We need, however, better ammonia inv entory data for the photochemical models used to simulate fine particle formation and transport in the eastern United States. Because the USEPA does not reg ulate ammonia as a criteria pollutant or as a criteria pollutant precursor, the se data do not presently exist at the same level of detail or certainty as for NO X and SO 2

. Ammonium ion (formed from ammonia emissions to the atmosphere) is an important constituent of airborne particulate matte r, typically accounting for 10–20
percent of total fine particle mass. Reductions in ammoniu m ion concentrations can be extremely beneficial because a more-than-proportion al reduction in fine particle mass can result. Ansari and Pandis (1998) showed that a one μ g/m 3 reduction in ammonium ion could result in up to a four μ g/m 3 reduction in fine particulate matter. Decision mak ers, however, must weigh the benefits of ammonia reducti on against the significant role it plays in neutralizing acidic aerosol. 21

21 SO 2 reacts in the atmosphere to form sulfuric acid (H 2 SO 4 ). Ammonia can partially or fully neutralize this strong acid to form ammonium bisulfate or ammonium sulfate. If planners focus future control strategies on ammonia and do not achieve corresponding SO 2 reductions, fine particles formed in the atmosphere will be substantially more acidic than those presentl y observed.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-10

Figure 4-8. Primary PM 10 (Bar graph: Percentage fraction of four source cate gories,

Circle: Annual emissions amount in 10 6 tons per year)

0% 20% 40% 60% 80% 100%

C o nne c t icut

D e l a w ar e

Distr i ct o f Col u m b ia Maine

Mar y la nd Massachus e t t s

N ew Hampshi

r e

N ew Jersey

N e w Y o rk Pennsylvania

R h ode I s la nd

V er mo ntmillion t/y

0.0 0.1 0.2 0.3 0.4 0.5

AREA NONROAD ONROAD POINT Emission

Figure 4-9. Primary PM 2.5 (Bar graph: Percentage fraction of four source categories, Circle: Annual emissions amount in 106 tons per year)

0% 25% 50% 75% 100%

C on necticu t Delawa re

D istrict o f C o lu mb ia Ma in e Ma ry la n d Massachusetts

N ew Hampsh i re

N ew Jers e y New York Pennsyl va nia Rhode Island Ve

r mon tmillion t/y

0.00 0.05 0.10 0.15 0.20

AREA NONROAD ONROAD POINT EmissionTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-11

To address the need for improved ammonia inventorie s, MARAMA, NESCAUM and USEPA funded researchers at Carnegie Mellon Unive rsity (CMU) in Pittsburgh to develop a regional ammonia inventory (Davidson et a l., 1999). This study focused on three issues with respect to current emissions esti mates: (1) a wide range of ammonia emission factor values, (2) inadequate temporal and spatial resolution of ammonia emissions estimates, and (3) a lack of standardized ammonia source categories.

The CMU project established an inventory framework with source categories, emissions factors, and activity data that are readi ly accessible to the user. With this framework, users can obtain data in a variety of fo rmats 22 and can make updates easily, allowing additional ammonia sources to be added or emissions factors to be replaced as better information becomes available (Strader et al. , 2000; NESCAUM, 2001b).

Figure 4-10 shows that estimated ammonia emissions w ere fairly stable in the 1996, 1999, and 2002 NEI for MANE-VU states, with s ome increases observed for Massachusetts, New Jersey and New York. Area and o n-road mobile sources dominate the ammonia inventory, according to Figure 4-11. Spec ifically, emissions from agricultural sources and livestock production accou nt for the largest share of estimated ammonia emissions in the MANE-VU region, except in the District of Columbia. The two remaining sources with a significant emissions contribution are wastewater treatment systems and gasoline exhaust from highway vehicles.

Figure 4-10. State Level Ammonia Emissions

0.000 0.025 0.050 0.075 0.100

Connecticut Delaware

District of Colu

m bia M ai ne

M aryland

M assa chusetts

N ew H am pshire New Jersey

N ew Y or k Pennsyl van i a

R h ode I s l and Verm o n tmillion t/y NEI 1996

NEI 1999

MV 2002 v.2

22 For example, the user will have the flexibility to choos e the temporal resolution of the output emissions data or to spatially attribute emissions based on land-use da ta.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-12

Figure 4-11. NH 3 (Bar graph: Percentage fraction of four source cat egories, Circle:

Annual emissions amount in 10 6 tons per year)

0% 25% 50% 75% 100%

Co nnec t icut
De l aware

Di str i c t o f Col u mbia Mai ne Mar y land

M as s achu set t s New Hampshire

Ne w J e rs ey New Y o r k Pennsylvania

Rh o de Is l an d Vermontmillion t/y

0.00 0.06 0.12

AREA NONROAD ONROAD POINT Emission



### 4.2. Contribution Assessments Based on Emissions Invento ries

Two data analysis methods have been developed that directly combine emission inventory data with meteorological data in order to provide first-order contributions to

observed sulfate from individual states. The first approach, known as “Q/d,” evaluates the state contribution as a proportion of the ratio of the total SO 2 emissions from that state and the distance from the state to the receptor. St ates and sources are assigned wind sectors to account for prevailing wind patterns in establishing contributions. The second approach, known as “Emissions times Upwind Probabili ty,” evaluates the state contribution through the use of ensemble back traje ctories (See Appendix A for a more detailed description of trajectory methods). The b ack trajectory-derived residence times of air parcels have been mapped onto a grid to crea te a “residence time probability field,” which is then multiplied by an SO 2 emissions field to obtain estimated source contributions. The results of the two approaches a re compared for receptor sites in and around the MANE-VU region.



### 4.2.1. Sulfur Dioxide Emissions Divided by Distance

Aggregated over long periods of time and large geog raphic areas, the total atmospheric sulfate contribution from a specific so urce, state, or region should be approximately proportionate to its SO 2 emissions. For specific receptor locations, like a Class 1 visibility area, relative impacts decrease with increasing distance from the source. Impacts diminish over distance as pollutants are di spersed in the atmosphere and removed through deposition. For non-reactive primar y pollutant emissions, theTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-13

relationship between atmospheric concentrations and distance (d) can be approximated as

a function of 1/d 2 . For secondary pollutants like sulfate, reductions in ambient concentrations that occur as a result of dispersion and deposition mechanisms are partially offset by the formation of secondary aero sol such that an increasing fraction of the remaining downwind sulfur is converted to aeros ol sulfate. In these cases, the effects of distance are better characterized by the functio n 1/d. During regional sulfate episodes when sulfur conversion rates are enhanced by the pr esence of gas and aqueous-phase oxidants, pollutant concentrations decline even les s rapidly with distance as accelerated aerosol formation rates work to both generate more sulfate and reduce the remaining sulfur available for deposition (deposition rates a re roughly an order of magnitude slower

for sulfate than for SO 2 ).

One simple technique for deducing the relative impa ct of emissions from specific point sources on a specific receptor site involves calculating the ratio of annual emissions (Q) to source-receptor distance (d). 23 This empirical relationship is reasonable based o n simple dispersion assumptions. Results from SO 2 modeling using the CALPUFF (California Puff) model (EarthTech, 2004) further bo lster its validity by showing a strong relationship between emissions and distance. In fa ct, this extremely simple method of estimating impact can be significantly improved to account for some aspects of meteorology by scaling results according to the ext remely linear relationships between CALPUFF and Q/d values within specific wind sectors.

The geographic domain of the sources included in th e Q/d study consisted of U.S. states in the CENRAP, MANE-VU, VISTAS, and MIDWEST RPO r egions. Canadian provinces in the lower eastern region were also inc luded. The categories of SO 2 emission sources included in this analysis were area sources (e.g., residential boilers and heaters), non-road mobile sources (e.g., tractors and constru ction vehicles), and point sources (e.g., industrial smokestacks and power generation facilit ies). 24 Results were calculated for seven receptors including: Acadia National Park, Br igantine Wilderness in the Forsythe Wildlife Preserve, Dolly Sods Wilderness, Lye Brook W ilderness, Moosehorn Wilderness, Presidential Range-Dry River Wilderness, and Shenandoah National Park.

The empirical formula that relates emission source strength and estimated impact can be expressed through the equation I=C i *Q/d. In this equation, the strength of an emission source, Q, is linearly related to the impa ct, I, that it will have on a receptor located a distance, d, away. The effect of meteoro logical prevailing winds can be factored into this approach by establishing the con stant, C i , as a function of the sectors relative to the receptor site. This relationship c an be established by comparing Q/d values to modeled impacts, which are also dependent on prevailing wind patterns at the site of impact. By establishing a different consta nt for each sector, based on prior modeling results – in this case, CALPUFF results – we are in effect “scaling” Q/d results

23 We calculated distances using the Haversine formula, which u ses spherical geometry to calculate the distance between two points on the surface of a sphere. Becaus e the Earth is not an exact sphere, use of

this formula introduces a small amount of error — on the o rder of 0.5% — in the distance calculations for any two locations on the Earth’s surface (see http://mathforum.org/library/drmath for further details).

24 On-road mobile sources contribute about 2% of the SO 2 inventory nationally (See Figure 4-2 for regional breakdown) and were not considered significant enough to include in this analysis, which does not provide results to that level of precision.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-14

by CALPUFF-calculated source impacts. The absolute impacts produced are then dependent on the CALPUFF results, however the relativ e contributions of each source within a wind sector is established completely inde pendent of the CALPUFF calculation, yielding a quasi-independent method of apportionmen t to add to our weight-of-evidence approach.

To determine the appropriate constant for each wind sector relative to a given receptor, a linear regression analysis was performe d on 778 sources in the eastern U.S.

with emissions data available from the continuous e missions monitoring system (CEMS) for 2002. The Q/d values were calculated for these sources and compared with their modeled source impacts from the CALPUFF model (see Phas e I modeling discussed in Appendix D). The sites were grouped by angle into “wind sectors” such that each wind sector had a best-fit line with as high a correlati on coefficient (R 2 ) value as possible.

Most sectors had an R 2 above or near 0.90. The slopes of the resulting be st-fit lines were used as the constants in the above equation. 25

To calculate the impact that each state had on a gi ven receptor, the area and nonroad SO 2 emission sources were summed across the entire sta te, and the distance to the receptor site for those emission sources was calcul ated based on that state’s geographic center, adjusted for population density. 26 In this way, the area and non-road emissions were treated as a single point source located at th e population-weighted center of each state. These impacts were then added to the impact of the point sources that were calculated individually. The sum of area, non-road , and point source impacts for each state was used to compare the contributions relativ e to other states in the eastern U.S. and parts of Canada.

The principal contributors to the MANE-VU receptors , according to this method, include the midwestern states of Indiana and Ohio, as well as Pennsylvania and New York. This is due not only to the large emissions from these states, but also to the predominantly westerly winds that carry Midwest pol lution eastward (the Midwest was located in the wind sector with the highest C i -value, five times that of the lowest Civalue). Table 4-1 shows the relative contribution of eastern states and Canadian provinces on several receptor sites in the region. Figure 4-12 and Figure 4-13 show the corresponding Q/d rankings across a set of northern and southern Class I areas in or near

MANE-VU.

25 The analysis resulted in best-fit lines that did not always go through the origin. By forcing the regression lines through the origin, we ensure that a source with zero emissions would correspond to zero impact at the receptor. After having forced the best-fit lines throu gh the origin, R 2 values remained greater than 0.77 and changed less than 0.01 from the original regression. The changes to the slope were considered insignificant, with an average change of 4%, ranging fro m -11% to 16%; the extremes occurred for plots with relatively few points and on the low end of R-squared correlations. Some angle ranges were not

associated with a wind sector because of insufficient data for that angle range. For example, there was a lack of data for Lye Brook Wilderness receptor in the 0-144 o angle range. This angle sector and similar sectors lacking adequate data were assigned the lowest C i -value amongst the other wind sectors of the same receptor site. The impact of this decision should be small gi ven the relatively few sources in these directions and their tendency to be downwind of the receptor

. 26 Calculations using county-level emissions and distance to co unty centroid to receptor were compared to the approach used here. This added complexity, however, di d not substantially change the predicted impacts nor the relative rankings among states.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-15

Table 4-1. 2002 SO 2 CALPUFF-scaled Emissions over Distance Impact ( μ g/m 3 )

STATE ACADIA LYE BROOK BRIGANTINE SHENANDOAH EMISSIONS Pennsylvania 0.19 0.30 0.38 0.43 1,090,562

Ohio 0.19 0.23 0.27 0.46 1,273,755 West Virginia 0.08 0.09 0.16 0.32 573,136 Maryland 0.05 0.06 0.24 0.21 292,970

New York 0.12 0.15 0.15 0.13 341,493

Indiana 0.11 0.11 0.14 0.18 914,039

North Carolina 0.07 0.06 0.14 0.26 510,452 Virginia 0.06 0.04 0.14 0.17 309,709 Georgia 0.07 0.07 0.11 0.14 605,040 Kentucky 0.06 0.06 0.11 0.14 521,583 Michigan 0.08 0.08 0.06 0.10 432,166

