Appendix B 

Conceptual Description
Appendix B - 1
Introduction 

Air Resource Specialists, Inc. (ARS) was contracted by Visibility Improvement State and Tribal 

Association of the Southeast (VISTAS) 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 and 

visibility in Class I areas, to discuss 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, and to describe special 

monitoring studies undertaken by VISTAS to better 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 PM2.5 data and visibility across monitoring 

networks in the Southeastern United States. 

Appendix B.4 contains a journal article by Ivar Tombach and 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 PM2.5 and haze. 

Appendix B.6 contains a powerpoint presentation about a VISTAS special monitory studies on 

continuous measures of speciated PM2.5.

Appendix B.7 contains the final report from the VISTAS contractor ICF International for 

characterization of meteorology and its relationships to fine particulate mass and visibility in the 

VISTAS Region. 

ii

Appendix B - 2
Appendix B.1 

Conceptual Description
Air Resource Specialists, Inc. 

VISTAS Conceptual Description 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, 2007

Appendix B.1 - 1
i

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 CONDITIONS 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 Revised 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 (Baseline, 2018 and Natural Conditions) F-1 

Appendix B.1 - 2
APPENDIX 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 International Attribution Plots L-1 

ii

Appendix B.1 - 3
iii

LIST OF FIGURES 

Figure Page

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

3-1 Comparison of Measured Light Scattering (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 extinction calculated using the new IMPROVE 

 algorithm (left), the old IMPROVE algorithm (right), and measured light 

 scattering (grey line) in 2004 3-4 

3-3 Stacked bar chart comparing 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 3-5 

3-4 Stacked bar chart comparing 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 3-5 

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 3-6 

3-6 Flow chart of data substitution methods used 3-9 

3-7 Comparison of OC and Estimated Organic 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 substituted data at the SAMA1 site 3-14 

3-10 10-year annual average extinction trends 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 regional grouping of sites. Slopes and p-values are 

 indicated in the legend 3-16 

3-12 2000-2004 baseline conditions and 2018 projected conditions at the 

 GRSM1 site simulated using WinHaze Visual Air Quality Modeling program 3-19 

Appendix B.1 - 4
LIST 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 VISTAS 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 VISTAS and neighboring Class I areas 3-22 

3-16 Contributions to mass (μg/m3

) for the average of the 20% worst visibility 

 days in 2000-2004 at VISTAS and neighboring Class I areas 3-23 

3-17 Contributions to mass (μg/m3

) for the average of the 20% best visibility 

 days in 2000-2004 at VISTAS and 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 visibility 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 visibility days in 

 2000-2004 at the ROMA1 site 3-27 

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 natural 

 background conditions (NB2) 3-28 

3-25 DV Glidepaths for the ROMA1 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 natural 

 background conditions (NB2) 3-29 

3-26 Glidepath depicting total bext (Mm-1) for 20% worst days at the GRSM1 site 3-30 

iv

Appendix B.1 - 5
LIST OF FIGURES (CONTINUED) 

Figure Page

3-27 Glidepath depicting Ammonium Sulfate for 20% worst days at the GRSM1 site 3-31 

3-28 Glidepath depicting Ammonium Nitrate for 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 bext (Mm-1) for 20% worst days at the ROMA1 site 3-32 

3-31 Glidepath depicting Ammonium Sulfate for 20% worst days at the ROMA1 site 3-33 

3-32 Glidepath depicting Ammonium Nitrate for 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 trajectories 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 

v

Appendix B.1 - 6
vi

LIST OF TABLES 

Table Page

1-1 Summary of Data Products Available 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 IMPROVE 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 Background Visibility Conditions 20% Worst 

Visibility Days 3-20 

 

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

Visibility Days 3-21 

Appendix B.1 - 7
1.0 INTRODUCTION 

The primary purpose of this document is to provide data, results and methods 

documentation for analysis performed by Air Resource Specialists, Inc. (ARS) 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 governments, and various 

federal agencies established to initiate and coordinate activities associated with the management 

of regional haze, visibility and other air quality issues in the Southeastern United States. VISTAS 

is working to develop the technical basis for 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 presented 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 and 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 establish “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 impair visibility. The national 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 

1-1 

Appendix B.1 - 8
Implementation Plans for the first phase of the regional haze regulation are due in December 

2007. 

