# Appendix B Conceptual Description

### Introduction

Air Resource Specialists, Inc. (ARS) was contract ed by Visibility Improvement State and Tribal Association of the Southeast (VIS TAS) to describe current visibi lity conditions and particulate matter concentrations at Class I areas in the VISTAS region, to investigate the relative contributions of regional and local sources of em issions to fine particle concentrations and visibility in Class I areas, to di scuss the rate of improvement in visibility that would demonstrate a uniform rate of progress toward visibility improvement goals between current visibility conditions and natural background visibility co nditions in 2064, and to describe special monitoring studies undertaken by VISTAS to bette r understand temporal and spatial variability in fine particles that contribute to visibility impairment and source contributions to measured organic carbon. Attachment B.1 contains the ARS report submitted to VISTAS.
Appendix B.2 contains a white paper written by VISTAS’s contractor Ivar Tombach that discusses the new IMPROVE Equation and why this new equation should be used to calculated visibility.
Appendix B.3 contains a journal article by Pat Brewer, VISTAS technical coordinator and Joe Adlhoch, ARS, about the trends in speciated PM 2.5 data and visibility across monitoring networks in the Southe astern United States.
Appendix B.4 contains a journa l article by Ivar Tombach a nd Pat Brewer about natural background visibility and regional haze goals in the Southeastern United States.
Appendix B.5 contains a powerpoint presentation about a VISTAS special monitory studies on the carbon analyses for PM 2.5 and haze.
Appendix B.6 contains a powerpoint presentation about a VISTAS special monitory studies on continuous measures of speciated PM 2.5. Appendix B.7 contains the final report from the VISTAS contractor ICF International for characterization of meteorology and its relationships to fine partic ulate mass and visibility in the VISTAS Region.

ii

# Appendix B.1 Conceptual Description

# Air Resource Specialists, Inc. VISTAS Conceptual Descri ption Support Document

Prepared for the:
Visibility Improvement State and Tribal Association of the Southeast The Interchange Building 59 Woodfin Place Asheville, North Carolina 28801

Prepared by:
Air Resource Specialists, Inc. 1901 Sharp Point Drive, Suite E

Fort Collins, Colorado 80525 (970) 484-7941 www.air-resource.com

September 20, 2007i TABLE OF CONTENTS

Section Page

1.0 INTRODUCTION 1-1

1.1 Overview 1-1 1.2 Data Summary Product FTP Site 1-2

2.0 ANALYTICAL METHODS 2-1
2.1 The IMPROVE Monitoring Network 2-1 2.2 Back Trajectory Modeling 2-5

3.0 VISIBILITY CO NDITIONS 3-1
3.1 Comparison of Old and New IMPROVE Algorithm 3-1 3.2 RHR Data Completeness 3-7

3.2.1 Additional Data Substitutions 3-7

3.2.1.1 Carbon Substitutions 3-10 3.2.1.2 Donor Site Substitutions 3-10 3.2.1.3 Data Completeness Following Substitutions 3-11

3.3 Extinction Trends 3-15 3.4 Baseline (2000-2004) and Natural (2064) Visibility Conditions 3-17 3.5 Glidepaths 3-28 3.6 Attribution Analysis 3-35
3.6.1 Back Trajectory Maps 3-35 3.6.2 Residence Time Maps 3-35 3.6.3 Extinction-Weighted Residence Time 3-36 3.6.4 International Contributions 3-37
APPENDIX A Nephelometer Comparisons A-1
APPENDIX B Original and Revise d Algorithm Comparisons by Site B-1
APPENDIX C File Key for Substituted Data C-1

APPENDIX D Data Substitution Results for Individual Sites D-1
APPENDIX E Extinction Trend Charts (Mm -1 , DV and SVR) E-1
APPENDIX F WinHaze Images (Base line, 2018 and Natural Conditions) F-1APPENDIX G 2000-2004 Timelines G-1
APPENDIX H DV Glidepaths H-1
APPENDIX I Speciated Glidepaths I-1
APPENDIX J Back Trajectory Maps J-1
APPENDIX K Residence Time Maps K-1

APPENDIX L Internati onal Attribution Plots L-1

iiiii LIST OF FIGURES
Figure Page

2-1 Map of Class I Areas and IMPROVE Mon itoring sites in and around VISTAS 2-1

3-1 Comparison of Measured Light Scatteri ng (Nephelometer) and Light Scattering Calculated Using the Original (Old) and Revised (New) IMPROVE Algorithms for the GRSM1 Site, 1993-2004 3-3
3-2 Stacked bar chart comparing extinct ion calculated usi ng the new IMPROVE algorithm (left), the old IMPROVE al gorithm (right), and measured light scattering (grey line) in 2004 3-4
3-3 Stacked bar chart comparing light extinc tion calculated using both the new (left) and old (right) IMPROVE algorithm for the average of the 20% worst visibility days in 2000-2004 at VISTAS a nd neighboring Class I areas 3-5

3-4 Stacked bar chart comparing light extincti on calculated using both the new (left) and old (right) IMPROVE algorithm for th e average of the 20% best visibility days in 2000-2004 at VISTAS a nd neighboring Class I areas 3-5

3-5 Stacked bar chart comparing 20% wors t days in 2002 at the GRSM1 site calculated using both the new (left) and old (right) IMPROVE algorithm 3-6

3-6 Flow chart of data substitution methods used 3-9
3-7 Comparison of OC and Estimated Organi c H, and EC and OC at SHRO1, NC Using Second Quarter Raw OC and Organic H Data, 2000-2004 3-10

3-8 Comparison of Aerosol Species Mass Between SHRO1, NC (y-axis) and GRSM1, TN (x-axis) 3-12
3-9 2000-2004 annual bar charts indicating subs tituted data at the SAMA1 site 3-14
3-10 10-year annual average extinction tr ends for sites in and around VISTAS Blue triangles indicate decreasing trends and blank diamonds indicate insignificant trends (p-value<0.20) 3-15

3-11 10-year trend plots for a re gional grouping of sites. Slopes and p-values are indicated in the legend 3-16

3-12 2000-2004 baseline conditions and 2 018 projected conditions at the GRSM1 site simulated using WinHaze Vi sual Air Quality Modeling program 3-19LIST OF FIGURES (CONTINUED)
Figure Page

3-13 2064 natural conditions at the GRSM1 site simulated using WinHaze Visual Air Quality Modeling program 3-19

3-14 Contributions to light extinction (Mm -1 ) for the average of the 20% worst visibility days in 2000-2004 at VIST AS and neighboring Class I areas 3-22

3-15 Contributions to light extinction (Mm -1 ) for the average of the 20% best visibility days in 2000-2004 at VIST AS and neighboring Class I areas 3-22

3-16 Contributions to mass (μg/m 3 ) for the average of the 20% worst visibility days in 2000-2004 at VISTAS a nd neighboring Class I areas 3-23

3-17 Contributions to mass (μg/m 3 ) for the average of the 20% best visibility days in 2000-2004 at VISTAS a nd neighboring Class I areas 3-23
3-18 Bar charts by year indicating speciation of all data collected at the GRSM1 site 3-24
3-19 Bar charts by year indicating speciation of all data collected at the ROMA1 site 3-25
3-20 Contributions to light extinction (Mm -1 ) for the 20% worst visibility days in 2000-2004 at the GRSM1 site 3-26
3-21 Contributions to light extinction (Mm -1 ) for the 20% best vi sibility days in 2000-2004 at the GRSM1 site 3-26
3-22 Contributions to light extinction (Mm -1 ) for the 20% worst visibility days in 2000-2004 at the ROMA1 site 3-27
3-23 Contributions to light extinction (Mm -1 ) for the 20% best vi sibility days in 2000-2004 at the ROMA1 site 3-27

3-24 DV Glidepaths for the GRSM1 site, calculated using bot h the original IMPROVE algorithm using EPA defau lt natural conditi ons (Default NB) and the new IMPROVE algorithm using the revised estimate of natural background conditions (NB2) 3-28

3-25 DV Glidepaths for the ROMA1 site, calculated using bot h the original IMPROVE algorithm using EPA defau lt natural conditi ons (Default NB) and the new IMPROVE algorithm using the revised estimate of natural background conditions (NB2) 3-29
3-26 Glidepath depicting total b ext (Mm -1 ) for 20% worst days at the GRSM1 site 3-30

ivLIST OF FIGURES (CONTINUED)
Figure Page
3-27 Glidepath depicting Ammonium Sulfate fo r 20% worst days at the GRSM1 site 3-31
3-28 Glidepath depicting Ammonium Nitrate fo r 20% worst days at the GRSM1 site 3-31

3-29 Glidepath depicting Particulate Organic Material for 20% worst days at the GRSM1 site 3-32
3-30 Glidepath depicting total b ext (Mm -1 ) for 20% worst days at the ROMA1 site 3-32
3-31 Glidepath depicting Ammonium Sulfate fo r 20% worst days at the ROMA1 site 3-33
3-32 Glidepath depicting Ammonium Nitrate fo r 20% worst days at the ROMA1 site 3-33

3-33 Glidepath depicting Particulate Organic Material for 20% worst days at the ROMA1 site 3-34

3-34 Back trajectories for the 20% Worst extinction days at the Great Smoky Mountains National Park (GRSM1 ) IMPROVE monitoring site 3-35

3-35 Residence times for 72-hour back traj ectories with 100m end heights for 20% worst extinction days at the GRSM1 IMPROVE monitoring site 3-36

3-36 Stacked bar charts of mass at the GRSM1 site depicting domestic and international contributions to the 20% worst days in 2002 3-38

3-37 Stacked bar charts for mass at the GRSM1 site depicting domestic and international contributions to the 20% best days in 2002 3-38

3-38 Stacked bar charts of mass at the ROMA1 site depicting domestic and international contributions to the 20% worst days in 2002 3-39

3-39 Stacked bar charts for mass at the ROMA1 site depicting domestic and international contributions to the 20% best days in 2002 3-39

vvi LIST OF TABLES

Table Page
1-1 Summary of Data Products Avai lable on ARS VISTAS FTP Site 1-3

2-1 IMPROVE Aerosol Monitor Sites in and around the VISTAS Region 2-2
2-2 IMPROVE Aerosol Elements and Species Used to Reconstruct Light Extinction 2-3
2-3 Back Trajectory Model Parameters 2-6

3-1 RHR Data Completeness From IMPR OVE Data Sets for 2000-2004 Baseline Years for Sites in and Around the VISTAS Region 3-8
3-2 Data Completeness Following Data Substitution 3-13
3-3 Baseline (2000-2004) and Natural Bac kground Visibility Conditions 20% Worst Visibility Days 3-20

3-4 Baseline (2000-2004) and Natural Backgr ound Visibility Conditions 20% Best Visibility Days 3-211.0 INTRODUCTION The primary purpose of this document is to provide data, results and methods documentation for analysis performed by Air Resour ce Specialists, Inc. (A RS) in support of the Visibility Improvement State and Tribal Association of the Southeast (VISTAS).
Any questions or comments regarding contents of this report should be addressed to:

Cassie Archuleta

or Joe Adlhoch Air Resource Specialists, Inc. 1901 Sharp Point Drive, Suite E

Fort Collins, Colorado 80525

Telephone: 970-484-7941 Fax: 970-484-3423
carchuleta@air-resource.com jadlhoch@air-resource.com

1.1 OVERVIEW

VISTAS is a collaborative effort of state governments, tribal gove rnments, and various federal agencies established to initiate and coor dinate activities associated with the management of regional haze, visibility and ot her air quality issues in the S outheastern United States. VISTAS is working to develop the technical basis fo r the Regional Haze Rule (RHR) promulgated in 1999 by the Environmental Protection Agency (EPA ) to address visibility at 156 designated Class I areas. Specific objectives of analyses pr esented in this report to support the VISTAS states are:

• To describe current visibility conditions and particulate matter concentrations at Class I areas in the VISTAS region.

• To investigate the relative contributions of regional and local sources of emissions to fine particle concentrations a nd visibility in Class I areas.

• To understand the rate of improvement in visibility that would demonstrate a uniform rate of progress toward visibility improvement goals between current visibility conditions and natural background visibility conditions in 2064. States and tribes are required to establis h “reasonable progress goals” for each Class I area to improve visibility on the 20% haziest days and to prevent visibility degradation on the 20% clearest days. States are to evaluate their contributions to visibility impairment at Class I areas both within and outside the State and to develop long-term control strategies to reduce emissions of air pollutants that impa ir visibility. The nati onal goal is to return visibility to natural background levels by 2064. Using the period 2000 to 2004 as the baseline period, States are to evaluate progress in improving visibility by 2018 and every 10 years thereafter. State

1-1Implementation Plans for the first phase of th e regional haze regulatio n are due in December 2007.

1.2 DATA SUMMARY PRODUCT FTP SITE
Methods descriptions and analys is results are provided in this document. Analysis results for VISTAS are also available on the ARS VISTAS project public ftp site ( ftp://ftp.airresource.com/VISTAS ). Table 1-1 lists products curre ntly available on the ftp site.

1-21-3Table 1-1Summary of Data Products Available on ARS VISTAS FTP SiteSamplePathDescription[data files]...Aerosol_dataset_VISTAS_2000-2004/SAMA1_daily_budgets_sub_nia.xlsData sets including substituted data for VISTAS andneighboring Class I Areas,old and new IMPROVEalgorithm.Average Extinction for 20% Worst DaysNew IMPROVE Algorithm (nia)2000-2004050100150200250300 SWAN1 ROMA1 OKEF1 EVER1 CHAS1 SAMA1 DOSO1 SHEN1 JARI1 LIGO1 SHRO1 GRSM1 COHU1 SIPS1 MACA1 BRIG1 BRET1 MING1 HEGL1 UPBU1 CACR1VISTAS coastalVISTAS inlandnon-VISTAS

Extinction (Mm -1 )Sea SaltCMSoilECPOMAmm NO3Amm SO4Rayleigh...Aerosol_stacked_bar_charts/Average_BestWorstDays_2000-04/VISTAS_nia_00-04_20070129.xlsRegional stacked bar charts depicting 20% best and worstdays, extinction and mass, old and new algorithm.GRSM1Baseline Extinction, nia20% W > 1420100200300400 J -0 2 F-02 M -02 A-02 M -02 J -02 J -0 2 A-02 S-02 O- 0 2 N-02 D-02

Extinction (Mm -1 )20% W > 1400100200300400 J-0 1 F-01 M-0 1 A-01 M-0 1 J -01 J -01 A-01 S-01 O- 0 1 N-01 D-01

Extinction (Mm -1 )20% W > 1390100200300400 J-00 F - 0 0 M -0 0 A - 00 M -00 J-00 J -00 A - 0 0 S - 00 O-00 N-0 0 D-00

Extinction (Mm -1 )20% W > 1390100200300400 J-0 3 F-03 M -03 A-03 M -03 J -03 J-0 3 A-0 3 S -0 3 O -0 3 N-0 3 D-03

Extinction (Mm -1 )20% W > 1300100200300400 J -0 4 F - 0 4 M -04 A -0 4 M-04 J-04 J-04 A -0 4 S -0 4 O -0 4 N -0 4 D -0 4

Extinction (Mm -1 )...Aerosol_stacked_bar_charts/Timelines_AllDays_2000-04/GRSM.pdfStacked bar charts depictingextinction for all monitoreddays during the baseline years, 2000-2004. For VISTASsites requiring data substitution, two charts are provided,where the first page distinguishes substituted days fromoriginal days.GRSM12000-2004 Reconstructed ExtinctionNew IMPROVE Algorithm20% Worst Days050100150200250300350400450 3/8 5/6 5/17 6/3 6/10 6/28 7/1 7/8 7/22 8/5 8/16 8/19 8/23 8/26 10/15 10/18 10/21 10/24 10/27 11/2 5/1 5/7 6/9 6/12 6/21 6/27 7/3 7/12 7/18 7/21 7/24 7/30 8/2 8/5 8/8 8/11 8/14 8/17 8/23 8/26 9/7 9/13 9/19 11/18 2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/22 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17 3/16 4/27 5/24 5/27 6/5 6/11 6/26 6/29 7/5 7/17 7/20 7/26 8/7 8/10 8/16 8/19 8/22 8/25 8/28 9/3 9/6 9/9 9/12 9/21 1/10 2/21 6/8 6/11 6/20 6/26 6/29 7/8 7/17 7/20 7/29 8/4 8/10 8/16 8/19 8/25 8/28 8/31 9/3 9/12 9/15 9/30 10/3 10/3020002001200220032004

Extinction (Mm -1 )Sea SaltCMSoilLACPOMAmm NO3Amm SO4Rayleigh...Aerosol_stacked_bar_charts/Timelines_BestWorstDays_2000-04/GRSM1_20bw_nia.xlsStacked bar charts that selectively show just the 20% bestand worst days for 2000-2004 baseline period.GRSM1Old and New IMPROVE Algorithm (oia/nia) Extinction by Species20% Best Days (2002)0102030405060 nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia1/8 1/11 1/14 1/23 2/1 2/7 2/19 2/28 3/3 3/9 3/30 4/26 7/13 9/14 9/26 10/26 10/29 11/4 11/16 11/22 12/7 12/13 12/22 Avg.

Extinction (Mm -1 )RayleighAmm SO4Amm NO3POMLACSoilCMSea Salt...Aerosol_stacked_bar_charts/Timelines_nia_oia_comparison_2002/GRSM1_nia_oia.xlsRegional and site specific stacked bar charts whereextinction calculations using the new and old IMPROVEalgorithm are presented side by side.All Daysy = 0.93x + 9.16R2= 0.870501001502002503000 50 100 150 200 250 30024-hr Nephelometer bsp(Mm-1)

24-hr Aerosol b sp (Mm -1 )Q4y = 1.10x + 1.49R2= 0.960501001502002503000 50 100 150 200 250 300Q1y = 0.77x + 13.88R2= 0.480501001502002503000 50 100 150 200 250 300Q2y = 0.93x + 12.17R2= 0.810501001502002503000 50 100 150 200 250 300Q3y = 0.86x + 14.28R2= 0.820501001502002503000 50 100 150 200 250 300-100-75-50-250255075100 2000 2001 2002 2003 2004

Difference (Mm -1 ) (Aer. b sp - Neph. b sp )Aerosol Scattering vs. Nephelometer ScatteringUsing New IMPROVE Algorithm (nia) and Daily f(RH)GRSM, 2000 - 2004...Nephelometer_Aeosol_Comparisons/GRSM_neph.xlsCharts comparing collocated IMRPROVE nephelometersand IMPROVE aerosol samplers, old and newIMPROVE algorithm.Glide Path to Natural Conditions (2004-2064)(5-yr Rolling Average for 20% Haziest Days - New IMPROVE equation and NB II )05101520253035 1986-1990 1988-1992 1990-1994 1992-1996 1994-1998 1996-2000 1998-2002 2000-2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064

Deciviews (dv)Default Glide Path GRSMNew Glide Path GRSMDefault 5-yr Rolling Avg GRSMNew 5-yr Rolling Avg GRSMAnnual g90 - OldAnnual g90 - NewBase old Base new Default NB NB2Great Smokey Mtns 28.8 30.3 11.4 dv 11.2 dv...Glidepaths/Revised Glide Path new IMPROVEequation_Reynolds.pptDeviview Glidepaths (developed by Scott Reynolds) byspecies depicting baselineconditions and estimatednatural conditions for boththe new and old algorithm.GRSM1Total ExtinctionGlide Slope for 20% Worst Days050100150200250 2000 2002 2004 2018 2064

Extinction (Mm -1 )Total BextBaselineGlide Slope2018 Base GNatural Conditions...Glidepaths/Speciated_Glidepaths/Glideslopes_GRSM1.xlsGlidepaths by species depicting baseline condition, 2018modeled predictions and estimated natural conditions.1-410 Year Temporal TrendsAll Days151719212325 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

DVOKEF1m = -0.13, p = 0.05ROMA1m = -0.24, p = 0.06SWAN1N/A (< 5 years)10 Year Temporal Trends20% Best Days101214161820 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

DVOKEF1m = -0.05, p = 0.43ROMA1m = -0.23, p = 0.02SWAN1N/A (< 5 years)10 Year Temporal Trends20% Worst Days202224262830 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

DVOKEF1m = -0.17, p = 0.01ROMA1m = -0.28, p = 0.24SWAN1N/A (< 5 years)...IMPROVE_Trend_Charts/Trends_DV.xls10 Year temporal trends for sites grouped regionally,includes Standard Visual Range (SVR), Deciview (DV)and Mm-1.GRSM1Domestic and International Aerosol Mass by Species20% Worst Days (2002)05101520253035 Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International Domestic International2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/22 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17

Mass (μg/m 3 )Amm SO4Amm NO3POMLACSoilCMSea Salt...International_Attribution/International_Attribution_mass_worst.xlsComparison of domestic and international attribution ofmass on 20% best and worst days, 2002....Back_Trajectory_Analyis/IMPROVE_ResidenceTimeMaps_2000-04_nia/GRSM_2002_20high.bmpBack trajectory maps for 20% worst extinction days.GRSM12000-0420% Worst Monitored Days100m Residence Times (%)< 0.010.01 - 0.020.02 - 0.040.04 - 0.080.08 - 0.160.16 - 0.32> 0.32...Back_Trajectory_Analyis/IMPROVE_ResidenceTimeMaps_2000-04_nia/GRSM_2002_20high.bmpResidence time maps for 20% worst days, 2000-2004.1-52.0 ANALYTICAL METHODS

2.1 THE IMPROVE MONITORING NETWORK
The Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring program collects speciated PM 2.5 , and PM 2.5 and PM 10 total mass. IMPROVE is a nation-wide network which began in 1988 and expanded signi ficantly in 2000 in response to the EPA’s Regional Haze Rule (RHR). It is data from this program that states and tribes must use to track progress under the RHR.

The IMPROVE network collects 24-hour inte grated filter samples every third day (Wednesdays and Saturdays prior to 2001). Each monitoring location operates four samplers (designated Module A through D) designed to quantify PM 2.5 and PM 10 mass, and PM 2.5 mass constituents, including numerous trace elemen ts, ions, Elemental Carbon (EC), and Organic Carbon (OC).
A map of the IMPROVE sites in and around the VISTAS region and their proximity to Class I areas is presented in Figure 2-1. A lis ting of IMPROVE sites, locations, and operational start dates is presented in Table 2-1. The list also includes star t (and end) dates for collocated nephelometers, which are continuous monitoring in struments that make direct measurements of light scattering due to particles.

IMPROVE Site Federal Tribal Class I Areas

BRET1 BRIG1

CACR1 CADI1

CHAS1 COHU1 DOSO1

EVER1 GRSM1 HEGL1 JARI1

LIGO1 MACA1 MING1

OKEF1 ROMA1

SAMA1 SHEN1

SHRO1
SIPS1 SWAN1

UPBU1

Figure 2-1. Map of Class I Areas and IMPR OVE Monitoring sites in and around VISTAS.

2-1Table 2-1 IMPROVE Aerosol Monitor Sites in and around the VISTAS Region

Region State Class I Area (Site) Start Date Latitude Longitude Nephelometer Start Date ( - End Date) AL Sipsy Wilderness (SIPS1) 3/92 34.34 -87.34 -- Chassahowitzka NWR (CHAS1) 4/93 28.75 -82.55 -- Everglades NP (EVER1) 9/88 25.39 -80.68 -- FL St. Marks (SAMA1) 6/00 30.09 -84.16 -- Cohutta (COHU1) 5/00 34.79 -84.63 1/04 – 3/07 GA Okefenokee NWR (OKEF1) 9/91 30.74 -82.13 1/93 – 6/97

KY Mammoth Cave NP (MACA1) 9/91 37.13 -86.15 1/93 - current Linville Gorge (LIGO1) 3/00 35.97 -81.93 -- Shining Rock Wilderness (SHRO1) 7/94 35.39 -82.77 4/94 – 9/99 NC Swanquarter (SWAN1) 6/00 35.45 -76.21 -- SC Cape Romain NWR (ROMA1) 9/94 32.94 -79.66 1/04 - current

TN Great Smoky Mountains NP (GRSM1) 3/88 35.63 -83.94 4/93 - current

James River Face Wilderness (JARI1) 6/00 37.63 -79.51 10/00 – 12/03 VA Shenandoah NP (SHEN1) 3/88 38.52 -78.43 1/00 - current VISTAS

WV Dolly Sods Wilderness (DOSO1) 9/91 39.11 -79.43 10/93 – 12/97
Caney Creek (CACR1) 6/00 34.45 -94.14 1/93 – 12/97 9/04 - current AR Upper Buffalo Wilderness (UPBU1) 12/91 35.83 -93.20 -- LA Breton (BRET1) 6/00 29.12 -89.21 -- Hercules-Glades (HEGL1) 3/01 36.61 -92.92 -- MO Mingo (MING1) 5/00 36.97 -90.14 --NonVISTAS

NJ Brigantine NWR (BRIG1) 9/91 39.47 -74.45 --

The IMPROVE program has developed methods for estimating light extinction from speciated aerosol and relative humidity data. Th e three most common metrics used to describe visibility impairment are:

• Extinction (b ext ) – Extinction is a measure of the fr action of light lost per unit length along a sight path due to s cattering and absorption by gase s and particles, expressed in inverse Megameters (Mm -1 ). This metric is useful for representing the contribution of each aerosol species to visibility impai rment and can be practically thought of as the units of light lost in a million meter distance.

• Visual Range (VR) – Visual range is the greatest di stance a large black object can be seen on the horizon, expressed in k ilometers (km) or miles (mi).

2-2• Deciview (dV) – The deciview index was designed to be linear with respect to human perception of visibility. A one deciview change is approxi mately equivalent to a 10% change in extinction, whether visibility is good or poor. A one deciview change in visibility is generally considered to be the minimum change the average person can detect. This is the metric used for tracking regional haze in the RHR.
The IMPROVE network estimates light ex tinction based upon the measured mass of various contributing aerosol species. EPA’s 2003 guidance (http://vista.cira.colostate.edu/improve/Publi cations/GuidanceDocs/guidancedocs.htm) for calculating light extincti on is based on the original protoc ol defined by the IMPROVE program in 1988. In December 2005, the IMPROVE Steer ing Committee voted to adopt a revised algorithm for use by IMPROVE as an alte rnative to the original approach.
The elements and species meas ured by the IMPROVE network relevant for reconstructed light extinction are listed in Table 2-2.

Table 2-2

IMPROVE Aerosol Elements and Species Used to Reconstruct Light Extinction
Species Composite Mass Algorithm * Comment

Ammonium Sulfate [(NH 4 ) 2 SO 4 ] 4.125× [S]

-or1.375× [SO 4 ] [S] is derived from the Teflon filter (A module). [SO 4 ] is derived from the nylon filter (B module). Ammonium Nitrate [(NH 4 )NO 3 ] 1.29× [NO 3 ] [NO 3 ] is derived from the nylon filter (B module). Particulate Organic Matter [POM] 1.4× [OC] (original algorithm) 1.8× [OC] (revised algorithm) [OC] is derived from the quartz filter (C module).
Elemental Carbon [EC] 1.0[EC] [EC] is derived from the quartz filter (C module).

Soil 2.20× [Al] + 2.49× [Si] + 1.63× [Ca] + 2.42× [Fe] + 1.94× [Ti] Soil elements are derived from the Teflon filter (A module).

Coarse Mass [CM] [PM 10 ] - [PM 2.5 ] [PM 10 ] is derived from the Teflon filter (D module). [PM 2.5 ] is derived from the Teflon filter (A module).
Sea Salt (revised algorithm only) 1.8× [Chl](Chloride)

-or1.8×[Cl](Chlorine) [Chl] and [Cl] are derived from the Teflon filter (A module). [Cl] only used if {Chl] is below the detection limit.
The original algorithm used by IMPROVE/regional haze guidance to reconstruct extinction (b ext ) is:

2-3

## [ ] [ ] [ ]

[ ] [ ] [ ] (10) Scattering Rayleigh CM6.0 Soil Fine1 EC10 POM4 Nitrate Amm. f(RH)3 Sulfate Amm. f(RH)3b

+ ×+ ×+ ×+ ×+ ××+ ××≈ ext

Reconstructed b ext is expressed in Mm -1 , dry extinction efficienci es (leading numbers) are expressed as m 2 /g and species mass concentrations are expressed in μ g/m 3 . The f(RH) factor is a water growth term that is a function of climatologically representative monthly average relative humidity (RH). The f(RH) factors are designed to account for the absorption of water by sulfate and nitrate species. In the original IM PROVE algorithm, a constant value of 10 Mm -1 is also added to account for extinction due to scattering from molecules in a clean atmosphere (Rayleigh conditions).
The original IMPROVE algorithm produces reas onable estimates of light scattering over a broad range of conditions, but it tends to u nderestimate the highest extinction values and overestimate the lowest extinction values. The re vised algorithm for esti mating light extinction recommended for use by the IMPROVE steering committee is: 

## [ ] [ ] [ ][ [ ][ ] [ ] [ ] [ ] [ ]

[ ] Specific) (Site Scattering Rayleigh (ppb)NO33.0 CM6.0 Salt Sea(RH)f7.1 Soil1 EC10 POM Large1.6POM Small8.2 Nitrate Amm. Large (RH)f1.5Nitrate Amm. Small (RH)f4.2 Sulfate Amm. Large (RH)f8.4Sulfate Amm. Small (RH)f2.2b

2 ss L s L s

+ ×+ ×+ ××+ ×+ ×+ ×+ ×+ ××+ ××+ 

## ] × × + ××≈ ext

The revised algorithm splits ammonium sulfate, ammonium nitrate, and POM concentrations into small and large size fractions as follows:



## [ ] [ ] [ ] [ ]

[ ] [ ][
[ ] [ ][ ] Total Large, μ g/m20TotalFor Large Total Small Total 20 Total Large
μ g/m20TotalFor

3 3

= ≥ ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧

−= ×=
< 

## ]

As noted in table 2-2, the organic mass con centration is 1.8 times the organic carbon

2-4mass concentration in the revised algorithm, wher e it was calculated as 1.4 times carbon mass in the original algorithm. The new algorithm contai ns three distinct water growth factors, designated f S (RH), f L (RH), and f SS (RH) for water absorption due small and large sulfate and nitrate fractions, and for sea salt, respectively. New terms have al so been added for sea salt and for absorption by gaseous NO 2 . NO 2 is not available at most IMPROVE sites, and is not included in analysis presented here.