Illinois 0.07 0.07 0.07 0.10 642,264

Tennessee 0.04 0.04 0.07 0.09 423,705 New Jersey 0.02 0.02 0.14 0.07 64,437

Alabama 0.05 0.05 0.07 0.08 548,054

Texas 0.04 0.04 0.05 0.06 849,831

Florida 0.04 0.03 0.06 0.07 537,327

Massachusetts 0.08 0.02 0.03 0.05 123,754

South Carolina 0.04 0.02 0.05 0.07 262,867

Delaware 0.02 0.02 0.10 0.04 83,549

Missouri 0.04 0.04 0.05 0.05 361,911

Wisconsin 0.03 0.03 0.03 0.04 263,040

Maine 0.05 <0.01 <0.01 0.01 39,423

Kansas 0.01 0.01 0.01 0.01 136,104 New Hampshire 0.03 <0.01 0.01 0.01 53,772

Minnesota 0.01 0.01 0.01 0.01 124,151 Mississippi 0.01 0.01 0.01 0.02 126,456

Iowa 0.01 0.01 0.01 0.01 230,676

Connecticut 0.01 0.01 0.01 0.01 41,093

Oklahoma 0.01 0.01 0.01 0.01 139,327

Louisiana 0.01 0.01 0.01 0.02 346,170

Arkansas <0.01 <0.01 0.01 0.01 140,096

Nebraska 0.01 <0.01 <0.01 0.01 46,074

Rhode Island <0.01 <0.01 <0.01 <0.01 2,531

Vermont <0.01 <0.01 <0.01 <0.01 1,575

Dist. of Columbia <0.01 <0.01 <0.01 <0.01 1,715

Ontario 0.01 0.24 0.12 0.15 5,010

New Brunswick 0.15 0.01 0.02 0.02 1,261 Quebec 0.09 0.02 0.03 0.05 6,567

Nova Scotia 0.08 0.01 0.02 0.02 7,566

Newfoundland 0.01 <0.01 <0.01 0.01 15,287

Prince Edward Is. <0.01 <0.01 <0.01 <0.01 10,157Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-16

Figure 4-12. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on emissions divided by distance (Q /d) results

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70CanadaPAOHINNYMIWVILGAMAKYNCVAMDALTNTXMOFLWINJMESCNHDEKSMNIACTOKMSLANEVTARRIDCAnnual Average Contribution (μg/m3 SO4)

Acadia

Great Gulf

Lye Brook

Moosehorn

Figure 4-13. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I receptors based on emissions divided by distance (Q /d) results

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70OHPAWVNCCanadaMDINVAKYGANYILTNMIALNJTXFLDESCMOWIMAMSKSMNOKIALACTNHARNEMEDCRIVTAnnual Average Contribution (μg/m3 SO4)

Brigantine
Dolly Sods

ShenandoahTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-17

It is difficult to draw firm conclusions from what is essentially an empirical relationship between emission source strength, dist ance and observed impacts at receptor sites, but the addition of the CALPUFF-derived scale f actors to this approach yields important insights as to the abilities of fairly si mple screening techniques to accurately predict potential contributions to downwind recepto rs. This is borne out by the high degree of correspondence between the relative contr ibutions of regions as identified by this and other techniques shown in Chapter 8.



### 4.2.2. Emissions times Upwind Probability

The Emissions times Upwind Probability method of ass essing contribution to pollution involves multiplying the back-trajectory calculated residence time probability for a grid cell with the total emissions – over the same time period – from that grid cell. The product is an emissions-weighted probability fi eld that can be integrated within state boundaries to calculate relative probabilities of e ach state contributing to pollution
transport.

A back trajectory is the path that a parcel of air is calculated to have taken prior to its arrival at a given receptor (See Chapter 5). Th e back trajectories used in this study were calculated by the HYSPLIT system (Draxler, 1997 and 1998). Five years of back trajectories, calculated eight times per day result s in 14,600 back trajectories. The back trajectories are 72-hours in length and have calcul ated endpoints, or locations, at hourly intervals that specify the air mass path. The endp oints from all trajectories are mapped into a matrix of residence times spent in individua l grid cells over the five year period. The resulting sum expresses the likelihood that air spent time in a particular quarter degree longitude by quarter degree latitude grid ce ll over a domain between 25 o and 57 o

latitude and -110 o to -50 o longitude. These residence times are then multipl ied by the MANE-VU base year SO 2 emission inventory that has been allocated to a 12 km horizontal grid based on a Lambert Conformal projec tion. 27 The resulting product matrix

contains the SO 2 -weighted residence times that are then numerically integrated within the

boundaries of each state to define a “contribution” for each state. This provides a relative ranking of contribution by state that can be used t o compare with other methods of

attribution. 28

The area of analysis included states from Maine to Mississippi. Several states lie on the periphery of our available SO 2 emissions field and were used in the study despite an incomplete inventory of SO 2 emissions for the far edges of each state; these i ncluded

27 Since the latitude-longitude projection of the residence tim e grid is different than the Lambert conformal projection of the emissions grid, there is not a one-to-o ne mapping. We therefore interpolated each residence time grid cell to increase the spatial resolution to 1/ 20 o latitude by 1/20 o longitude. Each

residence time cell was then associated with the nearest SO 2 emission cell to ensure that each SO 2 emission component of the inventory was associated with the approxim ate residence time that was spent in nearest proximity to the emissions region. A distance of one-q uarter degree between associated grid cells was used as a cutoff for the analysis. In other words, the produ ct of a particular SO 2 cell and residence time cell would not be used if the geographical distance between them w as greater than one-quarter degree (latitude or longitude).

28 Note that the absolute units are expressed as nmole/hr, whic h represent a fractional contribution of a grid cell’s emission rate that is likely to influence a downwind r eceptor. The physical meaning of this contribution is not clear, so this has been used in a relat ive sense only.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-18

Missouri, Arkansas, Mississippi, Alabama, and Georg ia. 29 Canada has significant SO 2 emissions in the domain of the SO 2 grid, hence contributions have been calculated for portions of Ontario, Quebec and New Brunswick that were within the SO 2 emission grid. Table 4-2 provides a ranking of state contributions and Figure 4-14 and Figure 4-15 show the ranked contribution for two groupings of Class I sites in or near MANE-VU.

Table 4-2. 2002 SO 2 Upwind Probability (percent contribution)

ACADIA LYEBROOK BRIGANTINE SHENANDOAH West Virginia 0.06 0.07 0.09 0.19

Ohio 0.09 0.11 0.10 0.12 Pennsylvania 0.09 0.13 0.13 0.07 Kentucky 0.04 0.05 0.06 0.09

Indiana 0.05 0.05 0.05 0.06

New York 0.07 0.11 0.04 0.02 Virginia 0.03 0.02 0.06 0.06

North Carolina 0.02 0.01 0.05 0.07

Illinois 0.06 0.05 0.04 0.04 Georgia 0.02 0.02 0.04 0.05 Michigan 0.04 0.04 0.02 0.02

Tennessee 0.02 0.01 0.02 0.04 Maryland 0.02 0.02 0.04 0.03 New Jersey 0.02 0.02 0.07 0.01

Alabama 0.01 0.01 0.02 0.02

South Carolina 0.01 0.01 0.02 0.02

Wisconsin 0.02 0.02 0.01 0.01

Missouri 0.01 0.01 0.01 0.01

Delaware <0.01 0.01 0.02 <0.01

Massachusetts 0.02 0.01 <0.01 <0.01 New Hampshire 0.02 0.01 <0.01 <0.01

Minnesota 0.01 0.01 <0.01 <0.01

Connecticut 0.01 0.01 <0.01 <0.01

Maine 0.02 <0.01 <0.01 <0.01

Iowa 0.01 <0.01 <0.01 <0.01

Dist. of Columbia <0.01 <0.01 <0.01 <0.01

Arkansas <0.01 <0.01 <0.01 <0.01 Mississippi <0.01 <0.01 <0.01 <0.01

Vermont <0.01 <0.01 <0.01 <0.01

Louisiana <0.01 <0.01 <0.01 <0.01

Rhode Island <0.01 <0.01 <0.01 <0.01

Texas <0.01 <0.01 <0.01 <0.01

Canada 0.23 0.20 0.08 0.05

29 These states still had significant areas that were not covered by the SO 2 grid. Thus only a fraction of

these states’ emissions were included in the total state contr ibution. The following are estimates of the area

not covered by the SO 2 grid: MO-20%, AR-10%, MS-25%, AL-20%, GA-5%.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-19

Figure 4-14. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on emissions times upwind probabili ty (E x UP) results

0 1000 2000 3000 4000 5000 6000CanadaOHPANYWVINILKYMIVAMDGAWINJNCTNNHMAMEMOALCTSCMNDEIAVTARDCRILAMSTXAnnual Average Contribution (nmoles/hr)

Acadia
Lye Brook

Moosehorn

Great Gulf

Figure 4-15. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I receptors based on emissions times upwind probabili ty (E x UP) results

0 1000 2000 3000 4000 5000 6000 7000 8000 9000WVOHPAKYNCCanadaVAINGAILTNNJMDNYMIALSCMOWIDEMNDCIAARMACTMSNHLAMERIVTTXAnnual Average Contribution (nmoles/hr)
Brigantine

ShenandoahTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 4-20



### References

Ansari, A. S., and Pandis, S.N., “Response of inorgani c PM to precursor concentrations,”

Environ. Sci. Technol

. , 32, 2706-2714, 1998.

Davidson, C., Strader, R., Pandis, S., and Robinson, A

., Preliminary Proposal to MARAMA and NESCAUM: Development of an Ammonia Emissions I nventory for the Mid-Atlantic States and New England . Carnegie Mellon University, Pittsburgh, PA. 7-Jan . 1999.

Draxler, R.D. and Hess, G.D., “Description of the H YSPLIT-4 Modeling System,” NOAA Technical Memorandum ERL, ARL-224 , Air Resources Laboratory, Silver Springs, Maryland, 24 pgs., 1997.

Draxler, R.D. and Hess, G.D., “An Overview of the H YSPLIT-4 Modeling System for Trajectories, Dispersion, and Deposition,” Australian Meteorological Magazine , 1998, 47, 295-308.

Duyzer, J., “Dry Deposition of Ammonia and Ammonium Aerosols over Heathland,” J. Geophys. Res., 99(D9):18,757 – 18,763, 1994.

EarthTech, 2004, http://src.com/calpuff/calpuff1.htm

MARAMA 2004, http://www.marama.org/visibility/2002%20NEI/index.h tml

NESCAUM, “Regional Haze and Visibility in the Northe ast and Mid-Atlantic States,” January 2001a.

NESCAUM, “Development of an Improved Ammonia Emissio ns Inventory for the United States,” December 2001b.

NESCAUM, “2002: A Year in Review,” December, 2004.

Odum, J.R., Jungkamp, T.P.W., Griffin, R.J., Flagan, R.C., and Seinfeld, J.H., “The Atmospheric Aerosol-forming Potential of Whole Gasol ine Vapor,” Science , 276, 96-99, 1997.

Strader, R., Anderson, N., and Davidson, C., Development of an Ammonia Inventory for the Mid-Atlantic States and New England, Progress R eport, October 18, 2000 , available

online: http://marama.org/rt_center/MARAMAprogress10-18-00. pdf , 2000.

USEPA, National Air Quality and Emission Trends Report, 19 98 , EPA 454/R-00-003,

available online: http://www.epa.gov/oar/aqtrnd98/ , 2000a.

USEPA, National Air Pollutant Trends, 1900 – 1998 , EPA 454/R-00-002, available

online: http://www.epa.gov/ttn/chief/trends/trends98/trends 98.pdf , 2000b.

USEPA 2005, http://www.epa.gov/ttn/chief/eiinformation.html

.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-1



## 5. DATA ANALYSIS TECHNIQUES

Trajectory analyses have historically been used to trace the path of polluted air masses prior to their arrival at a given receptor s ite. Such analyses, by linking downwind measurements of ambient air quality with specific g eographic areas upwind, can be very helpful in exploring the relative contribution of t ransported emissions from potential source regions on high and low pollution days. As w ith all of the tools and modeling techniques discussed in this report, trajectory ana lysis is not without some uncertainties

and limitations. One such limitation is the fact th at these analyses are typically unable to distinguish emission contributions from one point a long the length of the trajectory from a different point along the path. In addition, the accuracy of any individual back trajectory calculation for a single observation or episode may be compromised by inherent limitations in the underlying Lagrangian t rajectory models, which tend to become less accurate as the calculation progresses further back in time. Fortunately, a variety of techniques are available to mitigate the se uncertainties and enhance confidence in the results obtained using trajectory analysis. These include techniques for triangulating results across multiple sites, ensemb le techniques that combine the results of large numbers of back trajectories, clustering algo rithms that group similar trajectories based on their spatial characteristics, and techniq ues for combining trajectory analyses with source apportionment models. All of these stra tegies can be useful in improving and refining traditional trajectory analyses.

This chapter describes the results of back trajecto ry analyses that have been conducted to date for key pollutant species observe d at MANE-VU and nearby receptor sites. In addition, we explore novel techniques fo r improving the accuracy of individual trajectories by grouping meteorologically similar b ack-trajectories into trajectory “clusters” and examining the relationship between t he transport pathways defined by these clusters and downwind air quality observation s. We then turn to source apportionment models which can be used to group ava ilable monitoring data for various components of PM 2.5 in logical combinations that best explain the varia tion in observed species concentrations in terms of specific “source profiles.” These source profiles are used to distinguish the emissions from common pollu tion sources (e.g., mobile sources, coal combustion). The information obtained through source apportionment analysis can then be used in combination with back trajectory an alysis to link specific geographic source regions with downwind air quality conditions and to establish the relative contribution of different source regions to visibil ity impacts at the receptor site.

This chapter provides further description of severa l trajectory analysis techniques, before proceeding to a review of the insights gaine d to date by applying these techniques to analyze source regions for particulate pollution in the MANE-VU region. Preliminary results and interpretation are presented and used t o support and bolster the basic conceptual model of regional haze outlined in Chapt er 2.



### 5.1. Trajectory Analysis

The Hybrid Single Particle Lagrangian Integrated Traj ectory (HYSPLIT) model (Draxler, 1997 and 1998) was used to calculate back trajectories for 13 sites in the

northeastern United States. Most of these sites are located in Class I areas that areTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-2

subject to the Haze Rule, but several others are lo cated in areas where potential

nonattainment with the PM 2.5 NAAQS warrant analysis. Back trajectories were calculated eight times per day for starting heights of 200, 500, and 1,000 meters above ground level using meteorological wind fields for t he five-year period from 2000 through 2004. Meteorological data from the National Oceani c and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL) archives were used. These include wind fields from the Eta Data Assimilation System (EDAS), which cover North America with an 80 km spatial resolution and are based on 3-hour ly variational analyses (Rolph, 2003). For the analyses presented here, we exclusively used the 500 meter EDAS trajectories from the baseline period (2000-2004).