1.2 DATA SUMMARY PRODUCT FTP SITE 

 Methods descriptions and analysis 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 currently available on the ftp site. 

1-2 

Appendix B.1 - 9
1-3 

Table 1-1 

Summary of Data Products Available on ARS VISTAS FTP Site 

Sample Path Description 

[data files] ...Aerosol_dataset_VISTAS_2000-2004/ SAMA1_daily_budgets_sub_nia.xls Data sets including substituted data 

neighboring Class I Areas, old and algorithm. 

Average Extinction for 20% Worst Days

New IMPROVE Algorithm (nia)

2000-2004

0

50

100150200250300

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm-1)

Sea SaltCMSoilECPOMAmm NO3Amm SO4Rayleigh

...Aerosol_stacked_bar_charts/ 

Average_BestWorstDays_2000-04/ 

VISTAS_nia_00-04_20070129.xls 

Regional stacked bar charts depictindays, extinction and mass, old and n

GRSM1

Baseline Extinction, nia

20% W > 142

0

100200300400

J-02 F-02 M-02 A-02 M-02 J-02 J-02 A-02 S-02 O-02 N-02 D-02

Extinction (Mm-1)

20% W > 140

0

100200300400

J-01 F-01 M-01 A-01 M-01 J-01 J-01 A-01 S-01 O-01 N-01 D-01

Extinction (Mm-1)

20% W > 139

0

100200300400

J-00 F-00 M-00 A-00 M-00 J-00 J-00 A-00 S-00 O-00 N-00 D-00

Extinction (Mm-1)

20% W > 139

0

100200300400

J-03 F-03 M-03 A-03 M-03 J-03 J-03 A-03 S-03 O-03 N-03 D-03

Extinction (Mm-1)

20% W > 130

0

100200300400

J-04 F-04 M-04 A-04 M-04 J-04 J-04 A-04 S-04 O-04 N-04 D-04

Extinction (Mm-1)

...Aerosol_stacked_bar_charts/ 

Timelines_AllDays_2000-04/ 

GRSM.pdf 

Stacked bar charts depicting extinctdays during the baseline years, 200sites requiring data substitution, twwhere the first page distinguishes suoriginal days. 

GRSM1

2000-2004 Reconstructed Extinction

New IMPROVE Algorithm

20% Worst Days

0

50

100150200250300350400450

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

10/30

2000 2001 2002 2003 2004

Extinction (Mm-1)

Sea SaltCMSoilLACPOMAmm NO3Amm SO4Rayleigh ...Aerosol_stacked_bar_charts/ 

Timelines_BestWorstDays_2000-04/ 

GRSM1_20bw_nia.xls 

Stacked bar charts that selectively sand worst days for 2000-2004 base
GRSM1

Old and New IMPROVE Algorithm (oia/nia) Extinction by Species

20% Best Days (2002)

0

102030405060

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

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 12/22 Avg.

Extinction (Mm-1)

Rayleigh Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

...Aerosol_stacked_bar_charts/ 

Timelines_nia_oia_comparison_2002/ 

GRSM1_nia_oia.xls 

Regional and site specific stacked bextinction calculations using the nealgorithm are presented side by side

All Days

y = 0.93x + 9.16

R2 = 0.87

0

50

100150200250300

0 50 100 150 200 250 300

24-hr Nephelometer bsp (Mm-1)

24-hr Aerosol bsp (Mm-1)