IMPROVE data are availabl e on the IMPROVE Web site ( http://vista.cira.colostate.edu/improve/ ) and the Visibility Inform ation Exchange Web System (VIEWS) Web site ( http://vista.cira.colostate.edu/views/ ). The starting data sets for analysis presented here are the IMPROVE RHR1 data set (updated 11/05), and the IMPROVE RHR2 data set (updated 3/06), where RHR1 is the IMPROVE designati on indicating that extinction was calculated using the original/old IMPROVE algor ithm, and RHR2 indicates that extinction was calculated using the revised/ new IMPROVE algorithm.

VISTAS chose to use the new IMPROVE al gorithm because it takes into account the most recent review of the science and because it is recommended by the IMPROVE Steering Committee. Comparisons between the old and new algorithm are presented in Section 3.1, but most analysis presented here uses the revised algorithm.

2.2 BACK TRAJECTORY MODELING Back trajectory analyses were undertaken to identify the geographi c source areas most likely to contribute to visibility impairment on th e 20% worst visibility days at the Class I areas. Back trajectory analyses use interpolated measur ed or modeled meteorological fields to estimate the most likely central path over geographical areas that provided air to a re ceptor at a given time. The method essentially follows a parcel of air backward in hourly steps for a specified length of time. Back trajectories account fo r the impact of wind direction and wind speed on delivery of emissions to the receptor, but do not account for chemical transformation, dispersion and deposition of emissions.
Trajectories were generated using the Hybr id-Single Particle La grangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Labora tory (ARL). HYSPLIT uses archived 3- dimensional meteorological fields generated fr om observations and short-term forecasts. HYSPLIT can be run to generate forward or b ackward trajectories using several available meteorological data archives.
The data archives used in this analysis were the National Weather Service's National Centers for Environmental Prediction Eta Data A ssimilation System (EDAS). The EDAS fields are archived by the ARL across the continental U.S., including a buffer zone, at a horizontal resolution of 80 km before 2004, and at a horizo ntal resolution of 40 km starting in 2004. Detailed information regarding the trajectory model and these data sets can be found on NOAA’s Web site ( http://www.arl.noaa.gov/ready/hysplit4.html ).
The major model parameters selected for these analyses are presented in Table 2-3. The choice of these parameters affects the trajectori es generated and the final attribution analyses based on them. In particular, trajectories tend to become increasingly uncertain the further back in time they are used. Vertical motion in the model is sometimes best represented by following

2-5actual vertical motion measuremen ts (represented by model data), surfaces of constant entropy, or surfaces of constant pressure, depending on th e meteorological conditions at a given location and time. The impact of receptor height (or e nd height) on an individual trajectory is also important. Low-ending trajectories represent air parcels nearer to ground level and high-ending trajectories may better represen t boundary layer flow above th e local terrain. Specific back trajectory analyses performed by ARS fo r VISTAS are described in Section 3.6.1.

Table 2-3 Back Trajectory Model Parameters

Model Parameter: Value Trajectory duration 72 hours backwards in time Top of model domain 14,000 meters Vertical motion option used model data Meteorological Field EDAS End Times 0600, 1200, 1800 and 2400 EST End Heights 100 and 500 m

2-63-1 3.0 VISIBILITY CONDITIONS
The Regional Haze Rule sets a 60 year timelin e for states to improve visibility within Federal Class I Areas (CIAs) from “baseline” (2000-2004) levels to “natural conditions” by 2064. The following sections describe some of the major components for tracking reasonable progress at each CIA in the VISTAS region. Anal yses include comparisons of the old and new IMPROVE algorithms, determinatio n of baseline (2000-2004) condi tions, determination of the glide slope between baseline and natural conditions, and determ ination of geographic source areas using back trajectory analysis.

3.1 COMPARISON OF OLD AND NEW IMPROVE ALGORITHM
One of the most compelling reasons for de veloping the revised IMPROVE algorithm was to reduce the biases in light scattering estimates at the high and low extremes, when compared to nephelometer measurements. Nephelometers make a direct measurement of light scattering due to particles and are collocated with the IMPROVE aerosol samplers at some IMPROVE sites. These measurements can be compared to light scattering derived from the particulate data.
Figure 3-1 presents scatter plots for the GRSM 1 site comparing measured light scattering (nephelometer) and reconstructed light scattering calculated using the revi sed (New) and original (old) IMPROVE algorithms. A stacked bar chart fo r the most recent year of comparable record, in this case 2004 at the GRSM1 s ite, is included in Figure 3-2. Nephelometers make continuous hourly measurements of light scattering and RH . For these comparisons hourly nephelometer scattering is averaged into daily values, and ae rosol light scattering is calculated using daily f(RH) based upon measured RH, as opposed to th e IMPROVE method of us ing climatologically representative monthly average RH. Also light absorbing carbon (LAC) is not included in the reconstructed extinction because nephelometers measure atmospheric scattering but do not measure atmospheric absorption. For reconstruc ted extinction, coarse ma ss scattering was also scaled by a factor of 0.5 to account for the tendency of a nephelometer to underestimate coarse mass scattering. Charts are included for each VI STAS nephelometer site in Appendix A for both the total available data set and each quarter separately.
The GRSM1 site has one of the longest peri ods of nephelometer data in the VISTAS region, and has been in operation since 1993. In ge neral, the original algorithm tends to under estimate light scattering on those days with high scattering and over estimate scattering on days with low values. The new algorithm is more accurate as it does not have the same biases, but the data is less precise, with more spread in th e data and generally sli ghtly lower correlation.
Figures 3-3 and 3-4 present comparisons of th e average 20% worst an d best days in the 2000-2004 baseline period at VISTAS and neighboring sites calculated using both the revised and original IMPROVE algorithm. For the wors t days, extinction calculations using the new algorithm are higher than extinction calculations using the old algorithm Difference in total extinction range between 8 Mm -1 (BRET1) and 35 Mm -1 (MACA1). For the best days, the calculated extinction values were within 3 Mm -1 of each other.
Figure 3-5 presents a compar ison of the 20% worst days in 2002 at the GRSM1 sitecalculated using both the new and old IMPROVE algorithm. The 20% best and worst days are very similar using either the original and revi sed algorithms. In the case of GRSM1, 9/23 was a worst day in 2002 using the old algo rithm, but this one day is trad ed for 7/22 when extinction is calculated using the new algorithm. Other days cha nge in magnitude, but the same days are still counted among the 20% worst days. Additional plots comparing extincti on calculated using the old and new IMPROVE algorithms for 20% best a nd worst days in 2002 at all VISTAS sites are available in Appendix B.

3-2Old Algorithmy = 0.71x + 14.70R2= 0.890501001502002503000 50 100 150 200 250 30024-hr Nephelometer bsp(Mm-1)

24-hr Aerosol b sp (Mm -1 )New Algorithmy = 0.98x + 5.97R2= 0.870501001502002503000 50 100 150 200 250 30024-hr Nephelometer bsp(Mm-1)

24-hr Aerosol b sp (Mm -1 )Figure 3-1. Comparison of measured lightscattering (nephelometer) andlight scattering calculated using the original (old) and revised (new)IMPROVE algorithms for the GRSM1 site, 1993-2004.3-3050100150200250 1/1 1/4 1/7 1/13 1/16 1/19 1/22 1/28 1/31 2/9 2/21 2/24 3/1 3/4 3/7 3/10 3/13 3/19 3/22 3/25 3/28 4/3 4/6 4/9 4/15 4/18 4/21 4/24 4/27 4/30 5/9 5/12 5/15 5/21 5/24 5/27 5/30 6/2 6/5 6/11 6/14 6/17 6/20 6/29 7/5 7/8 7/11 7/23 7/29 8/1 8/4 8/7 8/10 8/16 8/19 8/25 8/28 9/3 9/6 9/12 9/15 9/18 9/21 9/24 9/27 10/6 10/9 10/15 10/18 10/27 10/30 11/2 11/5 11/8 11/11 11/14 11/17 11/26 11/29 12/2 12/5 12/8 12/17 12/26 12/29

Extinction (Mm -1 )Amm SO4Amm NO3POMSoilCMSea SaltnephFigure 3-2. Stacked bar chart comparing extinction calculated using thenew IMPROVE algorithm(left), the old IMPROVE algorithm(right), and measured light scattering (grey line) in 2004.3-40 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-3. Stacked bar chart comp aring light extinction calculated using both the new (left) and old (right) IMPROVE algorithm for the average of the 20% worst visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

0 10 20 30 40 50 60SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-4. Stacked bar chart comp aring light extinction calculated using both the new (left) and old (right) IMPROVE algorithm for the average of the 20% best visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

3-5GRSM1Old and New IMPROVE Algorithm (oia/nia) Extinction by Species20% Worst Days (2002)050100150200250300350400 nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia nia oia2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/22 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17 9/23 Avg.

Extinction (Mm -1 )RayleighAmm SO4Amm NO3POMLACSoilCMSea SaltFigure 3-5. Stacked bar chart comparing 20%worst days in 2002 at the GRSM1 site calculated using both the new (left) and old(right)IMPROVE algorithm.3-63- 7 3.2 RHR DATA COMPLETENESS
Once extinction has been calculated for each species at a monitoring site, the Regional Haze Rule provides some provisions for handling missing data. Treatment of missing data is necessary because the RHR requires that the base line period average (2000- 2004) contain at least 3 complete years of data. RHR guidance requi rements for IMPROVE aerosol data completeness include the following conditions:

• Individual samples must contain all speci es required for the calculation of light extinction (sulfate, nitrate, organic carbon, elemental car bon, soil, coarse mass, and, for the new IMPROVE algorithm, chloride or chlorine)

• Individual seasons must contain at least 50% of all possible daily samples

• Individual years must contain at le ast 75% of all possible daily samples

• Individual years must not contain more than 10 consecutive missing daily samples

• The baseline period (2000-2004) must contai n at least 3 complete years of data RHR guidelines provide provisions to fill in missing data under specific circumstances. There are currently two methods routinely used in preparing the RHR data set to substitute data for missing samples:

• The use of a surrogate in the data set:

− Total sulfate is generally determined as 3 times the sulfur measured on the A module filter. If sulfur is missing, the sulfur measurement from the B module filter is used to calculate sulfate.

− For the new IMPROVE algorithm, sea salt is calculated from chloride measured on the B module filter. If chloride is missing or below detection limit, the chlorine measurement from the A module f ilter is used to calculate sea salt.

• The application of “patching” missing data described by the RHR guidance:

− Missing samples not substituted using a surrogate as described above can be patched, or replaced, by a seasonal average if the patching exercise passes a series of tests outlined in the guidance document.
Once these methods have been applied to th e data, the resulting complete years are eligible for use in calculation of baseline conditions and tracking pr ogress under the RHR. Further details can be found in the RH R guidance document for tracking progress ( http://www.epa.gov/ttn/oarpg/ t1/memoranda/rh_tpurhr_gd.pdf ).

3.2.1 Additional Data Substitutions
Table 3-1 summarizes data completeness fo r the 2000-2004 baseline years for IMPROVEsites in and around the VISTAS region. After routine RHR data substitutions, five IMPROVE monitoring sites in the VISTAS states (CHAS1, SAMA1, COHU1, SH RO1 and SWAN1), and two regional site (BRET1 and MING1) did not ach ieve RHR data completeness requirements. VISTAS performed additional data substitutions for these sites. Additional substitutions included estimating missing species from other on- site measurements and appropriately scaling data collected at selected donor sites which ha d favorable long-term comparisons. Additional substitutions were also made for the CHAS1 site , because it is the only VISTAS site that met RHR baseline period requirements, but still had 2003 and 2004 as incomplete years. Additional substitutions for the MING1 site were performed separately by UCDavis. Details of data substitutions performed by ARS for VISTAS are outlined below.

Table 3-1
RHR Data Completeness From IMPROVE Data Sets for 2000-2004 Baseline Years for Sites in and Around the VISTAS Region

Region State Site 2000 2001 2002 2003 2004 2000-04 Count AL SIPS1 0 1 1 1 1 4 CHAS1 1 1 1 0 0 3 EVER1 0 1 1 1 1 4 FL SAMA1* 0 0 1 1 0 2 COHU1* 0 0 1 0 1 2 GA OKEF1 1 1 1 1 1 5 KY MACA1 1 1 1 1 1 5 LIGO1 0 1 1 1 1 4 SHRO1* 0 0 0 1 1 2 NC SWAN1* 0 1 1 0 0 2 SC ROMA1 1 1 1 1 1 5 TN GRSM1 1 1 1 1 1 5 JARI1 0 1 1 1 1 4 VA SHEN1 1 1 1 1 1 5 VISTAS

WV DOSO1 1 1 1 1 1 5 CACR1 0 0 1 1 1 3 AR UPBU1 1 1 1 1 1 5 LA BRET1* 0 0 0 0 0 0 HEGL1 0 0 1 1 1 3 MO MING1* 0 1 0 0 0 1NonVISTAS

NJ BRIG1 1 1 1 1 1 5 * Indicates site has <3 complete years for RHR baseline calculations 0 indicates an incomplete year with no substitutions made 1 indicates a complete RHR year

Figure 3-6 presents a flow ch art of the VISTAS data substitution methods, and details for these methods are described below. The starti ng data set was the RHR2 IMPROVE data using the new IMPROVE algorithm. This data set in cludes the routine surroga te and patched data
3-

8substitutions allowed by RHR guidance. Note that only years deemed incomplete under RHR guidance were candidates for additional data substitutions. Years deemed complete were not changed, even thought there may have been mi ssing individual sample s during those years.

Start with IMPROVE RHR dataset (daily_budgets_nia_20060306.csv)

3 complete years (including 2003/2004)?

Check year for completeness Substitute OC based on Organic H, and EC based on OC (use raw data linear regression statistics) For each missing year, check each incomplete day

Final Data Set Substitute missing species from a nearby donor site (use raw data linear regression statistics) yes

Check year for completeness

no yes

yes no no

Year remains incomplete

Figure 3-6. Flow chart of data substitution methods used.

3-

93.2.1.1 Carbon Substitutions
The first substitution method relied on using a surrogate for carbon mass measurements when the C module data was not available. H ydrogen (H) is measured on the A module filter, and is assumed to be primarily associated with orga nic carbon and inorganic compounds such as ammonium sulfate. Organic ca rbon (OC) can be estimated using the historical comparison between estimated organic H and OC. Organic H is estimated by subtracting the portion of H that is assumed to be associated with the i norganic compounds from the total H (Org_H = H – 0.24*S). Once OC has been estimated using this method, elemental carbon (EC) mass is determined using long-term comparisons between OC and EC at the site.
Figure 3-7 presents sample comparisons for data collected at the Shining Rock Wilderness Area (SHRO1) site in North Caro lina during the second quarter between 2000-2004 for OC vs. organic H, and EC vs. OC. Statistic s were calculated and appl ied quarterly to account for seasonal variations.

Q2

r2 = 0.55

0 2 4 6 8 10

0.0 0.2 0.4 0.6 0.8 1.0 org_H (μg/m3)OC (μg/m3) Q2

r2 = 0.65

0.0 0.2 0.4 0.6 0.8 1.0

0 2 4 6 8 10 OC (μg/m3)EC (μg/m3)

Figure 3-7. Comparison of OC and Estimated Or ganic H, and EC and OC at SHRO1, NC Using Second Quarter Raw OC and Organic H Data, 2000-2004

3.2.1.2 Donor Site Substitutions
In VISTAS, the carbon data substitution methods were not sufficient to complete the required years. A second method involved identi fication of another nearby IMPROVE site (or, in the case of BRET1, a nearby SEARCH site ) which had the most favorable long-term comparisons and similar regional character istics to be used as a donor site.
Figure 3-8 presents a sample inter-site ma ss comparison by species for data collected during the second quarter, 2000-2004, between the Shining Rock Wilderness Area (SHRO1) site and the Great Smoky Mountains National Park (GRS M1) site in Tennessee. Component specific correlations were calculated and applied quarterly. Note that only species missing in a given sample were substituted based on donor site data. Species collected at the site were not replaced with data from a donor site.

3- 10For BRET1 substitutions, ch loride/chlorine was not available from the GFP SEARCH site. In this case, sea salt could not be calcula ted from the GFP data, so seasonal averages of sea salt from the BRET1 site were used as substitute s. Missing coarse mass at the SEARCH site was also substituted using SEARCH methodologies prior to use as a donor for the BRET1 site.

3.2.1.3 Data Completeness Following Substitutions
Table 3-2 indicates the years that require d some degree of substitution, where a 2 indicates a substituted year, a 1 indicates the ye ar was already complete under RHR guidelines, and dashes indicate the year di d not meet RHR guidelines and no additional substitutions were made. The table also lists sites that were select ed as donor sites. The minimum data requirement of 3 complete years was met for each site, and additional substitutions beyond these requirements were made with the exception of th e year 2000, which was not substituted at any site either because monitoring started in late 2000, or the additional substitutions did not fill in enough data to make a complete RHR year.
Figure 3-9 presents bar charts by year for th e SAMA1 site depicti ng substituted data. The original RHR data is indicated in blue, and substituted data by species in specific colors. Substituted days are also indicated by a black ba r underneath the day. The red line indicates the threshold above which days are counted in the 20% worst days for that year. A red line is not included for any year that was incomplete a nd not substituted. In the case of SAMA1, substitutions were made for the years 2001 a nd 2004. In 2001, data from the CHAS1 site was scaled and substituted for about 33% of monitore d days, mostly for mid-April through May, and August through mid-September. In 2004, about 34% of the days have some degree of substitution, but most of these days only requi red the OC/EC substitutions from the organic H collected at the SAMA1 site. The substituted days are mostly between June and November, but only a very small portion of extinction for these days was substituted with most data being salvaged from the samples actually collected at the site.
The substituted data are available on the VIEWS website, http://vista.cira.colostate.edu/views/web/documents/substitutedata.aspx , and a key for the substituted data files is include d in Appendix C. Additional char ts and tables for all VISTAS substituted sites are in cluded in Appendix D.

3- 11Q2

r2 = 0.68

0 10 20 30 40 50

0 10 20 30 40 50 AmmSO 4 (μg/m3)AmmSO4(μg/m3) Q2

r2 = 0.62

0 1 2 3 4 5

012345 AmmNO 3 (μg/m3)AmmNO3(μg/m3)

Q2

r2 = 0.6

0 2 4 6 8 10

0 2 4 6 8 10 OC (μg/m3)OC (μg/m3) Q2

r2 = 0.49

0.0 0.5 1.0 1.5 2.0 2.5

0.0 0.5 1.0 1.5 2.0 2.5 LAC (μg/m3)LAC (μg/m3)

Q2

r2 = 0.8

0 1 2 3 4 5

012345 Soil (μg/m3)Soil (μg/m3) Q2

r2 = 0.73

0 5 10 15 20

0 5 10 15 20 CM (μg/m3)CM (μg/m3)

Figure 3-8. Comparison of Aerosol Species Ma ss Between SHRO1, NC (y-axis) and GRSM1, TN (x-axis).

3- 12Table 3-2 Data Completeness Following Data Substitution

State Site Donor Site 2000 2001 2002 2003 2004 2000-04 Count CHAS1 SAMA1 1 1 1 2 2 5 FL SAMA1* CHAS1 -- 2 1 1 2 4 GA COHU1* GRSM1 -- 2 1 2 1 4 SHRO1* GRSM1 -- 2 2 1 1 4 NC SWAN1* ROMA1 -- 1 1 2 2 4 LA BRET1* GFP -- 2 2 2 2 4 -- indicates an incomplete year with no substitutions made 1 indicates a complete RHR year 2 indicates a year is considered complete with some substituted values

3- 13SAMA1 (CHAS1 donor) Baseline Extinction, New IMPROVE Algorithm

20% W > 105

0 50 100 150 200

J-0

2 F-0 2 M- 0 2 A - 0 2 M - 02 J-0 2 J-02 A -02 S-0 2 O - 02 N-02 D-0 2Extinction (Mm-1) ■ ■ ■ ■■■■■■■■■■■■■■■■■■■ ■ ■ ■■ ■■■■■■■■■■ ■■■ ■ 0 50 100 150 200

J-

0 1 F- 01 M-01 A - 0 1 M -0

1 J-01 J- 0 1 A -01 S-0 1 O -0

1 N-01 D-0 1Extinction (Mm-1) 20% W > 101 0 50 100 150 200

J-00 F- 00 M-0 0 A-00 M-00 J- 0 0 J-0 0 A-00 S -0

0 O - 0 0 N-0 0 D-0 0Extinction (Mm-1)

20% W > 109

0 50 100 150 200

J-03 F -03 M-0 3 A - 03 M-0 3 J-03 J-03 A - 03 S-03 O-03 N-03 D-03Extinction (Mm-1)

■■■■■■■ ■ ■■■■■■■■■■■■■■■■■■■■ ■■■■■■■■■■■■■ 0 50 100 150 200

J-04 F -04 M-04 A-04 M-0 4 J- 0 4 J-04 A -0

4 S-0 4 O-04 N-0 4 D-04Extinction (Mm-1) 20% W > 117

Figure 3-9. 2000-2004 annual bar char ts indicating substituted data at the SAMA1 site. RHR data are indicated in blue, and substituted data by species in specific colors. Substituted days are also indicated by a black bar underneath the day. The red line indicate s the threshold above which days are counted in the 20% worst days for that year.

3- 143.3 EXTINCTION TRENDS
10-year extinction trends for the period 1995-20 04 were computed for each site that had at least 5 years of complete data. Emissions reductions were implemented in the eastern US during this period under the Acid Rain provisi ons of the 1990 Clean Air Act Amendments and under the NOx SIP call for ozone. Theil slopes we re calculated to determine the trend, and pvalues were calculated using Ma nn-Kendall trend analysis to dete rmine the significance of each slope. Lower p-values indicate higher conf idence levels in the computed slopes.
Figure 3-10 is a map indicating 10-year annual av erage extinction trends for sites with at least 5 years of complete data. For sites in and around VISTAS, the BRIG1 and SHEN1 sites indicated insignificant trends (p -value < 0.20) and 8 sites show ed decreasing trends (using pvalue<0.20 as indicator ). No s ites indicated increasing trends.
Figure 3-11 presents 10-year trend plots for a regional group of sites. The legend indicates the slope and p-value for the 20% worst days, 20% best days, and all monitored days. For these plots the GRSM1 and SHRO1 sites were the only sites with sufficient data for trend calculation, but annual observations for other site s are included on the plot for reference. Additional trend plots for other regional groupi ngs of sites are included in Appendix E, represented in terms of SVR, dV and Mm -1

.



### +4 Mm-1 +2 Mm-1 Insig. Trend -2 Mm-1 -4 Mm-1 <5 years

Total Extinction Trends IMPROVE 1995-2004 Light Extinction Change Per Year

BRIG1

SHEN1

CHAS1 DOSO1

GRSM1 MACA1

OKEF1 ROMA1 SHRO1 UPBU1

Figure 3-10. 10-year annual average extinction trends for sites in and around VISTAS Blue triangles indicate decreasing tr ends and blank diamonds indica te insignificant trends (pvalue<0.20). No sites sh owed increasing trends. 3- 15

### 10 Year Temporal Trends All Days

0 20 40 60 80 100 120 1401995199619971998199920002001200220032004TBext (Mm-1) LIGO1 N/A (< 5 years) GRSM1 m = -1.52, p = 0.01 SHRO1 m = -1.62, p = 0.20 COHU1 N/A (< 5 years) SIPS1 N/A (< 5 years) 

### 10 Year Temporal Trends 20% Best Days

0 10 20 30 40 50 601995199619971998199920002001200220032004TBext (Mm-1) LIGO1 N/A (< 5 years) GRSM1 m = -0.65, p = 0.19 SHRO1 m = -0.54, p = 0.09 COHU1 N/A (< 5 years) SIPS1 N/A (< 5 years) 

### 10 Year Temporal Trends 20% Worst Days

0 50 100 150 200 250 3001995199619971998199920002001200220032004TBext (Mm-1) LIGO1 N/A (< 5 years) GRSM1 m = -6.77, p = 0.00 SHRO1 m = -4.61, p = 0.20 COHU1 N/A (< 5 years) SIPS1 N/A (< 5 years)

Figure 3-11. 10-year tr end plots for a regional grouping of sites. Slopes and p-values are indicated in the legend.

3- 163.4 BASELINE (2000-2004) AND NATURAL ( 2064) VISIBILITY CONDITIONS
Baseline (2000-2004) visibility conditions we re determined using the new revised IMPROVE algorithm to select the 20% worst a nd best visibility days, and using VISTAS substituted data when appropriate.
Natural background visibility as defined in EPA guidance ( http://vista.cira.colostate.edu/ views/Web/RHR/RHR_Planning.aspx ) is based on annual average concentrations of fine particle components. The same annual average natural background mass concentrations are assumed for all Class I areas in the eastern US (separate values are estimated for the western US). Natural background visibil ity for the 20% worst days is estimated by assuming that fine particle concentrations fo r natural background are normally distributed and the 90 th percentile of the annual distribution repres ents natural background visibility on the 20% worst days. These assumptions are somewhat over simplified because the distribution of visibility conditions is likely not a normal distribution and the 92 nd percentile of a normal distribution would be a better re presentation of the mean of th e 20% worst days than the 90 th percentile 16 . EPA’s guidance did not estimate the co ntributions of indivi dual fine particle components to natural background visibility on the 20% worst days. Following development of the new IMPROVE algorithm, a workgr oup under the IMPROVE Steering Committee recommended revised assumptions to estimate natural background visibility on the 20% worst days ( http://vista.cira.colostate.edu/TSS/Tools/WOEChecklist.aspx ). For each Class I area, the natural background distribution of ea ch fine particle component is assumed to be the same as the current (2000-2004) distribution of that component . Original errors in NC2 calculations were corrected by Scott Copeland, USFS, in August, 2007.
For visualization purposes, haze conditions using images available for several of the VISTAS sites were simulated using ARS’ Wi nHaze Visual Air Quality Modeler (Ver. 2.9.6). Figure 3-12 presents a spit im age depicting simulated baselin e conditions and 2018 projected conditions at the GRSM1 site. There is a discerna ble difference in visibility between these time periods. Figure 3-13 depicts the estimated natu ral conditions goal for the GRSM1 site. These images were not generated using Scott Copeland’ s revised numbers. Images for all VISTAS sites available through the WinHaze program (12 sites) are included in Appendix F.
Tables 3-3 and 3-4 present the average baseline conditions for individual species, including the percent co ntribution to aerosol ex tinction (Rayleigh not in cluded), and 2064 natural condition estimates generated using the revised ca lculations discussed in Section 2.5, updated in August, 2007. Figures 3-14 and 3-15 present stacked bar charts indicating species contributions to light extinction (Mm -1 ) for the average of the 20% worst and best visibility days for 2000- 2004 at VISTAS regional sites. Figures 3-16 a nd 3-17 indicate species contribution to mass (μg/m 3 ) on the 20% worst and best extinction days.
Ammonium sulfate is the largest contributor to visibility impairme nt on the 20% haziest days in the baseline 2000-2004 pe riod (69-74%) at all the IMPROV E sites in the VISTAS region except Everglades National Park in Florida, wh ere Ammonium sulfate is a close second to Particulate Organic Mate rial, POM (40 and 45%, respectively). Particul ate Organic Material (also referred to as organic car bon) is the second larg est contributor to aeros ol extinction at all other sites, contributing to between 13 and 18% of aerosol extinction on the worst days. Baseline conditions for 20% worst da ys at the inland sites (182.2 - 241.4 Mm -1 ) average higher
3- 17than conditions measured at the coastal site s (116.4 - 147.3 Mm -1 ).

Ammonium sulfate is also the largest contributor to visibility impairment on the 20% best days (45-59%), with large contributions from ammonium nitrate (9-21%) POM (11-19%). Sea salt is not a factor on th e 20% worst days, but for the 20% best days it contribu tes to between 2 and 7% of the aerosol extinction at the VISTAS coastal sites.
Ammonium sulfate, CM and PO M are the largest contributo rs to total mass on the 20% best and worst days. CM, although it is a factor for total mass, has a low extinction efficiency and does not contribute significantly to aerosol extinction. Ammoni um sulfate contributes more significantly to aerosol extinction because it readily absorbs water vapor in the air.
Figures 3-18 and 3-19 present stacked bar charts including all samples days at the GRSM1 and ROMA1 sites in the 2000 through 2004 baseline period. The red line is a threshold line, above which days are counted in the 20% worst days. Figures 3-20 and 3-21 select just the 20% worst and best days at the GRSM1 site, and Figures 3-22 and 3-23 select the 20% worst and best visibility days at the ROMA1 site. The same charts are presented for additional Class I areas in Appendix G

. For the GRSM1 site, the 20% haziest days ar e most likely to occur in the summer, and the 20% best days occur most frequently durin g the winter. For coastal ROMA1 site, the 20% haziest days can occur year round, with the summe r days being dominated by ammonium sulfate, while the worst days that occur between Octobe r and February have large contributions from ammonium sulfate, POM and some times ammonium nitrate.

3- 18

### 2000-04 Baseline 216.3 Mm -1 

### 2018 Projected 113.6 Mm -1

Figure 3-12. 2000-2004 baseline conditions and 2018 projected conditions at the GRSM1 site simulated using WinHaze Visual Air Quality Modeling program.



### 2064 Natural Conditions 31.3 Mm -1

Figure 3-13. 2064 natural conditions at the GRSM 1 site simulated using WinHaze Visual Air Quality Modeling program.