Each trajectory was matched with corresponding moni toring data collected as close in time as possible to the “start” time of th e back trajectory calculation. The analysis included ambient measurements for PM 2.5 and ozone (O 3 ), as well as all particulate matter constituents that are routinely measured as part of the IMPROVE

program.
The resulting database of air quality monitoring re sults and associated back trajectories was used to develop several statistica l measures of the probability or likelihood that a given upwind source region is ass ociated with good or poor air quality at the receptor sites analyzed. Appendix A provides a detailed description of the metrics that were developed for this purpose and how they w ere calculated using both traditional trajectory analysis and cluster analysis techniques . This appendix also provides sitespecific results.



### 5.1.1. Incremental Probability

The incremental probability (IP) field represents a measure of the likelihood that a given source region contributes more than “average” to high concentrations of a particular pollutant at a downwind receptor site (s ee Appendix A for a more complete definition). This technique can also be used to id entify locations that are less likely to contribute to poor air quality at a given receptor site, thus allowing for more robust conclusions to be drawn about likely source regions for individual fine particle

constituents.

Calculating IP fields for a subset of back trajector ies within a complete sample can help further illuminate the different roles of different source regions. For example, it is interesting to note distinct differences between the IP field for back trajectories corresponding to the 10 percent highest observed su lfate values in the Northeast (three sites are shown that bracket the MANE-VU region’s C lass I sites) and the IP field for trajectories corresponding to the lowest sulfate va lues in the Northeast (specifically,

sulfate values in the lowest 10 th percentile). Figure 5-1 and Figure 5-2 illustrate t he IP fields for each set of observations, respectively.

In Figure 5-1 and Figure 5-2, note that the red color indicates areas with greater probability of contributing to transport on the sel ected days. These show that the very highest observed sulfate values across the region a re strongly associated with transport from a source region that encompasses the Ohio Rive r Valley, western Pennsylvania, andTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-3

the urban East Coast corridor. On the days with the lowest measured sulfate, transport is associated with northwesterly winds from Canada and weather patterns off the Atlantic.

Figure 5-1. Incremental Probability (Top 10% Sulfat e) at Acadia, Brigantine and Lye Brook 2000-2004

Acadia Brigantine 

### Lye Brook

Figure 5-2. Incremental Probability (Bottom 10% Sul fate) at Acadia, Brigantine and Lye Brook 2000-2004

Acadia Brigantine Lye Brook



### 5.1.2. Clustered Back-Trajectories

Each of the IP fields shown in Figure 5-1 or Figure 5- 2 incorporate results from over 14,000 back trajectories over the five-year pe riod analyzed. In cases like these, where IP fields are calculated from a very large set of data points, the error in the calculation of any individual trajectory — which ca n be as high as 30 percent or more of the total transport distance involved in a given tr ajectory — is not likely to affect the overall result. Assuming that such errors are rand omly distributed (i.e., no systematic bias exists in the calculations used by the traject ory model to calculate wind speed or direction), the use of large numbers of individual trajectories will effectively ensure that

the random errors cancel out. To further minimize the effect of any errors with respect to individual trajectories, it is also possible to clu ster large numbers of back trajectories according to their three-dimensional similarity (se e Appendix A for a detailed description of several methodologies used). Figure 5-3 shows re sidence-time probability fields for clusters of similar back trajectories grouped accor ding to their proximity to unique meteorological pathways. This metric yields probab ilistic representations of the meteorological pathways which were most likely to b e associated with the highest observed sulfate concentrations at the receptor sit e. Such probabilistic representations reduce the reliance on any one back trajectory and ensure that the general pattern used to associate a transport pathway with a downwind recep tor site is more likely to be accurate.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-4

Figure 5-3. Proximity based cluster with the highes t associated sulfate value for three sites in the MANE-VU region, Acadia (sulf=3.1 9 μ g/m 3 ), Brigantine

(sulf=6.79 μ g/m 3 ), and Lye Brook (sulf=3.92 μ g/m 3 )



### Acadia 

### Brigantine Lye Brook



### 5.1.3. Cluster-Weighted Probability

The clusters derived above can be used individually or combined in an “ensemble cluster” approach similar to how individual traject ories are combined to develop the IP metric. This second method for associating transpo rt patterns with downwind pollution measurements involves using all clusters generated by the clustering algorithms described in the preceding section (and in detail in Appendix A) and weighting them by their average observed sulfate value. Simply averaging t he residence-time probability of all clusters would yield the “everyday” probabilities t hat are used in calculating IP fields. Instead, weighting each cluster before the averaging process serves to highlight transpor t patterns that are associated with high sulfate leve ls at the receptor site, while downplaying patterns that are associated with low v alues. Figure 5-4 shows the resulting cluster-weighted probability (CWP) field. Results a re similar to those obtained using the incremental probability metric described previously , but they now include all clusters, not just the high-day values.

A noteworthy feature of the clustering process is t hat while it reduces uncertainty about prevailing transport patterns, it is not help ful in taking advantage of weather variations to identify specific source regions. Th us, results for a particular site should be interpreted as showing that observed air quality co nditions have an increased probability of being associated with the transport of a specifi c pollutant, as opposed to being associated with a particular source region for a gi ven pollutant. Put another way, it is difficult to make an association with a specific po int along the pathway defined by a cluster. As with the IP approach described earlier, however, multi-site averaging can address this ambiguity by making it possible to tri angulate on regions that are associated with the transport of pollution to multiple sites i n different locations, as shown in Figure

5-4.

Both trajectory-based approaches (i.e., IP and CWP) h ave also been applied to Class I receptor sites in the nearby VISTAS region, w hich includes the Dolly Sods and Otter Creek Wilderness Areas in West Virginia as we ll as Shenandoah National Park and the James River Face Wilderness Area in Virginia. R esults for the VISTAS Class I sites are presented at the conclusion of Appendix A.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-5

Figure 5-4. Cluster Weighted Probability at Acadia, Brigantine and Lye Brook 2000-2004

Acadia Brigantine Lye Brook



### 5.2. Source Apportionment Models and Ensemble Trajectory Analysis of Source Apportionment Results

Previous sections of this chapter have discussed a c ategory of receptor-based assessment techniques known more generally as ensem ble trajectory analysis. The latter category includes residence time analysis (RTA) as well as potential source contribution function (PSCF) and cluster analysis (see also Appendi x A). In this section we turn to multivariate mathematical models for analyzing sour ce contributions, such as chemical mass balance (CMB) models, principal component anal ysis (PCA), positive matrix factorization (PMF), and UNMIX.

Receptor-based models begin with ambient air qualit y measurements at one or more receptor locations and work “backward” to iden tify logical combinations of pollutant species that best fit a “source profile.” Sources matching that profile are assumed to have contributed to the ambient pollutan t concentrations historically observed at the receptor locations. These models are typica lly driven by variations in PM constituent concentrations across multiple observat ions at one or more sites. An advantage of PCA, PMF, and UNMIX is that source profi les do not need to be known in advance; however, this does mean that the results m ust be subjectively interpreted to identify and distinguish likely sources.

Because of these complexities and because the multi variate models typically rely entirely on measurements of PM constituents without regard to meteorology, it can be extremely useful to consider results obtained throu gh the ensemble trajectory techniques (which rely on meteorology only) when interpreting or evaluating the outputs from a multivariate modeling exercise.

Appendix B provides details of numerous source appo rtionment and associated ensemble back trajectory analyses. These details c over results obtained for many of the most significant components of fine particulate mas s and resulting light extinction. Here we focus on the “secondary sulfate” or “coal” sourc e profile that was identified at nearly every site in the eastern United States. Secondary su lfate typically accounts for 30–60 percent of overall fine particle mass and 60–80 per cent of visibility impairment on the haziest days in the Northeast.

Figure 5-5 shows results from one of the broadest st udies conducted to date of

sulfate sources and characteristics at nine eastern IMPROVE sites. The bars on the leftTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-6

show the fraction of total sulfate measured at each site that is contributed by the “sulfate/coal” source profile as determined by the source apportionment models. The bars on the right show the fraction of each “sulfate/coa l” source profile that is composed of sulfate. Figure 5-5 suggests that: (1) large source s contribute 70–90 percent of the total sulfate measured at these sites, and (2) that the c ontribution from these large sources consists of 50–90 percent sulfate.

Figure 5-5. Sulfate characteristics of “secondary s ulfate” (coal)

sources identified at eastern sites

0 10 20 30 40 50 60 70 80 90 100

ACAD BRIG DOSO GRSM JARI LYBR MACA SHEN WASHPercent Sulfate at Site or in Source

% SO4 at site % SO4 in source

When large sulfate sources are associated with upwi nd states or regions through the use of back trajectories (Figure 5-6), it become s clear that many Class I and urban sites in MANE-VU and adjoining areas are influenced by a common source region. These findings suggest that reductions in coal-rela ted SO 2 emissions would have substantial benefits in terms of improved visibilit y and reduced PM concentrations over a large part of the eastern United States and eastern Canada.

This conclusion is further reinforced by comparing regions with significant emissions that match the “source profiles” generate d by available mathematical modeling tools to regions identified through trajectory anal ysis as having a high probability of being upwind on days with high sulfate levels and h igh reconstructed extinction values. As shown in Figure 5-6, the degree of correspondence between these regions is substantial. This indicates that the “secondary sul fate/coal combustion” source profile prominent at several eastern sites is strongly link ed to regions associated with the highest 10 percent of recorded sulfate and reconstructed ex tinction values. It is noteworthy that the upwind regions identified in Figure 5-7 are deri ved from measurements spanning the entire IMPROVE network, suggesting that the source r egion for “secondary sulfate/coal combustion,” which is a dominant contributor to vis ibility impairment in parts of the eastern United States, is also a major contributor t o observed sulfate and extinction outside the MANE-VU region.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-7

Figure 5-6. Incremental Probabilities for "Secondar y Sulfate" (Coal)

Sources in Eastern U.S.



### 5.3. Trajectory Model Evaluation

and Future Work

The geographical correspondenceexhibited in Figure 5-7 extends to the multisite average IP fields calculated for the MANE-VU region and shown previously in Figure 5-1. It also extends to the multi-site average IP field calculated using the ATAD model and shown in Figure B-30 in Appendix B. Essentially, both figures are versions of the same thing, but they do exhibit some

subtle differences. These differences are highlighted in Figure 5-8 which compares the

results of ATAD and HYSPLIT IP calculations for the top 10 percent of sulfate, selenium, and nickel observations at Lye Brook, Vermont. Sulfate is a secondary pollutant that tends to peak in the summer, whereas nickel and selenium are primary pollutants that typically peak in the

wintertime. Ni and Se serve as excellent

markers for residual oil and coal combustion respectively. The figure indicates strong agreement between the two models in terms of Figure 5-7. Comparison of probability fields for observed sulfate, “sulfate” source profiles for

seven eastern sites and reconstructed deciviewsTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-8

the IP fields they calculate for nickel, suggesting that — during wintertime — primary pollutants are tracked well by both techniques. Th ere is less agreement between the IP fields for sulfate, suggesting either a southerly b ias to the HYSPLIT calculations for this secondary pollutant, or a westerly bias to the ATAD results.

Seasonal differences in the meteorology that affects Lye Brook and other East Coast sites during the summer versus during the win ter may help to explain these model discrepancies. Some of the largest absolute differe nces between the ATAD and HYSPLIT estimates occur for the highest sulfate days. While there are many differences between the models, one key difference is in their trajectory start heights. The HYSPLIT trajectories all start at 500 meters above ground l evel while the ATAD model first estimates a “transport layer depth” (TLD) and then initiates the trajectory (while constraining subsequent trajectory endpoints) at a point roughly half way between ground level and the TLD. During summer, when the largest sulfate events occur, the resulting ATAD start heights are roughly twice as high as the 500 m HYSPLIT start heights (see Figure 5-9). Hence the ATAD calculations tend to ext end over a greater distance to the west, while the summer HYSPLIT trajectories may be mo re reflective of flows that are nearer the surface and more frequently east of the Appalachian Mountains. Both flow regimes are important. In fact, Blumenthal et al. ( 1997) have observed that the highest ozone concentrations in the Northeast (which often coincide with episodes of high sulfate concentrations) tend to occur when surface flows up the Northeast urban corridor combine with synoptic flows over the Appalachian Mo untains from the west, a pattern that is often accompanied by lower level nocturnal jets along the Northeast corridor and through gaps in the Appalachians.

Figure 5-8. Comparison of IP contours generated by ATAD and HYSPLIT (both EDAS and FNL) for sulfate, nickel and selenium at L ye BrookTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-9

An extensive evaluation of the performance of HYSPLIT , ATAD, and Capita Monte Carlo trajectory models using a variety of di fferent meteorological drivers, ensemble trajectory techniques, and performance tra cers was recently conducted as part of the Big Bend Regional Aerosol and Visibility Obs ervational (BRAVO) study (Pitchford et al., 2004). No one model consistently out-performed the others at that site, hence results from these and more sophisticated pho tochemical grid models (REMSAD and CMAQ) were merged to produce a best-estimate, “ consensus” apportionment of sulfate in the BRAVO study.

MANE-VU is using all available trajectory models, t rajectory-related metrics, and improved understanding of transport phenomena to fu rther explore and support the development of emission control strategies for redu cing regional haze.

Figure 5-9. ATAD Transport Layer Depth (TLD) by mon th. Color indicates the

length of time prior to arriving at the receptor.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 5-10



### References

Blumenthal, D.L., Lurmann, F.W., Kumar, N., Ray, S.E, Korc, M.E., Londergan, R., Moore, G., Transport and mixing phenomena related t o ozone exceedances in the Northeast U.S. Working Draft No. 1.1 prepared for Oz one Transport Assessment Group Air Quality Analysis Workgroup by Sonoma Technology, Inc., Santa Rosa, CA and Earth Tech, Concord, MA, STI-996133-1710-WD1.1, February 19 97.

Draxler, R.D. and Hess, G.D., “Description of the H YSPLIT-4 Modeling System,” NOAA Technical Memorandum ERL, ARL-224 , Air Resources Laboratory, Silver Springs, Maryland, 24 pgs., 1997.