Q4

y = 1.10x + 1.49

R2 = 0.96

0

50

100150200250300

0 50 100 150 200 250 300

Q1

y = 0.77x + 13.88

R2 = 0.48

0

50

100150200250300

0 50 100 150 200 250 300

Q2

y = 0.93x + 12.17

R2 = 0.81

0

50

100150200250300

0 50 100 150 200 250 300

Q3

y = 0.86x + 14.28

R2 = 0.82

0

50

100150200250300

0 50 100 150 200 250 300

-100

-75-50-25

0

255075

100

2000

2001

2002

2003

2004

Difference (Mm-1) 

(Aer. bsp - Neph. bsp

)

Aerosol Scattering vs. Nephelometer Scattering

Using New IMPROVE Algorithm (nia) and Daily f(RH)

GRSM, 2000 - 2004

...Nephelometer_Aeosol_Comparisons/ 

GRSM_neph.xls 

Charts comparing collocated IMRPand IMPROVE aerosol samplers, oIMPROVE algorithm. 

Glide Path to Natural Conditions (2004-2064)

(5-yr Rolling Average for 20% Haziest Days - New IMPROVE equation and NB II )

05

101520253035

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 GRSM New Glide Path GRSMDefault 5-yr Rolling Avg GRSM New 5-yr Rolling Avg GRSMAnnual g90 - Old Annual g90 - New

 Base old Base new Default NB NB2 Great Smokey Mtns 28.8 30.3 11.4 dv 11.2 dv

...Glidepaths/ 

Revised Glide Path new IMPROVE 

equation_Reynolds.ppt 

Deviview Glidepaths (developed by 

species depicting baseline conditionnatural conditions for both the new 

GRSM1

Total Extinction

Glide Slope for 20% Worst Days

0

50

100150200250

2000

2002

2004

2018

2064

Extinction (Mm-1

)

Total BextBaselineGlide Slope2018 Base GNatural Conditions ...Glidepaths/ 

Speciated_Glidepaths/ 

Glideslopes_GRSM1.xls 

Glidepaths by species depicting basmodeled predictions and estimated 

1-4 
10 Year Temporal Trends

All Days

151719212325

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

DV

OKEF1 m = -0.13, p = 0.05ROMA1 m = -0.24, p = 0.06SWAN1 N/A (< 5 years)

10 Year Temporal Trends

20% Best Days

101214161820

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

DV

OKEF1 m = -0.05, p = 0.43ROMA1 m = -0.23, p = 0.02SWAN1 N/A (< 5 years)

10 Year Temporal Trends

20% Worst Days

202224262830

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

DV

OKEF1 m = -0.17, p = 0.01ROMA1 m = -0.28, p = 0.24SWAN1 N/A (< 5 years)

...IMPROVE_Trend_Charts/ 

Trends_DV.xls 

10 Year temporal trends for sites grincludes Standard Visual Range (SVand Mm-1. 

GRSM1

Domestic and International Aerosol Mass by Species

20% Worst Days (2002)

05

101520253035

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

International

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

Mass (μg/m

3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

...International_Attribution/ 

International_Attribution_mass_worst.xls 

Comparison of domestic and internmass on 20% best and worst days, 2

...Back_Trajectory_Analyis/ 

IMPROVE_ResidenceTimeMaps_2000-04_nia/ 

GRSM_2002_20high.bmp 

Back trajectory maps for 20% wors

GRSM1

2000-04

20% Worst Monitored Days

100m Residence Times (%)

< 0.01

0.01 - 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.bmp 

Residence time maps for 20% wors

1-5 
2.0 ANALYTICAL METHODS 

2.1 THE IMPROVE MONITORING NETWORK 

The Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring 

program collects speciated PM2.5, and PM2.5 and PM10 total mass. IMPROVE is a nation-wide 

network which began in 1988 and expanded significantly 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 integrated 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 PM2.5 and PM10 mass, and PM2.5 mass 

constituents, including numerous trace elements, 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 listing of IMPROVE sites, locations, and operational 

start dates is presented in Table 2-1. The list also includes start (and end) dates for collocated 

nephelometers, which are continuous monitoring instruments 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 IMPROVE Monitoring sites in and around VISTAS. 