3- 19Table 3-3 Baseline (2000-2004) and Natural Background Visibility Conditions 20% Worst Visibility Days

2000-04 Component Extinction (Mm-1) (% of aerosol extinction) 2000-04 Baseline 2064 Natural Conditions Site Amm. SO 4 Amm. NO 3 POM LAC Soil CM Sea Salt b ext (Mm -1 ) dV VR (km) b ext (Mm -1 ) dV

SWAN1 83.5 (74%) 5.8 (5%) 14.8 (13%) 3.7 (3%) 0.6 (1%) 3.5 (3%) 0.2 (0%) 124.2 24.7 31.5 32.6 11.5

ROMA1 96.5 (71%) 5.3 (4%) 23.8 (18%) 5.7 (4%) 0.6 (0%) 3.2 (2%) 0.2 (0%) 147.3 26.5 26.6 34.2 12.1

OKEF1 102.9 (69%) 4.9 (3%) 30.3 (20%) 6.2 (4%) 0.8 (1%) 3.1 (2%) 0.2 (0%) 159.4 27.1 24.5 31.7 11.2

EVER1 42.5 (40%) 4.8 (5%) 47.1 (45%) 5.9 (6%) 0.9 (1%) 3.0 (3%) 1.2 (1%) 116.4 22.3 33.6 42.7 12.1

CHAS1 87.6 (71%) 5.2 (4%) 20.7 (17%) 6.7 (5%) 0.9 (1%) 3.0 (2%) 0.2 (0%) 135.2 25.8 28.9 30.5 11.0VISTAS coastal

SAMA1 93.1 (71%) 4.5 (3%) 24.2 (18%) 5.6 (4%) 0.8 (1%) 2.6 (2%) 0.2 (0%) 142.1 26.3 27.5 33.1 11.7

DOSO1 160.5 (87%) 3.4 (2%) 13.4 (7%) 4.7 (3%) 0.8 (0%) 1.7 (1%) 0.1 (0%) 194.6 29.0 20.1 28.4 10.4

SHEN1 155.5 (83%) 5.8 (3%) 16.1 (9%) 5.7 (3%) 0.7 (0%) 2.5 (1%) 0.1 (0%) 196.5 29.3 19.9 32.3 11.4

JARI1 136.4 (76%) 7.8 (4%) 22.8 (13%) 7.4 (4%) 0.8 (0%) 3.0 (2%) 0.2 (0%) 189.5 29.1 20.6 30.8 11.1

LIGO1 146.0 (85%) 2.3 (1%) 16.1 (9%) 5.2 (3%) 0.8 (0%) 2.2 (1%) 0.1 (0%) 183.6 28.8 21.3 30.9 11.2

SHRO1 149.0 (87%) 2.7 (2%) 14.6 (8%) 3.6 (2%) 0.9 (0%) 1.3 (1%) 0.1 (0%) 182.2 28.5 21.5 31.8 11.5

GRSM1 172.7 (84%) 3.4 (2%) 20.6 (10%) 5.7 (3%) 0.8 (0%) 1.9 (1%) 0.2 (0%) 216.3 30.3 18.1 30.8 11.1

COHU1 171.6 (84%) 3.5 (2%) 22.1 (11%) 5.1 (2%) 1.0 (0%) 1.8 (1%) 0.1 (0%) 216.0 30.3 18.1 29.8 10.8

SIPS1 133.0 (75%) 8.5 (5%) 24.0 (14%) 6.5 (4%) 0.8 (0%) 3.2 (2%) 0.3 (0%) 187.3 29.0 20.9 30.1 10.9VISTAS inland

MACA1 188.9 (82%) 9.7 (4%) 22.3 (10%) 6.2 (3%) 0.9 (0%) 2.2 (1%) 0.1 (0%) 241.4 31.4 16.2 30.7 11.1

BRIG1 127.1 (70%) 15.7 (9%) 24.2 (13%) 7.0 (4%) 1.0 (1%) 5.4 (3%) 0.4 (0%) 192.7 29.0 20.3 35.7 12.1

BRET1 97.0 (76%) 8.5 (7%) 11.7 (9%) 5.2 (4%) 1.0 (1%) 3.8 (3%) 0.7 (1%) 139.0 26.0 28.2 33.7 11.9

MING1 100.2 (61%) 27.8 (17%) 21.8 (13%) 5.9 (4%) 1.4 (1%) 6.6 (4%) 0.2 (0%) 175.9 28.3 22.2 32.4 11.6

HEGL1 87.9 (63%) 17.9 (13%) 25.3 (18%) 5.2 (4%) 0.9 (1%) 2.8 (2%) 0.2 (0%) 151.2 26.7 25.9 31.9 11.2

UPBU1 83.2 (63%) 13.3 (10%) 22.5 (17%) 4.7 (4%) 1.2 (1%) 6.8 (5%) 0.2 (0%) 142.9 26.3 27.4 32.3 11.5non-VISTAS

CACR1 87.1 (65%) 13.8 (10%) 23.4 (17%) 4.8 (4%) 1.1 (1%) 3.7 (3%) 0.2 (0%) 145.1 26.4 27.0 32.1 11.6

3-

20Table 3-4 Baseline (2000-2004) and Natural Background Visibility Conditions 20% Best Visibility Days

2000-04 Component Extinction (Mm-1) (% of aerosol extinction) 2000-04 Baseline 2064 Natural Conditions Site Amm. SO 4 Amm. NO 3 POM LAC Soil CM Sea Salt b ext (Mm -1 ) dV VR (km) b ext (Mm -1 ) dV

SWAN1 11.3 (52%) 2.3 (10%) 2.3 (11%) 0.8 (4%) 0.2 (1%) 3.2 (15%) 1.5 (7%) 33.6 12.0 116.3 17.3 5.5

ROMA1 15.7 (51%) 3.1 (10%) 4.7 (15%) 1.7 (5%) 0.3 (1%) 2.9 (10%) 2.2 (7%) 42.4 14.3 92.2 18.1 5.9

OKEF1 19.7 (55%) 3.7 (10%) 6.8 (19%) 2.2 (6%) 0.4 (1%) 2.2 (6%) 0.7 (2%) 46.7 15.2 83.8 17.1 5.3

EVER1 9.6 (45%) 2.2 (10%) 3.5 (16%) 1.1 (5%) 0.4 (2%) 2.7 (13%) 1.9 (9%) 32.3 11.7 121.0 16.9 5.2

CHAS1 18.3 (50%) 4.2 (11%) 6.3 (17%) 2.9 (8%) 0.6 (2%) 3.3 (9%) 1.0 (3%) 47.6 15.5 82.1 18.1 5.9VISTAS coastal

SAMA1 18.6 (59%) 2.8 (9%) 4.9 (16%) 1.7 (6%) 0.5 (2%) 2.3 (7%) 0.6 (2%) 42.4 14.3 92.2 17.2 5.4

DOSO1 14.3 (56%) 3.1 (12%) 4.9 (19%) 2.1 (8%) 0.2 (1%) 0.7 (3%) 0.1 (0%) 35.4 12.3 110.5 14.5 3.6

SHEN1 11.2 (53%) 4.2 (20%) 2.9 (14%) 1.6 (8%) 0.2 (1%) 1.1 (5%) 0.1 (1%) 31.2 10.9 125.4 13.8 3.1

JARI1 17.5 (56%) 3.7 (12%) 5.6 (18%) 3.0 (9%) 0.2 (1%) 1.2 (4%) 0.1 (0%) 42.3 14.2 92.5 15.5 4.4

LIGO1 11.3 (56%) 2.2 (11%) 3.8 (19%) 1.8 (9%) 0.2 (1%) 0.8 (4%) 0.1 (1%) 31.2 11.1 125.4 15.1 4.1

SHRO1 7.0 (58%) 1.5 (12%) 2.1 (17%) 0.9 (7%) 0.2 (1%) 0.4 (3%) 0.1 (1%) 22.2 7.7 176.6 12.9 2.5

GRSM1 16.4 (56%) 3.9 (14%) 4.9 (17%) 2.2 (8%) 0.2 (1%) 1.3 (5%) 0.2 (1%) 40.2 13.6 97.3 15.8 4.5

COHU1 16.1 (55%) 5.2 (18%) 4.8 (16%) 2.0 (7%) 0.2 (1%) 1.0 (3%) 0.1 (0%) 40.5 13.7 96.6 15.4 4.3

SIPS1 18.5 (50%) 7.1 (19%) 6.5 (17%) 2.6 (7%) 0.4 (1%) 2.1 (6%) 0.1 (0%) 48.3 15.6 81.0 16.6 5.0VISTAS inland

MACA1 22.1 (53%) 8.8 (21%) 6.3 (15%) 3.0 (7%) 0.3 (1%) 1.4 (3%) 0.1 (0%) 53.0 16.5 73.8 16.5 5.0

BRIG1 14.8 (49%) 3.9 (13%) 4.5 (15%) 2.4 (8%) 0.2 (1%) 3.2 (11%) 1.4 (4%) 42.4 14.3 92.2 17.3 5.4

BRET1 17.0 (57%) 3.1 (10%) 3.2 (11%) 1.8 (6%) 0.4 (1%) 3.4 (11%) 1.1 (4%) 41.1 14.0 95.2 17.1 5.3

MING1 14.3 (46%) 4.6 (15%) 6.2 (20%) 2.3 (7%) 0.4 (1%) 3.1 (10%) 0.1 (0%) 42.9 14.4 91.1 17.1 5.4

HEGL1 10.8 (42%) 4.9 (19%) 5.1 (20%) 2.0 (8%) 0.3 (1%) 2.3 (9%) 0.2 (1%) 36.6 12.8 106.9 16.0 4.7

UPBU1 8.7 (39%) 4.6 (21%) 4.2 (19%) 1.5 (7%) 0.3 (2%) 2.8 (13%) 0.1 (0%) 33.2 11.7 117.9 15.2 4.1non-VISTAS

CACR1 8.5 (41%) 4.0 (20%) 4.3 (21%) 1.5 (7%) 0.2 (1%) 1.8 (9%) 0.2 (1%) 31.6 11.2 123.7 15.3 4.2

3-

21Average Extinction for 20% Worst Days New IMPROVE Algorithm (nia) 2000-2004

0 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-14. Contributions to light extinction (Mm -1 ) for the average of the 20% worst visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

Average Extinction for 20% Best Days New IMPROVE Algorithm (nia) 2000-2004

0 10 20 30 40 50 60SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-15. Contributions to light extinction (Mm -1 ) for the average of the 20% best visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

3-

220 5 10 15 20 25 30 35SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASMass (μg/m3) Sea Salt CM Soil EC POM Amm NO3 Amm SO4

Figure 3-16. Contributions to mass (μg/m 3 ) for the average of the 20% worst visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

0 2 4 6 8 10 12SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASMass (μg/m3) Sea Salt CM Soil EC POM Amm NO3 Amm SO4

Figure 3-17. Contributions to mass (μg/m 3 ) for the average of the 20% best visibility days in 2000-2004 at VISTAS and neighboring Class I areas.

3-

23GRSM1 Baseline Extinction, New IMPROVE Algorithm

20% W > 142

0 100 200 300 400

J-0

2 F-0 2 M- 0 2 A - 0 2 M - 02 J-0 2 J-02 A -02 S-0 2 O - 02 N-02 D-0 2Extinction (Mm-1) 20% W > 140

0 100 200 300 400

J-

0 1 F- 01 M-01 A - 0 1 M -0

1 J-01 J- 0 1 A -01 S-0 1 O -0

1 N-01 D-0 1Extinction (Mm-1) 20% W > 139

0 100 200 300 400

J-00 F- 00 M-0 0 A-00 M-00 J- 0 0 J-0 0 A-00 S -0

0 O - 0 0 N-0 0 D-0 0Extinction (Mm-1)

20% W > 139

0 100 200 300 400

J-03 F -03 M-0 3 A - 03 M-0 3 J-03 J-03 A - 03 S-03 O-03 N-03 D-03Extinction (Mm-1)

20% W > 130

0 100 200 300 400

J-04 F -04 M-04 A-04 M-0 4 J- 0 4 J-04 A -0

4 S-0 4 O-04 N-0 4 D-04Extinction (Mm-1)

Figure 3-18. Bar charts by year indicating speciat ion of all data collected at the GRSM1 site. The red line indicates the thre shold above which days are counted in the 20% worst days.

3-

24ROMA1 Baseline Extinction, New IMPROVE Algorithm

20% W > 92

0 50 100 150 200 250 300

J -0

2 F- 0 2 M-0 2 A-02 M-02 J -0

2 J-0 2 A-02 S-02 O - 0 2 N-0 2 D- 02Extinction (Mm-1) 20% W > 102

0 50 100 150 200 250 300

J -0

1 F-01 M-0 1 A-01 M-01 J-0 1 J-01 A-01 S-01 O - 0 1 N-01 D-01Extinction (Mm-1) 20% W > 133

0 50 100 150 200 250 300

J-0

0 F- 00 M- 00 A - 00 M-00 J-0 0 J-0 0 A - 00 S- 0 0 O-00 N- 0 0 D- 00Extinction (Mm-1)

20% W > 96

0 50 100 150 200 250 300

J -0

3 F-03 M-0 3 A - 03 M -03 J-03 J-0 3 A - 03 S - 03 O - 03 N- 0 3 D-03Extinction (Mm-1)

20% W > 116

0 50 100 150 200 250 300

J -0

4 F- 0 4 M-04 A -04 M- 04 J-0 4 J-04 A -0

4 S-04 O-04 N- 04 D- 04Extinction (Mm-1)

Figure 3-19. Bar charts by year indicating speciation of all data collected at the ROMA1 site. The red line indicates the thre shold above which days are counted in the 20% worst days.

3-

25y

0 50 100 150 200 250 300 350 400 4503/85/65/176/36/106/287/17/87/228/58/168/198/238/2610/1510/1810/2110/2410/2711/25/15/76/96/126/216/277/37/127/187/217/247/308/28/58/88/118/148/178/238/269/79/139/1911/182/255/25/85/176/16/136/196/227/17/77/107/167/227/318/38/68/98/128/158/219/59/89/119/173/164/275/245/276/56/116/266/297/57/177/207/268/78/108/168/198/228/258/289/39/69/99/129/211/102/216/86/116/206/266/297/87/177/207/298/48/108/168/198/258/288/319/39/129/159/3010/310/30

2000 2001 2002 2003 2004Extinction (Mm-1) Sea Salt CM Soil LAC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-20. Contributions to light extinction (Mm -1 ) for the 20% worst visibility days in 2000- 2004 at the GRSM1 site.

0 10 20 30 40 50 60 70 801/81/121/151/221/261/292/123/113/294/54/84/124/154/224/2610/911/1411/2012/1412/231/71/101/191/221/312/62/152/242/273/143/204/135/256/309/19/49/2510/710/1010/1610/2511/3012/1512/211/81/111/141/232/12/72/192/283/33/93/304/267/139/149/2610/2610/2911/411/1611/2212/712/1312/221/302/22/52/172/263/193/314/94/185/125/217/210/610/1510/2711/511/2011/2612/212/512/812/1412/171/11/71/161/251/312/62/122/152/273/73/314/66/239/1810/1511/1111/1411/2311/2611/2912/212/812/1112/23

2000 2001 2002 2003 2004Extinction (Mm-1) Sea Salt CM Soil LAC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-21. Contributi ons to light extinction (Mm -1 ) for the 20% best vi sibility days in 2000- 2004 at the GRSM1 site.

3- 26y

0 50 100 150 200 250 300 350 4001/12/122/235/37/17/87/127/157/198/128/168/198/269/1510/1510/1811/511/812/812/111/11/42/213/24/105/135/165/195/285/316/157/97/127/158/88/238/268/299/199/229/2810/3111/911/1812/31/171/292/132/162/224/205/26/46/77/17/47/77/167/197/257/318/249/119/149/1710/2010/2310/2612/71/92/202/263/255/275/306/56/116/206/236/267/117/298/198/259/69/99/129/2110/310/610/1210/211/101/131/222/243/134/95/65/125/276/207/117/147/207/237/268/48/199/39/249/3010/310/2710/3012/5

2000 2001 2002 2003 2004Extinction (Mm-1) Sea Salt CM Soil LAC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-22. Contributions to light extinction (Mm -1 ) for the 20% worst visibility days in 2000- 2004 at the ROMA1 site.

0 10 20 30 40 50 60 70 801/51/151/292/264/224/266/146/176/287/298/28/239/99/129/1810/910/2412/2312/261/101/312/123/144/255/16/276/307/38/28/59/109/1610/710/1010/1310/2211/311/1511/2411/2712/612/1812/241/141/233/33/63/94/24/144/175/56/106/196/226/257/108/158/309/29/59/209/239/2610/1112/191/242/52/233/314/95/215/246/146/177/147/237/268/169/39/159/2711/511/1411/1711/2012/1112/2012/231/72/122/152/273/13/43/73/314/156/146/177/298/18/139/2111/211/511/1111/2612/212/1112/1412/20

2000 2001 2002 2003 2004Extinction (Mm-1) Sea Salt CM Soil LAC POM Amm NO3 Amm SO4 Rayleigh

Figure 3-23. Contributions to light extinction (Mm -1 ) for the 20% best visibility days in 2000- 2004 at the ROMA1 site.

3- 273.5 GLIDEPATHS
The RHR guidance requires reasonable prog ress to be tracked using the Haze Index (deciviews), which is determined as a logar ithmic transformation of the sum of all light extinction terms in the IMPROVE light extinction algorithm. The rate of visibility improvement between baseline conditions in 2000-2004 and natu ral background conditions in 2064 is defined as the glidepath for the uniform rate of progress toward visibility goals. Glidepaths calculated using the original and revised IMPROVE al gorithm for the GRSM1 and ROMA1 sites are presented in Figures 3-24 and 3-25 and for other Class I area s in Appendix H. Natural conditions for the glidepath usi ng the original IMPROVE equati on are EPA default calculations, and natural conditions for the path using the re vised IMPROVE equations are the revised natural condition calculations discussed in Section 2.5, updated in August, 2007. Current conditions at the interior sites are poorer than at the coasta l sites and thus a higher rate of improvement is required in each decade to ach ieve a uniform rate of progr ess toward natural background conditions in 2064.

Glide Path to Natural Conditions (2004-2064) (5-yr Rolling Average for 20% Haziest Days - New IMPROVE equation and NB II )

0 5 10 15 20 25 30 351986-19901988-19921990-19941992-19961994-19981996-20001998-20022000-2004200620082010201220142016201820202022202420262028203020322034203620382040204220442046204820502052205420562058206020622064Deciviews (dv) Default Glide Path GRSM New Glide Path GRSM Default 5-yr Rolling Avg GRSM New 5-yr Rolling Avg GRSM Annual g90 - Old Annual g90 - New

Base old Base new Default NB NB2 Great Smokey Mtns 28.8 30.3 11.4 dv 11.1 dv

Figure 3-24. DV Glidepaths for the GRSM1 site , calculated using both the original IMPROVE algorithm using EPA default natural conditions (Default NB) and the new IMPROVE algorithm using the revised estimate of na tural background conditions (NB2).

3-

28Glide Path to Natural Conditions (2004-2064) (5-yr Rolling Average for 20% Haziest Days - New IMPROVE equation and NB II )

0 5 10 15 20 25 30 351986-19901988-19921990-19941992-19961994-19981996-20001998-20022000-2004200620082010201220142016201820202022202420262028203020322034203620382040204220442046204820502052205420562058206020622064Deciviews (dv) New Glide Path ROMA Default Glide Path ROMA New 5-yr Rolling Avg ROMA Default 5-yr Rolling Avg ROMA Annual g90 - New Annual g90 - Old

Base old Base new Default NB NB2 Cape Romain 25.3 26.5 11.4 dv 12.1 dv

Figure 3-25. DV Glidepaths for the ROMA1 site, calculated usi ng both the original IMPROVE algorithm using EPA default natural conditions (Default NB) and the new IMPROVE algorithm using the revised estimate of na tural background conditions (NB2).

The glidepath defined using deciview does not provide information regarding the relative contributions of individual specie s to overall visibility. This in formation is useful because some species (sulfate and nitrate) originate from larg ely anthropogenic sources, while others (organic carbon, elemental carbon) originate from a mixtur e of both anthropogenic and natural sources.
To look at individual species contribution, species specific glidepaths were constructed using extinction in a manner similar to that outlined in RHR guidance for total deciviews. Figures 3-26 through 3-29 present glidepath ch arts for total extinction and for ammonium sulfate, ammonium nitrate and POM extinction at the GRSM1 site, a nd figures 3-30 through 3- 33 present the glidepaths for the ROMA1 site. The dV glidepath is linear as defined in the RHR, which, because it is a logarithmic transformation of extinction data, transl ates to a curved line when converted to total extinction in Mm -1 . The dV contribution can not be constructed for individual species, so li near approximations in Mm -1 are used for the species specific glidepaths to approximate progress goals. 2018 projecte d extinction values were provided by ENVIRON based on CMAQ modeling of the 2018 Base G1 inve ntory. Natural conditions presented here do not use the revised NC2 numbers that became available in Augus t, 2007 but differences between the original set provided by IMPROVE , and the revised set were minima l. Plots for each site in and around the VISTAS region are included in Appendix I.

3-

29For total extinction, the proj ected 2018 value at the GRSM1 site is well below the glidepath to natural conditions. This is in large part due to the substantial improvement in projected ammonium sulfate values, from 173 Mm -1 to 70 Mm -1 in 2018. POM needs little improvement to reach natural conditions, and projected 2018 values fall slightly below the glidepath. For Ammonium nitrate, baseline conditions are low, and are similar to estimated natural conditions. Projected 2018 nitrate values are actually higher than the baseline emissions, but still low at 9 Mm -1 when compared to the baseline ammonium sulfate emissions of 173 Mm -1

. At the ROMA1 site, the baseline is lower than for the GRSM1 site, and the projected 2018 value for total extinction at the ROMA1 site is on the glidepath. Projected ammonium sulfate is below the glidepath, with a projecte d value of 54 Mm -1 in 2018. POM again needs little improvement to reach natural c onditions, and projecte d 2018 values are on the glidepath. For ammonium nitrate, projected 2018 values are lower than the ba seline emissions, slightly above the glidepath, but still low, w ith projected 2018 values of 5 Mm -1

.

0 50 100 150 200 25020002002200420182064Extinction (Mm-1) Total Bext Baseline Glide Slope 2018 Base G Natural Conditions

Figure 3-26. Glidepat h depicting total b ext (Mm -1 ) for 20% worst days at the GRSM1 site.

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300 20 40 60 80 100 120 140 160 180 20020002002200420182064Extinction (Mm-1) Ammonium Sulfate

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-27. Glidepath depicti ng Ammonium Sulfate for 20% wo rst days at the GRSM1 site.

0 1 2 3 4 5 6 7 8 9 1020002002200420182064Extinction (Mm-1) Ammonium Nitrate

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-28. Glidepath depicti ng Ammonium Nitrate for 20% wo rst days at the GRSM1 site.

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310 5 10 15 20 25 30 35 4020002002200420182064Extinction (Mm-1) Particulate Organic Matter

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-29. Glidepath depicti ng Particulate Organic Material for 20% worst days at the GRSM1 site.

0 20 40 60 80 100 120 140 160 180 20020002002200420182064Extinction (Mm-1) Total Bext Baseline Glide Slope 2018 Base G Natural Conditions

Figure 3-30. Glidepat h depicting total b ext (Mm -1 ) for 20% worst days at the ROMA1 site.

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320 20 40 60 80 100 120 14020002002200420182064Extinction (Mm-1) Ammonium Sulfate

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-31. Glidepath depicti ng Ammonium Sulfate for 20% worst days at the ROMA1 site.

0 1 2 3 4 5 6 7 820002002200420182064Extinction (Mm-1) Ammonium Nitrate

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-32. Glidepath depicti ng Ammonium Nitrate for 20% wo rst days at the ROMA1 site.

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330 5 10 15 20 25 30 3520002002200420182064Extinction (Mm-1) Particulate Organic Matter

Baseline Glide Slope
2018 Base G
Natural Conditions

Figure 3-33. Glidepath depicti ng Particulate Organic Material for 20% worst days at the ROMA1 site.

3- 343.6 ATTRIBUTION ANALYSIS
An attribution analysis was performed to allo w states to understand what source regions impact Class I areas in the VISTAS region. The analysis included construction of back trajectory and residence time maps, and compar isons of international and domestic mass attribution.

3.6.1 Back Trajectory Maps
For the year 2002, back trajectories were computed for IMPROVE monitoring sites in and around the VISTAS region. These analyses used the VISTAS substituted data set, with extinction calculated using the original IMPROVE algorithm. The original algorithm was used because the maps were generated and mapped be fore VISTAS made the decision to use the revised algorithm, but the days identified as the 20% worst days are similar, if not exactly the same, between algorithms. Each ho urly point for back trajectori es with end dates corresponding to the 20% worst visibility days was plotted on a map. A back trajectory map for the 20% worst measured extinction days at the GRSM1 sites is pr esented in Figure 3-34. Back trajectory maps for all VISTAS sites are included in Appendix J.

Figure 3-34. Back trajectories for the 20% Wo rst extinction days at the Great Smoky Mountains National Park (GRSM1) IM PROVE monitoring site.

3.6.2 Residence Time Maps
Residence time maps were constructed us ing 2000-2004 back trajectories. 20% worst extinction days were determined using th e substituted IMPROVE RHR2 (new IMPROVE algorithm, updated 3/06) data set. Each hourly point for back trajecto ries with end dates
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35corresponding to the 20% worst visibility da ys was counted and summed into 1⁄4 degree horizontal grid cells of latitude and longitude. The percent of hourly point s in each grid cell was calculated and mapped using color gradients, where darker colors indicate regional areas where air parcels spent the most time before reachin g the IMPROVE monitors. A residence time map for the 20% worst measured extinction days at th e GRSM1 site for trajectories with end heights of 100m is presented in Figure 3-31. 100m an d 500m residence time Maps for all VISTAS and regional sites are incl uded in Appendix K.

Residence time over an area is indicative of general flow patterns, but since it does not account for emissions and removal processes, it does not necessarily imply specific areas contributed significantly to h aze compounds at a receptor site.

GRSM1 2000-04 20% Worst Monitored Days 100m Residence Times (%)

< 0.01 0.01 - 0.02 0.02 - 0.04 0.04 - 0.08 0.08 - 0.16 0.16 - 0.32 > 0.32

Figure 3-35. Residence times fo r 72-hour back trajectories with 100m end heights for 20% worst extinction days at the GRSM1 IMPROVE monitoring site.

3.6.3 Extinction-Weighted Residence Time
For the years 2000-2004 ARS genera ted extinction-weighted resi dence time data for each IMPROVE sites in and around the VISTAS region. These analyses used the VISTAS substituted data set, with extinction calculated using th e original IMPROVE algorithm. The original algorithm was used because these da ta files were generated prior to VISTAS decision to use the new algorithm, and it was determined that updating these data with the revised algorithm would not affect interpretation. Each hourly point for back traject ories with end dates corresponding to one the 20% worst visibility days were weighted with calculated extinction values for that day. Trajectory data were weighted by total extinction, and by ex tinction components including

3-

36ammonium sulfate (AmmSO4), ammonium nitrate (AmmNO3), particulate organic material (POM), Light Absorbing Carbon (LAC), Soil, a nd Coarse Mass (CM). The extinction values associated with hourly points were summed into 1 degree horizontal grid cells of latitude and longitude.
These extinction-weighted residence time data files were provided to ENVIRON to use in conjunction with emissions information to relate emissions and transport to components of haze, and to apply distance weighting to estimate remova l processes. These char ts are available from ENVIRON.

3.6.4 International Contributions

To account for contribution to mass measured at VISTAS sites from international emissions, VISTAS generated model results with zeroed out Mexican and Canadian emissions, and boundary conditions (as defined by the GEOS-CHEM global model). The difference between the model run with and without the in ternational emissions wa s used to represent species mass attributable to international sources.
Figure 3-36 and 3-37 present st acked bar charts separating domestic and international attribution of mass on the 20% worst and best extinction days in 2002 at the GRSM1 site, and Figures 3-38 and 3-39 present charts for the RO MA1 site. For this comparison all sulfate and nitrate were assumed to be fu lly neutralized by particulate NH 4 + (similar to IMPROVE monitoring network assumptions). Negative number s in the international results indicated that species concentration went up when the non-US anthropogenic emissions were removed. The negative impacts were usually small, and these values were set to zero for purposes of this comparison. Domestic contributions were comput ed as the difference between total measured mass and international modeled mass.

International attribution at the GRSM1 site ra nges between 2 and 7% of total mass on the 20% worst days. Magnitudes on the 20% best days were similar, but accounted for up to 41% of total measured mass. At the ROMA1 site, attr ibution was between 0 a nd 18% on the worst days, and up to 21% on the best days. Similar charts for the other Class I areas are included in Appendix L.

3- 37GRSM1 Domestic and Internationa l Aerosol Mass by Species 20% Worst Days (2002)

0 5 10 15 20 25 30 35DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/22 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt Figure 3-36. Stacked bar charts of mass at the GRSM1 site depict ing domestic and international contributions to the 20% worst days in 2002.
GRSM1 Domestic and Internationa l Aerosol Mass by Species 20% Best Days (2002)

0 2 4 6 8 10 12DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

1/8 1/11 1/14 1/23 2/1 2/7 2/19 2/28 3/3 3/9 3/30 4/26 7/13 9/14 9/26 10/26 10/29 11/4 11/16 11/22 12/7 12/13 12/22Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt Figure 3-37. Stacked bar charts for mass at the GRSM1 site depicting dome stic and international contributions to the 20% best days in 2002.