Draxler, R.D. and Hess, G.D., “An Overview of the H YSPLIT-4 Modelling System for Trajectories, Dispersion, and Deposition,” Australian Meteorological Magazine , 47, 295-308, 1998.

Pitchford, M.L., Schichtel, B.A., Gebhart, K.A., Barn a, M.G., Malm, W.C., Tombach, I.H., and Knipping, E.M., “Causes of Haze at Big Be nd National Park – Results of the BRAVO Study and More” Regional and Global Perspective s on Haze: Causes, Consequences and Controversies – Visibility Specialt y Conference, Air & Waste Management Association, Asheville, NC October 25-29 , 2004.

Rolph, G.D., Real-time Environmental Applications a nd Display sYstem (READY) Website (http://www.arl.noaa.gov/ready/hysplit4.htm l). NOAA Air Resources Laboratory, Silver Spring, MD., 2003.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-1



## 6. CHEMICAL TRANSPORT MODELS

Eulerian or “grid” models have traditionally served as the workhorse of air quality planning programs. These tools strive to be compre hensive in accounting for emissions, meteorological dynamics, chemical production, trans formation, and destruction as well as wet and dry deposition and microphysical processes. With this degree of sophistication comes attendant uncertainty. Many of the more comp lex processes (e.g., cloud processes and boundary layer dynamics) are handled through pa rameterizations that attempt to approximate the real atmosphere at an appropriate l evel of detail. Chemical transport models for ozone and fine particles have improved m arkedly over the past several years as various groups have developed competing models a nd as the different strengths and weaknesses of these models help to shed light on va rious aspects of the underlying

science.

Two regional-scale air quality models have been eva luated and used by NESCAUM to perform air quality simulations. These ar e the Community Multi-scale Air Quality modeling system (CMAQ) 30 and the Regional Modeling System for Aerosols and Deposition (REMSAD). 31 Appendix C provides detailed descriptions of thes e models and of their use by NESCAUM, together with performan ce evaluations and preliminary results. A brief overview of the two modeling plat forms in terms of their relevance to future SIP work is provided here, along with highligh ts of the findings.



### 6.1. Chemical Transport Model (CTM) platforms – Overview

Both REMSAD and CMAQ are being used with a 12 km gr id 32 in the eastern U.S. domain (see Figure 6-1(b)). Air quality is modeled on 22 vertical layers with hourly temporal resolution for the entire calendar year 20 02. REMSAD has simplified chemistry but allows for emissions tracking of sulf ate, nitrate, and mercury through a tagging feature that calculates the contribution of specific sources to ambient concentrations, visibility impacts, and wet or dry deposition. REMSAD has shown good performance when reproducing annual or seasonal sta tistics for sulfate and mercury chemistry, while CMAQ has shown good performance fo r multiple species. A new release of CMAQ (version 4.5) may improve performan ce for sulfate, nitrate and organics over what Appendix C presents and will be used with the quality-assured meteorology and emission inventory inputs described below for final SIP submissions in

2007 or 2008.

Meteorological inputs have been developed by the Un iversity of Maryland (UMD) using the Fifth-Generation Pennsylvania State Un iversity/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) s ystem. 33 A modified Blackadar boundary layer scheme is used as well as physics options including explicit representations of cloud physics with simple ice mi crophysics (no mixed-phase processes) and the Kain-Fritsch cumulus parameteriza tion.

30 See Byun and Ching, 1999.

31 See ICF/SAI, 2002.

32 12 km grid describes a 12 by 12 km grid cell

33 http://www.mmm.ucar.edu/mm5/Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-2

The New York Department of Environmental Conservati on and NESCAUM are processing emissions inputs using the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System. To model biogenic emissions , SMOKE uses the Biogenic Emission Inventory System, version 2.3 (BEIS2) and ve rsion 3.09 and 3.12 (BEIS3). SMOKE has also been integrated with the MOBILE6 mode l for on-road emissions. MANE-VU has developed a quality-assured 2002 emissi ons inventory which is being merged with the regional inventories for other RPOs in order to provide a comprehensive emissions inventory for the entire Northeast domain shown in Figure 6-1(b).

A dynamic 3-dimensional boundary condition feeds am bient concentration fields in at the domain boundaries which are representativ e of actual concentrations during 2002. This dynamic boundary condition was develope d by applying the output of a global model run (Park et al., 2004) with 4 degree l ongitude by 5 degree latitude

horizontal resolution at the boundaries of the 36 k m grid domain shown in Figure 6-1(a). The results of this annual simulation are then appl ied at the boundary of our 12km grid domain, ensuring acceptable representation of the g eneral trends and sulfate patterns that were present during the simulation period.

Figure 6-1. Modeling domains used in NESCAUM air qu ality modeling studies. (a) Domain 1: 36 km National US grid domain with lo cation of 12 km grid domain highlighted; (b) Domain 2: 12km Northeast US grid domain. The g ridlines are shown at 180 km intervals (5 x 5 36 km cells or 15 x 15 12 km cells).Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-3



### 6.2. Preliminary Results

CMAQ has been run for a complete set of baseline si mulations including 2002, 2009 and 2018. These preliminary runs are describe d in greater detail in Appendix C, but include inventory and meteorological drivers which will be updated for final SIP submissions. Nonetheless, these preliminary result s suggest that implementation of existing regulations (including USEPA’s Clean Air Int erstate Rule, or CAIR) will continue to yield significant improvements in visib ility over the next decade, primarily as a result of regional sulfate reductions (See Figure 6 -2 a and b below for visibility improvement and see Figure C-27 in Appendix C for s ulfate mass reductions). Despite these potential improvements, not all MANE-VU Class I areas are anticipated to achieve uniform progress goals as described by current USEPA guidance. 34 Brigantine Wilderness Area in New Jersey is projected to fall about a half deciview short of the uniform rate under existing emission reduction plan s.
A significant difference between the CMAQ and the R EMSAD results presented here is that NESCAUM has taken the additional step o f reprocessing the SO 2 emission sources from each state such that these model input s are formatted to take advantage of

34 We note that uniform progress goals do not necessarily dict ate visibility levels required by statute, but do represent a point of comparison for states when establishing reasonable progress goals toward our national visibility goal of no anthropogenic visibility impairmen t by 2064. Figure 6-2(a) and (b): CMAQ Integrated SIP Modelin g Platform simulation results for 2002, 2009 and 2018 relative to Uniform Progress Goals calcula ted according to current USEPA guidance for (a) Northeast Class I sites in MANE-VU and (b) Mid-Atlantic Class I sites in or near MANE-VU.

17 18 19 20 21 22 23 24 25 26

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020Deciview 

### Acadia Uniform Goal Camp Dodge Uniform Goal Lye Brook Uniform Goal

Moosehorn Uniform Goal

Acadia Modeled Camp Dodge Modeled
Lye Brook Modeled

Moosehorn ModeledTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-4

REMSAD’s tagging capabilities. Thus, all SO 2 emissions included in the model for the eastern half of the country, Canada and the boundar y conditions have been tagged according to state of origin. This allows for a rou gh estimation of the total contribution from elevated point sources in each state to simula ted sulfate concentrations at eastern receptor sites. The tagging scheme employed for th is analysis is illustrated in Figure 6-3. Using identical emission and meteorological inputs to those prepared for the Integrated SIP (CMAQ) platform, REMSAD was used to simulate the a nnual average impact of

each state’s SO 2 emission sources on the sulfate fraction of PM 2.5 over the northeastern

United States.

Results of these tagged runs indicate that elevated point sources in Pennsylvania, Ohio, and New York contribute significantly, on an annual basis, to sulfate

concentrations at all MANE-VU sites. Northern site s (e.g., Acadia) are more influenced by sources in upper midwestern states (e.g., Wiscon sin and Michigan) whereas southern sites like Brigantine are more influenced by source s in more southerly states such as West Virginia, Maryland, and Virginia. Shenandoah, a VIST AS Class I site appears to be most strongly influenced by sources in Ohio, Pennsylvania , and West Virginia, followed by other nearby Southeast and Midwest states. Figure 6- 4 through Figure 6-7 present these results showing the breakout of sulfate by individu al tag. Note that the large “other”

fraction of sulfate includes all sources outside th e analysis domain, which includes some portions of the VISTAS and CENRAP RPO, Northern and Wes tern Canada in addition to all other (i.e., inter-continental) sources of SO 2 . Figure 6-8 shows similar results summarized by RPO for the 20% worst days. Figure 6-2(b).

22 23 24 25 26 27 28 29

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020Deciview 

### Brigantine Uniform Goal Dolly Sods Uniform Goal

Shenandoah Uniform Goal
Brigantine Modeled Dolly Sods Modeled

Shenandoah ModeledTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-5

Figure 6-3. REMSAD modeling tagging schemes. (black: group 1, red: group 2, and blue: group 3)

Note: Sulfur species from anthropogenic emission sources are tagged by states for three sets of tags. Tag group 3 also includes boundary conditions . The color of the numbers represents tag groups (black: group 1, red: group 2, and blue: grou p 3)Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-6

Figure 6-4. 2002 Eastern states’ contribution to an nual PM sulfate in Acadia, ME

OTHER

33.7%

MA

10.1% CANADA

8.7% PA

6.8% ME

6.5% OH

5.6% NY

4.7% WV

2.4% NH

2.2% MD

2.2% IN

2.1% MI

2.0% VA

1.5% NC

1.5% NJ

1.4% CENRAP

0.9%

IL

1.4% KY

1.2% DE

1.0% GA

0.7%
CT

0.8% WI

0.8% RI

0.3%

DC

0.0% MS

0.0% VT

0.1% TN

0.6%

AL

0.3% SC

0.4%

Figure 6-5. 2002 Eastern states’ contribution to an nual PM sulfate in Brigantine, NJ

PA

12.8%

OH

9.1%

CANADA

7.1% NY

5.6% MD

5.0% WV

4.7% CT

0.5% ME

0.2% NH

0.6%

VT

0.1% MS

0.0%

DC

0.0% RI

0.1%

WI

1.2% CENRAP

1.1% AL

1.1% SC

1.7%

IL

1.8% KY

2.3% TN

1.6%

GA

2.3% MA

2.7%

MI

2.8% DE

3.2%

IN

3.3%

NJ

4.0%

NC

4.2% VA

4.3% OTHER

16.7%Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-7

Figure 6-6. 2002 Eastern states’ contribution to an nual PM sulfate in Lye Brook, VT

CANADA

12.4%

PA

11.7%

OH

9.9% NY

9.0% IN

3.9% OTHER

19.0%

WV

3.7% SC

0.6% CT

0.6%

AL

0.8% RI

0.1%
MS

0.0% DC

0.0% ME

0.3% TN

1.2%

VT

1.0% DE

0.9%

NJ

1.4% WI

1.5% CENRAP

1.7% GA

1.3%

NH

1.7% NC

1.9% VA

2.0% KY

2.2%

IL

2.4% MA

2.4%

MD

2.7% MI

3.7%

Figure 6-7. 2002 Eastern states’ contribution to an nual PM sulfate in Shenandoah, VA

OTHER

12.4%

PA

12.0%

WV

9.6% VA

6.2% IN

5.2% OH

14.8%

NC

4.8% NH

0.1% MS

0.1% CT

0.1%

ME

0.0% RI

0.0% VT

0.0% DC

0.0% DE

0.6% NJ

0.5% MA

0.3% SC

1.3%

CENRAP

1.5% AL

1.8% WI

1.1%

NY

2.0% IL

2.4%
TN

2.7%

MI

3.2%

GA

3.5% KY

4.4%

CANADA

4.7% MD

4.8%Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-8

Figure 6-8. Comparison of Sulfate Extinctions on 20 % Worst Visibility Days

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

DOSO SHEN BRIG LYBR GRGU ACAD MOOS

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAINTools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-9



### References

Byun, D.W., and Ching, J.K.S., Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , EPA/600/R-99/030, March 1999.

Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M., Natural and transboundary pollution influences on sulfate-nitra te-ammonium aerosols in the United States: implications for policy , J. Geophys. Res., D15204, 10.1029/2003JD004473,

2004.

SAI, User’s Guide to the Regional Modeling System for Ae rosols and Deposition (REMSAD), Version 7, ICF Consulting/SAI, San Francisco, CA, 2002.Tools and Techniques for Apportioning Fine Particl e/Visibility Impairment in MANE-VU Page 6-10Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-1



## 7. LAGRANGIAN DISPERSION MODELS

Dispersion models are commonly used to study the im pacts of pollutant plumes or specific point source emissions on surrounding area s. The scale of these models has traditionally been limited to a few hundred kilomet ers because of a perceived lack of ability to accurately reproduce horizontal dispersi on beyond these distances. Recent advances in the CALPUFF system (USEPA, 2006) — including enhancements to its horizontal diffusion and dispersion algorithms as w ell as the addition of chemical transformation parameterizations — have resulted in improved performance over much greater distances. In fact, the most recent propos ed guidance for implementing the BART (Best Available Retrofit Technology) requireme nts of the Regional Haze Rule provide for the use of CALPUFF to analyze dispersion over distances exceeding 200 km as long as a detailed modeling protocol is included for approval by the appropriate reviewing authority (40 CFR Part 51, pg. 25194, May 5 , 2004).

Appendix D provides specific information related to two CALPUFF platforms that have been developed for a large domain (see Fig ure 7-1) by the Vermont Department of Environmental Conservation (VT DEC) Air Pollution Control Branch and by the State of Maryland’s Department of the Environment (MDE) a nd Department of Natural Resources (MDNR) with contract assistance provided by Environmental Resources Management (ERM). Appendix D contains detailed des criptions of the two platforms; the processing and evaluation of both MM5- and Nati onal Weather Service (NWS)-based meteorological data; the processing and evaluation of CEMS (Continuous Emissions Monitoring System)- and 2002 RPO-based emissions data ; performance evaluations of

Figure 7-1. CALPUFF modeling domain utilized by MAN E-VUTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-2

the overall modeling system; preliminary results of modeling to determine annual average and maximum 24-hour impact by individual un it and by state; and discussion of the future application of these platforms to the BA RT program. This chapter provides an overview of the two modeling platforms, a summary o f initial results, and a brief analysis of the differences between the two platforms.

While CALPUFF will certainly play a role in helping MA NE-VU assess potential visibility impacts for BART-eligible sources, the d evelopment of twin CALPUFF platforms utilizing both MM5-based and NWS-based met eorological drivers further expands the suite of analytical tools available for assessing contributions — at both the facility and state level — to downwind visibility i mpairment in the MANE-VU region.



### 7.1. Platform Overview

The VT DEC developed meteorological inputs for CALPU FF using observationbased inputs (i.e., rawinsonde and surface measurem ents) from the NWS and by applying CALMET. VT DEC also developed hourly emissions and exhaust flow data from the Acid Rain Program’s CEMS data files for 869 large ele ctric generating units (EGUs). These emissions data were utilized as inputs to CAL PUFF, along with emissions data for four additional source sectors: non-EGU point sour ces, mobile (on-road), mobile (offroad), and general area sources. The emission inpu ts for these source sectors were

derived from the 2002 RPO inventories.

The MDNR and MDE developed meteorological inputs fo r CALPUFF using MM5 data developed by the University of Maryland fo r the MANE-VU and Ozone Transport Commission SIP modeling work. The Maryland agencies utilized the CEMS data files developed by VT DEC, and independently d eveloped emissions and source parameters for the other four source sectors based on the same inter-RPO 2002

inventories.

Both platforms were used to model the entire calend ar year 2002. These simulations have been configured to provide estimat es for both individual source impacts and cumulative state impacts and to allow for inter -platform comparisons. The modeling domain has been designed to be consistent with the other modeling systems described in this report (e.g., REMSAD, CMAQ), so that conclusion s regarding the most significant sources of sulfate-related visibility impacts in MA NE-VU can be compared. Consistency across a broad range of approaches will add credibi lity to the conclusions reached in the

overall contribution assessment.



### 7.2. CALPUFF Modeling Results for Individual Sources

To explore differences between the two CALPUFF modelin g platforms, each was

used to create a ranked list of the 100 emissions s ources that contribute most to ambient

sulfate levels at each of several eastern Class I s ites. Of the 100 top sources identified for the Brigantine Wilderness Area, 70 sources appeared on the lists generated by both platforms. At Acadia, Lye Brook, and Shenandoah, the re was even more agreement between the model results, with both platforms iden tifying 78, 76, and 85 out of 100 of the same top sources for each of these sites, respe ctively. Figure 7-2 shows the correlation between estimated annual average impact s for the sources that were identified by both platforms as among the top 100 sulfate cont ributors. While theTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-3

NWS/rawindsonde-based meteorology consistently produ ced slightly lower estimates of impact than the MM5-based platform, the correlation s are relatively robust, ranging from 0.89 at Brigantine to 0.93 at Lye Brook.

Overall, the CALPUFF modeling results to date demonst rate reasonably good comparability between the two platforms (as illustr ated by Figure 7-2 and Table 7-1), but they also suggest a consistent pattern of under pre diction for one platform relative to the

other.



### 7.3. CALPUFF Modeling Results Overview

Table 7-1 provides further comparisons of the resul ts of CALPUFF modeling utilizing the two different platforms described ear lier in this chapter: VT DEC (NWS/rawinsonde-based meteorology) and Maryland (MM5 -based meteorology). 35 The table summarizes annual average sulfate concentrati ons by source category for each of the two platforms relative to observed concentratio ns.

Table 7-1. CALPUFF Overall Modeling Summary

Annual Average SO 4 Ion Concentration (μg/m 3 )
NWS/Rawinsonde-based Meteorology MM5-based Meteorology

CEMS

EGU Non-CEMS

Point Area/Mobile Total CEMS

EGU Non-CEMS

Point Area/Mobile Total Observed

Shenandoah 2.271 0.412 0.106 2.789 2.98 0.46 0.22 3.6 6 4.61
Brigantine 1.847 0.421 0.257 2.526 2.6 0.51 0.38 3.4 8 4.06

Acadia 0.965 0.385 0.218 1.569 1.42 0.42 0.28 2.13 1. 86 Lye Brook 1.178 0.342 0.178 1.698 1.65 0.36 0.25 2.2 6 2.17

Generally, the NWS/rawinsonde platform predicts lowe r sulfate ion concentrations than the MM5 platform. On an annual average basis, the concentrations predicted using the MM5 platform are much closer to observed values than the concentrations predicted using the NWS/rawindsonde p latform.



### 7.4. CALPUFF Results for Ranked State Sulfate Contributi ons

This section focuses on the ranked contribution of emissions from individual states to overall sulfate levels at specific recept or sites (additional results are summarized in a number of different ways in Appendix D). The r ankings were calculated by summing impacts from EGUs included in the 2002 data base fo r each state. State contributions are then sorted by total annual impact. Predicted annua l average sulfate ion concentrations

from other source sectors were added to these data in Table 7-2(a-d) for both platforms. As in previous chapters, estimated contributions to receptor impact by state (using the results presented in Table 7-2) are depicted graphi cally in Figure 7-3 and Figure 7-4 for the observation-based and MM5-based platforms, resp ectively. States are ranked along the horizontal axis by averaging the individual res ults calculated for each state using the two CALPUFF platforms.

35 The Maryland Department of the Environment is contributi ng toward this work through the Maryland Department of Natural Resources and their contractor ERM, Inc. who have developed the MM5-based meteorology and CALPUFF platform.Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VUPage 7-4Figure 7-2. Correlation between MM5-based source contributions (Maryland/ERM) and NWS/rawindsonde-based sourcecontributions (VT DEC) for common EGUs modeled at four receptor sites in or near MANE-VUAcadiay = 1.6062x - 0.0003R2= 0.898800.0050.010.0150.020.0250.030 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)Brigantiney = 1.5134x - 0.0003R2= 0.889900.0050.010.0150.020.0250.030.0350.040.0450.050 0.005 0.01 0.015 0.02 0.025 0.03VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)Lye Brooky = 1.3802x + 0.0002R2= 0.933300.0050.010.0150.020.0250.030.0350.040 0.005 0.01 0.015 0.02 0.025 0.03VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)Shenandoahy = 1.6379x - 0.0023R2= 0.914300.010.020.030.040.050.060.070.080 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-5

Table 7-2a. Sulfate Ion Impacts by State (Annual Av erage)

Acadia National Park

NWS-based Meteorology (VT DEC) MM5-based Meteorology (M DE/MDNR)

μ g/m 3 μ g/m 3

STATE CEM

PT NonCEM PT Area/

Mobile TOTAL

PT CEM

PT Non-CEM

PT Area/

Mobile TOTAL
AL(a) 0.0086 0.0013 0.0003 0.0102 0.0139 0.0009 0.0011 0.0159
AR(a) 0.0039 0 0 0.0039 0.0054 0.0020 0.0010 0.0083

CT 0.0041 0.0012 0.0085 0.0138 0.0074 0.0011 0.0072 0.0156

DC 0.0001 0.0001 0.0002 0.0004 6.9E-05 0.0001 0.0003 0.0005

DE 0.0087 0.002 0.0008 0.0115 0.0093 0.0109 0.0018 0.0219
GA(a) 0.0142 0.0008 0.0005 0.0155 0.0259 0.0009 0.0019 0.0287

IA 0.0097 0.0122 0.0001 0.0219 0.0149 0.0120 0.0030 0.0299

IL 0.0342 0.0157 0.0004 0.0504 0.0486 0.0172 0.0034 0.0693

IN 0.0758 0.0103 0.001 0.087 0.1089 0.0119 0.0099 0.1307
KS(a) 0.0081 0 0 0.0081 0.0137 0.0012 0.0010 0.0159

KY 0.0411 0.0054 0.0023 0.0487 0.0632 0.0038 0.0069 0.0740

MA 0.0653 0.0127 0.0579 0.136 0.0860 0.1544 0.0773 0.3176

MD 0.0398 0.0019 0.0034 0.0451 0.0780 0.0062 0.0040 0.0882

ME 0.0032 0.0243 0.0294 0.057 0.0030 0.0356 0.0236 0.0622

MI 0.0611 0.0083 0.0031 0.0726 0.0656 0.0095 0.0093 0.0844

MN 0.0089 0.0043 0.0005 0.0137 0.0107 0.0022 0.0023 0.0151

MO 0.014 0 0 0.014 0.0215 0.0115 0.0041 0.0371
MS(a) 0 0.0002 0.0002 0.0003 0 0.0002 0.0002 0.0004

NC 0.0342 0.0081 0.0014 0.0437 0.0554 0.0057 0.0019 0.0630
ND(a) 0 0.0009 0.0012 0.0021
NE(a) 0.0017 0 0 0.0017 0.0028 0 0.0009 0.0037

NH 0.0386 0.0022 0.0071 0.0479 0.0666 0.0020 0.0065 0.0750

NJ 0.013 0.0025 0.0076 0.0232 0.0187 0.0033 0.0133 0.0354

NY 0.0577 0.0118 0.0505 0.12 0.0736 0.0363 0.0578 0.1677

OH 0.1402 0.0081 0.0013 0.1496 0.2248 0.0457 0.0055 0.2759
OK(a) 0.0059 0 0 0.0059 0.0071 0.0015 0.0006 0.0092

PA 0.1383 0.0196 0.0126 0.1706 0.2354 0.0214 0.0156 0.2725

RI 0 0 0.0074 0.0074 5.9E-06 0.0007 0.0043 0.0050

SC 0.0092 0.003 0.001 0.0132 0.0134 0.0036 0.0012 0.0182
SD(a) 0.0009 0 0 0.0009 0.0012 2.8E-05 0.0009 0.0022

TN 0.0192 0.0045 0.0024 0.0261 0.0286 0.0076 0.0031 0.0393
TX(a) 0 0 0 0 1.1E-05 0 2.3E-05 3.5E-05

VA 0.0319 0.0082 0.0007 0.0407 0.0389 0.0081 0.0029 0.0499

VT 0 0.0004 0.0169 0.0173 4.0E-06 0.0004 0.0026 0.0030

WI 0.0152 0.0196 0.0005 0.0353 0.0254 0.0085 0.0019 0.0358

WV 0.0583 0.0053 0.0006 0.0642 0.0865 0.0086 0.0016 0.0966
Canada(b) 0 0.1914 0 0.1914

Totals 0.96511 0.3854 0.21832 1.5688 1.45 0.44 0.28 2.17

Notes: (a) Only sources in that portion of the state within th e RPO modeling domain were modeled.

(b) 52 Canadian point sources > 250 tons/yr SO 2 emissions during 2002 (from Canadian NPRI).Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-6

Table 7-2b. Sulfate Ion Impacts by State (Annual Av erage)

Brigantine Wilderness Area

NWS-based Meteorology (VT DEC) MM5-based Meteorology (M DE/MDNR)

μ g/m 3 μ g/m 3

STATE CEM

PT NonCEM PT Area/

Mobile TOTAL

PT CEM

PT NonCEM PT Area/

Mobile TOTAL

AL(a) 0.0317 0.0055 0.0011 0.0383 0.0304 0.0017 0.0020 0.0341
AR(a) 0.0047 0 0 0.0047 0.0088 0.0032 0.0017 0.0137

CT 0.0041 0.0013 0.0099 0.0153 0.0044 0.0009 0.0063 0.0116

DC 0.0009 0.0004 0.0008 0.0021 0.0012 0.0005 0.0013 0.0030

DE 0.0395 0.0111 0.0073 0.0579 0.0524 0.0549 0.0138 0.1211
GA(a) 0.0576 0.0044 0.0030 0.0649 0.0672 0.0024 0.0057 0.0753

IA 0.0156 0.0176 0.0001 0.0333 0.0152 0.0137 0.0032 0.0321

IL 0.0521 0.0192 0.0005 0.0719 0.0535 0.0190 0.0043 0.0768

IN 0.1165 0.0125 0.0011 0.1302 0.1632 0.0162 0.0128 0.1921
KS(a) 0.0113 0 0 0.0113 0.0107 0.0009 0.0008 0.0124

KY 0.0846 0.0098 0.0039 0.0982 0.1285 0.0076 0.0135 0.1496

MA 0.0240 0.0049 0.0191 0.0480 0.0234 0.0406 0.0168 0.0808

MD 0.1351 0.0073 0.0165 0.1589 0.2191 0.0228 0.0210 0.2630

ME 0.0004 0.0017 0.0016 0.0037 0.0002 0.0017 0.0011 0.0030

MI 0.0579 0.0077 0.0028 0.0685 0.0810 0.0110 0.0120 0.1040

MN 0.0120 0.0056 0.0007 0.0183 0.0114 0.0025 0.0027 0.0166

MO 0.0179 0 0 0.0179 0.0202 0.0108 0.0036 0.0346
MS(a) 0 0.0006 0.0003 0.0009 0 0.0006 0.0005 0.0012

NC 0.1414 0.0360 0.0060 0.1835 0.1609 0.0160 0.0054 0.1823
ND(a) 0 0.0011 0.0015 0.0026
NE(a) 0.0031 0 0 0.0031 0.0025 0 0.0009 0.0035

NH 0.0064 0.0004 0.0012 0.0080 0.0100 0.0003 0.0010 0.0113

NJ 0.0426 0.0081 0.0518 0.1024 0.0625 0.0124 0.0805 0.1553

NY 0.0658 0.0120 0.0719 0.1497 0.0810 0.0307 0.0779 0.1896

OH 0.2611 0.0130 0.0017 0.2757 0.4297 0.0836 0.0088 0.5221
OK(a) 0.0068 0 0 0.0068 0.0077 0.0014 0.0007 0.0098

PA 0.2538 0.0460 0.0339 0.3336 0.4407 0.0553 0.0461 0.5421

RI 0 0 0.0042 0.0042 2.1E-06 0.0003 0.0016 0.0019

SC 0.0362 0.0139 0.0042 0.0542 0.0341 0.0101 0.0032 0.0475
SD(a) 0.0011 0 0 0.0011 0.0012 3.4E-05 0.0012 0.0024

TN 0.0477 0.0138 0.0049 0.0664 0.0630 0.0188 0.0061 0.0879
TX(a) 0 0 0 0 2.5E-07 0 2.9E-05 3.0E-05

VA 0.1442 0.0447 0.0035 0.1924 0.1577 0.0331 0.0119 0.2027

VT 0 0.0002 0.0033 0.0035 1.5E-06 0.0001 0.0006 0.0008

WI 0.0216 0.0312 0.0007 0.0535 0.0315 0.0106 0.0026 0.0447

WV 0.1499 0.0118 0.0016 0.1633 0.2340 0.0202 0.0046 0.2588
Canada(b) 0 0.0807 0 0.0807

Totals 1.84732 0.42121 0.25746 2.526 2.61 0.51 0.38 3.49

Notes: (a) Only sources in that portion of the state within th e RPO modeling domain were modeled.