3- 38ROMA1 Domestic and Internationa l Aerosol Mass by Species 20% Worst Days (2002)

0 5 10 15 20 25 30 35 40DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

1/17 1/29 2/13 2/16 2/22 4/20 5/2 6/4 6/7 7/1 7/4 7/7 7/16 7/19 7/25 7/31 8/24 9/11 9/14 9/17 10/20 10/23 10/26 12/7Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt Figure 3-38. Stacked bar charts of mass at the ROMA1 site depict ing domestic and international contributions to the 20% worst days in 2002.
ROMA1 Domestic and Internationa l Aerosol Mass by Species 20% Best Days (2002)

0 2 4 6 8 10 12 14 16 18 20DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

1/14 1/23 3/3 3/6 3/9 4/2 4/14 4/17 5/5 6/10 6/19 6/22 6/25 7/10 8/15 8/30 9/2 9/5 9/20 9/23 9/26 10/11 12/19Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 3-39. Stacked bar charts for mass at the ROMA1 site depicting domestic and international contributions to the 20% best days in 2002.

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39

# Appendix B.2 Justification for New IMPROVE Equation

# Appendix B.3 Trends in Speciated PM2.5 and Visibility Across Monitoring Networks in the Southeastern US1



## Trends in Speciated PM2.5 and Visibility across Monitoring 1 

## Networks in the Southeastern US 2

3
Patricia F. Brewer 4 VISTAS, 2090 US Highway 70, Swannanoa, NC 28778 5 Joseph P. Adlhoch 6 Air Resource Specialists, Inc., 1901 Sharp Point Dr. Suite E, Fort Collins, CO 80525-4429 7 8
ABSTRACT 9 10 Trends in fine particulate matter less than 2.5 microns in diameter (PM 2.5 ) and visibility in the 11 Southeastern U.S. were evaluated for sites in the Interagency Monitoring of Protected Visual 12 Environments (IMPROVE), Speciated Trends Network (STN), and Southeastern Aerosol 13 Research and Characterization Study (SEARCH) networks. These analyses are part of the 14 technical assessment by VISTAS, the regional planning organization for the Southeastern states, 15 in support of State Implementation Plans for the regional haze rule. 16
17 At all VISTAS IMPROVE sites, ammonium sulfate and organic carbon are the largest and 18 second largest contributors, respectively, to light extinction on both the 20% haziest and 20% 19 clearest days. Ammonium nitrate, elemental carbon, soils, and coarse particles make 20 comparatively small contributions to PM 2.5 mass and light extinction on most days at the Class I 21 areas. At Southern Appalachian sites, the 20% haziest days occur primarily in the late spring to 22 fall, while at coastal sites, the 20% haziest days can occur through out the year. Levels of 23 ammonium sulfate in Class I areas are similar to those in nearby urban areas, and generally 24 higher at the interior sites than the coastal sites. Concentrations of organic carbon, ammonium 25 nitrate and, sometimes, elemental carbon, tend to be higher in the urban areas than in nearby 26 Class I areas, although differences in measurement methods complicated comparisons between 27 networks. Results support regional controls of sulfur dioxide for both regional haze and PM 2.5 28 implementation and suggest that controls of local sources of organic carbon, elemental carbon, or 29nitrogen oxides might also be considered for urban areas that are not attaining the annual 30 National Ambient Air Quality Standard for PM 2.5 . 31 32
IMPLICATIONS 33 34 State Implementation Plans for the first phase of implementation of the Regional Haze Rule are 35 due in December 2007. This paper describes the pollutant contributions on the haziest and 36 clearest days in Class I areas in the southeastern U.S. and compares fine particle composition in 37 Class I areas to those in nearby urban areas. Several of the urban areas are also nonattainment 38 areas for the national ambient air quality standard for fine particulate matter. States are using the 39 results of the analyses described in this paper to design more effective emissions control 40 strategies for regional haze and related air quality issues. 41
42
INTRODUCTION 43
44 Visibility is a term that refers to the color, contrast, and outline of objects viewed in a landscape. 45 Particulate matter and gases in the atmosphere scatter or absorb light and reduce visibility by 46 reducing the clarity and distance that can be discerned by the human eye. Regional haze 47 describes the cumulative visibility impact from numerous air emission sources across a broad 48 geographic region. 49 50 The 1977 Amendments to the Clean Air Act set a national visibility goal to prevent “any future, 51 and remedy any existing, impairment of visibility in mandatory Class I Federal areas” which 52 results from manmade air pollution. In 1999 the U.S. Environmental Protection Agency (EPA) 53 promulgated regional haze regulations to address visibility at 156 designated Class I areas 54 (national parks greater than 6000 acres and wilderness areas greater than 5000 acres) 1 . The 55 regional haze rule calls for States to establish “reasonable progress goals” for each Class I area to 56 improve visibility on the 20% haziest days and to prevent visibility degradation on the 20% 57 clearest days. States are to evaluate their contributions to visibility impairment at Class I areas 58 both within and outside the State and to develop long-term control strategies to reduce emissions 59 of air pollutants that impair visibility. The national goal is to return visibility to natural 60background levels by 2064. Using the period 2000 to 2004 as the baseline period, States are to 61 evaluate progress in improving visibility by 2018 and every 10 years thereafter. State 62 Implementation Plans for the first phase of the regional haze regulation are due in December 63 2007. The southeastern states illustrated in Figure 1 are working together through a regional 64 planning organization, Visibility Improvement – State and Tribal Association of the Southeast 65 (VISTAS) to conduct the technical analyses to support the states’ regulatory planning for 66 regional haze and related air quality issues. 67 68 The National Ambient Air Quality Standard for particulate matter was revised in 1997 to set 69 annual and 24-hr limits (15.0 and 65 μ g/m 3 , respectively) for particles with diameters less than 70 2.5 microns (PM 2.5 ). In December 2004, several counties in the southeastern states were 71 designated as not attaining the annual PM 2.5 standard. These counties include several urban 72 areas near Class I areas, for example: Atlanta, GA; Birmingham, AL; Charleston, WV; 73 Louisville, KY; and Chattanooga and Knoxville, TN (PM 2.5 design value for years 2002-2004 in 74 these cities ranged between 17.5 and 15.7 μ g/m 3 ) 2 . By April 2008, affected states are required 75 to submit a State Implementation Plan demonstrating control strategies to attain the annual PM 2.5 76 standard by April 2010. 77 78 This paper summarizes current fine particle and visibility conditions at Class I areas in the 79 southeastern U.S, and compares fine particle loadings at Class I areas with nearby urban areas in 80 the region. These analyses are being used by VISTAS states to better understand source 81
contributions to PM 2.5 and regional haze in the southeastern United States and to design effective 82 emissions control strategies for State Implementation Plans. 83 84
METHODS 85 86
Fine Particle Measurements 87 Fine particle mass and components are being measured at several sites in the southeastern U.S. 88 The locations of the 18 Class I areas in the VISTAS region (plus Breton Wildlife Refuge located 89 on the Louisiana coast of the Gulf of Mexico), and the locations of monitoring sites included in 90 analyses described in this paper are illustrated in Figure 1. 9192 The Interagency Monitoring of Protected Visual Environments (IMPROVE) network monitors 93 fine particle concentrations at Class I areas throughout the U.S. 3 including 15 of the 18 Class I 94 areas in the VISTAS region. IMPROVE collects fine particle samples on filters for 24 hours 95 every third day. EPA and the State and Local Agencies monitor fine particle components at 96 Speciated Trends Network (STN) sites in both urban and rural areas using filter samples 97 collected every third or every sixth day. 4 The Southeastern Aerosol and Research 98 Characterization Study (SEARCH) monitors fine particle concentrations at 4 paired urban and 99 rural sites (8 sites total) in Alabama, Georgia, and the Gulf Coast of Florida and Mississippi. 5 100 SEARCH filter samples are collected once every third day (daily in Atlanta, GA). IMPROVE 101 monitoring began between 1988 and 1993 for several Class I areas in the VISTAS states; 102 monitors were installed at additional sites during 2000. SEARCH operations began in 1999, 103 while STN sites reported in this paper began monitoring in 2001 or 2002. 104 105 The three networks use similar methods for sample collection and analysis, but differences 106 between programs complicate inter-comparison of monitoring results. All three networks collect 107
PM 2.5 on Teflon, nylon, and quartz filters. For all networks, the Teflon filters are weighted 108 gravimetrically to define PM 2.5 . IMPROVE uses X-Ray Fluorescence (XRF), and Proton Elastic 109 Scattering Analysis (PESA) to analyze elements. SEARCH uses XRF and STN uses Energy 110 Dispersive X-Ray Fluorescence (EDXRF) to analyze elements. IMPROVE and STN define 111 “fine soils” as a weighted sum of aluminum (Al), calcium (Ca), iron (Fe), silicon (Si), and 112 titanium (Ti). SEARCH defines “major metal oxides” (similar to fine soils) as a somewhat 113 different weighted sum of these elements and potassium (K). 114 115 IMPROVE uses a sodium carbonate denuder in the nylon filter sample inlet to remove acidic 116 gases. SEARCH uses both sodium carbonate and citric acid denuders upstream of its 117 Teflon/nylon/glass fiber filter packs to remove acidic gases and ammonia (NH 3 ), respectively. 118 Some STN samplers use a magnesium oxide denuder to remove acidic gases prior to the nylon 119 filter. All three networks analyze the nylon filters using ion chromatography and report sulfate 120 (SO 4 ), chloride (Cl), and nitrate (NO 3 ). (Valences are omitted for brevity.) SEARCH includes 121
non-volatilized NO 3 from the Teflon filter in reported NO 3 . STN and SEARCH both report 122ammonium (NH 4 ). SEARCH uses a citric acid coated cellulose filter behind the Teflon filter to 123 capture volatilized NH 4 ; STN does not. 124 125
IMPROVE infers NH 4 concentrations from the combined concentrations of SO 4 and NO 3 , by 126 assuming both are fully neutralized by NH 4 as ammonium sulfate, (NH 4 ) 2 SO 4 and ammonium 127 nitrate, NH 4 NO 3 . To test this assumption, for 13 of 15 IMPROVE sites in the VISTAS region, 128 the nylon filters have been analyzed for NH 4 (beginning 1997 for Dolly Sods, Shenandoah, and 129 Great Smoky Mountains and since October 2002 for 10 additional Southern Appalachian and 130 Coastal sites). 131 132 The quartz filters are used to analyze carbon. SEARCH uses a carbon denuder upstream of its 133 quartz filter to remove organic gases from the sample. IMPROVE uses a backup quartz filter on 134 a small fraction of samples to estimate the extent of adsorbed organic gases. The STN data 135 reported on the EPA Air Quality System website 6 do not include a correction factor for carbon 136 artifacts. However, EPA has evaluated carbon on backup filters and now recommends a 137
correction factor for each of the STN monitor brands 7 . The separation of particulate organic 138 carbon (OC) from elemental carbon (EC) is operationally defined. IMPROVE and SEARCH use 139 Thermal Optical Reflectance (TOR) while STN uses Thermal Optical Transmittance (TOT) for 140 carbon analysis. The temperature of separation of EC is higher for TOT than for TOR. Using 141 the TOT method, STN reports higher OC values and lower EC values than IMPROVE and 142 SEARCH. All three networks use a mass multiplier of 1.4 to account for species associated 143
with carbon. 144 145 IMPROVE uses a second Teflon filter to collect particles with diameters less than 10 microns 146 (PM 10 ). SEARCH uses a separate sampler to collect PM 10 . STN does not measure PM 10 . 147
Coarse mass is calculated as the difference between PM 10 and PM 2.5 and can be calculated for 148
IMPROVE and SEARCH but not for STN sites. The difference between mass measured 149 gravimetrically on the PM 2.5 filter and the reconstructed mass is reported as an unidentified 150 component. The unidentified component may be positive or negative and is the net result of 151 positive and negative artifacts on the filters, unmeasured species, and measurement errors. 152 153Data for the years 2000 – 2003 8, 9 for IMPROVE sites in the VISTAS region are reported here. 154 Data were compared to SEARCH 10 for the years 2000 – 2003 and to STN 6 for the years 2002 – 155 2003. Data completeness requirements outlined by the EPA for tracking progress under the 156 Regional Haze Rule 11 (valid data for at least 75% of days in a year and for at least 50% of the 157 days in each quarter) were adopted and applied to IMPROVE, SEARCH, and STN data sets. 158 Table 1 lists the years determined to have complete data sets for IMPROVE sites under these 159 requirements. All SEARCH and STN sites used in this analysis met these same requirements for 160 the time periods analyzed. 161 162 Calculating Light Extinction 163 Light extinction is a measure of visibility that can be estimated from the measured (or modeled) 164 concentrations of the components of fine particle mass. Total light extinction consists of both 165 scattering of light by particles and absorption of light by gases (e.g., nitrogen dioxide) and 166 particles (e.g., light-absorbing carbon). A particle’s chemical composition, size, shape, and 167 mixture with other chemical components affect the efficiency of light extinction. Particles in the 168 size range of 0.1 to 1.0 microns are most efficient at scattering light. Ammonium sulfate, 169 (NH 4 ) 2 SO 4 , and ammonium nitrate, NH 4 NO 3 , particles are hygroscopic and absorb increasingly 170 more water at higher relative humidity. The acidity of SO 4 particles also affects water 171 absorption; fully neutralized ammonium sulfate, (NH 4 ) 2 SO 4 , absorbs less water than the more 172 acidic ammonium bisulfate (NH 4 HSO 4 ) or sulfuric acid (H 2 SO 4 ). Some organic carbon species 173
absorb water but the amounts are variable and are less well defined. 174 175
EPA guidance for calculating light extinction 11 , which is based on the protocol defined by the 176 IMPROVE program 12-15 was followed for this analysis. The extinction (scattering or absorption) 177 efficiency of each species is multiplied by the measured species mass. Particle components are 178 assumed to act as if in an external mixture (single chemical species per particle) even though 179 particles actually occur in internal mixtures of different chemical species (chemical interactions 180 could alter extinction efficiency). Sulfate is assumed to be fully neutralized. The extinction 181 efficiencies for ammonium sulfate and ammonium nitrate are assumed to be equal (3 m 2 /g) and 182 less than that of organic carbon mass (4 m 2 /g). Scattering enhancement factors are included for 183 ammonium sulfate and ammonium nitrate to account for increased scattering at higher relative 184humidity. Monthly average “climatologically-representative” enhancement factors appropriate 185 for each Class I area are used in the calculation. The measured organic carbon is multiplied by a 186 factor of 1.4 to account for other chemical species associated with carbon in the total organic 187 carbon mass. A value of 10 inverse megameters (Mm -1 ) is added to account for extinction due to 188 scattering from molecules in a clean atmosphere (Rayleigh conditions). The full extinction 189 equation is: 190 191
b ext = 3 f s (RH) [(NH 4 ) 2 SO 4 ] + 3 f n (RH) [NH 4 NO 3 ] + (1) 192 4 [OCM] + 1 [SOIL] +0.6 [Coarse] + 10 [LAC] + 10 193 194
Where b ext = calculated extinction, in units of inverse megameters (Mm -1 ) 195 f(RH) = scattering enhancement factor based on relative humidity 196 OCM = Organic Carbon Mass defined as 1.4 times measured Organic Carbon 197 LAC = Light Absorbing Carbon (typically represented by measured Elemental Carbon) 198 199 Uncertainties in the calculation of light extinction include: 200

• measurement methods for fine particle mass and composition 201

• the factor used to calculate total organic carbon mass 202

• the assumed extinction efficiencies for the fine mass components and for the unidentified 203 components of coarse mass 204

• the lack of contribution by unidentified fine mass 205

• the assumed scattering enhancement factors for relative humidity 206 207 The IMPROVE Steering Committee is currently reviewing the assumptions used to calculate 208 light extinction and will make recommendations for modifications of the above assumptions 209
based on the most recent data 16, 17 . This paper does not attempt to address the accuracy of the 210 current assumptions or potential modifications to these assumptions. 211
212
RESULTS 213
214 Visibility and Fine Particle Trends in VISTAS Class I areas 215Eight of the 15 IMPROVE sites in the VISTAS region have complete data sets for the four year 216 period 2000-2003 (see Table 1), as defined by EPA’s Guidance for Tracking Progress Under the 217 Regional Haze Rule 11 . Three sites had valid data for 2001-2003, but not for 2000. There are 218 only two years of valid data for St. Marks, FL, and Swanquarter, NC and only one year of valid 219 data for Cohutta, GA and Shining Rock, NC. For the latter four sites, contributions to light 220 extinction (Figures 2-3) may be skewed by smaller sample sizes and seasonal bias in available 221 data. All IMPROVE, STN, and SEARCH sites used in quarterly analyses (Figures 4-8) meet 222 EPA’s data completeness criteria for the illustrated quarters of 2002 (at least 50% of days in the 223 quarter have valid data.) Breton, LA had no years meeting the minimum annual criteria, but data 224 do meet the quarterly criteria for first and third quarters of 2002. 225 226 Ammonium sulfate, (NH 4 ) 2 SO 4 , is the largest contributor to light extinction at the IMPROVE 227 sites in the VISTAS region on both the 20% haziest and 20% clearest days in the period 2000 – 228 2003 (Figures 2 and 3). Ammonium sulfate is also the largest contributor to fine particle mass 229 at the IMPROVE sites on the 20% haziest days and the 20% clearest days (not shown). 230 Extinction from (NH 4 ) 2 SO 4 on the 20% haziest days is higher in the interior and northern tier of 231 sites (116-161 Mm -1 ) than at the coastal and southern IMPROVE sites (44-85 Mm -1 ). On the 232 20% clearest days, extinction from (NH 4 ) 2 SO 4 (11-24 Mm -1 ) shows smaller spatial variation. 233 The exception is Shining Rock, NC, which on the 20% clearest days shows the lowest extinction 234 from (NH 4 ) 2 SO 4 (6 Mm -1 ) of all the VISTAS IMPROVE sites. Shining Rock, at 1621 m 235 elevation, may be influenced on the clearest days by regional transport of air masses above the 236 boundary layer. Because only one year (2003) at Shining Rock met the data completeness 237 criteria established for these analyses, these results may be skewed compared to the other sites, 238 but are not inconsistent with the site’s topography. Other sites with missing data show similar 239 (NH 4 ) 2 SO 4 contributions to nearby sites with complete records. 240
241 Organic carbon mass (OCM) is the second largest contributor to fine particle mass and light 242 extinction at VISTAS Class I areas on the 20% haziest days (Figure 2). On the 20% clearest 243 days, OCM is the second largest contributor to light extinction at all the coastal sites and most 244 interior sites (Figure 3). On the 20% clearest days, OCM can exceed (NH 4 ) 2 SO 4 mass at some 245
coastal IMPROVE sites and NH 4 NO 3 ,mass at all sites. Due to the hygroscopic nature of 246(NH 4 ) 2 SO 4 and NH 4 NO 3 , these components still contribute more to extinction on the 20% 247 clearest days than OCM. 248 249 Ammonium nitrate, EC, and fine soil have small contributions to fine particle mass and light 250 extinction on most days. At coastal sites, fine soil has a larger relative contribution to extinction 251 and mass than at Southern Appalachian sites. Coarse mass is higher at coastal sites than at 252 Southern Appalachian sites, possibly due to the presence of sea salt. Due to the low extinction 253 efficiency assigned to coarse mass, the contribution of this component to light extinction is low 254
at all sites. 255 256
The fractional contribution of NH 4 NO 3 to extinction is higher on the clearest days than on the 257 haziest days, even though the absolute contribution is similar between the clearest (1-9 Mm -1 ) 258 and haziest (2-12 Mm -1 ) days. In the southeastern U.S., NH 4 NO 3 formation is generally limited 259 by availability of NH 3 in the atmosphere 18 . Ammonia preferentially reacts with SO 4 to neutralize 260 the ion’s two negative charges. Until SO 4 particles are fully neutralized, little NH 3 is available to 261 react with nitric acid (HNO 3 ) and form NH 4 NO 3 . Ammonium nitrate formation is dependent on 262 temperature and relative humidity. Because NH 4 NO 3 volatilizes at higher temperatures, 263
NH 4 NO 3 concentrations in VISTAS Class I areas are lowest during the summer months and 264 highest during the winter months. 265 266 For the Southern Appalachian and interior IMPROVE sites, the 20% haziest days are most likely 267 to occur May through October. The 20% clearest days occur most frequently in the cooler 268 winter months. For the southern and coastal IMPROVE sites, the 20% haziest days occur year 269 round. Light scattering measured by nephelometer at Great Smoky Mountains, Mammoth Cave, 270 and Shenandoah National Parks is under estimated by the IMPROVE default assumptions on the 271 haziest days and over estimated on the clearest days 19 . Alternative assumptions that increase the 272 OCM multiplier and reflect greater extinction efficiency for SO 4 , NO 3 , and OCM at greater mass 273
concentrations 16, 17 result in slightly greater extinction on the 20% haziest days and slightly lower 274 extinction on the 20% clearest days. They do not change the conclusion that the haziest days at 275 Class I areas in the VISTAS region are dominated by SO 4 with significant contributions from 276
OCM. 277278 The relative importance of components of fine particle mass varies seasonally and spatially, as 279 illustrated in Figure 4 for Mammoth Cave, Great Smoky Mountains, and Everglades National 280 Parks, and Okefenokee and Cape Romain National Wildlife Refuges for the year 2002. 281 Ammonium sulfate is highest in the summer months at the Southern Appalachian and interior 282 sites but less variable seasonally at the southern and coastal sites. Note that NH 4 NO 3 is elevated 283 in the winter months (quarters 1 and 4) at Mammoth Cave compared to other sites. Note also 284 that fine soil is the largest component of PM 2.5 mass at Everglades in the summer quarter of 2002 285
while SO 4 mass is least in the summer quarter at Everglades. Fine soil is episodically elevated 286 at the Florida IMPROVE sites (and to a lesser extent at other IMPROVE sites) during the 287 summer months due to fine dust transported from Africa (Saharan Desert 20 ). When air masses 288 are influenced by transport from the Atlantic Ocean and African continent, SO 4 mass is low. The 289 impact of a single large fire in May 2002 near Okefenokee, GA, is illustrated in the elevated 290 average OCM for the second quarter at Okefenokee. 291 292 Network Measurement Intercomparison and Source Implications 293 VISTAS is using the year 2002 as the base year for emissions inventories and air quality 294 modeling for both the regional haze and PM 2.5 State Implementation Plans 21 . To evaluate the 295
relative contributions to PM 2.5 mass at Class I and nearby urban areas, quarterly average 296 components of PM 2.5 mass in 2002 were compared at the IMPROVE, nearby STN, and 297 SEARCH sites. Average components of PM 2.5 mass for the third quarter of 2002 (July, August, 298 September) are illustrated for selected sites in the Southern Appalachian area in Figure 5 and in 299 the Gulf and Atlantic coastal areas in Figure 6. Average particulate concentrations for the same 300 sites during the first quarter of 2002 (January, February, March) are illustrated in Figures 7 and 301 8. Birmingham, AL; Louisville, KY; Chattanooga and Knoxville, TN; and Atlanta, GA 302 (illustrated in Figures 5 and 6) are PM 2.5 nonattainment areas. 303

Sulfate. Sulfate measurement methods are comparable across the three networks and 304 measurement uncertainty is lower for SO 4 than for other components of PM 2.5 . Sulfate values in 305 quarter 3 (July, August, September) are very similar (6-9 μ g/m 3 ) across IMPROVE, STN, and 306 SEARCH sites in the southern Appalachians (Figure 5) and are generally higher than SO 4 values 307(2-6 μ g/m 3 ) across the coastal sites (Figure 6). Sulfate levels in urban areas are generally within 308
1 μ g/m 3 of those in nearby rural and Class I areas. Larger differences between Shining Rock and 309 Charlotte, NC, and between Shenandoah and Roanoke, VA on the winter days are at least 310 partially attributable to difference in elevation that lead to differences in air masses trajectories 311 and influences from regional transport between these sites. Large differences in SO 4 between 312 Cape Romain and Charleston, SC, and Okefenokee and Savannah, GA (2 μ g/m 3 during the 313 summer quarter) indicate local as well as regional source contributions in these southern coastal 314 areas. Sulfate is attributable primarily to utility and industrial sources and these are broadly 315 distributed across a large geographic region. Sulfate levels in the southern Appalachian and 316 interior urban sites are lower in winter (2-4 μ g/m 3 , Figure 7) than summer, reflecting lower rates 317 of chemical production of SO 4 from sulfur dioxide, SO 2 , in the winter (due to low winter 318 temperatures and low availability of ozone, hydrogen peroxide, and the hydroxyl radical.) 319 Sulfate levels at the coastal areas are fairly similar in winter (2-4 μ g/m 3 , Figure 8) and summer 320 and do not show as strong seasonal patterns as the interior sites. Rao, et al. compared annual 321 average constituents of PM 2.5 mass for the period March 2001 to February 2002 at 13 paired 322
IMPROVE-STN sites nationwide 7 . Quarterly average SO 4 values reported here show greater 323 variability between urban-rural pairs than reported by Rao, et al. for annual average SO 4 (0.0-0.5 324

μ g/m 3 ) for eight paired IMPROVE-STN sites in the eastern U.S. Greater rural-urban variability 325 in this analysis is attributable to a combination of seasonal variability, smaller geographic scope, 326 and local as well as regional contributions to SO 4 at these sites. 327 328
Nitrate. Nitrate measurement uncertainty is higher than that for SO 4 and is attributable to 329 differences in measurement methods between the networks. SEARCH measurements are likely 330 to have the highest accuracy because the citric acid denuder reduces the positive NO 3 artifact due 331
to reaction of NH 3 gas onto the Teflon filter. Nitrate values are low at all sites during quarter 3 332 but are higher at urban sites than at IMPROVE sites. In contrast to SO 4 , NO 3 values are higher 333 during winter months (less than 1 to greater than 2 μ g/m 3 across IMPROVE sites, Figure 7) than 334 summer months (less than 1 μ g/m 3 ). Because NH 4 NO 3 formation is generally limited by NH 3 in 335 the southeastern U.S., lower SO 4 values during the winter months mean more NH 3 is available to 336 react with nitric acid gas, HNO 3 . Also NH 4 NO 3 volatilizes at higher temperature so less is 337 present in particulate phase during the warmer summer months. As a result, NH 4 NO 3 has a 338larger contribution to mass and light extinction on the 20% clearest days, which tend to occur 339 during the winter months, than on the haziest days. 340 341
Ammonium. In Figures 5 through 8, NH 4 is illustrated separately from SO 4 and NO 3 , using 342
inferred NH 4 for IMPROVE and measured NH 4 for STN and SEARCH. Inferred NH 4 values at 343
IMPROVE sites are similar to measured NH 4 at nearby SEARCH sites. STN sites generally 344
have lower measured NH 4 values than IMPROVE and SEARCH. 345 346
NH 4 measurements in all three networks are subject to positive and negative artifacts. The 347
SEARCH NH 4 measurements likely have greater accuracy and fewer artifacts than the 348 IMPROVE and STN measurements due to the use of a citric acid coated backup filter behind the 349 nylon filter, specifically designed to capture volatilized NH 4 . In special studies at Great Smoky 350 Mountains National Park, the potential loss of NH 4 off the nylon filter was evaluated using a 351 denuder/filter pack system with a phosphorous acid-coated denuder after the nylon filter to 352 capture NH 3 22 . Results indicated that for summertime samples at Great Smoky Mountains, an 353 average of 18% (with a range of 10-35%) of the NH 4 volatilized off the nylon filter. Losses of 354
NH 4 off the routine IMPROVE nylon filter may be of similar magnitude. 355 356 The validity of the assumption used in the IMPROVE equation, Eq. 1, that SO 4 is fully 357 neutralized as (NH 4 ) 2 SO 4 was tested by measuring NH4 on the nylon filters for routine 358 IMPROVE samples from 13 sites in the VISTAS region. Molar ratios of NH 4 to SO 4 were used 359 to describe the degree of neutralization of SO 4 by NH 4 . A molar ratio of 2.0 indicates fully 360 neutralized (NH 4 ) 2 SO 4 , while a molar ratio of 1.0 implies NH 4 HSO 4 (or mix of forms with 361 average molar ratio of 1.0), and a molar ratio of 0.0 indicates H 2 SO 4 . Because the more acidic 362
forms of SO 4 aerosol absorb more water and scatter more light than (NH 4 ) 2 SO 4 , three separate 363 curves have been developed to describe the light extinction enhancement factor for water 364 absorption, f(RH), for (NH 4 ) 2 SO 4 , NH 4 HSO 4 , and H 2 SO 4 . The f(RH) curve for H 2 SO 4 is much 365 steeper than the f(RH) curve for (NH 4 ) 2 SO 4 and NH 4 SO 4 , so if the molar ratio of NH 4 to SO 4 is 366 less than 1.0 (indicating a greater fraction of H 2 SO 4 ), light extinction would be greater than that 367 calculated by the IMPROVE equation, Eq. 1. Monthly average molar ratios for interior and 368 coastal IMPROVE sites are always less than 2.0, frequently less than 1.5, and sometimes below 3691.0. Some but not all sites show seasonal variation in molar ratios with the lowest ratios in the 370 fall and summer and higher ratios in the spring. Molar ratios are generally lower at the coastal 371 sites than at the interior sites. Given the likely underestimate in NH 4 values reported for 372 IMPROVE nylon filters, these monthly average molar ratios of NH 4 to SO 4 should be interpreted 373
as lower limits. 374 375 For comparison, monthly average molar ratios for NH 4 to SO 4 were calculated for SEARCH 376 sites using NH 4 collected on both the nylon filter and the backup filter. For the SEARCH sites, 377 the monthly average molar ratio is higher than at nearby IMPROVE sites and frequently close to 378 2.0 (example IMPROVE and SEARCH sites are illustrated in Figure 9.) Values are lower at 379 coastal SEARCH sites than at urban interior SEARCH sites but rarely fall below 1.4. There is 380
much less seasonal variation in the SEARCH values than seen in the IMPROVE data. It is 381 difficult to separate differences due to loss of NH 4 from IMPROVE filters from those attributable 382 to spatial differences in NH 3 emissions. We can conclude, based on SEARCH NH 4 383 measurements, that SO 4 may not be fully neutralized in the coastal areas and possibly interior 384 sites in the summer, and that light extinction by SO 4 may be slightly underestimated using the 385 IMPROVE equation, Eq. 1. Improved methods for measuring NH 4 at the IMPROVE sites are 386 required to verify this conclusion. 387 388 Organic Carbon mass. The greatest uncertainties and the largest differences in measured mass 389 are related to organic carbon mass, OCM. STN reported values for OCM do not include a 390 correction for background carbon on the quartz filter, however OC correction factors have been 391
estimated for each of the STN monitors 7 . Quarterly average OCM values for STN sites 392 illustrated in Figures 5-8 include the appropriate carbon correction factors. Corrected quarterly 393 average OCM values are 20-45% lower than reported OCM values in quarters 1 and 3 of 2002. 394 Quarterly average STN OCM values after correction are 1-5 μ g/m 3 greater than OCM values at 395 nearby IMPROVE sites. These differences are partially attributable to differences in TOR vs 396 TOT analytical methods and partially attributable to differences in source strengths between 397 urban and rural sites. STN OCM values are greater than SEARCH OCM values in Birmingham, 398 AL for both quarters 1 and 3 and in Atlanta for quarter 3, while SEARCH OCM is higher than 399 STN OCM in Atlanta, GA for quarter 1 (Figure 5). While local-scale variations in emissions 400sources may contribute to differences in measured OCM in Birmingham and Atlanta (the 401 SEARCH and STN monitors are not collocated), differences between analytical methods are 402 likely the major contributors to OCM differences. 403 404 Measurement of total carbon on quartz filters is comparable across the three networks, but the 405 TOT method used by STN applies a higher temperature of separation than the TOR method 406 (used by IMPROVE and SEARCH). More of the total measured carbon is reported by STN as 407 OC and less as EC than that reported by IMPROVE and SEARCH. Because analytical 408 laboratories differ subtly in application of the TOR or TOT methods, there is not currently a 409 single consistent correlation factor that can be used to compare OC reported by STN with that 410 reported by IMPROVE and SEARCH 23

. 411
412 Another source of uncertainty in the reported OCM is the accuracy of the assumption that a 413 common OC multiplier of 1.4 can be used at all sites to account for the mass of other 414 components of organic carbon compounds (e.g. hydrogen, oxygen, nitrogen, etc.). Currently 415 IMPROVE, SEARCH, and STN consistently use an OC multiplier of 1.4. Several studies and 416 reviews have recommended that a higher multiplier would be appropriate for organic carbon in 417 urban areas influenced by fossil fuels (~1.6 +/- 0.2) 24 . Multipliers of 2.1 (+/- 0.2) and 2.1-2.4 418 (+/- 0.2) are recommended for carbon from vegetative emissions and biomass burning, 419 respectively 24, 25 . If a carbon mass multiplier of 1.6 were applied to OC for measurements in 420 urban areas and a multiplier of 1.8 were applied for rural sites, the differences in OCM between 421 IMPROVE sites and urban STN and SEARCH sites would be less than illustrated in Figures 5-8. 422
423 Sources contributing to OCM vary spatially for both biogenic sources (e.g. vegetative emissions, 424 wildfire) and anthropogenic sources (e.g. automotive, truck, or construction equipment, 425 residential wood smoke, prescribed fire or land-clearing emissions). Chemical Mass Balance 426 analyses use chemical profiles as indicators for gasoline, diesel, wood smoke, meat cooking, and 427 vegetation to evaluate the relative contributions of different sources of primary organic carbon 26

. 428 Analysis of the 14 C isotope is used to identify the percentage of a carbon sample attributed to 429 fossil fuel combustion compared to recent carbon sources (e.g. wood burning or vegetative 430 emissions). Analyses of PM 2.5 samples from SEARCH sites by Zheng, et al. 26 and Edgerton, et 431al. 27 indicate higher proportions of recent carbon in rural areas than in urban areas and higher 432 contributions from fossil carbon in urban areas than in rural areas. At both urban and rural areas, 433 primary carbon sources dominate in the winter, while secondary carbon sources are more 434 important in the summer. Wood burning contributes to the majority of the recent carbon in the 435 winter, but it is difficult to separate the contributions of wood smoke due to anthropogenic 436 activities (land clearing, prescribed fire, and residential wood burning) from those due to 437
wildfire. 438 439 At Great Smoky Mountains, analysis of 14 C isotope in PM10 samples indicates that 55-85% of 440 the total organic carbon mass measured in spring, summer, and fall is modern carbon (from 441 biomass burning or secondary conversion from biogenic emissions) and 15-40% is attributable to 442
fossil carbon 28 . Using PM 2.5 samples, fossil carbon was found to have a larger contribution in a 443 nearby urban area, Chattanooga, TN, than in the Great Smoky Mountains, but still less than 444 modern carbon for most samples 29 . To develop a better understanding of the sources of carbon, 445 VISTAS supported high-volume PM 2.5 filter samplers at Mammoth Cave, Great Smoky 446 Mountains, and Shenandoah National Parks, Cape Romain Wilderness Area, and Millbrook, NC 447 (suburban location outside Raleigh, NC) for the period April 2004-May 2005. Twenty-four 448 hour samples were collected on quartz filters every third day. Chemical Mass Balance analyses 449 and analyses of 14 C isotope are underway and will provide better characterization of fossil fuel 450 versus biogenic influences at these sites. 451 452
Elemental Carbon. Elemental carbon values are higher at urban STN sites than at nearby 453 IMPROVE sites, despite the fact that the TOT method yields lower EC than the TOR method. 454 EC values are also higher at urban SEARCH sites than at rural SEARCH and IMPROVE sites. 455 Emissions inventories indicate that industrial point sources and fires are generally localized 456 sources of EC while vehicles and equipment engines are more regionally dispersed sources. 457 458
Total Carbon Mass. Total carbon mass, TCM, (calculated as 1.4 times OC, plus EC) can be 459 compared between networks to eliminate the effect of difference between the TOR and TOT 460 analytical methods. Because EC is such a small fraction of TCM, the range for TCM between 461 urban STN sites and IMPROVE sites illustrated in Figures 5-8 is the same as the range for OCM, 4621-5 μ g/m 3 . These differences in quarterly average TCM between rural and urban sites are 463 similar to those reported by Rao, et al. 7 for annual average TCM for IMPROVE-STN pairs in the 464
eastern U.S. 465 466
Fine Soil. Measurement methods for fine soils are comparable across the three networks, 467 although SEARCH uses a slightly different calculation of component elements. Fine soil is 468 higher at the southern and coastal sites than at the southern Appalachian sites, especially during 469 the summer months. Construction, road dust, and agriculture are major anthropogenic sources 470 of fine soil. Saharan dust is a major contributor to fine soil mass at the southern and coastal 471 sites, especially Everglades, while Asian dust is seen in fine soil at northern tier sites such as 472 Shenandoah in spring and early summer months 20 . Because fine soil has low extinction 473 efficiency, its average contribution to light extinction is generally low. 474 475
Coarse Mass. Coarse mass is higher at coastal IMPROVE sites than Southern Appalachian 476 IMPROVE sites or SEARCH sites. Sea salt is an unspeciated component of coarse mass and 477 likely contributes to higher values of coarse mass at coastal areas. 478
479
DISCUSSION 480 481 Implications for Future Monitoring 482 The IMPROVE, STN, and SEARCH monitoring networks have similar, but differing objectives 483 and differing analytical methods that complicate inter-comparison across networks. Additions of 484 an acid denuder before the nylon filter to collect NH 3 , a backup nylon filter to collect volatilized 485
NH 4 , and an acid denuder after the nylon filter to collect volatilized NH 3 would improve NO 3 486
and NH 4 measurements at IMPROVE sites. Given that these methods are labor intensive and 487 expensive to implement in the routine network, we recommend additional intensive studies in 488
different seasons and IMPROVE locations to better understand NO 3 and NH 4 chemistry in the 489
southeastern U.S. 490 491 Collated IMPROVE and STN monitors are providing information on comparability of the 492 measurements in the two networks, and EPA is already evaluating changes to the STN protocol, 493particularly for carbon, to be more similar to that used by IMPROVE. Studies currently 494 underway in VISTAS will assist in definition of contributions of primary vs secondary 495 carbonaceous species and fossil fuel vs recent carbon sources in the southeastern U.S. This 496 information, combined with other studies, can be used to improve estimates of the OCM 497 multiplier appropriate to the urban and rural areas in the southeastern U.S. 498 499 Implications for Atmospheric Modeling 500 VISTAS is modeling air quality in 2002 21 as the base year for regional haze and PM 2.5 State 501 Implementation Plans. A critical component of this analysis is to demonstrate that the 502 performance of the atmospheric model meets established performance goals. Model 503 performance is constrained by measurement uncertainty as well as uncertainties in emissions 504 inventories and representation of chemical and transport processes in the model. Differences in 505 network measurement methods contribute to measurement uncertainty, especially for organic 506 carbon mass, and these differences need to be considered when evaluating model performance 507 across the networks. Sulfate measurements are comparable across the networks. SEARCH NO 3 508 measurements are likely more accurate than IMPROVE and STN measurements, but all three 509 networks report fairly low NO 3 concentrations. Because NO 3 concentrations are low, model 510 performance can tolerate larger bias and error than for SO 4 and OCM components 21

. Given the 511 likely underestimate in NH 4 from the IMPROVE nylon filters, we cannot recommend using the 512
measured NH 4 mass from IMPROVE nylon filters as a better estimate of NH 4 for IMPROVE 513
sites than inferred NH 4 based on measured SO 4 and NO 3 . For STN sites, OCM values should be 514 corrected for background carbon before comparison to modeled OCM values. Model 515 performance evaluation should also consider that OC reported by STN is defined by TOT 516 analysis and is likely at least 20% higher than OCM reported by SEARCH and IMPROVE based 517 on TOR analysis. Consideration should be given to using a higher carbon mass multiplier than 518 1.4 for all three networks (in the range between 1.6-2.0, possibly selecting a lower multiplier for 519 urban areas than rural areas) to account for chemical species associated with carbon. 520
521
CONCLUSIONS 522 Fine particle mass and light extinction are dominated by ammonium sulfate in the southeastern 523 U.S. Ammonium sulfate levels are generally higher in the northern tier of the VISTAS states 524and lower at the coastal and southern sites. Regional SO2 controls should be effective in 525 reducing fine particulate sulfate and its contribution to light extinction and PM2.5 mass in the 526 VISTAS states. Levels of organic carbon, elemental carbon, and ammonium nitrate are 527 generally higher in urban areas than rural and Class I areas. A combination of local and regional 528 sources is likely contributing to these fine particle components. Improved understanding of the 529 relative contributions of forest fire, land clearing, or residential wood burning will be necessary 530 to determine what reductions in carbon from wood burning are feasible at urban and Class I 531 areas. Fossil carbon sources are important contributors to OCM in urban areas, suggesting 532 greater opportunity for reduction in OCM in response to anthropogenic emissions controls than 533 at Class I areas. Because secondary carbonaceous species from biogenic emissions are major 534 contributors to OCM in the southeastern U.S., particularly in the summer months, even after 535 anthropogenic controls, OCM will likely remain a major component of PM2.5 and visibility in 536 the VISTAS region. 537 538
ACKNOWLEDGEMENTS 539 540 The work reported in this paper was supported by the VISTAS regional planning organization 541 through its funding agency the Southeastern Air Resource Managers, Inc. and grants from the 542 Environmental Protection Agency. The authors thank the VISTAS Data Workgroup for 543 technical direction and are especially grateful to Dr. Ivar Tombach for technical review and 544
editorial recommendations. 545
546

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548

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27. Edgerton, E. S., Hartsell, B. E., and Jansen, J. J. Natural Sources of PM 2.5 and PM coarse 613 Observed in the SEARCH network. Regional and Global Perspectives on Haze: Causes, 614 Consequences and Controversies - Visibility Specialty Conference, Air and Waste 615 Management Association, Asheville, NC, October 25-29, 2004. 616
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Carbon based on 14 C measurements. Aerosol Sci. Tech. , 38(S1), 133-139, 2004. 618
29. Tanner, R. L., Zheng, M., Lin, K., Schauer, J. J., and McNichol, A.P. Source Contributions 619 to the Organic Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural 620 Differences. Presentation to AAAR International Specialty conference, Particulate Matter: 621 Supersites Program and Related Studies”, Atlanta, GA, Feb 7-11, 2005. 622
623Brewer, P. F. and Adlhoch, J. P. Trends in Speciated PM2.5 and Visibility across Monitoring Networks in the Southeastern U.S.

Table 1 – Years determined to have complete data sets at IMPROVE sites, indicated by an ‘X’. Data completeness requirements adopted from EPA’s Guidance for Tracking Progress Under the Regional Haze Rule . The IMPROVE data set used was the February, 2005, RHR summary data set available on the VIEWS web site.

IMPROVE Site 2000 2001 2002 2003 Dolly Sods, WV X X X X Shenandoah, VA X X X X James River Face, VA X X X Mammoth Cave, KY X X X X Sipsey, AL X X X X Great Smoky Mtns, TN X X X X Cohutta, GA X Shining Rock, NC X Linville Gorge, NC X X X Swanquarter, NC X X Cape Romain, SC X X X X Okefenokee, GA X X X X Saint Marks, FL X X Chassahowitzka, FL X X X X Everglades, FL X X XBrewer, P. F. and Adlhoch, J. P. Trends in Speciated PM2.5 and Visibility across Monitoring Networks in the Southeastern U.S.
Figure Captions

Figure 1. Locations of Class I areas in the VISTAS region and locations of IMPROVE, SEARCH and selected STN monitors.
Figure 2. Calculated light extinction on the 20% Haziest Days in 2000 to 2003 at IMPROVE sites in the VISTAS region.
Figure 3. Calculated light extinction on the 20% Clearest Days in 2000 to 2003 at IMPROVE sites in the VISTAS region.
Figure 4. Seasonal (quarterly) variation in fine particle mass and composition at example IMPROVE sites in Southern Appalachian (Mammoth Cave and Great Smoky Mountains) and coastal (Okefenokee, Cape Romain and Everglades) areas of the VISTAS region.
Figure 5. Comparison of measured fine particle mass and composition in quarter 3 (July, August, and September) 2002 at IMPROVE and nearby STN and SEARCH sites in VISTAS’ Southern Applachian and interior area.
Figure 6. Comparison of measured fine particle mass and composition in quarter 3 (July, August, and September) 2002 at IMPROVE and nearby STN and SEARCH sites in VISTAS’ Gulf and Atlantic coastal areas.
Figure 7. Comparison of measured fine particle mass and composition in quarter 1 (January, February, and March) 2002 at IMPROVE and nearby STN and SEARCH sites in VISTAS’ Southern Applachian and interior area.
Figure 8. Comparison of measured fine particle mass and composition in quarter 1 (January, February, and March) 2002 at IMPROVE and nearby STN and SEARCH sites in VISTAS’ Gulf and Atlantic coastal areas.
Figure 9. Molar ratio of ammonium (NH4) to sulfate (SO4) at example IMPROVE and SEARCH sites in VISTAS Southern Appalachian and coastal areas.ABOUT THE AUTHORS

Patricia Brewer is the Technical Coordinator for the VISTAS Regional Planning Organization for the southeastern US and is located at 2090 U.S. Highway 70, Swannanoa, NC 28778.
Joseph Adlhoch is a consultant with Air Resource Specialists, Inc., and is located at 1901 Sharp Point Dr. Suite E, Fort Collins, CO 80525-4429.

## 0

50 100 150 200 250Extinction (Mm-1) 

### Coarse Soil OCM EC NH 4 

### NO 3 

### (NH 4 

### ) 2 

### SO 4 

### RayleighDolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCShining Rock, NCCohutta, GASaint Marks, FLSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FLExtinction (Mm-1)



## 0

10 20 30 40 50 60Dolly Sods, WVShenandoah, VAJames Rvier Face, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNLinville Gorge, NCShining Rock, NCCohutta, GASaint Marks, FLSwan Quarter, NCCape Romain, SCOkefenokee, GAChassahowitzka, FLEverglades, FL 

### Coarse Soil OCM EC NH 4 

### NO 3 

### (NH 4 

### ) 2 

### SO 4 

### Rayleigh

## 0.0

5.0 10.0 15.0 20.0Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4μg/m3 

## Unidentified

Mammoth Cave, KY Okefenokee, GA Everglades,

FL Cape Romain, SC Great Smoky Mtn., TN Soil

OCM EC

NH 4 

## NO 3 

## (NH 4 

## ) 2 

## SO 4

## 0.0

5.0 10.0 15.0 20.0 25.0Fine Particle Mass -μg/m3 

### Unidentified Fine Soil EC OCM NO 3 

### NH 4 

### SO 4Shenandoah, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNShining Rock, NCAtlanta, GABirmingham, ALLouisville, KYKnoxville, TNAtlanta, GACharlotte, NCRoanoke, VACohutta, GADolly Sods, WV 

## IMPROVE SEARCH STNBirmingham, AL

## IMPROVE SEARCH STNSwan Quarter, NCCape Romain, SCTampa, FLSt. Marks, FLCharleston, SCOkefenokee, GASavannah, GAChassahowitzka, FLEverglades, FLPensacola, FLBreton Island, LAGulfport, MS 

## 0.0 5.0 10.0 15.0 20.0 25.0Fine Particle Mass -μg/m3 

### Unidentified Fine Soil EC OCM NO 3 

### NH 4 

### SO 4

## 0.0

4.0 8.0 12.0 16.0Fine Particle Mass -μg/m3



### Unidentified Fine Soil EC OCM NO 3 

### NH 4 

### SO 4Shenandoah, VAMammoth Cave, KYSipsey, ALGreat Smoky Mtns, TNShining Rock, NCAtlanta, GABirmingham, ALLouisville, KYChattanooga, TNAtlanta, GACharlotte, NCRoanoke, VACohutta, GADolly Sods, WV 

## IMPROVE SEARCH STNBirmingham, AL

## 0.0

4.0 8.0 12.0 16.0

IMPROVE SEARCH STNSwan Quarter, NCCape Romain, SCTampa, FLSt. Marks, FLCharleston, SCOkefenokee, GAChassahowitzka, FLEverglades, FLPensacola, FLBreton Island, LAGulfport, MSFine Particle Mass -μg/m3 

### Unidentified Fine Soil EC OCM NO 3 

### NH 4 

### SO 4

## 0.0

0.5 1.0 1.5 2.0 2.5

Jan-02 M a r- 02 M ay-02 Jul-02 Sep-02 Nov-02 Jan-03 M a r- 03 M ay-03 Jul-03 Sep-03Molar Ratio



### Great Smoky Mtn, TN Birmingham, AL Gulfport, MS Mammoth Cave, KY 

## SEARCH IMPROVE Cape Romain, SC

# Appendix B.4 Natural Background Visibility and Regional Haze Goals in the Southeastern United States

## Natural Background Visibility and Regional Haze

1 

## Goals in the Southeastern United States 2

Ivar Tombach 3 Consultant, 753 Grada Ave., Camarillo, CA 93010 4
Patricia Brewer 5 VISTAS, 2090 U.S. 70 Highway, Swannanoa, NC 28778 6

ABSTRACT 7 The goal of the regional haze mitigation program in the United States is to attain “natural 8 conditions” in national parks and wilderness areas by 2064. Results of research investigations on 9 background concentrations of sea salt and biogenic organic matter, of episodic Saharan and 10 Asian dust, and of carbon from natural fires were reviewed to provide a basis for making site11 specific estimates of what the concentrations of atmospheric fine particulate matter components 12 might be under natural conditions in the southeastern United States. Based on this review, rough 13 estimates were made of potential contributions of these aerosol components to natural 14 background visibility. Natural organic particles were the dominant influence on the rate of 15 visibility improvement required to reach natural conditions at an inland, mountainous location 16 and organic particles and sea salt were the dominant influences on the rate at a coastal location. 17 African dust also had a large episodic effect, but the current regulatory approach is not designed 18 to address episodic background variations. Insufficient data exist to quantify the contributions of 19 wildfires with any detail, although global air pollution modeling provides insight and their 20 emissions can be locally dominant. Conservative regional refinements to the default natural 21 background estimates do not greatly alter the region-wide rates of reduction of ambient 22 particulate matter concentrations that will be needed to accomplish the first phase of the regional 23 haze program. However, refinements at specific Class I areas may have considerable influence 24 on defining the nature (magnitude and spatial and temporal distribution) of local emission 25
reduction efforts there. 26

INTRODUCTION 272

The United States Environmental Protection Agency’s Regional Haze Rule 1 prescribes that 28 States are to make efforts to improve visibility in 156 national parks and wilderness areas (“Class 29 I areas”) at such rates that "natural conditions" would be achieved in each Class I area by 2064. 30 A "reasonable rate of progress" corresponds to linear improvement in visibility, as characterized 31 in units of deciviews, between current conditions during the base period of 2000-2004 and 32 natural conditions at the end point of 2064. 33 To provide a starting point for the regional haze planning process, the U.S. EPA has 34 issued guidance for estimating natural visibility conditions based on assumed natural background 35 concentrations of the chemical components of particulate matter. 2 Recognizing that these 36 “default” concentration estimates may not be appropriate for every Class I area, the guidance 37 document also provides a State with the option of developing a "refined" estimate of natural 38
conditions that reflects local circumstances. 39 This paper discusses the attributes of the default approach, identifies issues that might 40 suggest consideration of a refined method, and provides a survey of the current scientific 41 understanding of background and natural particulate matter (PM) component concentrations. 42 (Note that the concept of achieving “natural conditions” by a regulatory program in the United 43 States is an idealistic one, in that it assumes that the only pollutants ultimately transported into 44 the U.S. across its borders will be natural ones and ignores potential contributions from 45 anthropogenic sources outside the country. Although the issue of transboundary transport is 46 important in the implementation of U.S. regional haze policy, it will not be addressed here. 47 Rather, we focus on the scientific issues involved in estimating natural conditions in the absence 48 of transboundary anthropogenic contributions.) 49 The discussion here focuses on the geographic region encompassed by the VISTAS 50 Regional Planning Organization, essentially the southeastern United States. VISTAS (Visibility 51 Improvement – State and Tribal Association of the Southeast) is an association of ten States, the 52 Eastern Band of Cherokee Indians, and local air pollution control agencies. The VISTAS States 53 and the Class I areas within them are shown in Figure 1. 54 Of importance to VISTAS is whether the limitations in the default concentration 55 estimates are significant, particularly for the development of emission control strategies designed 56 to reach a first phase goal by 2018 -- i.e., could the decisions that VISTAS will be making about 57 initial emission management strategies be affected by uncertainties in the natural background 583

concentration estimates? And, as a corollary, will it be worthwhile to consider refining the 59 estimates for some components of particulate matter using the new information that has become 60 available since the default natural concentration estimates were developed? 61 Fortuitously, the global climate change research community has carried out substantial 62 research on global background and natural aerosols in the past decade. Their interest is to 63 separate the natural and anthropogenic contributions by particulate matter to global radiation 64 balance, a focus that neatly parallels the regional haze interests here. Consequently, the 65 information base now available for estimating natural background concentrations greatly exceeds 66 that of the pre-1990 research upon which the EPA’s default guidance is based. 67 To address the questions raised and to take advantage of the new data, this paper reviews 68 the basis for the default concentration estimates, discusses two technical issues concerning 69 estimating light extinction from those concentrations, surveys relevant research findings that 70 provide insight into natural conditions PM component concentrations in the Southeast, and 71 discusses the implications of this new information on control strategy planning. 72

BACKGROUND -- THE DEFAULT ESTIMATES OF NATURAL CONDITIONS 73 In September 2003, the U.S. EPA issued Guidance for Estimating Natural Visibility Conditions 74 Under the Regional Haze Rule
, 2 to provide initial guidance for estimating natural conditions. 75
That document recommends that natural extinction levels be estimated from the default 76 component concentrations by using the formula established by IMPROVE for estimating current 77 light extinction from measurements of the concentrations of the main components of fine 78 particulate matter. (IMPROVE stands for I nteragency M onitoring of Pro tected V isual 79
E nvironments, the national visibility-related PM component monitoring program.) The 80 IMPROVE formula estimates light extinction from the concentrations of fine ammonium sulfate, 81 ammonium nitrate, particulate organic matter, light absorbing carbon, and fine soil, plus coarse 82 particles: 83

b ext

= 3f(RH)[SULFATE] + 3f(RH)[NITRATE] + 4[POM] + 1[SOIL] 84 + 0.6[CM] + 10[LAC] + 10, (1) 85
where the concentrations, indicated by square brackets, are in μg/m 3 ; b ext is in Mm -1 ; and the 86 numerical coefficients before each term, which represent extinction efficiencies (light extinction 87 per unit mass), are in m 2 /g. SULFATE is assumed to be in the form of ammonium sulfate; 884

NITRATE is assumed to be ammonium nitrate; and POM is calculated as 1.4[OC] where [OC] 89 represents the organic carbon concentration in particulate organic matter [POM] . (The 90 IMPROVE program labels POM as OMC and, as with POM here, uses that label to describe only 91 the fine fraction.) CM represents coarse matter, particles between 2.5 and 10 μm diameter. Light 92 absorbing carbon ( LAC ) is sometimes interchanged with elemental carbon ( EC ). Although the 93 two are not exactly equivalent, we will use the terms interchangeably here. The constant 10 at the 94 end approximates Rayleigh scattering by air; it is the correct value at an altitude of about 1500 m 95 (5000 ft). A humidity-dependent enhancement factor, f(RH) , is used to account for the increased 96 light scattering by sulfates and nitrates as the relative humidity increased. Monthly-average 97 "climatologically-representative" values of f(RH) for each Class I area have been published by 98 the U.S. EPA and are to be used for the purposes of the Regional Haze Rule. 2 99 Much of the Regional Haze Rule describes visibility in terms of a haze index ( HI ), in 100 units of deciviews (dv). The haze index is determined via the formula 101
HI = 10 ln(b ext /10) , (2) 102
using the b ext

(in Mm -1 ) derived in eq 1. 103 Estimates of natural background concentrations of the particulate matter components in 104 eq 1 were developed in 1990 by Trijonis for the National Acid Precipitation Assessment Program 105 (NAPAP). 3 He estimated annual average concentrations of chemical components of particulate 106 matter for both the eastern United States and the mountain/desert regions of the western United 107 States. The East was defined as extending up to one tier of states west of the Mississippi River, 108 thus the VISTAS domain falls entirely within that region. 109 The U.S. EPA has adopted Trijonis’ estimates of natural background concentrations, after 110 adjusting his values to make them consistent with current IMPROVE measurement conventions. 2 111 The resulting default concentrations for the East are listed in Table 1, along with Trijonis’ 112 estimates of the errors in his values. The last two columns list the dry extinction efficiencies (the 113 numerical coefficients in eq 1) for each component and the estimated contribution of each 114 component to light extinction under “dry” conditions (relative humidity ≤ 36%), as calculated 115 using eq 1 with f(RH) = 1

. 116 Note that particulate organic matter is overwhelmingly the largest contributor to natural 117 background extinction, in contrast to the present situation in the East where sulfates usually 118 dominate light extinction. 1195 Trijonis’ error factors show that there are large uncertainties in his concentration 120 estimates, typically by a factor of two or so. These uncertainties reflect the limited information 121 he had available for developing his estimates and the result of assigning of a single set of 122 numbers to characterize concentrations throughout the year over the large geographic area of the 123
eastern United States. 124 Trijonis arrived at his estimates for the eastern United States as follows, using the 125
information that was available to him in 1990: 126

• Fine organic particulate matter (POM) – He made a d hoc adjustments to 127 measurements then available for the rural East (i.e., he assumed that 1/3 of the 128 average measured concentration of 4 μg/m 3 is natural), which were supported by 129 measurements of organic aerosol concentrations in remote areas (which suggest a 130 background of 1.5 to 2.25 μg/m 3 in the northern hemisphere). (Note that there is a 131 difference between background concentrations in remote areas today and 132 concentrations under natural conditions. The former can include global background 133 from anthropogenic emissions and thus may differ from the natural value.) 134

• Fine sulfates – He estimated that 3% of sulfur emissions in the East are due to natural 135 sources (based on the NAPAP emission inventory), considered rural measurements in 136 the southern hemisphere, and considered background estimates made for central 137
Alaska. 138

• Fine elemental carbon – He estimated that 2.8% of EC emissions are due to wildfires 139 and prescribed burning (from fire emissions information in the NAPAP emission 140 inventory) and considered that global background EC concentrations are very small. 141

• Fine ammonium nitrate – He considered nitrate levels (about 0.1 μg/m 3 ) in tropical 142 forests and observed that the NAPAP emission inventory indicated that natural NO x 143 emissions are 4.5 times as great as those of gaseous sulfur (on a molar basis) but that 144 atmospheric nitrate production should be relatively less that that of sulfur. 145

• Fine soil -- Measurements suggested an average fine soil concentration of about 1 146 μg/m 3 in the East. He arbitrarily assumed that half of that amount is natural. 147