(b) 52 Canadian point sources > 250 tons/yr SO 2 emissions during 2002 (from Canadian NPRI).Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-7

Table 7-2c. Sulfate Ion Impacts by State (Annual Av erage)

Lye Brook Wilderness Area

NWS-based Meteorology (VT DEC) MM5-based Meteorology (M DE/MDNR)

μ g/m 3 μ g/m 3

STATE CEM

PT NonCEM PT Area/

Mobile TOTAL

PT CEM

PT NonCEM PT Area/

Mobile TOTAL

AL(a) 0.0151 0.0023 0.0005 0.0179 0.0209 0.0013 0.0015 0.0238
AR(a) 0.0053 0 0 0.0053 0.0072 0.0029 0.0015 0.0116

CT 0.0015 0.0004 0.0038 0.0057 0.0024 0.0006 0.0045 0.0075

DC 0.0001 0.0002 0.0003 0.0005 7.9E-05 0.0002 0.0004 0.0006

DE 0.0045 0.0017 0.0007 0.0068 0.0076 0.0123 0.0020 0.0219
GA(a) 0.0270 0.0016 0.0011 0.0296 0.0351 0.0012 0.0029 0.0392

IA 0.0151 0.0175 0.0001 0.0326 0.0184 0.0158 0.0041 0.0383

IL 0.0473 0.0173 0.0005 0.0651 0.0550 0.0208 0.0047 0.0805

IN 0.1039 0.0120 0.0011 0.1170 0.1369 0.0148 0.0128 0.1645
KS(a) 0.0115 0 0 0.0115 0.0167 0.0016 0.0013 0.0195

KY 0.0647 0.0075 0.0031 0.0753 0.0820 0.0047 0.0099 0.0967

MA 0.0106 0.0040 0.0125 0.0270 0.0161 0.0291 0.0203 0.0655

MD 0.0452 0.0025 0.0040 0.0518 0.0686 0.0088 0.0052 0.0826

ME 0.0001 0.0020 0.0017 0.0038 0.0003 0.0024 0.0018 0.0044

MI 0.0841 0.0113 0.0041 0.0995 0.0798 0.0121 0.0120 0.1039

MN 0.0130 0.0062 0.0007 0.0200 0.0147 0.0031 0.0035 0.0213

MO 0.0191 0 0 0.0191 0.0253 0.0140 0.0052 0.0445
MS(a) 0 0.0004 0.0002 0.0006 0 0.0006 0.0004 0.0011

NC 0.0424 0.0088 0.0016 0.0528 0.0680 0.0058 0.0022 0.0760
ND(a) 0 0.0014 0.0020 0.0035
NE(a) 0.0027 0 0 0.0027 0.0032 0 0.0012 0.0044

NH 0.0072 0.0007 0.0020 0.0098 0.0137 0.0008 0.0023 0.0167

NJ 0.0071 0.0017 0.0051 0.0139 0.0128 0.0029 0.0115 0.0272

NY 0.0637 0.0289 0.0586 0.1511 0.0985 0.0613 0.0842 0.2440

OH 0.2108 0.0112 0.0016 0.2237 0.2963 0.0649 0.0078 0.3690
OK(a) 0.0086 0 0 0.0086 0.0097 0.0020 0.0009 0.0127

PA 0.1918 0.0255 0.0169 0.2342 0.3050 0.0288 0.0219 0.3558

RI 0 0 0.0013 0.0013 1.4E-06 0.0002 0.0010 0.0012

SC 0.0088 0.0037 0.0013 0.0138 0.0133 0.0040 0.0014 0.0187
SD(a) 0.0014 0 0 0.0014 0.0017 4.3E-05 0.0014 0.0031

TN 0.0281 0.0065 0.0032 0.0378 0.0407 0.0098 0.0042 0.0546
TX(a) 0 0 0 0 8.4E-06 0 3.2E-05 4.0E-05

VA 0.0295 0.0088 0.0008 0.0391 0.0454 0.0104 0.0037 0.0596

VT 0 0.0006 0.0499 0.0505 4.0E-06 0.0017 0.0083 0.0100

WI 0.0229 0.0293 0.0007 0.0529 0.0351 0.0116 0.0028 0.0495

WV 0.0852 0.0079 0.0009 0.0939 0.1232 0.0121 0.0023 0.1375
Canada(b) 0 0.1211 0 0.1211

Totals 1.1780 0.3416 0.1781 1.6977 1.65 0.36 0.25 2.27

Notes: (a) Only sources in that portion of the state within th e RPO modeling domain were modeled.

(b) 52 Canadian point sources > 250 tons/yr SO 2 emissions during 2002 (from Canadian NPRI).Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-8

Table 7-2d. Sulfate Ion Impacts by State (Annual Av erage)

Shenandoah National Park

NW S-based Meteorology (VT DEC) MM5-based Meteorology (MDE /MDNR)

μ g/m 3 μ g/m 3

STATE CEM

PT NonCEM PT Area/

Mobile TOTAL

PT CEM

PT NonCEM PT Area/

Mobile TOTAL

AL(a) 0.0521 0.0084 0.0018 0.0623 0.0504 0.0029 0.0034 0.0567
AR(a) 0.0074 0 0 0.0074 0.0087 0.0035 0.0019 0.0141

CT 0.0005 0.0002 0.0011 0.0018 0.0007 0.0001 0.0009 0.0017

DC 0.0004 0.0004 0.0008 0.0016 8.1E-05 0.0003 0.0009 0.0013

DE 0.0101 0.0029 0.0011 0.0141 0.0086 0.0136 0.0021 0.0243
GA(a) 0.0879 0.0056 0.0040 0.0975 0.0963 0.0032 0.0079 0.1073

IA 0.0192 0.0181 0.0001 0.0374 0.0152 0.0130 0.0036 0.0318

IL 0.0646 0.0222 0.0006 0.0874 0.0561 0.0189 0.0045 0.0794

IN 0.1782 0.0156 0.0015 0.1952 0.1907 0.0181 0.0155 0.2243
KS(a) 0.0137 0 0 0.0137 0.0091 0.0007 0.0006 0.0104

KY 0.1273 0.0135 0.0057 0.1465 0.1741 0.0106 0.0184 0.2031

MA 0.0036 0.0005 0.0020 0.0060 0.0029 0.0047 0.0023 0.0098

MD 0.1045 0.0116 0.0118 0.1280 0.1365 0.0373 0.0109 0.1847

ME 0 0.0004 0.0003 0.0007 2.8E-05 0.0003 0.0002 0.0006

MI 0.0830 0.0082 0.0036 0.0948 0.0860 0.0100 0.0125 0.1085

MN 0.0148 0.0055 0.0007 0.0210 0.0109 0.0023 0.0028 0.0160

MO 0.0255 0 0 0.0255 0.0180 0.0104 0.0034 0.0318
MS(a) 0 0.0009 0.0004 0.0013 0 0.0010 0.0007 0.0017

NC 0.1669 0.0251 0.0050 0.1970 0.2257 0.0148 0.0062 0.2467
ND(a) 0 0.0011 0.0016 0.0027
NE(a) 0.0038 0 0 0.0038 0.0023 0 0.0009 0.0032

NH 0.0010 0.0001 0.0002 0.0012 0.0013 5.3E-05 0.0002 0.0016

NJ 0.0102 0.0018 0.0046 0.0166 0.0119 0.0022 0.0071 0.0212

NY 0.0350 0.0027 0.0141 0.0519 0.0468 0.0141 0.0167 0.0776

OH 0.4678 0.0256 0.0027 0.4960 0.6483 0.1088 0.0114 0.7685
OK(a) 0.0080 0 0 0.0080 0.0081 0.0016 0.0009 0.0105

PA 0.2774 0.0354 0.0214 0.3342 0.4517 0.0318 0.0247 0.5082

RI 0 0 0.0004 0.0004 3.1E-07 2.9E-05 0.0002 0.0002

SC 0.0242 0.0117 0.0041 0.0401 0.0232 0.0093 0.0035 0.0359
SD(a) 0.0011 0 0 0.0011 0.0011 4.0E-05 0.0014 0.0025

TN 0.0781 0.0207 0.0073 0.1061 0.0929 0.0304 0.0086 0.1319
TX(a) 0 0 0 0 1.7E-07 0 3.2E-05 3.2E-05

VA 0.1102 0.0398 0.0047 0.1547 0.1124 0.0469 0.0263 0.1856

VT 0 0 0.0006 0.0007 3.6E-07 2.6E-05 0.0001 0.0002

WI 0.0259 0.0311 0.0007 0.0577 0.0289 0.0096 0.0026 0.0410

WV 0.2691 0.0259 0.0045 0.2995 0.4657 0.0402 0.0111 0.5170
Canada(b) 0 0.0781 0 0.0781

Totals 2.271 0.412 0.106 2.789 2.98 0.46 0.22 3.66

Notes: (a) Only sources in that portion of the state within th e RPO modeling domain were modeled.

(b) 52 Canadian point sources > 250 tons/yr SO 2 emissions during 2002 (from Canadian NPRI).Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-9

Figure 7-3a. Ranked state percent sulfate contribut ions to Northeast Class I receptors based on observation-based (VT) CALPUFF r esults

0 0.05 0.1 0.15 0.2 0.25 0.3PACaOHNYINMIMAWKYILWINCMDVAMETNNHIAVTGANJMNMOSCALKSCTDEOKRINEDCMSARAnnual Average Contribution (μg/m3) SO4 Acadia

Great Gulf

Lye Brook

Moosehorn

0 0.05 0.1 0.15 0.2 0.25 0.3PACaOHNY 0 0.05 0.1 0.15 0.2 0.25 0.3PACaOHNYINMIMAWKYILWINCMDVAMETNNHIAVTGANJMNMOSCALKSCTDEOKRINEDCMSARAnnual Average Contribution (μg/m3) SO4 Acadia

Great Gulf

Lye Brook

Moosehorn

Figure 7-3b. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I receptors based on observation-based (VT) CALPUFF r esults



### 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7OHPAWVINNCKYVAMDTNMIILNYCanadaGAWIALNJSCIADEMOMNMAKSOKCTNENHMERIVTDCMSARAnnual Average Contribution (μg/m3 SO4)

Brigantine
Dolly Sods

ShenandoahTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-10

Figure 7-4a. Ranked state percent sulfate contribut ions to Northeast Class I

receptors based on MM5-based (MD) CALPUFF results

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40OHPANYMAINWVMIKYILMDNCVATNNHWIMOIAGAMENJDEALSCMNKSCTOKARVTNERINDSDMSDCTXAnnual Average Contribution (μg/m3 SO4) Acadia
Lye Brook

Figure 7-4b. Ranked state percent sulfate contribut ions to Mid-Atlantic Class I

receptors based on MM5-based (MD) CALPUFF results

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90OHPAWVMDNCINVAKYNYTNMIGANJILDEALMAWISCMIAMNARKSOKCTNHNENDSDDCMEMSRIVTTXAnnual Average Contribution (μg/m3 SO4) Brigantine

ShenandoahTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-11



### 7.5. Future work and potential uses of CALPUFF results f or BART

determinations

Modeling efforts to date have provided a solid basi s for contributing to a weightof-evidence assessment of state contributions. In addition, the two CALPUFF platforms

can be used to evaluate the relative contributions to fine PM and visibility impacts of individual sources in the MANE-VU region. It is an ticipated that MANE-VU will provide all states with a consistent set of modelin g results from each of these platforms to serve as a preliminary basis for BART visibility de terminations and states will have several options with regard to how these results ar e used:

• States may accept the MANE-VU modeling as an adequat e basis for determining whether BART controls at a facility are justified b y its contribution to visibility degradation.

• States may conduct additional modeling on their own to determine whether BART controls at a facility are justified by its co ntribution to visibility degradation.

• States may require a source to conduct additional mo deling to determine whether BART controls at a facility are justified by its co ntribution to visibility degradation.

These options and the use of modeling results for B ART determinations are

discussed in more detail in the MANE-VU BART Resource Book (NESCAUM, 2006), and

the reader is referred to that resource for additio nal information.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 7-12



### References

NESCAUM, BART Resource Guide , Northeast States for Coordinated Air Use Management, 2006.

USEPA, CALPUFF Modeling System, Available at: http://www.epa.gov/ttn/scram , 2006.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-1



## 8. SYNTHESIS OF RESULTS USING DIFFERENT SOURCE ASSESSMENT TECHNIQUES

By synthesizing results from a variety of data sour ces and analysis techniques MANE-VU has taken a first step toward identifying s ources of visibility impairment in the Northeast generally, and toward understanding t he role of transported sulfate in particular. The variety of approach and complexity of analytical tools utilized for this purpose provides numerous metrics and means of comp arison into how SO 2 emissions are chemically transformed, transported and combine d with various local constituents of fine particle pollution in the MANE-VU region. Bey ond reviewing these results, additional sections of this chapter describe opport unities for further synthesizing the available data to solidify a weight-of-evidence app roach to implementing the contribution assessment and pollution apportionment requirements of the Haze Rule



### 8.1. Ranked Contribution

Chapter 4 of this report describes two crude method s of ranking state

contributions based on the ratio of source emission s to source-receptor distance as well as the gridded product of emissions and upwind residen ce time probability. Chapter 5 describes the qualitative evidence available from s everal different trajectory-based techniques and source apportionment studies. These include source region comparisons, source profile examinations, and the development of other techniques and metrics to support the more quantitative ranking techniques. Chapter 6 describes results obtained using Eulerian grid models such as the Regulatory M odeling System for Aerosols and Deposition (REMSAD) and the Community Multi-scale Ai r Quality (CMAQ) model. Ultimately these types of models are likely to yiel d the most definitive assessments of contribution from different sources. Chapter 7 expl ores the use of lagrangian puff dispersion models such as CALPUFF for estimating sourc e contributions and compares

two related but distinct versions of the CALPUFF mode ling system that demonstrate the sensitivity of this tool to emissions and meteorolo gy inputs.