• Coarse matter – He used the lower end of the range of coarse mass concentration 148
measurements at "remote" sites in the U.S. 1496 Thus, Trijonis based his estimates on the limited base of data that was available at the 150 time and interpreted it with a healthy dose of intuition, so the large error factors he assigned to 151 his own estimates are not surprising. 152 Figure 2 displays the annual average estimates of b ext that result from applying the default 153 concentrations of Table 1 together with the EPA’s site-specific, climatologically-representative 154 f(RH) at four of the Class I areas in the VISTAS region, and identifies the contribution of each 155 particulate matter component (and any water associated with it) to that extinction. The bars in the 156 figure are labeled with the IMPROVE abbreviations for the names of the Class I areas the 157 monitors represent – GRSM = Great Smoky Mountains National Park; MACA = Mammoth 158 Cave National Park; ROMA = Cape Romain Wilderness within the Cape Romain National 159 Wildlife Refuge; and EVER = Everglades National Park. 160 The extinction coefficient and haze index values that result from the application of eqs 1 161 and 2 (using the concentrations in Table 1 and site-specific, climatologically-representative 162 monthly f(RH) values) are intended to represent default estimates of the average natural 163 conditions light extinction during each month, though based on annually constant default 164 concentrations. Since the Regional Haze Rule specifies that reasonable progress in mitigating 165 regional haze is to consist of improvement on the 20% of the days that are most hazy and of no 166 deterioration on the 20% of the days that are least hazy (clearest), the natural conditions 167 extinction value that is most relevant is the one that applies to the 20% of the days that are the 168 most hazy. 169
Ames and Malm 4 developed a statistical procedure to estimate the average of the 170 background extinction on the 20% haziest days (which they assumed is represented by the 90 th 171 percentile of extinction values) from the annual average default values. They assumed that the 172 haze index under natural conditions would be normally distributed, in which case the 10 th and 173
90 th percentile values can be related to the mean and standard deviation of the distribution. The 174 mean values were calculated using Trijonis' default concentrations. To estimate the standard 175 distribution of natural conditions, they adjusted IMPROVE-measured frequency distributions of 176 today's haze indices at each Class I area by reducing actual sulfate and nitrate concentrations 177 until, on average, they matched the Trijonis default values in the East and the West. Measured 178 concentrations of organic carbon, elemental carbon, fine soil, and coarse matter were not reduced 1797

similarly, under the assumption that at Class I areas they represent local concentrations that 180 already derive largely from natural sources. 181 From such synthetic frequency distributions at several Class I areas, Ames and Malm 182 estimated one representative value of the standard deviation for the East and another one for the 183 West, enabling calculation of 10 th and 90 th percentile estimates. They concluded that, for the 184 East, the 90 th percentile value for any given Class I area is 3.84 dv greater than the annual mean 185 haze index (in deciviews) that is calculated from the default concentrations and f(RH) . Based on 186 this procedure, Appendix B of EPA's guidance document 2 presents default estimates of the haze 187 index that are intended to represent the 20% haziest and 20% clearest days under natural 188
conditions at each Class I area. 189

SOURCES OF NATURAL BACKGROUND PARTICULATE MATTER 190 Sources of gas and particulate emissions that contribute to natural particulate matter can be 191 grouped into two classes depending on their variability during the year – essentially steady or 192 episodic. Since haze calculations under the Regional Haze Regulations are based on 24-hour 193 averages, diurnal variations are not of import to us here. 194 Considering potential contributors, relatively steady-state natural sources (i.e., ones that 195 vary slowly throughout the year or within a season, though they may reflect diurnal variability 196 and even flare up at times) include the following: 197

• Oceans and tidal zones– sources of sea salt (affected by the amount of wind-raised 198 spray) and also sulfates and organic carbon (both primary and secondary). The 199 primary on-land effect occurs within tens of kilometers to, at most, a few hundred 200
kilometers of the shoreline. 201

• Forests and vegetation – sources of organic carbon (both primary and secondary), 202 coarse organic matter (from plant detritus), sulfates, and nitrates. Emissions result 203 from plant photosynthesis and respiration and also from decaying vegetation. 204 Ammonia produced in forests plays a role in the formation of ammonium sulfate, 205 ammonium nitrate, and even organic particles. 206

• Lightning – source of nitrates (relatively steady from day to day in some regions and 207 seasons). 2088 • The earth's crust – source of fine and coarse soil, including sulfates and nitrates, due 209
to wind and natural disturbances. 210

• Volcanoes – sources of sulfates and nitrates (both primary and secondary) and of soil 211 particles. The main volcano currently affecting the VISTAS region is Popocatepetl in 212 southern Mexico, whose SO 2 emissions alone were estimated by the BRAVO Study 213 to average 3500 tonnes (3800 tons) per day. 5 These emissions are from the ultimate 214 "tall stack" at 5465 m (18050 feet) above sea level (before plume rise), and thus can 215 be transported very long distances along different pathways than lower-elevation 216
emissions. 217 The principal episodic natural sources include: 218

• Windstorms – sources of soil particles, which can be transported long distances, even 219 intercontinentally (examples are documented North African and Asian dust impacts 220 on the United States). 221

• Wildfires – sources of organic carbon and, to a lesser degree, elemental carbon and 222 soil, which can also be transported long distances (an example is the observed effect 223 of transported Central American biomass burning emissions on the U.S. South, 224 although most of this burning is man-caused). 225
If EPA’s default natural concentration values are valid, the contributions of all of these 226 steady-state and episodic sources should already be reflected in those default concentration 227 estimates, at least on average. At any specific location and time, however, conditions could 228 deviate considerably from the default values. 229 Subsequent to the development of the default values, natural background concentrations 230 over the entire globe have been estimated by applying air quality models. As examples, such 231 simulations have produced estimates for the southeastern U.S. of concentrations of OC and EC 232 from natural sources, biomass burning, and all sources, with the Grantour model; 6 of 233 concentrations of all components, with the GEOS-CHEM model; 7, 8 and of concentrations of 234 anthropogenic and biogenic secondary organic aerosol (SOA, the secondary fraction of POM), 235
with the MOZART-2 model. 9 Specifically, the most recent application of the GEOS-CHEM 236
model 8 yielded the following estimates of annual average natural background concentrations in 237 the U.S. East: ammonium sulfate, 0.11 μg/m 3 ; ammonium nitrate, 0.03 μg/m 3 ; organics, 1.17 2389

μg/m 3 , and elemental carbon, 0.04 μg/m 3 . These can be compared with the default values in 239 Table 1, with the caveat that the GEOS-CHEM modeling values include effects of worldwide 240 “climatological” (i.e., multi-year average) fire emissions, both natural or anthropogenic, and thus 241 may overstate the natural portion of carbon concentrations alone. 242 Such global modeling could provide finer spatial resolution than the East-West split of 243 the default values and could be used to develop seasonal or monthly estimates of the natural 244 background. As uncertainties in emission inventories, particularly for natural emissions, are 245 being reduced, global modeling is improving in accuracy and its results should be considered in 246 parallel with the default estimates of Trijonis when establishing refined natural background 247
values. 248 We conducted a review of the scientific understanding of the emissions and ambient 249 concentrations of all the source types mentioned above, to evaluate which of those source types 250 are most likely to cause appreciable impact to natural conditions concentrations in any part of the 251 VISTAS region. 10 An important consideration was whether the scientific information base is 252 strong enough to develop credible quantitative estimates of aerosol concentrations resulting from 253 each of the above emissions categories. The potential impact of each emission category on 254 natural visibility in the VISTAS region, and the credibility and importance associated with each 255 impact relative to the default conditions, was estimated. 256 As a result of that review, contributions of the following four categories of sources were 257 considered for potential inclusion in refined estimates of natural conditions and thus have been 258 investigated further: 259

• organic and elemental carbon from wildfires, 260

• volatile organic compounds (VOCs) and POM from biological processes in forests, 261

• oceanic sea salt and POM, and 262

• long range transport of windblown dust from Asia and North Africa. 263 Impacts from other source categories mentioned earlier were considered to be too small 264 to be important or our understanding of them at this time was too limited to enable development 265
of credible concentration estimates. 26610 In addition, the statistical procedure used by the EPA to derive extinction under natural 267 conditions for the average 20%-haziest days and the empirical factor used to calculate POM 268 concentrations from measured OC concentrations were considered worthy of review. 269 Alternative assumptions for extinction efficiencies in eq 1 would also affect calculated 270 light extinction under both current and natural background conditions. These alternatives are not 271 discussed in this paper and the reader is referred to recent reviews of the topic. 11 - 13 272 The rest of this paper discusses the following issues: (1) statistical estimation of 273 extinction under natural conditions for the average of the haziest 20% days, (2) the choice of the 274 scaling factor for OC mass, and (3) current knowledge about the four categories of sources listed 275 above. Included are initial estimates (sometimes crude) of the potential magnitudes of these 276 issues on natural background concentrations and their potential impacts on developing 277 reasonable progress goals. The questions then addressed in conclusion are whether such effects 278 are substantial enough at some locations to materially affect the slope of the required "reasonable 279 progress" line, and might there be areas where additional effort to refine natural background 280 estimates would be justified? 281

POTENTIAL REFINEMENTS TO THE DEFAULT VALUES 282 Statistical Estimation of the Haziest 20% of Days 283
Ames and Malm 4 assumed that the frequency distribution of haze index values over a year is 284 normally distributed. For a normal distribution, the haze indices at the 10 th and 90 th percentiles of 285 the haze distribution under natural conditions can be calculated using the following formula: 286

HI = <HI> ± 1.28 σ , (3) 287

where HI

is the haze index (in deciviews), <HI> is the annual mean haze index for the Class I 288 area of interest (from the EPA guidance document 2 ), and σ is the standard deviation of the 289 assumed normal distribution of HI. Using the plus sign produces the 90 th percentile value and 290 using the minus sign produces the 10 th percentile value. The term 1.28 σ represents the distance 291
from the mean to the 10 th and 90 th percentiles. 292 Using the synthesized natural concentrations frequency distribution described earlier, 293
Ames and Malm estimated σ to average 3 in the East and 2 in the West under natural conditions, 294 so the formula for the East becomes simply 29511 HI = <HI> ± 3.84

. (4) 296
However, since the HI frequency distribution from the 80 th to the 100 th percentiles is not 297 uniform in a normal distribution, the HI value at the midpoint (90 th ) percentile of that range does 298 not actually represent the mean of the 20% haziest days that is prescribed by the Regional Haze 299 Regulations. Rather, the mean value for that range in a normal distribution is represented by the 300
haze index at the 92 nd percentile. 14 Consequently, the 1.28 factor should be 1.42 if the 301 distribution is normal, in which case the formula for the East becomes 302

HI = <HI> ± 4.26

. (5) 303
Thus, for the East, the haze indices for the average of the haziest 20% of the natural days should 304 be 0.42 dv greater than the default values that are presented in the EPA's guidance document 305 (and those for the clearest 20% of the natural days should be 0.42 dv less), if a normal 306
distribution is assumed. 307
308 Mass Concentration of Particulate Organic Matter 309 Trijonis estimated the natural background concentration of organic carbon (OC) in the East to 310 average 1 μg/m 3 . This estimate should include both emissions from natural fires and biogenic 311 emissions from vegetation. To account for the oxygen, hydrogen, nitrogen, and other elements 312 that are contained in POM, he multiplied his OC value by 1.5; to conform with current 313 IMPROVE practice, the EPA has used a factor of 1.4 for the same adjustment for Table 1. The 314 global aerosol research community generally appears to use a factor of 1.6 to 1.8 (see, for 315 example, references 15 and 16). Considering individual species, the known range of the 316 multiplier spans from 1.17 for large saturated hydrocarbons to 3.75 for oxalic acid. 17 317 A comprehensive review of experimental and theoretical research results by Turpin and 318
Lim 18 led to the conclusions that the appropriate multiplier for ambient OC will vary depending 319 on the origin and age of the aerosol and that the value of 1.4 does not accurately represent a non320 urban aerosol. Rather, they concluded that the appropriate factor for a non-urban aerosol is 2.1 ± 321 0.2. They also concluded that the appropriate value for an urban aerosol is 1.6 ± 0.2, which 322 approximates the 1.5 and 1.4 factors used by Trijonis and the EPA and IMPROVE. Turpin and 323 Lim’s value for an aerosol heavily impacted by wood smoke is 2.4 ± 0.2. 32412 It thus seems reasonable that the OC multiplier that would best represent natural 325 conditions most of the time would be about 2.1 (Turpin and Lim’s value for a non-urban 326 aerosol), while 2.4 could represent natural conditions during episodic fire events. Using the 2.1 327 factor, the estimated mass concentration of POM in Table 1 would be 2.1 μg/m 3 and the 328
extinction contribution would become 8.4 Mm -1 , which increases the overall dry extinction of 329
9.09 Mm -1 to 11.89 Mm -1 , an increase of 2.8 Mm -1 or 31%. This corresponds to an increase of 330 2.7 deciviews under dry conditions. 331 A different multiplicative factor than 2.1 is needed for deducing current POM 332 concentrations from IMPROVE OC measurements, though, because today's organic aerosol may 333 include an urban component or reflect smoke from intentional burning of vegetation. For 334 example, ratios of POM concentration to OC concentration in Class I areas have been found to 335
be 1.8 and 2.0 in recent research studies. 19, 20 A recent evaluation of the literature recommends a 336 value of 1.8 for IMPROVE monitoring sites. 11 337
Concentrations of POM mentioned in the next few sections will be based on the factor of 338 1.4, unless otherwise indicated. If the factor used by the investigator to scale measured or 339 modeled OC concentrations is different or if the model directly presents POM concentrations, 340 such a situation will be identified. The implications of changing to a different factor for natural 341 conditions will be addressed after we have surveyed results of scientific research concerning the 342
contributions of the sources listed earlier. 343
344
Carbon from Wildfires 345 Wildfires can affect air pollutant concentrations over large geographic areas. Effects of large 346 wildfires in northwestern Canada have been observed on air pollutant concentrations in 347
Tennessee 21 and transport and impact of smoke from wildfires and biomass burning in southern 348 Mexico and Central America have been observed in the central and southeastern portions of the 349
United States. 22, 23 350 The contributions of natural fires to background carbon concentrations have just begun to 351 be quantified. They are, of course, highly episodic at any given location. In the VISTAS domain, 352 using a combination of chemical mass balance (CMB) modeling and 14 C analyses of particulate 353 matter, average wood smoke contributions to POM were estimated to lie between 0.8 and 2.4 354 μg/m 3 at a rural location near Atlanta, Georgia. 24 These values would also include the 35513

contribution of intentional burning of vegetation, and thus are likely to overstate the natural 356
contribution. 357 Another estimate is provided by the global air pollution model GEOS-CHEM, 7, 8 which 358 was described earlier. The mean concentration of EC in the East, attributable almost entirely to 359 biomass fires (natural and anthropogenic, worldwide), was estimated to be 0.04 μg/m 3 , twice the 360
natural default EC value in Table 1. The modeled estimate for POM from the same fires and 361 from all other natural sources is 1.17 μg/m 3 for the East, which is slightly less than the 362 corresponding natural default value of 1.4 μg/m 3 for POM in Table 1. 363 Both the default POM concentration in Table 1 and the upper bound GEOS-CHEM value 364 cited above lie in the range determined by the measurements at the rural location near Atlanta, so 365 there is no inconsistency between the three sources of information. 366 Another type of bounding estimate is provided by 14 C analyses at Great Smoky 367 Mountains National Park during two recent summers. 25, 26 Those measurements, which are 368 described in the next section, found average modern carbon concentrations of 2.45 and 3.42 369 μg/m 3 , respectively. Modern carbon would be derived naturally from wildfires and as a result of 370 emissions from vegetation (discussed below) and from anthropogenic agricultural and prescribed 371 biomass burning. It is likely that most of the modern carbon at Great Smoky Mountains in 372 summer is attributable to biogenic emissions from vegetation, thus the natural burning 373 contribution to carbon at these times will necessarily be substantially less than 2.45 or 3.42 374 μg/m 3 (or the corresponding POM concentration will be substantially less than 3.43 or 4.79 375 μg/m 3 , if the 1.4 factor is used). 376 The average seasonal regional contributions of natural wildfires throughout the Southeast 377 need to be better quantified before default concentrations can be adjusted to reflect their 378
contributions. 379 As an example of a potential approach that could be used for this purpose, the total 380 current biomass-burning contribution could be measured by creating and using local biomass381 burning particle signatures analogous to those developed for the Intercontinental Transport and 382 Chemical Transformation (ITCT) mission from measurements in a forest fire plume in Utah. 27 383 Once the total biomass burning contribution is quantified, the anthropogenic and natural biomass 384 burning contributions could be separated based on their proportions in (rapidly improving) 385 emission inventories for wildfires, prescribed burning, and agricultural burning. 38614 Alternatively, the contribution of biomass-burning smoke could be estimated by 387 speciation of organic matter in particulate matter samples and then using organic species tracers, 388

14 C dating, and CMB analysis of the data to deduce source types. VISTAS is currently carrying 389 out such a study; one year of measurements at five locations was completed in May 2005 and 390 sample and data analyses are now under way. 391
392 Emissions of POM from Forests and Other Vegetation 393 Forest vegetation is the principal global source of atmospheric organic carbon in particles 394 (both primary and secondary), with global biogenic organic emissions exceeding anthropogenic 395 emissions by as much as an order of magnitude. 28, 29 In the United States, biogenic sources 396 account for 1/2 to 3/4 of the hydrocarbon emissions. 29, 30 The tropospheric burden of fine 397 particulate carbon in low latitude regions of the globe is dominated by long-range transport from 398 the tropics. 31 399 A visible manifestation of biogenic organic aerosol is the blue haze often observed over 400 forested regions, which results from oxidative particle formation, probably from biogenic 401 terpenes. It is unclear, though, to what extent such secondary terpenoid aerosols contribute to the 402 total biogenic organic aerosol burden. 32 403 The largest biogenic emissions occur in the tropics, where there is a combination of high 404 temperatures, high biomass density, and ample sunlight. During summer in the southeastern 405 United States, though, the emission flux (emissions rate per unit area of land) of biogenic 406 hydrocarbon emissions is estimated to be as large as at that in the tropics 30 , such as in the 407
Amazon basin. 9, 29 408 Studies at several locations on the globe have concluded that the formation of biogenic 409 organic aerosol begins with the formation of minute particles (a few nanometers in size) through 410 nucleation involving sulfuric acid, ammonia, water, and possibly some volatile organic 411 compounds. These particles grow to optically-important sizes via condensation of both inorganic 412 and photochemically-oxidized organic vapors. Aspects of the formation process in different parts 413
of the world are described in several articles. 33 - 40 414 The exact role of VOCs in the nucleation process is a subject of debate, although the fact 415 that biogenic VOCs are much more reactive than anthropogenic VOCs 41 suggests that they play a 416 role from the outset. Also, the acidity of seed particles appears to enhance the formation of 41715

SOA, 42-44 which may mean that current organic particle formation rates from natural emissions 418 are greater than they would be under natural conditions because of the presence today of global 419 anthropogenic inorganic acidic pollution. 420 Concentrations of POM have been found to be quite high in tropical forests. 421 Measurements of submicrometer (< 1 μm) particulate matter in an equatorial forest in the Ivory 422 Coast (west coast of Africa) found the average particulate carbon concentration (i.e., before 423 multiplication by a factor such as the 1.4 used by EPA and IMPROVE) to be 9 μg/m 3 during the 424 wet (growing) season. 31 Since biomass burning, which would have contributed carbonaceous 425 particles during the dry season, did not take place during the wet season, the measured 426 particulate carbon was attributed to natural emissions from the forest. 427 Measurements made by low-flying aircraft over a tropical Amazon Basin forest in Brazil 428
resulted in similar conclusions. 45 The measurements were made during the early to middle "dry" 429 season, when the forest was moist during the measurements and biomass burning did not yet 430 appear to be an important source. Their sampler used for carbonaceous particles did not have a 431 specific size cut, although it is likely that most of the particles sampled were in the fine size 432 fraction. The average particulate organic carbon concentration in the mixed layer was 740 433
nanomoles C/m 3 , which works out to 8.9 μg/m 3 of carbon (or a POM concentration of 12.5 434 μg/m 3 , if a factor of 1.4 is used). 435 In another study in the Amazon Basin, Absolute Principal Component Analysis (APCA) 436 of five years of field measurements at two remote locations yielded that biogenic particles 437
accounted for 57-63% of the total PM 2.5 mass during the wet (non-burning) season. 46 The 438 average biogenic particle concentrations during those seasons were estimated to be 3.9 and 5.5 439 μg/m 3 at the two sites. The biogenic material was considered to be composed principally of 440 organic carbon compounds plus a small amount of sulfur compounds. 441
These measured concentrations are consistent with concentrations simulated with the 442
MOZART-2 model 9 , which estimated that SOA concentrations in the Amazon basin were as 443 much as 10 μg/m 3 in summer. 444 These tropical data show that the potential concentrations of biogenic POM can be quite 445 large in and near forests. The question is how to translate these tropical research results into 446 biogenic POM concentrations that would apply in the VISTAS region. Because of the cooler 447 climate, it is likely that the average concentrations over the year will be lower in forests in the 44816

much of the VISTAS region than in the tropics. As noted above, though, several analyses have 449
concluded that summertime (June to September) biogenic VOC emission fluxes in the Southeast 450 (except near the coast) are comparable to those in the Amazon basin. 9, 29 451 In a more northerly setting, research carried out on formation of POM in remote forests in 452 Finland provides further insight. Overall PM 2.5 mass concentrations observed during the spring 453 there were lowest (2.6 μg/m 3 ) when the air mass was polar or arctic, and higher concentrations 454 were observed when the air came from other directions that reflected warmer air and impacts 455 from distant anthropogenic sources. 47 456 Carbon concentrations in June, measured by high-volume sampler (needed to collect 457 sufficient sample for speciation of the organic carbon compounds) at the same location in 458 Finland, were 3.2 μg/m 3 for OC and 0.19 μg/m 3 for elemental carbon (or, more rigorously, black 459 carbon). 48 Direct comparison with the values in Table 1 is not possible, though, because the 460 particle size range collected by the hi-vol is much broader than that of PM 2.5 . 461
Estimates of natural SOA in the southeastern U.S. have been made via the 14 C 462 measurements and CMB analyses that were cited earlier. 26 Based on measurements made during 463 one month of each quarter during one year at a rural, predominantly agricultural, location near 464 Atlanta, Georgia, the average natural SOA concentration was 1.6 μg/m 3 , the peak concentration 465 observed was about 2.5 μg/m 3 in July, and the concentration in winter (January) was zero. Data 466
from another 14 C study, 49 this time in Nashville, Tennessee, allow us to conclude that biogenic 467 OC concentrations there during a one-month period from mid-June to mid-July ranged from 468 about 2 to about 5 μg/m 3 , which correspond to POM concentrations of 2.8 to 7 μg/m 3 or more. 469
Two 14 C studies at Great Smoky Mountains National Park in August 2002 25 and the 470
summer of 2004 26 concluded that averages of 83% and 90% of the sampled POM concentrations 471 were of modern origin, respectively. These translate into average concentrations of modern OC 472 during those periods of 3.42 and 2.45 μg/m 3 , which are considered most likely to be mainly SOA 473 derived from biogenic VOC emissions. 26 (The corresponding POM concentrations are 4.78 and 474 3.43 μg/m 3 , respectively, calculated with a factor of 1.4.) 475
Simulation with the MOZART-2 model finds current fine SOA concentrations 476 (attributable to both biogenic and anthropogenic emissions) to be around 2 μg/m 3 throughout the 477 inland South in June and September, and 1 μg/m 3 or less in March and December. These values 478 are somewhat less than the average POM concentrations (sum of both primary and secondary) 47917

measured by IMPROVE samplers in these regions, 50 but not greatly so, which suggests that they 480 are not far off the mark. These modeled values may thus provide an indication of the upper 481 bound to the typical biogenic contribution alone in the Southeast today. 482 The information given here suggests that biogenic POM concentrations in forested 483 portions of the VISTAS region could reach 4 μg/m 3 or more in summer. This value is 484 comparable to currently observed summertime levels of organic carbon in forested areas in the 485
southeastern U.S. 50 Since biogenic contributions will be less the rest of the year, the annual 486 average impact is likely in the range of 1 to 2 μg/m 3 . Such a biogenic contribution alone is 487 comparable to the 1.4 μg/m 3 average default concentration for all organics, while the 488 summertime peak is many times the default average. 489
490 Oceanic Emissions of Sea Salt and Organics 491 Sea Salt. Sea salt contributes to ambient particulate matter near the ocean. Over the ocean in 492 remote areas it accounts for about half of the submicrometer (< 1 μm) aerosol in the boundary 493 layer and for about 3⁄4 of the aerosol that is larger than 1 μm and smaller than 10 μm. 16 The 494 concentration will vary with the state of the ocean; average concentrations measured in the 495 remote southern hemisphere ocean during the ACE 1 research study were 0.6 μg/m 3 for the 496 submicrometer sea salt and about 9.5 μg/m 3 for the salt larger in sizes between 1 and 10 μm. 16 497 The chemical composition of sea salt, which is a mixture of NaCl and other constituents, 498 is complex and variable. In particular, sea salt loses chloride as it ages because exposure to 499 atmospheric H 2 SO 4 and HNO 3 and certain non-acidic gases results in the release of gaseous HCl. 500 Consequently, the mass scattering efficiency of aged sea salt differs from that of fresh sea salt 501 (whose scattering is similar to that of pure NaCl). 51 Being hygroscopic, sea salt has the potential 502 to contribute disproportionately to haze when the humidity is high. 503 Although IMPROVE measures elemental Na and Cl, and hence estimating the amount of 504 sea salt at Class I areas is conceptually feasible, its effect is not included in the EPA/IMPROVE 505 formula for estimating light extinction (eq 1). An assessment of the effect of this omission, using 506 IMPROVE data taken at 59 sites from 1993 to 1999, led to the conclusion that including the 507 mass concentration of NaCl consistently improved the comparison between reconstructed fine 508
mass concentrations and measured PM 2.5 at all sites. 52 Fine NaCl accounted for 1% to 33% of the 509 reconstructed fine mass concentrations (the 33% was at Point Reyes National Seashore, 51018

California). In the VISTAS region, NaCl accounted for 5% of the fine mass concentration at 511 Chassahowitzka National Wildlife Refuge (NWR) in Florida, 3% at the more inland Okefenokee 512 NWR in Georgia, and 1-2% elsewhere. (Another VISTAS coastal site, Cape Romain NWR in 513 South Carolina, was not included in the analysis.) 514 The average reconstructed PM 2.5 concentrations between 1996 and 1998 at 515 Chassahowitzka and Okefenokee were 9.8 and 9.7 μg/m 3 , respectively. 50 We can thus infer from 516 the percentages above that the average fine sea salt concentration at those two locations is in the 517 range of 0.3 to 0.5 μg/m 3 , which corresponds to 13 to 22% of the default natural fine mass 518 concentrations there. Such concentrations are comparable to the default natural concentration of 519 soil. Further, since the sea salt size distribution approximates that of soil (see, for example, a 520 comparison of size distributions in reference 30), the extinction impact of comparable amounts 521 of fine soil and dry fine sea salt can be expected to be comparable. Sea salt is hygroscopic 522 however, the effect of which will be addressed later in this section. 523 In an experiment in which the composition of particulate matter arriving at Miami from 524 over the Atlantic ocean was studied for 23 years, the annual mean sea salt concentration in TSP 525 (calculated from the measured Na concentration and the Na/Cl ratio of seawater) was 8.67 μg/m 3 526 for those periods when the flow arrived from over the ocean. 53 The highest values were in the 527 winter (attributed to stronger winds creating more sea spray then) and lower than average 528 concentrations were found from May through September. The minimum daily concentration 529 measured during every month of the year exceeded 4.5 μg/m 3

. 530 The size cut point of the sampler used for these measurements is not known, but the 531 investigator estimated that 1/3 to 1/2 of the sampled mass was in the fine (PM 2.5 ) range. 53 If the 532 same fraction applied to the sea salt, that would suggest an annual average fine sea salt 533 contribution in the range of 3 to 4.5 μg/m 3 . The corresponding estimate of minimum daily fine 534 sea salt concentration (on days with onshore flow) would be in the range of 1.5 to 2.2 μg/m 3

. 535 These concentrations are greater than the highest averages inferred above from analyses of 536 IMPROVE samples. 52 A possible reason for the discrepancy is that the Miami concentrations 537 only reflect those measured at the shoreline during the times when airflow came from over the 538 ocean, while the IMPROVE data represent all wind directions. Also, the Miami measurements 539 were taken much closer to the shoreline than the IMPROVE measurements at Okefenokee, and 54019

thus they could be influenced by sea spray from breaking surf, although the investigator states 541 that the concentrations were not strongly influenced by salt spray from the surf zone. 53 542 Sea salt concentrations from IMPROVE measurements at Cape Romain and Okefenokee 543 were also calculated by Paine and colleagues. 54 Their estimate of current sea salt concentrations 544
included both NaCl and Na 2 SO 4 , where the sulfate was assumed to have replaced chloride that 545 was lost due to chemical reactions of NaCl with acidic gases and other substances in the 546 atmosphere. Because such reactants might not be present under natural conditions, they also 547 calculated the concentration of NaCl alone. Considering the latter concentration, which is the 548 lower one, they estimated annual average fine sea salt concentrations to be 1.3 μg/m 3 at Cape 549 Romain and 0.85 μg/m 3 at Okefenokee. The Cape Romain figure of 1.3 μg/m 3 is consistent with 550 the slightly higher range of 1.5 to 2.0 μg/m 3 that was deduced above from measurements at 551 Miami during onshore flow alone. The Okefenokee value is somewhat larger than the 0.3 to 0.5 552 μg/m 3 range inferred above for Okefenokee from data in reference 52. 553 Another study 24 estimated the fine sea salt concentrations along the coast of the Gulf of 554 Mexico (the southern boundary of the VISTAS domain), to be in the range of 0.4 to 0.5 μg/m 3 at 555 the coast of the Gulf of Mexico. This range of concentrations is lower than the ranges cited 556 above, but the investigators point out that the Gulf may generate less sea salt spray than the more 557 highly energetic ocean along the Atlantic coastline. 558 Meaningful sea salt concentrations can extend well inland from the shoreline. Particulate 559 matter measurements by low-flying aircraft (at about 150 m altitude) in the Amazon Basin of 560 Brazil found that that, assuming that aerosol Na + was derived exclusively from sea salt, the 561 average concentration of sea salt larger than 1 μm in size in the mixed layer was about 4 μg/m 3 562 near the coast, 1.5 μg/m 3 250 km inland, and less than 1 μg/m 3 more than 400 km inland. 45 563 Without further information on size distribution, one cannot say how much of this material 564 would show up as PM 2.5 and how much would be coarser. A corresponding description for 565 submicrometer sea salt was not possible because concentrations were below the detection 566 method for the analytical method used. 567 Studies of the light scattering efficiency and hygroscopic growth of sea salt particles 51, 55 568 don't, unfortunately, provide information on the scattering of light by sea salt particles in the 569 actual size range found in the atmosphere. Over the Indian Ocean, an extinction efficiency of 1.0 570
to 1.2 m 2 /g was estimated for submicrometer (< 1 μm diameter) sea salt at 55% RH, 15 which is in 57120

the same range as the 1.0 m 2 /g used by IMPROVE for dry fine soil particles. The estimate for 572 supermicrometer (> 1 μm diameter) particles was 5.1 to 6.0 m 2 /g at 55% RH, which is well 573
above the 0.6 m 2 /g used by IMPROVE for dry coarse particles, even after allowance is made for 574 hygroscopic growth, although some of the difference may be explained by the difference 575 between the 1 μm lower size cuts used for this work and the 2.5 μm size cut of IMPROVE. 576 In the absence of better information, the light scattering efficiency of fine dry sea salt is 577
sometimes taken to be the same as the 1 m 2 /g used by IMPROVE for fine soil, and that of the 578 coarse fraction of dry sea salt is taken to be the 0.6 m 2 /g scattering efficiency of coarse 579 particulate matter. This is the approach used by Paine et al., 54 who also deduced a humidity 580 scaling function f(RH) for fine sea salt based on measurements made over the Atlantic Ocean 581 during the ACE 2 experiment. The resulting function is similar to the EPA/IMPROVE f(RH) for 582 sulfates and nitrates, except that hygroscopic growth begins at a lower relative humidity for sea 583 salt than for sulfate and nitrate and the increase above 90% RH is not as rapid for sea salt. The 584 f(RH) curves for fine sea salt and sulfate/nitrate are almost identical from 60% RH to 90% RH. 585 Humidity-related growth can also be expected for the coarse fraction of sea salt, though not with 586
the same f(RH) curve as for the fine sea salt. 56 587 Using their empirical f(RH) curve and their lower sea salt concentration estimates at Cape 588 Romain and Okefenokee, Paine et al. calculated seasonal and annual average extinction impacts 589
of fine sea salt concentrations. 45 They concluded that the contribution of natural background fine 590
sea salt to extinction was 4.3 Mm -1 at Cape Romain and 3.0 Mm -1 at Okefenokee. Using the data 591
in Table 1 and the same annual f(RH) that Paine et al. used, we calculate that the annual default 592 particulate matter extinctions without sea salt are 11.4 Mm -1 at Cape Romain and 11.6 Mm -1 at 593 Okefenokee. Adding the above sea salt contribution to these values increases the estimated 594 annual-average natural extinction due to particulate matter by 38% at coastal Cape Romain and 595
26% at the more-inland Okefenokee. 596
597
Oceanic POM. Turning to organic particles from the ocean, mass concentrations of fine organic 598 particles at the northeastern tip of Puerto Rico (and thus exposed to the easterly trade winds 599 coming over the ocean) and also during ship sampling away from the coast there were found to 600 be in the range of 0.3 to 0.4 μg/m 3 during a February to June study. 57 Black (elemental) carbon 601 was not found in the samples, which indicated that the aerosol was relatively free of combustion 60221

products. Consequently, a substantial fraction of this organic material may be associated with 603
natural oceanic emissions. 57, 58 604 As another indication of background levels of oceanic carbon, average POM 605 concentrations (defined as 1.6 x OC) in marine air masses over the Indian Ocean south of the 606 equator were found to be 0.07 to 0.13 μg/m 3 in submicrometer particles and 0.14 to 0.56 μg/m 3 607 in supermicrometer particles during the February-March monsoon season. 15 These 608 concentrations again represent values obtained for oceanic air masses, with the lowest 609 concentrations associated with transport from the south. According to the investigators, these 610 values are comparable with what has been measured in remote areas of the Atlantic and Pacific 611 Oceans. In contrast, in the North Atlantic during the phytoplankton bloom periods, 612 submicrometer OC concentrations in clean marine air at a coastal site averaged 0.9 μg/m 3 (which 613 corresponds to a POM concentration of about 1.4 μg/m 3 , using the same 1.6 factor). 59 614 It is not clear how to relate open-ocean POM concentrations to concentrations of natural 615 POM of oceanic origin at coastal IMPROVE sites such as Cape Romain. Available data aren’t 616 sufficient to give insight into variability due to temperature or season. Nor do the oceanic 617 concentrations reflect enhanced POM emissions from breaking waves at the shoreline and 618 biological tidal zone POM formation, both of which are likely to be very site-specific. 619 As a first guess, if one assumes 0.4 μg/m 3 for natural fine POM over the ocean and 620 assumes that the wind blows onshore from the ocean half of the time (a good assumption at Cape 621 Romain), then the annual average contribution of fine oceanic POM at Cape Romain might be 622 0.2 μg/m 3 . This corresponds to an increase in the default POM concentration by about 15% and 623 the default light extinction by 0.8 Mm -1 . The oceanic POM contribution would be smaller at 624
more inland non-coastal sites such as Okefenokee. 625
626 Long Range Transport of Windblown Dust 627 To estimate the average fine soil concentration, Trijonis elected to "arbitrarily assume" (his 628 words) that half of the current average measured fine soil concentrations are natural. 3 The limited 629 data available to him indicated that average rural fine soil concentrations were about 1 μg/m 3 in 630 both the East and the desert/mountainous West, which he divided by two to arrive at his natural 631 background estimate of 0.5 μg/m 3 for both regions. Recent measurements and data analyses 632 allow us to assess the applicability of this estimate to the VISTAS region. 63322

634 IMPROVE Fine Soil Measurements in the VISTAS Region. IMPROVE measurements of fine 635 soil concentrations at nine locations throughout the VISTAS region during 1996-1998 produce a 636 composite annual average fine soil concentration of 0.62 μg/m 3

. 50 The annual average values are 637 least at the northern sites (0.38 μg/m 3 at Dolly Sods Wilderness, West Virginia and 0.42 μg/m 3 at 638 Shenandoah National Park, Virginia) and greatest at the southern sites (0.77 μg/m 3 at 639 Okefenokee NWR, 0.80 μg/m 3 at Sipsey Wilderness, Alabama, and 1.1 μg/m 3 at 640 Chassahowitzka NWR). The measured soil values in the southern part of the VISTAS region 641 approximate the 1 μg/m 3 assumed by Trijonis as representative of the current fine soil 642 concentration in the East, but the current measured values in the northern portion are closer to his 643 default value of 0.5 μg/m 3 for natural conditions alone. Thus the default soil concentration may 644 overstate natural fine soil concentrations in the northern portion of the VISTAS region. 645 The soil concentrations measured by IMPROVE vary greatly by season. Winter 646 concentrations are relatively uniform throughout the VISTAS domain, in the vicinity of 0.2 to 647 0.3 μg/m 3 , while summer concentrations range from around 0.5 μg/m 3 to around 2 μg/m 3 as one 648 goes from north to south in the VISTAS region. 50 As we discuss below, long-range transport of 649 dust appears to be a major contributor to the large spatial variability in the summer. Such dust 650 has two origins – Asia (including the Gobi Desert) and Africa (especially the Sahara Desert). 651
652 Asian Dust Contributions to IMPROVE Samples. The elemental composition of IMPROVE 653 filter samples taken throughout the United States from 1989 to 1999 was evaluated by VanCuren 654 and Cahill to determine the origins of the soil elements in those samples. 60 Using cluster analysis 655 of known Asian air masses to develop signatures for the Asian aerosols, they concluded that dust 656 transported across the Pacific Ocean from Asia affects most of the U.S. during most of the year. 657 Satellite images have demonstrated that Asian aerosols can easily cross North America and reach 658
the western Atlantic Ocean. 659 Asian dust impacts are greatest in the western U.S. 61 and subside toward the East Coast, 660 and are generally greater in the northern part of the country than they are to the south 661 (presumably because westerly flow is more prevalent in the northern latitudes). This geographic 662 distribution is not universal, however. A large Asian dust episode in mid April 2001 (discussed 663 further below) had its greatest impacts along the central West Coast, the Rocky Mountains, and 66423

in the Southeast, while the impacts along the northern boundary of the United States were 665 smaller, though still significant. 62 666 The frequency of appearance of Asian dust in the United States is greatest in the spring 667 and summer months, with the most noticeable seasonal variation in northern states and, 668 generally, a flatter temporal distribution in the southeastern U.S. 669 Focusing now on the VISTAS region, graphs in the Van Curen and Cahill paper 60 show 670 that, on those days when Asian dust was a dominant contributor to fine soil concentrations at 671 Shenandoah National Park, the average concentration of that dust exceeded 0.6 μg/m 3 during 672 every month from March through October and was less the rest of the year. The average 673 contribution during Asian dust events during that 7-month period was about 0.7 μg/m 3 and the 674 peak monthly average was about 1.2 μg/m 3 , in August. Episodic concentrations (24-hr average) 675 were much higher. 676 As an extreme example, which occurred after the period analyzed by VanCuren and 677 Cahill, the Asian dust event of mid April 2001 (the largest such event ever observed in the U.S.) 678
caused 24-hr PM 10 concentrations to increase by 20-30 μg/m 3 (!) at the Shenandoah and 679 Okefenokee IMPROVE samplers. 62 This was followed by two additional episodes during the 680 first half of May. All three episodes also significantly impacted particulate matter concentrations 681 in the northeastern U.S. Interestingly, the trajectories to the Northeast for the first May episode 682 first passed through the VISTAS region. They entered the VISTAS states from the northwest, 683 circled clockwise through the VISTAS region, and then headed northeast to the northeastern 684
U.S. 63 685 Based on VanCuren’s and Cahill’s analysis, Asian dust events occurred at Shenandoah 686 during every month of the year, with frequencies between about 20 to 36% of the time in the 687 spring months, 16% of the time in October, and less than 10% of the time in the remaining 688 months. Combining these concentrations and frequencies of episodes, we estimate the average 689 Asian dust concentration (averaged over all days, irrespective of whether Asian dust impact 690 occurred on any given day) at Shenandoah from March through October is in the range of 0.1 to 691 0.2 μg/m 3 . The Asian dust statistics at Dolly Sods Wilderness are similar to those at Shenandoah. 692 Farther to the south, frequencies of Asian dust events were highest during summer at 693 Everglades National Park, Florida (averaging around 20% from May through September). The 694 average concentrations during those events were smaller than at Shenandoah (averaging about 69524

0.5 μg/m 3 from March to September), which suggests an average Asian dust contribution there 696 of around 0.1 μg/m 3 over the late spring and summer. 697 The fine soil concentrations measured by IMPROVE in the Appalachian region (which 698 includes Shenandoah and Great Smoky Mountains National Parks) average 0.45 μg/m 3 over the 699 year and peak at 0.61 μg/m 3 over the summer season. 50 The episodic fine dust concentrations at 700 Shenandoah that were attributed by VanCuren and Cahill to Asian dust events (and were 701 described above) are comparable to or greater than these measured averages. This implies that 702 Asian dust plays a dominant role in Shenandoah soil concentrations at times from spring into 703 fall, and its average 0.1 to 0.2 μg/m 3 contribution accounts for roughly 20 to 40% of the 704 measured fine soil concentration during that period. 705 At southeastern IMPROVE sites (encompassing Chassahowitzka, Okefenokee, and Cape 706 Romain), the average fine soil concentration in 1996-1998 was 0.81 μg/m 3 and the summer 707 average was 1.86 μg/m 3

. 03 As expected, the influence of Asian dust is much less noticeable here 708 than at the more northerly locations. Rather, as discussed next, African dust transport plays a 709 major role in the summertime peak. 710
711 African Dust Contributions to IMPROVE Samples. Dust transported across the Atlantic Ocean 712 from the Sahara Desert is another contributor to fine soil concentrations in the VISTAS region. 713 African dust impacts, which can be identified by their elemental signature, occur less frequently 714 in the VISTAS region than those from Asian dust but the magnitudes of the African 715 contributions are much larger whenever those impacts occur. 60 716 African dust impacts tend to be greatest along the Caribbean and diminish toward the 717 north. Specifically, at the Everglades, African dust events occurred regularly from June through 718 September over an 11-year period, with the soil composition on more than 40% of the days in 719 July dominated by African dust. 60 The mean 24-hr average African fine soil dust contribution 720 there during events occurring in June through August was between 7 and 9 μg/m 3 . The pattern is 721 similar at Okefenokee, but with somewhat lower frequency and concentrations than at the 722 Everglades. Farther north, the average African dust event contributed 4.5 μg/m 3 at Shenandoah 723 and Dolly Sods, but those events were infrequent and occurred almost exclusively in August. 724 IMPROVE measurements at the three southeastern monitoring sites, Chassakhowitzka, 725 Okefenokee, and Cape Romain do indeed show an average soil concentration of 1.86 μg/m 3 in 72625

the summer, compared to about 0.6 μg/m 3 in the spring and autumn. 50 The high frequency of 727 occurrence and the high concentration impacts of African dust events there imply that such dust 728 is the dominant cause of that summertime peak, which suggests that African dust alone accounts 729 for most of the summer increase, perhaps roughly 1 μg/m 3 of fine soil, averaged over the 730 summer. This amount alone, without considering other natural sources, is well in excess of the 731 0.5 μg/m 3 average natural concentration estimated by Trijonis. 732 Perry and colleagues 64 used IMPROVE samples from 1988 through 1995 to estimate the 733 impacts of transported North African dust at various locations in the Southeast. They concluded 734 that 57% of the samples taken at the Everglades in July showed an African dust impact greater 735 than 3 μg/m 3 . Corresponding percentages at other VISTAS locations were 27% at Okefenokee, 736 19% at Chassahowitzka, 16% at Sipsey and Great Smoky Mountains, 14% at Dolly Sods, and 737 8% at Shenandoah. A surprising 23% of the observations at Mammoth Cave National Park, 738 Kentucky in July contained African dust above 3 μg/m 3 , which is reinforced by an even higher 739 37% at Upper Buffalo Wilderness in Arkansas (outside the VISTAS region). Further outside the 740 VISTAS region, 19% of the observations at Big Bend National Park in western Texas in July 741 contained more than 3 μg/m 3 of African dust and the corresponding percentage was 76% at the 742 Virgin Islands. Percentages of African dust impact were less in other months, with the period of 743 impact concentrated on June through August in the southern locations in the VISTAS region and 744 including only July at the northern locations. 745 In contrast, the period of impact at the Virgin Islands covered the nine months from 746 February through October. As a result, Perry et al. found the annual mean fine soil concentration 747 at the Virgin Islands was 3.25 μg/m 3 , versus values in the vicinity of 1 μg/m 3 that were given 748 earlier for the Southeast, and the summertime fine soil concentrations there were a substantial 749 7.58 μg/m 3 , about four times as large as the highest concentrations in the VISTAS region. 750 From 23 years of measurements of the composition of particulate matter transported over 751 the Atlantic Ocean onto the coast at Miami, the mean concentration of soil dust in TSP during 752 onshore flow was 4.72 μg/m 3

. 53 The maximum concentrations occurred from June through 753 August. The concentration during onshore flow averaged 16.3 μg/m 3 in July. The investigator 754
estimated that one-third to one-half of these concentrations is in the PM 2.5 range. Based on 755 correlation with climate conditions (e.g., drought cycles) in Africa and satellite observations of 756 dust cloud transport, this soil dust is attributed to African emissions 75726 The BRAVO Study also found African dust at the Big Bend National Park, Texas, 758 IMPROVE site during July-October 1999. 65 Based on analyses of speciated particulate matter 759 measurements taken during that study, 66 we estimated that the average African dust contribution 760 at Big Bend National Park in July (averaged over all days, not just Saharan dust events) was 1.4 761 μg/m 3 and in the first half of August it was 0.9 μg/m 3 . Although Big Bend is well west of the 762 VISTAS domain, on several days the African dust there still accounted for more than 80% of the 763 measured fine soil concentration and contributed over 2.5 μg/m 3

. 764 African dust contributions over the VISTAS region can be expected to range between 765 those reported at Shenandoah, Everglades, and Big Bend. Although precise quantification is not 766 possible from the information provided here, it is reasonable to conclude that African dust 767 contributes substantially to fine soil concentrations at most VISTAS locations during the summer 768 (specifically, June through August). As a broad average for the region, daily impacts during 769 African dust events during those months may average 3 to 5 μg/m 3 and such events might occur 770 roughly 20% of the time. Thus, a rough estimate of the average Saharan fine dust contribution 771 during the summer could be in the range of 0.6 to 1.0 μg/m 3 -- more in Florida and a bit less at 772 Shenandoah and Dolly Sods. 773 These analyses of IMPROVE and BRAVO data provide strong evidence that the natural 774 contribution to fine soil during the summer is substantially greater than Trijonis’ average natural 775 soil estimate of 0.5 μg/m 3 for the year. If African dust is the only natural contributor, then the 776 foregoing data suggest that a summertime natural conditions estimate would be in the range of 777 0.6 to 1 μg/m 3 , out of a measured summertime average range of 0.5 to 2 μg/m 3 for all fine soil at 778 IMPROVE sites. Other natural sources will further increase the natural contribution, with Asian 779 dust alone adding another 0.1 to 0.2 μg/m 3 . Thus, at least for the summertime, it appears that 780 natural sources account for the overwhelming majority of the measured fine soil concentration in 781 the VISTAS area, not just the half postulated by Trijonis, and the concentration of natural soil 782 during that season far exceeds the default annual estimate of 0.5 μg/m 3

. 783
784 Implications of Transported Dust on Natural Background Extinction. Because of its episodic 785 nature, it is difficult to suggest an approach for dealing with the contributions of Asian and 786 African dust to the natural background. The annual average impact is relatively small, but its 787 monthly contribution can be substantial during the summer months (a range of 0.6 to 1.0 μg/m 3 78827

was estimated above) and the episodic contribution can be much larger at times. Some of the 789 average effect is probably reflected in Trijonis’ average estimate of 0.5 μg/m 3 , but the 790 seasonality, episodicity, and geographic variation is not. Since the scattering efficiency of fine 791
soil is assumed to be 1 m 2 /g, the average summertime impact translates to an extinction 792
increment of 0.6 to 1.0 Mm -1 . Some portion of this amount could be added to the default natural 793 conditions estimate for specific sites for those months. The effect during the rest of the year is 794 substantially smaller. 795

IMPLICATIONS FOR MANAGING REGIONAL HAZE 796 The discussion above has identified a variety of potential refinements to the default estimates of 797 natural background concentrations of PM components and light extinction in the VISTAS 798 region. The question to be addressed at this point is whether such refinements should be pursued 799 by VISTAS. Developing refined estimates could be worthwhile if they might materially affect 800 the slope of the "reasonable progress" line and perhaps affect the soundness of the emission 801 management strategies that are pursued. On the other hand, from a practical point of view, 802 developing and justifying refined estimates at this time may not be worthwhile if their impact on 803 the first round of emissions management steps (which have a 2018 target) is so small that it does 804 not materially affect the emissions management choices. Even in the latter case, there is still 805 merit in applying the best science available, even at this point, because the process of doing so 806 will lay the groundwork for the increasing importance of scientifically sound natural background 807 estimates to making technically and financially responsible emissions management policy 808
decisions in the future. 809 This issue is illustrated schematically in Figure 3. Changing the natural conditions 810 estimate changes the slope of the line required to achieve progress toward the natural conditions 811 goal. But the difference in the 2018 regional haze goal, in deciviews, is much smaller than the 812 change in the 2064 natural background estimate. 813 Note that, because of the nature of the refinements discussed here and the limitations in 814 the default estimates, it is likely, but not always necessary, that a natural conditions refinement 815 will increase the estimated natural extinction as illustrated in Figure 3. A refinement that 816 decreases a default estimate is not precluded, however. Also, some of the refinements under 817 consideration here for natural conditions (such as for the OC mass multiplier and sea salt) also 81828

imply parallel adjustments to corresponding estimates of current conditions, and therefore the 819 baseline at the top of the figure may also be subject to adjustment. 820 To put some numbers into this discussion, it will be useful to recap the estimated impacts 821 resulting from each of the natural contributions discussed in this paper: 822

• Adjusting the 90 th percentile extinction to the 92 nd percentile, to more properly 823 represent the mean of the haziest 20% of the days in a normal distribution, would 824 increase the default haze index by 0.42 dv. (The impact on the extinction coefficient, 825
in Mm -1 will vary depending on the extinction level.) 826

• Increasing the organic carbon multiplier to 2.1 would increase the default natural 827 extinction by 2.8 Mm -1 . An even greater multiplier would be appropriate for organic 828 carbon that originates from fires. Since the biogenic contribution to organics is likely 829 greater on a seasonal basis than the fire contribution, the 2.1 value seems appropriate 830 for the Southeast, instead of a higher value. One should also consider increasing the 831 present day organic carbon multiplier, but probably to a lesser value (say, 1.8) 832 because of the anthropogenic carbon content of the ambient aerosol today. 833

• Preliminary information on wildfire impacts from global modeling results suggests 834
that overall POM and elemental carbon concentrations for the East are about the same 835 as the default values. Presumably, Trijonis' estimates included average wildfire 836 impacts. The global modeling is able to improve on the default averages by providing 837 insight into spatial and seasonal variability, however. 838

• The contribution of vegetation to POM in forested areas is harder to quantify. The 839 potential impact within forests in the deep South during the warmer months appears 840
to be several times the default estimate of POM concentration and extinction and the 841 annual average impacts are comparable to the default values. If so, the impact on 842 background extinction in such areas could be as much as 10 Mm -1 during the warmest 843 months. More research is needed to ascertain the actual impact, which will depend on 844 the degree to which a given location is surrounded by forest and affected by forest 845 emissions. As an initial estimate for locations entirely in forested areas, adding 1 846 μg/m 3 of POM (for an extinction increase of 4 Mm -1 ) during spring and summer 847 seems conservative, or a corresponding annual average impact in the range of 1/3 to 84829 1/2 of that amount (i.e., 0.3 to 0.5 μg/m 3 , or 1.2 to 2 Mm -1 ) may be appropriate. If 849 geographic distribution is considered, the annual value in the northern part of the 850 VISTAS domain and at high elevation locations is likely to be smaller than that 851 farther south and in the lowlands. These estimates have high uncertainty. 852

• Sea salt has a major impact that can be estimated with fair confidence, at least near 853 the coast. The impact will vary with distance from the coast. Annual average fine 854 fraction concentration impacts of around 1.3 μg/m 3 at the coast and perhaps half that 855 at inland sites in Florida are suggested by current research results. It is unlikely that 856 much of this is reflected in Trijonis' estimates because of the limited monitoring data 857 that was available to him. The extinction impact of such an increase in fine 858
concentrations will be 1.3 Mm -1 or more (depending on relative humidity) at the coast 859 and less inland. There will also be a smaller coarse fraction impact. Sea salt is not 860 considered in the current IMPROVE algorithm for estimating visibility, so the same 861 impact should also be included in estimates of current extinction. 862

• POM from the sea may also affect coastal areas. An average mass concentration 863 impact of 0.2 μg/m 3 , as suggested earlier, translates into an extinction impact of 0.8 864
Mm -1 . Because few measurements are available except over open ocean, and coastal 865 values are dependent on the nature of the shoreline, this estimate has high uncertainty. 866

• In the northern portion of the VISTAS region, the average soil concentrations already 867 approximate the default background values, which suggests that the default values 868 there may be too high by, perhaps, 0.2 to 0.3 μg/m 3 (and thus that the default 869 extinction there may be too large by 0.2 to 0.3 Mm -1 ). Counterbalancing this, the 870 default concentrations may be too low at some locations, but this has not been 871
established. 872

• The 0.1 to 0.2 μg/m 3 of transported Asian dust during spring and summer would 873 impact the extinction by 0.1 to 0.2 Mm -1 then, and by perhaps 0.1 Mm -1 when 874 averaged over the full year. The question is whether that Asian dust is already 875 reflected in the default estimates, since it may have appeared in the measurement data 876 on which Trijonis based his estimates. 87730 • African dust has a very large episodic impact during the summer. If seasonal average 878 natural conditions concentrations were to be estimated, the summertime mass 879 concentration of soil could be increased by about 0.6 to 1.0 μg/m 3 over the annual 880 default values. (If different adjustments are made for different geographic areas, one 881 might use an even larger increase in the southern portion of the VISTAS region and a 882 smaller one in the northern portion.) The result would be to increase natural 883 background extinction by 0.6 to 1.0 Mm -1 during the summer months. One might 884 compensate with a small decrease during the rest of the year, or argue that the 885 episodic summer impacts were not reflected in the limited data base that Trijonis had 886 available. On an annual basis a net increase of 0.2 to 0.3 μg/m 3 (0.2 to 0.3 Mm -1 ) 887 might be added to the default soil value. 888 Table 2 summarizes the two suggested refinements to the method of estimating natural 889 conditions and the potential effects of the seven types of natural sources that are itemized in the 890
bulleted discussions above. The last column of the table indicates the authors’ assessment of 891 whether a refinement of a default method or concentration is warranted, based on the magnitude 892 of the expected impact, whether it might already be included in the default concentrations, and 893 the degree of scientific understanding of the impact (or the compensating degree of 894 conservativeness in the estimated impact). Note that the suggested refinements are within the 895 typical factor of two error estimates by Trijonis, as listed in Table 1. 896 Of course, it is unlikely that all of the refinements discussed above and summarized in 897 Table 2 should be applied simultaneously at every location. As already mentioned, some of the 898 contributions may already be included in the default estimates (possibly Asian dust, for 899 example). Also, some sources will not be applicable at some locations, e.g., sea salt in the inland 900 mountains or, perhaps, forest organics at the seashore. 901 One should also recognize that our conclusions are based on relatively uncritical review 902 of the literature. Measurement methods may be biased, measurements may be unrepresentative, 903 or scientific hypotheses may change with more information, Consequently, much of what is 904 involved in our assessment here has some degree of uncertainty and is itself subject to 905 refinement. In this light, any values that we assign to potential refinements should be taken as 906 approximations. 90731 To assess the significance of refinements that might realistically be considered for 908 specific locations, we consider next examples of some of their effects on the regional haze 909 progress goals at two specific locations -- Great Smoky Mountains National Park (inland, 910 mountainous) and Cape Romain National Wildlife Refuge (Atlantic coastal). 911
912 Implications at Great Smoky Mountains National Park 913 The average current extinction at Great Smoky Mountains National Park, at the eastern end of 914 Tennessee, can be approximated by the most recent five years (1998-2002) for which IMPROVE 915 particulate matter component concentration data were available on the VIEWS web site 67 on 10 916 October 2004. At the GRSM IMPROVE site adjacent to the park the average 20% haziest-days’ 917 haze index over those five years (using the EPA-prescribed protocols for the calculations 68 ) is 918 29.54 dv, which is equivalent to an extinction coefficient of 191.9 Mm -1 . From EPA's natural 919 conditions guidance document, 2 the long-term goal is default natural conditions estimated at 920 11.44 dv (31.4 Mm -1 ) for the 20% haziest days. 921 Consider four potential scenarios with different estimates of the natural conditions 922 endpoint. 923

• GRSM1: EPA's default natural conditions estimate. Calculate annual average from 924 Table 1 concentrations and f(RH) from U.S. EPA’s natural conditions guidance 925
document. 2 Estimate average of 20% haziest days based on estimated 90 th percentile, 926 which adds 3.84 dv to the annual average haze index value. 927

• GRSM2: Change OC mass multiplier from 1.4 to 2.1, which adds 2.8 Mm -1 to the 928 annual average. (Also, when computing progress slope, adjust the OC mass multiplier 929 for present day to 1.8, a value midway between the 1.6 and 2.1 values recommended 930 for urban and rural areas, respectively, by Turpin and Lim. 18 ) 931

• GRSM3: Assume the conditions of Scenario GRSM2 and also add 0.3 μg/m 3 of 932 biogenic POM from the forests (the lower bound of the range proposed in Table 2) to 933 the annual default concentrations. This increases the natural background annual 934 average light scattering contribution by 1.2 Mm -1