In Table 8-1 through Table 8-5 (and graphically in Figure 8-1), we have normalized the results obtained using five techniqu es for assessing state contribution by calculating the percentage contribution and plotted them on a common graph. The figure shows substantial consistency across a variety of i ndependent analyses using techniques that are themselves based on the application of dis parate chemical, meteorological and physical principles. Together, these findings creat e a strong weight-of-evidence case for identifying the most significant contributors to vi sibility impairment in MANE-VU Class

I areas.

In Figure 8-1, several features of the normalized re sults bear notice. First, we note that the apparent perfect agreement among the techniques for the “other” contribution that represents all emissions from out side the domain of study is a result of having substituted the REMSAD calculated “other” con tribution for all of the other methods. REMSAD is the only method that has a means of developing a comprehensive

estimate of the total out-of-domain contribution be cause the boundary condition used was derived from a global model run using global SO 2 emissions estimates. It is also worth noting how high the “other,” or out-of-domain, cont ribution is to observed sulfate atTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-2

Acadia National Park. This is not surprising given how close Acadia is to the domain boundaries on both the northern and eastern edge. There may be some recirculation of

in-domain SO 2 emissions that leave the modeling domain and re-en ter through the dynamic boundary condition, but lose their tag in t he process.

It is also worth noting the differences between the methods for certain states and Canada, such as Massachusetts and Maine in the case of Acadia, Maryland and Canada for Brigantine, Canada for Lye Brook, and Ohio and West Virginia for Shenandoah. Those states and Canada that are directly upwind a large fraction of the time, either because they are very large geographically or becau se they are very nearby, are likely to be treated differently by the percent-time-upwind m ethod relative to the other methods. In addition, the CALPUFF models appear to underestimat e the contribution from Canada relative to other methods. This is likely to resul t from an incomplete characterization of

the total SO 2 inventory for Canada relative to other methods tha t are based on the entire MANE-VU Canadian inventory.

Table 8-1. Annual Average Sulfate Impact from REMSA D (%)

RPO S TATE A CADIA B RIGANTINE D OLLY S ODS G REAT G ULF L YE B ROOK M OOSEHORN S HENANDOAH

CANADA 8.69 7.11 3.90 14.84 12.43 7.85 4.75

CENRAP 0.88 1.12 1.58 1.65 1.67 0.82 1.48

MANE-VU 36.17 34.83 14.81 27.83 31.78 30.08 20.59

Connecticut 0.76 0.53 0.04 0.48 0.55 0.56 0.08

Delaware 0.96 3.20 0.30 0.63 0.93 0.71 0.61

District of
Columbia 0.01 0.04 0.01 0.01 0.02 0.01 0.04

Maine 6.54 0.16 0.01 2.33 0.31 8.01 0.02 Maryland 2.20 4.98 2.39 1.92 2.66 1.60 4.84

Massachusetts 10.11 2.73 0.18 3.11 2.45 6.78 0.35 New Hampshire 2.25 0.60 0.04 3.95 1.68 1.74 0.08 New Jersey 1.40 4.04 0.27 0.89 1.44 1.03 0.48

New York 4.74 5.57 1.32 5.68 9.00 3.83 2.03 Pennsylvania 6.81 12.84 10.23 8.30 11.72 5.53 12.05

Rhode Island 0.28 0.10 0.01 0.11 0.06 0.19 0.01MANE-VU

Vermont 0.13 0.06 0.00 0.41 0.95 0.09 0.01

MIDWEST 11.98 18.16 30.26 20.10 21.48 10.40 26.84

Illinois 1.37 1.82 2.56 2.52 2.42 1.30 2.47

Indiana 2.13 3.29 5.40 3.94 3.93 2.02 5.23 Michigan 2.02 2.77 3.24 3.88 3.67 1.74 3.20

Ohio 5.62 9.11 17.98 8.33 9.96 4.62 14.87MIDWEST

Wisconsin 0.85 1.16 1.08 1.42 1.49 0.72 1.07

VISTAS 8.49 21.99 36.75 12.04 13.65 6.69 33.86

Alabama 0.32 1.07 2.13 0.65 0.81 0.25 1.77 Georgia 0.67 2.32 3.71 1.27 1.31 0.56 3.47 Kentucky 1.17 2.22 4.89 1.99 2.22 0.98 4.34 Mississippi 0.01 0.04 0.08 0.03 0.04 0.01 0.07

North Carolina 1.45 4.19 4.29 1.88 1.89 1.14 4.78

South Carolina 0.43 1.69 1.04 0.64 0.56 0.36 1.30

Tennessee 0.61 1.56 3.41 1.11 1.23 0.50 2.73 Virginia 1.48 4.30 2.82 1.52 1.95 1.13 6.20VISTAS

West Virginia 2.35 4.59 14.38 2.96 3.64 1.75 9.19

OTHER 33.79 16.78 12.70 23.54 18.99 44.17 12.48 TOTAL ( μ g/m 3 ) 2.026 3.444 3.867 1.780 2.137 1.767 3.919Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VUPage 8-3Table 8-2. Annual Average Sulfate Impact from Q/D (%)RPOSTATEACADIABRIGANTINEDOLLYSODSGREATGULFLYEBROOKMOOSEHORNSHENANDOAHCANADA11.916.010.008.9712.0018.776.76CENRAP1.741.641.592.331.991.351.72Arkansas 0.13 0.15 0.13 0.08 0.17 0.09 0.26Iowa 0.29 0.19 0.24 0.40 0.32 0.24 0.24Louisiana 0.02 0.03 0.02 0.02 0.03 0.02 0.04Minnesota 0.22 0.13 0.16 0.30 0.24 0.13 0.19

CENRAPMissouri 1.08 1.15 1.03 1.53 1.23 0.87 1.00MANE-VU20.1332.5320.1021.4825.6912.8424.50Connecticut 0.34 0.33 0.11 0.74 0.38 0.21 0.31Delaware 0.59 3.01 0.46 0.51 0.67 0.36 1.07District of Columbia 0.01 0.05 0.02 0.01 0.02 0.010.09Maine 1.74 0.15 0.08 0.71 0.15 1.13 0.15Maryland 1.83 7.26 3.86 0.43 2.67 1.27 5.27Massachusetts 2.89 0.95 0.46 4.61 1.06 1.33 1.22New Hampshire 1.07 0.30 0.14 0.42 0.08 0.60 0.18New Jersey 0.76 4.22 0.43 3.11 0.75 0.48 1.82New York 4.02 4.61 1.93 3.67 6.71 2.83 3.30Pennsylvania 6.64 11.57 12.58 6.62 13.07 4.50 11.00Rhode Island 0.12 0.05 0.02 0.08 0.04 0.06 0.06

MANE-VUVermont 0.10 0.03 0.02 0.57 0.10 0.07 0.04MIDWEST16.9917.4826.3025.3822.8412.4922.46Illinois 2.53 2.16 2.60 3.64 2.98 2.11 2.61Indiana 3.94 4.24 5.17 6.01 5.01 2.91 4.50Michigan 2.69 1.95 2.46 4.08 3.50 2.16 2.49Ohio 6.63 8.34 15.06 9.94 9.98 4.51 11.85

MIDWESTWisconsin 1.19 0.79 1.00 1.71 1.38 0.80 1.01VISTAS15.4425.5539.3218.3018.4810.3932.08Alabama 1.24 1.69 1.66 1.45 1.60 0.91 1.65Georgia 2.36 3.28 3.18 2.62 2.82 1.63 3.30Kentucky 2.07 3.36 3.99 3.18 2.79 1.50 3.54Mississippi 0.19 0.24 0.22 0.22 0.24 0.14 0.37North Carolina 2.27 4.16 9.03 2.59 2.69 1.44 6.60South Carolina 1.29 1.62 0.95 1.14 0.94 0.70 1.69Tennessee 1.45 2.14 2.49 1.74 1.92 1.06 2.40Virginia 1.93 4.36 2.49 1.97 1.78 1.12 4.25

VISTASWest Virginia 2.64 4.71 15.33 3.39 3.71 1.88 8.27OTHER3633.7916.7812.7023.5418.9944.1712.48TOTAL (μg/m3) 1.920 2.740 3.455 1.305 1.858 1.977 3.41736OTHER is % from REMSAD result; Florida is considered within OTHERTools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VUPage 8-4Table 8-3. Annual Average Sulfate Impact from CALPUFF (NWS Observations) (%)RPOSTATEACADIABRIGANTINEDOLLYSODSGREATGULFLYEBROOKMOOSEHORNSHENANDOAHCANADA8.072.652.307.225.779.452.45CENRAP2.762.983.345.064.502.303.42Iowa 0.93 1.09 1.13 1.65 1.55 0.80 1.17Kansas 0.34 0.37 0.41 0.64 0.55 0.28 0.43Louisiana 0.00 0.00 0.00 0.00 0.00 0.00 0.00Minnesota 0.58 0.60 0.62 1.16 0.95 0.49 0.65Missouri 0.59 0.59 0.81 1.00 0.91 0.49 0.80Nebraska 0.07 0.10 0.11 0.14 0.13 0.06 0.12

CENRAPOklahoma 0.25 0.22 0.26 0.47 0.41 0.20 0.25MANE-VU27.4129.1716.2120.9126.5221.1117.47Connecticut 0.58 0.50 0.03 0.26 0.27 0.41 0.06Delaware 0.48 1.90 0.21 0.31 0.32 0.38 0.44District of Columbia 0.02 0.07 0.02 0.03 0.02 0.020.05Maine 2.40 0.12 0.01 0.53 0.18 2.04 0.02Maryland 1.90 5.22 2.54 2.19 2.47 1.55 4.01Massachusetts 5.73 1.58 0.12 1.44 1.29 4.13 0.19New Hampshire 2.02 0.26 0.02 0.79 0.47 1.36 0.04New Jersey 0.98 3.37 0.28 0.63 0.67 0.75 0.52New York 5.06 4.92 1.24 4.67 7.20 4.03 1.63Pennsylvania 7.19 10.97 11.71 8.86 11.16 5.65 10.48Rhode Island 0.31 0.14 0.01 0.08 0.06 0.22 0.01

MANE-VUVermont 0.73 0.12 0.01 1.13 2.41 0.56 0.02MIDWEST16.8519.9933.0926.6826.9814.2129.46Illinois 2.12 2.37 2.86 3.36 3.11 1.84 2.74Indiana 3.67 4.28 6.52 5.83 5.57 3.19 6.11Michigan 3.06 2.25 3.28 4.74 4.74 2.67 2.97Ohio 6.31 9.07 18.33 9.82 10.66 5.07 15.55

MIDWESTWisconsin 1.69 2.03 2.10 2.93 2.90 1.44 2.09VISTAS11.1228.4332.3516.5917.248.7634.72Alabama 0.43 1.26 1.77 0.77 0.85 0.32 1.96Georgia 0.65 2.13 2.12 1.30 1.41 0.52 3.06Kentucky 2.05 3.23 5.29 3.39 3.59 1.64 4.59Mississippi 0.01 0.03 0.04 0.03 0.03 0.01 0.04North Carolina 1.84 6.03 3.20 2.52 2.51 1.42 6.18South Carolina 0.61 1.87 0.75 0.80 0.71 0.49 1.33Tennessee 1.10 2.19 3.27 1.72 1.80 0.86 3.33Virginia 1.72 6.33 2.42 1.80 1.86 1.32 4.85

VISTASWest Virginia 2.71 5.37 13.49 4.26 4.48 2.17 9.39OTHER3633.7916.7812.7023.5418.9944.1712.48TOTAL (μg/m3) 1.571 2.533 3.125 1.167 1.701 1.429 2.793Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VUPage 8-5Table 8-4. Annual Average Sulfate Impact from CALPUFF (MM5) (%)RPOSTATEACADIABRIGANTINEDOLLYSODSGREATGULFLYEBROOKMOOSEHORNSHENANDOAHCANADA8.052.655.762.46CENRAP3.262.855.082.74Arkansas 0.23 0.32 0.39 0.33Iowa 0.82 0.75 1.28 0.74Kansas 0.43 0.29 0.65 0.24LouisianaMinnesota 0.41 0.39 0.71 0.37Missouri 1.01 0.80 1.48 0.74Nebraska 0.10 0.08 0.15 0.07Oklahoma 0.25 0.23 0.42 0.24

CENRAPTexas 0.00 0.00 0.00 0.00MANE-VU28.0931.8327.6919.31Connecticut 0.43 0.27 0.25 0.04Delaware 0.01 0.07 0.02 0.03District of Columbia 0.60 2.81 0.73 0.57Maine 1.62 0.06 0.14 0.01Maryland 1.68 5.95 2.59 4.27Massachusetts 8.67 1.87 2.18 0.23New Hampshire 2.05 0.26 0.56 0.04New Jersey 0.97 3.60 0.91 0.49New York 4.41 4.30 8.08 1.79Pennsylvania 7.44 12.57 11.86 11.83Rhode Island 0.14 0.04 0.04 0.00

MANE-VUVermont 0.08 0.02 0.33 0.00MIDWEST16.2821.7925.5828.43Illinois 1.89 1.78 2.68 1.85Indiana 3.57 4.46 5.48 5.22Michigan 2.30 2.41 3.47 2.53Ohio 7.53 12.11 12.30 17.88