. 935

• GRSM3*: As an alternative to GRSM3, if all of the haziest days are in the growing 936 season/summer (which pretty much holds true at Great Smoky Mountains), add the 93732 peak effects of African dust (0.6 μg/m 3 , 0.6 Mm -1 ) and the peak season impacts of 938 vegetation emissions of biogenic organics (conservatively 1 μg/m 3 , 4 Mm -1 ) to the 939 natural background of Scenario GRSM2 during summer only. Concentrations during 940
the other three seasons are taken to be the same as those in GRSM2. 941 For scenarios GRSM1 through GRSM3, the contribution of each PM component to 942 annual average light extinction is displayed graphically in Figure 4. The largest changes are 943 produced by changing the assumed mass concentration of organic matter. Since the annual 944 average effect of the added dust is small, the annual average of extinction for scenario GRSM3* 945 would be virtually the same as that of GRSM3. 946 Table 3 compares current and natural conditions values of light extinction, annual 947 averages versus averages of 20% haziest days, and haziest days estimates based on the 90 th 948 percentile of the distribution (the default approach) versus the 92 nd percentile (the approach 949 supported here), for all four scenarios. An assumption implicit in this data tabulation and similar 950
calculations here is that refinements to the default natural conditions concentrations do not alter 951 the standard deviation of the frequency distribution of the natural conditions haze index, so that 952 the statistical adjustment used by the EPA to determine average conditions on the 20% haziest 953 days from the annual averages 4 is the same for both the original and the refined conditions. 954 Emissions control strategies are intended to focus on the 20% haziest days rather than the 955 annual average conditions. The control strategy implications for the four scenarios on the 20% 956 haziest days are summarized in Table 4. The first data column is the current haze index that 957 represents the average of the 20% haziest days. It was calculated under the current EPA 958 protocol, 68 with OC multiplied by 1.4 and the 90 th percentile used to define the 20% haziest 959 natural days for Scenario GRSM1(90). The same scenario, except that the 92 nd percentile was 960 used, is labeled GRSM1(92). The remaining scenarios also use the 92 nd percentile to define the 961 20% haziest natural days and have natural OC multiplied by 2.1 and current OC multiplied by 962 1.8. Both the Current Haze Index and the Natural Background Haze Index columns reflect the 963
effects of the refinements stated above. The difference between these two columns is the 964 required improvement over 60 years, so 1/6 of that is required over a decade, as listed in the third 965 data column. The corresponding change in b ext over the first decade is in the next column. The 966 next-to-last column gives the percentage reduction in particulate matter concentrations that 96733

would be required if all particulate matter components in Equation 1 were reduced by the same 968 percentage. 969 Since sulfates are the predominant contributors to haze in the VISTAS region most of the 970 time, the last column in Table 4 indicates the % reduction in non-natural sulfate concentrations 971 that would be needed during the first decade to produce the given b ext decrease if only sulfates 972 were reduced and all other components remained unchanged. These values were calculated using 973 an annual-average f(RH) of 3.32 (from Table A-2 in Reference 2), i.e., RH = 85%, and a 5-year 974 (1998-2002) average ammonium sulfate concentration of 14.25 μg/m 3 on the 20% haziest days, 975 of which 0.23 μg/m 3 is assumed uncontrollable natural background (from Table 1) . 976 The differences between scenarios GRSM1 through GRSM3 are relatively small, whether 977 equal control of all components is assumed, or only control of sulfates. Note, though, that the 978 percentage improvements given in the next-to-last column assume (unrealistically) that the 979 concentrations of all of the chemical components in Table 1 will be reduced by the same 980 percentage. The difference between a 27.5% and a 25.0% reduction in a given component 981 concentration will be much more significant if part of that component concentration is natural or 982 otherwise uncontrollable. For example, Trijonis postulated that fine soil is half natural and half 983 anthropogenic, so the percentage reductions in the anthropogenic part of the soil need to be twice 984 as large as the percentages just cited if the specified reduction in soil is to hold. This implies 985 requiring a 55% reduction of anthropogenic soil emissions under the default assumption versus 986 50% under the GRSM3 scenario. Similarly, if some of the universe of sources that emit a 987 specific pollutant is already well-controlled, the percentage reductions required of the other 988 sources will be greater than the values given in Table 4, in which case the differences between 989 the required reduction rates may be of economic importance when setting a control strategy. 990 The required percentage of concentration reduction decreases further when we consider 991 scenario GRSM3*. This scenario takes advantage of the fact that most of the haziest days at 992 Great Smoky Mountains occur in the summer, at the same time when the organic emissions from 993 vegetation and impacts from African dust are the greatest and thus the natural background is 994 high. In this situation, a smaller degree of emissions control is required to attain the required rate 995 of progress on the 20% haziest days. 996
99734 Implications at Cape Romain National Wildlife Refuge 998 A similar analysis, following the same protocols, can be carried out for the Cape Romain 999 National Wildlife Refuge, on the Atlantic coast of South Carolina. At the Cape Romain 1000 IMPROVE site (ROMA), the current extinction on the 20% haziest days can be approximated 1001 for our purposes here by the values in four recent years, 1999-2002. 67 (The data for 1998 are too 1002 incomplete to define the 20% worst days in that year.) The average 20% haziest days' haze index 1003 over those four years is 25.89 dv or 133.2 Mm -1 . The haziest days natural conditions target in the 1004 EPA guideline document 2 is 11.36 dv, or 31.1 Mm -1 , for this location. 1005 Similar to the previous analysis, consider five scenarios. The first two scenarios, ROMA1 1006 and ROMA2, are identical to those at Great Smoky Mountains, albeit with local data. The other 1007 three scenarios reflect the uniqueness of the coastal location of Cape Romain: 1008

• ROMA1: EPA's default natural conditions estimate, as described for GRSM1. 1009

• ROMA2: Adjust the OC mass multiplier for natural conditions to 2.1 and for present 1010 day data to 1.8, as described in GRSM2. 1011

• ROMA3: Add 1.3 μg/m 3 of sea salt to Scenario ROMA2. Using the annual average 1012 f(RH) of 3.15 at Cape Romain, this sea salt contributes 4.1 Mm -1 of light scattering. 1013 Since the EPA/IMPROVE formula (eq 1) does not consider sea salt, this amount is 1014
added to both current and natural conditions extinction estimates. 1015

• ROMA 4: Add 0.8 Mm -1 to natural conditions in Scenario ROMA3 to represent the 1016 contribution of 0.2 μg/m 3 of oceanic POM. 1017

• ROMA5: Add an annual average of 0.3 μg/m 3 of African dust, which increases 1018 natural extinction by 0.3 Mm -1 . (Being farther south than Great Smoky Mountains, 1019 the African dust impact here is greater and temporally more diffuse, although an 1020 annual average is still a crude approximation. Furthermore the 20% haziest days at 1021 Cape Romain are distributed throughout the year and thus are not well correlated with 1022 the summertime episodicity of African dust, so a seasonal analysis of the type used 1023 for Great Smoky Mountains is not viable here.) 1024 Figure 5 shows the effects of these scenarios on the contributions to natural conditions extinction 1025 by the various components of the particulate matter. The biggest changes result from increasing 102635

the organic carbon multiplier and from adding sea salt. The contributions of oceanic organics and 1027 African dust are relatively minor. 1028 Table 5 summarizes the extinction statistics for the various scenarios. Cape Romain is 1029 less hazy than Great Smoky Mountains, especially on the haziest 20% of the days. Note that the 1030 increased OC multiplier and the addition of sea salt affect the current extinction estimates as well 1031
as those of natural conditions. 1032 Table 6, which is in the same format as Table 4, shows the emissions control strategy 1033 implications of these four scenarios. The main difference from Table 4 is the smaller percentage 1034 concentration reductions that are needed because Cape Romain is substantially less hazy on 1035 average than Great Smoky Mountains, while the default natural conditions goals are virtually 1036 identical. Also, all of the refinements considered here have a bigger effect on the required 1037 percentage of reduction than did those considered for Great Smoky Mountains. Otherwise, the 1038 general conclusions concerning Table 4 also apply to Table 6. 1039 In overview, Figures 4 and 5 illustrate that most refinements in the annual average 1040 estimates of concentrations of soil, elemental carbon, and nitrates will have minimal effect 1041 because the underlying contributions of those components are quite small. Even the episodic 1042 African dust contribution in the summer in Scenario GRSM3* makes only about a 1% 1043 contribution to the extinction on the natural conditions 20% haziest days. On the other hand, 1044 changes in POM and sea salt concentrations can have important impacts on the light extinction 1045
and deserve first attention if a decision is made to refine the default concentrations. The 1046
combination of the use of the 92 nd percentile and the 2.1 multiplier for calculating POM from OC 1047 also has a material effect on the natural conditions extinction estimate. When the needed degree 1048 of emission control is considered, however, the impacts of some of these refinements to natural 1049 conditions are diluted because partially-compensating adjustments also need to be made to 1050
estimates of current extinction. 1051

CONCLUDING COMMENTS 1052 This review was prepared to illuminate potential approaches for refining natural conditions 1053 estimates and to provide some insight into the most promising approaches. The calculations in 1054 the previous section suggest that there is merit in establishing refined estimates of natural 1055 conditions at some Class I areas, even at this initial part of the 60-year period. 105636 We have found that conservative regional refinements to the default natural background 1057 estimates do not greatly alter the region-wide rates of reduction of ambient particulate matter 1058 concentrations that will be needed to attain the natural conditions goal. However, refinements at 1059 a specific Class I area may have considerable influence on defining the nature (magnitude and 1060 spatial and temporal distribution) of local emission reduction efforts to reach the natural 1061 conditions goal there. 1062 Three relatively non-controversial refinements that could be applied without extensive 1063 effort are (1) using a more-appropriate percentile to reflect the average of the haziest 20% of the 1064 days; (2) applying an organic carbon multiplier adjustment at most or all Class I areas and (3) 1065 adding the effect of sea salt at Class I areas that are relatively close to the ocean. In the last two 1066 cases, appropriate corresponding adjustments should also be made to present-day estimates 1067 before the rate of reasonable progress is calculated. In the case of the OC multiplier, though, the 1068 present day multiplier probably should not be the same as that for natural conditions. 1069 African dust is a large contributor episodically during the summer, but its episodicity 1070 means that creative thought will be needed to weave it into the mandated regional haze 1071 management process. Compared to other components, on an average basis it is not an important 1072 contributor to natural light extinction, though. 1073 POM created in forests and emitted from natural wildfires are two other promising 1074 contributors, but their effects are not well quantified and including them will be more 1075 challenging and require more research than the other refinements that have been mentioned. 1076 Asian dust has most of the complexities of African dust, but it has even smaller impact. 1077 Note that in this paper we have not considered changes to the numerical coefficients 1078 (extinction efficiencies) used in eq 1 to calculate light extinction. If the scaling factor for OC is 1079 increased, then total light extinction calculated via eq 1could possibly become an overestimate 1080 because of the greater POM concentration, unless the extinction efficiencies of carbon or other 1081 components in the equation are adjusted. These issues are addressed elsewhere 11 - 13 and have 1082
not been included in our calculations. 1083 As a closing perspective, one needs to remember that any refinements to the default 1084 natural conditions estimates will have to be made in a regulatory framework. In such a setting, 1085 refinements are most likely to be accepted if the scientific community and the regulators have 1086 confidence in the science supporting the changes and justifying the magnitudes of the changes, 108737

and there is minimal controversy concerning those aspects. This paper addresses the technical 1088 merits of potential refinements to natural background visibility at VISTAS Class I areas but does 1089 not make policy recommendations as to whether these refinements should be adopted by the 1090 VISTAS states as part of their state implementation plans for regional haze. 1091

ACKNOWLEDGMENTS 1092 The work reported in this paper was funded by the VISTAS regional planning organization, 1093 through its funding agency, the Southeastern State Air Resource Managers and grants from the 1094 Environmental Protection Agency. The ideas presented in this paper are those of the authors and 1095 do not represent policy decisions by the VISTAS states. We appreciate the comprehensive 1096 comments by an anonymous reviewer; they have substantially enhanced the quality of this paper. 1097

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128744

ABOUT THE AUTHORS 1288 Ivar Tombach is Technical Advisor to the VISTAS Regional Planning Organization. He is an 1289 independent air pollution consultant, located at 753 Grada Ave., Camarillo, CA 93010. Patricia 1290 Brewer is VISTAS’ Technical Coordinator at 2090 U.S. 70 Highway, Swannanoa, NC 28778. 1291
1292 1293 1294 TABLES 1295 1296 Table 1. Annual average default natural background concentration estimates for particle 1297 components in the East, and their estimated impacts on dry light extinction according to the 1298 IMPROVE algorithm (Eq 1). 1299 1300 Component Average Concentration, μg/m 3 Error

Factor Dry Extinction

Efficiency,
m 2 /g Dry

Extinction, Mm –1 Ammonium sulfate 0.23 2 3 0.69 Ammonium nitrate 0.1 2 3 0.3 POM 1.4 2 4 5.6 Elemental carbon 0.02 2-3 10 0.2 Fine soil 0.5 1.5-2 1 0.5 Coarse matter 3.0 1.5-2 0.6 1.8 Sum Fine = 2.25 Coarse = 3.0 9.09

1301 130245

Table 2. Summary of suggested refinements to default estimates of natural conditions in the 1303 VISTAS region and their potential effects, with authors’ assessments of whether refinement of 1304 the default method or concentration is warranted. 1305 1306 Issue/Source PM 2.5 Component Affected Annual Average

Concentration Impact (μg/m 3 ) Annual Light

Extinction Impact (Mm -1 ) Refining

Default Warranted? Methods Refinements Use 92 nd %-ile instead of 90 th Extinction -- +0.42 dv Yes

Increase OC multiplier to 2.1 POM 0.7 2.8 Yes

Component Concentration Impacts Wildfires POM and

EC 1.1 and 0.2 4.4 and 2 No

Forest organics POM 0.3 – 0.5 (or ~1 in spring and summer) 1.2 – 2

(or ~4 in spring and summer) Yes, in

heavily forested
areas Sea salt Sea salt 1.3 at coast, 0.7 inland (e.g., in Florida) 1.3 f(RH)

0.7 f(RH) Yes, near

coast; perhaps inland Ocean organics POM 0.2 0.8 Yes, near coast Fine soil Soil -0.2 in northern VISTAS states -0.2 No

Asian dust Soil 0.1 (or 0.1 to 0.2 in spring and summer) 0.1

(or 0.1 to 0.2 in spring and summer) No

African dust Soil 0.2 – 0.3 (or 0.6 to 1 in summer) 0.2 – 0.3

(or 0.6 to 1 in summer) Maybe,

episodically at some sites 130746

Table 3. Estimated annual and 20%-haziest days extinction averages (in Mm -1 ) for four 1308 hypothetical scenarios at Great Smoky Mountains National Park. 1309 1310 Annual Average b ext 20% Haziest Days b ext

Scenario Current Natural Current Natural (90%ile) Natural

(92%ile) GRSM1 85.8 21.4 191.1 31.4 32.8 GRSM2 89.0 24.2 198.0 35.5 37.1 GRSM3 89.0 25.4 198.0 37.3 38.9 GRSM3* 89.0 25.4 198.0 40.1 41.7 1311 1312

1313 1314

1315

Table 4. Implications of four natural conditions scenarios on the emissions reductions required 1316 to decrease haze on the 20% haziest days during the first decade at Great Smoky Mountains 1317 National Park. 1318 1319 Scenario Current HI, dv Natural Backgnd HI, dv Improvmnt

Rate, dv/decade ∆b ext in 1st

Decade, Mm -1 Required

Reduction, Total Conc. Required

Reduction, Sulfates GRSM1(90) 29.5 11.4 3.02 -52.2 27.5% 35.8% GRSM1(92) 29.5 11.9 2.95 -51.2 26.9% 35.1% GRSM2 29.9 13.1 2.79 -50.4 25.7% 34.6% GRSM3 29.9 13.6 2.71 -49.2 25.0% 33.7% GRSM3* 29.9 14.3 2.60 -47.4 24.1% 32.4% 1320 1321 132247

Table 5. Estimated annual and 20%-haziest days extinction averages (in Mm -1 ) for five 1323 hypothetical scenarios at Cape Romain National Wildlife Reserve. 1324 1325 Annual Average b ext 20% Haziest Days b ext

Scenario Current Natural Current Natural (90%ile) Natural

(92%ile) ROMA1 76.6 21.2 133.2 31.1 32.5 ROMA2 79.7 24.0 138.4 35.3 36.8 ROMA3 83.8 28.1 142.5 41.3 43.1 ROMA4 83.8 28.9 142.5 42.5 44.3 ROMA5 83.8 29.2 142.5 42.9 44.7 1326 1327 1328

1329

1330

Table 6. Implications of five scenarios on the emissions reductions required to decrease haze on 1331 the 20% haziest days during the first decade at Cape Romain National Wildlife Reserve. 1332 1333 Scenario Current HI, dv Natural Backgnd HI, dv Improvmnt

Rate, dv/decade ∆b ext in 1st

Decade, Mm -1 Required

Reduction, Total Conc. Required

Reduction, Sulfates ROMA1(90) 25.9 11.4 2.42 -28.7 23.3% 32.1% ROMA1(92) 25.9 11.8 2.35 -27.9 22.7% 31.2% ROMA2 26.3 13.0 2.21 -27.4 21.4% 30.7% ROMA3 26.6 14.6 1.99 -25.8 19.4% 28.8% ROMA4 26.6 14.9 1.95 -25.2 19.0% 28.2% ROMA5 26.6 15.0 1.93 -25.0 18.9% 28.0% 1334 1335FIGURES

Figure 1. States and Class I areas in the VISTAS domain.

Figure 2. Component contributions to estimated annual average light extinction under default natural conditions for dry aerosol and for climatologically-representative relative
humidities at four Class I areas.

Figure 3. Illustration of effects of changes in 2064 natural conditions estimates on 2018 regional haze goals.

Figure 4. Component contributions to estimated annual average light extinction under three assumed natural conditions scenarios at Great Smoky Mountains National Park.

Figure 5. Component contributions to estimated annual average light extinction under five assumed natural conditions scenarios at Cape Romain National Wildlife Refuge.

# Appendix B.5 Carbon Analyses for PM2.5 and Haze1Patricia Brewer1,Johann Engelbrecht2, Roger Tanner3,Scott Reynolds4, John Calcagni51VISTAS2Desert Research Institute3Tennessee Valley Authority4South Carolina Department of Health and Environmental Control5Environmental Protection AgencyVISTAS Special Monitoring StudiesCarbon Analyses for PM2.5 and Haze2Presentation Objectives• VISTAS Data Analysis• Special Monitoring Studies• Carbon Studies: C14isotope, Positive MatrixFactorization, and Chemical Mass BalanceAreas with Poorest Visibility are similar to PM2.5 Non-attainment AreasIMPROVE SiteFederalTribalClass I AreasBRET1BRIG1CACR1CADI1CHAS1COHU1DOSO1EVER1GRSM1HEGL1JARI1LIGO1MACA1MING1OKEF1ROMA1SAMA1SHEN1SHRO1SIPS1SWAN1UPBU1Average Extinction for 20% Haziest DaysNew IMPROVE Algorithm2000-2004VISTAS coastalVISTAS inlandnon-VISTAS050100150200250300 SWAN ROMA OKEF EVER CHAS SAMA DOSO SHEN JARI LIGO SHRO GRSM COHU SIPS MACA BRIG BRET MING HEGL UPBU CACR

Extinction (Mm -1 )Sea SaltCoarseSoilECPOMNH4NO3(NH4)2SO4RayleighSulfate dominates the 20% Haziest Days at VISTAS Class I areasAverage Extinction for 20% Best DaysNew IMPROVE Algorithm2000-20040102030405060 SWAN ROMA OKEF EVER CHAS SAMA DOSO SHEN JARI LIGO SHRO GRSM COHU SIPS MACA BRIG BRET MING HEGL UPBU CACRVISTAS coastalVISTAS inlandnon-VISTAS

Extinction (Mm -1 )Sea SaltCMSoilECPOMNH4NO3(NH4)2SO4RayleighSulfate also dominates the 20% Clearest Days at VISTAS Class I areasNatural Conditions -- New IMPROVE Algorithm -01020304050 Great Sm o ky Mtns Shenandoah Cape Romain Okefenokee Everglades B re ton *Left = B20%; Center =Average; Right = W20%*Breton based on 2001Q2-2002Q1 & 2002Q4-2003Q3

bext (Mm-1)Sea SaltCMSoilECPOMNH4NO3(NH4)2SO4Rayleigh8Monitoring ObjectivesIdentify sources of organic carbonUse high volume samplers with PM2.5 head andquartz filters at 5 sitesSamples collected April 2004 – May 2005Analyses by Desert Research InstituteSelect 250 samples for carbon analysis (5 sites,50 samples per site including field and lab blanks)Define carbon mass of each sampleCharacterize organic composition (GC/MS)Identify tracers of primary sources of carbon(Chemical Mass Balance and Positive MatrixFactorization)9Monitoring ObjectivesIdentify sources of organic carbonFor same samples, characterize C14isotope and ratio of modern to fossil carbonAnalyses by Woods Hole OceanographicInstitute’s National Oceanographic SciencesAccelerator Mass Spectrometry (NOSAMS)Sources of modern carbon: vegetativeemissions, wood burning, agriculturalburning, cookingSources of fossil carbon: gasoline, diesel,oil10URBANCLASS 1Great Smoky Mtns.Cape RomainRaleighVISTAS Carbon Study SitesShenandoahMammoth Cave11Sources of Organic Carbon:Lessons LearnedModern carbon fraction seldom <0.60,usually >0.70Higher modern carbon at forested sites(GRSM and MACA) than coastal (ROMA) orurban (MILL) sitesHigher total carbon and higher total PM2.5aturban site correspond to lower modern carbonNo clear seasonal trends in modern carbonHypothesize that greater wood burning in wintermonths and greater vegetative emissions inspring/summer12Apr 2004May 200513Fraction Modern Carbon vs. Aerosol CarbonCape Romain, All Seasonsy = 0.045x + 0.556R2= 0.4220.300.400.500.600.700.800.901.000.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0Aerosol C,μg/m3

Fraction Modern Carbon, f m1415Great Smoky Mtns, All Seasonsy = 0.038x + 0.609R2= 0.13620.300.400.500.600.700.800.901.001.100.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00Aerosol Carbon,μg/m3

Fraction Modern Carbon, f mFraction Modern Carbon vs. Aerosol Carbon1617Mammoth Cave, All Seasons0.300.400.500.600.700.800.901.001.100.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Fraction Modern Carbon, f mAerosol Carbon,μg/m3Fraction Modern Carbon vs. Aerosol Carbon1819Aerosol Carbon,μg/m3Fraction Modern Carbon vs. Aerosol CarbonMillbrook, All Seasons0.300.400.500.600.700.800.901.001.100.00 2.00 4.00 6.00 8.00 10.00 12.00

Fraction Modern Carbon, f m2021Shenadoah, All Seasonsy = 0.054x + 0.514R2= 0.4340.300.400.500.600.700.800.901.001.100.00 1.00 2.00 3.00 4.00 5.006.00 7.00 8.00 9.00 10.00Aerosol Carbon,μg/m3Fraction Modern Carbon vs. Aerosol Carbon

Fraction Modern Carbon, f m22Monitoring ObjectivesPositive Matrix Factorization applied tocluster organic compounds.Tracers identified forBiogenic EmissionsVegetative BurnGasoline emissionsDiesel emissionsCrude Oil (Commercial Marine, seen at ROMA)Analyses incomplete: tracers still need to berelated back to total carbon to understandrelative importance23Vegetative burnBiogenic EmissionsDiesel CrudeMotor vehicles2425262728Monitoring ObjectivesChemical Mass Balance analysesresults to date from few samples analyzed by MeiZheng, Ga TechChemical markers (profiles) for primarycarbon sources:Wood CombustionGasoline emissionsDiesel emissionsMeat CookingVegetative detritusOther Organic Matter (unidentified primary sourcesplus secondary organics)29GT-CMB Results0.001.002.003.004.005.006.00 ROMA 1-22-04 ROMA 4 - 10-04 GRSM1-22 - 04 GRSM 4 -10 -04 M A C A 1-22 -04 M A C A 4-10 -04 M I LL1-22 - 04 M I LL 4-10-04 SHEN 1-22 - 04 SHEN 4-10-04

μ g/m3DieselGasolineWood CombustionMeat CookingVegetative DetritusOther OM30Carbon Source Attribution:Lessons LearnedCMB Results from samples analyzed byMei Zheng, Ga TechIn April samples from all sites, unidentifiedcomponent is largest fraction (>70%)Likely secondary, likely biogenicWood combustion is largest identifiedcomponent in Jan samples, all sitesGasoline higher in Jan than Apr samples?Diesel at all sites in Apr, not Jan samples?31Carbon Source Attribution:Lessons LearnedCMB analyses together with C14resultsindicate that modern carbon is moreimportant than fossil carbon at Class Iareas and urban area sampled.Little seasonal variability in modern carbonwood burning vs biogenicStill need integration to understand sourcecontributions32Next StepsRelate organic tracers used as PMF factors tolarger population of organic compoundsComplete CMB organic analyses all samplesRelate organic profiles to measured carbonIntegrate results with C14 analysesInterpret source attribution for organic carbonIntegrate results with SOA production inCMAQ/CAMx air quality modelsFinalize project reportsSubmit manucript for publication

# Appendix B.6 Continuous Measures of Speciated PM2.51July 23, 2007Patricia Brewer1, Scott Reynolds2,Roger Tanner3, Eric Edgerton4, Ben Hartsell4,Jim Renfro5, Hoke Kimball6, John Calcagni71VISTAS2South Carolina Department of Health and Environmental Control3Tennessee Valley Authority4Atmospheric Research Associates5National Park Service6North Carolina Department of Natural Resources7Environmental Protection AgencyVISTAS Special Monitoring StudiesContinuous Measures of Speciated PM2.52Presentation Objectives• Special Monitoring Studies• Continuous Monitors: PM2.5, Sulfate,Nitrate, Total Carbon, Black Carbon3Monitoring ObjectivesUse continuous monitors of PM2.5mass,sulfate, nitrate, total carbon and blackcarbon to characterize daily and spatialtrendsData collected Mar 2003 - Dec 2004Raw data available in AQSNormalized data available from VISTASDraft VISTAS Report authored by RogerTanner, TVAURBANCLASS 1Great Smoky Mtns.Cape RomainRaleighVISTAS Focus SitesAtlanta5March 2003 - December 2004• Continuous monitoring:• Sulfate, Nitrate, Total Carbon (R&P instruments)• Black Carbon (Aethalometer)• Continuous Mass (TEOM)• Precursor gases - SO2, NOy, CO• Light Scattering (Nephelometer) at Class 1areas)• Meteorology• Alternative Sulfate and Nitrate methods• Filter-based Speciation:IMPROVE or STNFocus Sites6Lessons Learned fromContinuous Monitors• Continuous PM2.5 mass (TEOM) showsgood agreement to PM2.5 mass from filter7PM2.52003y = 1.3336x + 3.3987R2= 0.8542y = 1.0749x + 2.8358R2= 0.9629y = 0.8383x + 2.4046R2= 0.9051010203040500 10 20 30 40 50Filter

ContinuousMassContinuous PM2.5 mass shows good agreement to PM2.5 mass from filter8Lessons Learned fromContinuous Monitors• Aethalometer (BC) has good performance• R&P instruments for S, N, and Total C achievedgood data capture (>75%) when operating• Periodic failures R&P instruments• S, N, TC, BC continuous data needs to benormalized to filter-based data to interpret trends.• Correlations by Site and Quarter• Continuous speciated monitors have highupkeepand data management• Postscript: R&P monitors are being discontinued• Alternative commercial continuous monitors9V IS T A S F o c u s S ite T E O M P M2 5Q 3 200301 02 03 04 05 06 07 /17 /8 7 /1 5 7 /2 2 7 /2 98 /5 8 /1 2 8 /1 9 8 /2 69 /29 /99 /1 6 9 /2 3 9 /3 0

micrograms / cubic meterDK PMMB PMCR PMLR PMThird Quarter 2003V IS T A S F o c u s S ite S u lfa te200301 02 07/1 7/8 7/15 7/22 7/29 8/5 8/12 8/19 8/26 9/2 9/9 9/16 9/23 9/30

micrograms / cubic meterMB SO 4CR SO4LR SO 4MASSSULFATE10V IS T A S F o c u s S ite T o ta l C a rb o n200301 07/1 7/8 7/15 7/22 7/29 8/5 8/12 8/19 8/26 9/2 9/9 9/16 9/23 9/30

micrograms / cubic meterMB TCCR TCLR TCThird Quarter 2003V IS T A S F o c u s S ite T E O M P M2 5Q 3 200301 02 03 04 05 06 07 /17 /8 7 /1 5 7 /2 2 7 /2 98 /5 8 /1 2 8 /1 9 8 /2 69 /29 /99 /1 6 9 /2 3 9 /3 0

micrograms / cubic meterDK PMMB PMCR PMLR PMMASSCARBON11Lessons Learned fromContinuous Monitors• Continuous monitors high scattercompared to filter-based data• Capture same spatial and seasonaltrends as filter-based data12Filter comparisonsSulfate - IMPROVELook Rock SulfateQ3 200305101520257/1 7/8 7/15 7/22 7/29 8/5 8/12 8/19 8/26 9/2 9/9 9/16 9/23 9/30

micrograms / cubic mete rLR SO4fLR SO413Filter comparisonsSulfate - IMPROVECape Romain SulfateQ3 200305101520257/1 7/8 7/15 7/22 7/29 8/5 8/12 8/19 8/26 9/2 9/9 9/16 9/23 9/30

micrograms / cubic mete rCR SORfCR SO414Filter comparisonsSulfate – STNRVEMillbrook SulfateQ3 200305101520257/1 7/8 7/15 7/22 7/29 8/5 8/12 8/19 8/26 9/2 9/9 9/16 9/23 9/30

micrograms / cubic mete rMB STN SO4MB SO415Sulfate2003y = 1.0079x + 0.7062R2= 0.6451y = 0.6538x + 1.2038R2= 0.7711y = 0.5623x + 0.6845R2= 0.77750510152005101520Filter

ContinuousSulfate16Nitrate2003y = 0.322x + 0.1088R2= 0.1171y = 0.9853x + 0.0657R2= 0.7019y = 0.5556x + 0.1306R2= 0.35290.00.51.00.0 0.5 1.0Filter

ContinuousNitrate17Carbon2003y = 0.783x + 0.7507R2= 0.6823y = 1.2608x + 0.4168R2= 0.5383y = 0.4249x + 1.0943R2= 0.759904812160 4 812 16Filter

ContinuousTotalCarbon18Elemental Carbon2003y = 0.6704x + 0.7892R2= 0.6108y = 1.0152x + 0.5788R2= 0.4042y = 0.2646x + 0.8859R2= 0.59104812160 4 812 16Filter

ContinuousElementalto BlackCarbon19Normalizing ContinuousMonitoring Data• Continuous data was normalized to filterdata to facilitate comparison to modeledvalues• Regressions calculated for each site, eachquarter20Lessons Learned fromContinuous Monitors• Diurnal and seasonal trends:• Sulfate:• GRSM > MILL > ROMA in summer• Little regional difference other seasons• Consistent with regionally distributed sources• Highest in summer at all sites• Afternoon peak in warm seasons, not otherseasons• Consistent with photochemical production21Diurnal Pattern of Average Sulfate in Winter (Jan-Mar) 2004at Focus Sites0.01.02.03.04.05.000:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00Hour of Day

[SO 4

= ], μ g/m 3ROMAGRSMMILL22Diurnal Pattern of Average Sulfate in Spring (Apr-Jun) 2004at Focus Sites0.01.02.03.04.05.06.07.08.000:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00Hour of Day

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= ], μ g/m 3ROMAGRSMMILL