MIDWESTWisconsin 0.98 1.04 1.65 0.95VISTAS10.5324.1016.9034.57Alabama 0.43 0.79 0.79 1.32Georgia 0.78 1.74 1.30 2.50Kentucky 2.02 3.47 3.22 4.73Mississippi 0.01 0.03 0.04 0.04North Carolina 1.72 4.23 2.53 5.74South Carolina 0.50 1.10 0.62 0.84Tennessee 1.07 2.04 1.82 3.07Virginia 1.36 4.70 1.99 4.32

VISTASWest Virginia 2.64 6.00 4.58 12.03OTHER3633.7916.7812.7023.5418.9944.1712.48TOTAL (μg/m3) 2.424 3.589 2.430 3.761Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VUPage 8-6Table 8-5. Annual Average Sulfate Impact from percent time upwind method (%)RPOSTATEACADIABRIGANTINEDOLLYSODSGREATGULFLYEBROOKMOOSEHORNSHENANDOAHCANADA15.246.7019.2915.9113.454.33CENRAP1.891.771.731.661.521.72Arkansas 0.12 0.24 0.15 0.15 0.15 0.20Iowa 0.38 0.27 0.27 0.28 0.28 0.25Kansas 0.00 0.00 0.00 0.00 0.00 0.00Louisiana 0.04 0.08 0.06 0.04 0.04 0.09Minnesota 0.56 0.33 0.38 0.44 0.44 0.22Missouri 0.80 0.85 0.87 0.75 0.62 0.95

CENRAPTexas 0.00 0.00 0.00 0.00 0.00 0.00MANE-VU18.3325.8320.6425.3815.2311.38Connecticut 0.51 0.27 0.52 0.59 0.40 0.10Delaware 0.30 1.36 0.34 0.42 0.28 0.24District of Columbia 0.12 0.29 0.11 0.14 0.12 0.24Maine 1.49 0.08 0.68 0.26 1.53 0.05Maryland 1.32 3.06 1.31 1.31 0.96 2.29Massachusetts 1.10 0.33 0.86 0.81 0.90 0.12New Hampshire 1.21 0.17 1.48 0.72 0.77 0.06New Jersey 1.02 6.01 0.99 1.39 0.78 0.49New York 4.80 3.49 6.80 9.08 4.23 1.44Pennsylvania 6.21 10.71 7.10 10.36 5.07 6.33Rhode Island 0.11 0.05 0.08 0.08 0.09 0.02

MANE-VUVermont 0.14 0.03 0.37 0.23 0.10 0.01MIDWEST17.3519.5520.6721.6315.5622.03Illinois 3.79 3.47 3.31 3.74 3.22 3.76Indiana 3.37 4.36 4.33 4.13 3.21 5.08Michigan 2.73 2.07 3.03 3.27 2.34 1.80Ohio 6.10 8.65 8.73 9.23 5.77 10.64

MIDWESTWisconsin 1.36 1.00 1.28 1.25 1.02 0.76VISTAS13.4029.3714.1416.4310.0748.06Alabama 0.72 1.32 0.63 0.71 0.39 2.14Georgia 1.40 3.21 1.06 1.54 0.72 4.73Kentucky 2.65 4.71 3.59 3.83 2.31 7.82Mississippi 0.04 0.10 0.06 0.06 0.03 0.12North Carolina 1.29 4.35 0.92 0.99 1.18 6.11South Carolina 0.72 1.64 0.42 0.41 0.44 1.62Tennessee 1.05 1.91 1.04 1.16 0.86 3.67Virginia 1.80 4.83 1.48 1.67 1.32 5.45

VISTASWest Virginia 3.74 7.31 4.94 6.05 2.81 16.39OTHER3633.7916.7812.7023.5418.9944.1712.48Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-7

MANE-VU will continue to explore these differences, but it remains encouraging that the use of different platforms and approaches results in more agreement across the various techniques than difference. With the few, specific exceptions mentioned above, it is relatively easy — using the normalized result s from multiple techniques shown in Figure 8-1(a-d) — to identify those states that have the largest influence on sulfate levels

at each Class I site. MANE-VU believes that this i nformation can provide a solid basis for initiating consultation and planning efforts be tween upwind and downwind states and

RPOs.

Figure 8-1(a-d). Comparison of normalized (percent contribution) results using different techniques for ranking state contributions to sulfa te levels at the MANE-VU Class I sites

(a) Acadia National Park, ME, (b) Brigantine Wilder ness Area, NJ, (c)Lye Brook Wilderness Area, VT, and (d) Shenandoa h National Park, VA.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-8

An alternative means of displaying the above result s is in Table 8-6, which shows the individual state rankings produced by different assessment techniques for Acadia National Park, Maine. In the left-side column of Ta ble 8-6, states are colored according to their average ranking across the different asses sment methods. Those states that are ranked in the top five on average, across all techn iques are colored red, while states ranked in the top six through ten are colored magen ta, and so on for each group of five going down the left-side column. Through this colo r scheme, one can see how the states’ average ranking compares to their rankings under ea ch individual assessment method given in the other columns of the table. The fact that all techniques tend to come to Figure 8-1(a-d). ContinuedTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-9

consistent conclusions about which states are top c ontributors provides some confidence that the source regions with the most influence on sulfate levels at MANE-VU Class I sites can be correctly identified. Note that the CE NRAP states and several other states along the border of the analysis domain represent o nly partial state contributions.

Table 8-6. Ranked Contributing States to Acadia Sul fate

Average REMSAD Q/d CALPUFF (VT) CALPUFF

(MD) E x RTP

CANADA MA CANADA CANADA MA CANADA

PA CANADA PA PA CANADA PA

OH PA OH OH OH OH

MA ME NY MA PA NY

NY OH IN NY NY IL

IN NY MA IN IN WV

WV WV MI MI WV IN

ME NH WV WV CENRAP MI

MI MD IL ME MI KY

IL IN GA IL NH CENRAP

KY MI NC CENRAP KY VA

CENRAP VA KY KY IL ME

MD NC VA NH NC GA

NH NJ MD MD MD WI

NC IL CENRAP NC ME MD

VA KY ME VA VA NC

WI DE TN WI TN NH

GA CENRAP SC TN WI MA

TN WI AL NJ NJ TN

NJ CT WI VT GA NJ

SC GA NH GA DE AL

AL TN NJ SC SC SC

DE SC DE CT AL CT

CT AL CT DE CT DE

VT RI MS AL RI VT

RI VT RI RI VT DC

MS MS VT DC DC RI

DC DC DC MS MS MS

Yet one more way of combining the ranked contributi ons is shown in Figure 8-2,

which summarizes the relative contributions of four RPOs, Canada, and “outside domain” regions to ambient sulfate concentrations a t several Class I areas using four different assessment techniques. The techniques con sidered here include: tagged REMSAD modeling, two CALPUFF platforms (MM5-based meteo rology used by MDE and NWS observation-based meteorology used by VT DEC ), the empirical emissions divided by distance approach (Q/d), and emissions t imes residence time probability. The estimates of state-by-state sulfate mass contributi ons ( μ g/m 3 ) from each method have been aggregated by RPO, both in terms of their absol ute contribution (these values are displayed within the bars shown in the graphic) and in terms of their proportional

contribution relative to other RPOs. It should be n oted that the “outside domain”Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-10

contribution shown for each analysis method was der ived exclusively from the REMSAD result. Averaging estimated impacts at the Acadia, Brigantine, and Lye Brook sites over the four assessment techniques utilized, MANE-VU st ates account for about 20 to 30 percent of sulfate impacts in these three MANE-V U Class I areas, while the Midwest

RPO and VISTAS states each account for about 20 to 25 percent of the total sulfate contribution at Brigantine and Lye Brook and about 10 to 15 percent each at Acadia. The CENRAP states, Canada and “outside domain” add the r emainder. Although variation

exists across estimates of contribution for differe nt sites and using different techniques, the overall pattern is generally consistent. Relat ive contributions are somewhat reversed at Shenandoah, which is a VISTAS Class I area. There, VISTAS states and Midwest RPO states account for roughly 20 to 30 percent of o verall sulfate impacts, with MANEVU states contributing roughly 15 to 20 percent.

Figure 8-2. Estimated RPO contributions to sulfate concentrations at Class I areas

using different assessment techniques

0.73

0.65 1.03
0.58 1.20 0.89 1.37 1.07 0.68 0.56 0.83 0.59 0.81 0.56 0.83 0.96 0.24

0.40 0.60

0.49 0.63

0.61 0.94 0.58 0.46 0.57 0.77
0.52 1.05 0.94 1.22 0.88 0.18 0.26 0.39

0.45 0.77 0.87 1.04 0.84 0.29 0.36 0.51

0.42 1.36 1.11 1.49 1.25

0.02 0.05 0.09 0.05 0.04 0.07 0.10 0.05

0.04 0.07 0.12

0.05 0.06 0.08 0.09

0.07

0.18 0.19 0.29 0.35 0.24 0.08 0.11 0.20

0.27 0.12 0.17
0.28 0.19 0.08 0.11 0.26

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%REMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent UpwindREMSADCALPUFF(VT)CALPUFF(MD)E/DPercent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAINTools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-11

While the foregoing discussion has focused on quant itative methods for comparing contributions from individual states and regions, additional analyses have been conducted to verify and support these results using more qualitative means of identifying “regions of influence” for each Class I area. One such qualitative approach to synthesizing and interpreting the results obtained through different assessment techniques is illustrated in Figure 8-3 and Figure 8-4 below, wh ich show a series of maps shaded to

indicate different levels of contribution from diff erent states and regions as determined by the analysis platforms already discussed. In these maps, states are shaded darker the higher they rank in terms of percent contribution t o sulfate at a Class I site. For example, in Figure 8-3, states in a line from Indiana through Massachusetts are calculated to have the greatest impact on sulfate at Acadia. Overlaid on top of these maps are contours of

Figure 8-3. Ranked contributions of states to ambie nt sulfate concentrations at Acadia National Park, Maine.

Note: Shaded maps show contributions as estimated by REM SAD, Emissions divided by Distance, CALPUFF VT, and CALPUFF MD. Red and blue contours representing regions of high incremental probability (IP) and high clusterweighted probability (CWP) are overlaid onto the shaded st ate maps to indicate similarity of regional contributions as calculated by these independent receptor-based methods.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 8-12

Incremental Probability (red) and Cluster Weighted Pr obability (blue) of contributing to sulfate on the highest days. The substantial consi stency in the patterns support and bolster the quantitative results. The importance o f this finding is that the receptor-based results portrayed by the contours rely on methods t hat are completely independent of the source-based modeling approaches used to calculate the underlying ranks. This sort of internal consistency among approaches gives conside rable strength to the weight-ofevidence approach that MANE-VU has adopted for iden tifying sulfate source regions.

Figure 8-4. Ranked contributions of states to ambie nt sulfate concentrations

at Brigantine Wilderness Area, New Jersey.

Note: Shaded maps show contributions as estimated by REMS AD, Emissions divided by Distance, CALPUFF VT, and CALPUFF MD. Red and blue contours re presenting regions of high incremental probability (IP) and high cluster-weighted probability (CWP) are overlaid onto the shaded state maps to indicate similarity of regional contributions as calculated by these independent receptor-based methods.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 9-1



## 9. CONCLUSION

As MANE-VU prepares to implement the requirements o f the Regional Haze Rule, a significant technical effort has focused on developing multiple analysis tools for assessing contributions to fine particle pollution and thus visibility impairment at Class I areas in the eastern United States. These analysis tools span the discipline of atmospheric

science and include traditional Eulerian “source” o r “grid” models, Lagrangian dispersion models, back trajectory receptor techniques, source apportionment models, and simple approximations based on empirical relationships bet ween emissions and geography.

A review of the literature and of recent monitoring data has yielded a conceptual model of visibility impairment in the MANE-VU regio n that attributes a dominant role, on the worst visibility days, to the sulfate compon ent of fine particle matter. This model in turn suggests that the most effective near-term strategy for reducing fine particle pollution and visibility impairment in the East is to continue reducing anthropogenic

emissions of SO 2 . Reductions in both NO X and VOCs should also be considered. Given that sulfate, in particular, plays a dominant role in causing visibility impairment throughout the East, MANE-VU has focused on multipl e methods of apportioning the

sulfate mass found in ambient air at Class I sites to contributing states and regions. This weight-of-evidence approach is intended to overcome large uncertainties that would

otherwise undermine confidence in the results obtai ned using any one modeling or analysis technique in isolation.

The assessment techniques described in this report use numerous approaches to develop ranked lists of individual state contributi ons to sulfate levels in MANE-VU

Class I areas. When these results are normalized a nd compared, we find broad general agreement concerning the top contributing states at each site as well as some differences that suggest the magnitude of uncertainty inherent in these results.

The conclusions that emerge from this report regard ing the relative contributions of different upwind RPOs to downwind sulfate concen trations at MANE-VU Class I areas appear quite robust and the modest difference s presented here relative to the preliminary results presented in Spring of 2005 are a further indication that the general patterns of contribution presented here are unlikel y to change due to further refinements of the emissions and meteorological inputs. This s uggests that the MANE-VU findings are sufficiently robust to serve as a basis for int er-RPO consultations and the regional haze planning process. Given that as much as 30 to 50 percent of the ambient sulfate found at northeastern Class I sites on hazy days ap pears to originate within neighboring RPOs, coordination and consultation is likely to be critical if MANE-VU is to achieve its visibility goals for 2018 and beyond.Tools and Techniques for Apportioning Fine Particle /Visibility Impairment in MANE-VU Page 9-1



# Appendix A: Application of Trajectory Analysis

Methods to Sulfate Source Attribution Studies in

the Northeast U.S.

Appendix B: Source Attribution by ReceptorBased Methods

Appendix C: Chemical Transport Model Results

for Sulfate Source Attribution Studies in the

Northeast U.S.

Appendix D: Development of Parallel CALPUFF Dispersion Modeling Platforms for Sulfate Source

Attribution Studies in the Northeast U.S.