2-1 

Appendix B.1 - 13
Table 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) NC 7/94 35.39 -82.77 4/94 – 9/99 

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. The three most common metrics used to describe 

visibility impairment are: 

• Extinction (bext) – Extinction is a measure of the fraction of light lost per unit length 

along a sight path due to scattering and absorption by gases and particles, expressed 

in inverse Megameters (Mm-1). This metric is useful for representing the contribution 

of each aerosol species to visibility impairment 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 distance a large black object can be 

seen on the horizon, expressed in kilometers (km) or miles (mi). 

2-2 

Appendix B.1 - 14
• Deciview (dV) – The deciview index was designed to be linear with respect to human 

perception of visibility. A one deciview change is approximately 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 extinction based upon the measured mass of 

various contributing aerosol species. EPA’s 2003 guidance 

(http://vista.cira.colostate.edu/improve/Publications/GuidanceDocs/guidancedocs.htm) for 

calculating light extinction is based on the original protocol defined by the IMPROVE program

in 1988. In December 2005, the IMPROVE Steering Committee voted to adopt a revised 

algorithm for use by IMPROVE as an alternative to the original approach. 

The elements and species measured 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 

[(NH4)2SO4] 

4.125× [S] 

-or1.375× [SO4] 

[S] is derived from the Teflon 

filter (A module). 

[SO4] is derived from the nylon 

filter (B module). 

Ammonium Nitrate 

[(NH4)NO3] 1.29× [NO3] [NO3] 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] [PM10] - [PM2.5] 

[PM10] is derived from the 

Teflon filter (D module). 

[PM2.5] 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 (bext) is: 

2-3 

Appendix B.1 - 15
[ ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh (10) Scattering

CM6.0

Soil Fine1

EC10

POM4

f(RH)3 Nitrate Amm.

f(RH)3b Sulfate Amm.

+

×+

×+

×+

×+

××+

ext ××≈

Reconstructed bext is expressed in Mm-1, dry extinction efficiencies (leading numbers) are 

expressed as m2/g and species mass concentrations are expressed in μg/m3. 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 IMPROVE 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 reasonable estimates of light scattering over 

a broad range of conditions, but it tends to underestimate the highest extinction values and 

overestimate the lowest extinction values. The revised algorithm for estimating light extinction 

recommended for use by the IMPROVE steering committee is: 

[ ] [ ]

[ ][

[ ][ ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh Specific) (Site Scattering

(ppb)NO33.0

CM6.0

Salt Sea(RH)f7.1

Soil1

EC10

POM Large1.6POM Small8.2

(RH)f4.2 (RH)f1.5Nitrate Amm. Small Nitrate Amm. Large

(RH)f2.2b (RH)f8.4Sulfate Amm. Small Sulfate Amm. Large

2

ss

s L

s L

+

×+

×+

××+

×+

×+

×+ ×+

××+ ××+ ] ext ××≈ + × ×

The revised algorithm splits ammonium sulfate, ammonium nitrate, and POM 

concentrations into small and large size fractions as follows: 

[ ]

[ ] [ ] [ ]

[ ] [ ][

[ ] [ ][ ] μg/m20TotalFor Large, Total

Small Total Large

Total

20

Total Large

μg/m20TotalFor 

3

3

≥ =

⎪

⎪

⎩

⎪

⎪

⎨

⎧

−=

×=

<

]

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

2-4 

Appendix B.1 - 16
mass concentration in the revised algorithm, where it was calculated as 1.4 times carbon mass in 

the original algorithm. The new algorithm contains three distinct water growth factors, 

designated fS(RH), fL(RH), and fSS(RH) for water absorption due small and large sulfate and 

nitrate fractions, and for sea salt, respectively. New terms have also been added for sea salt and 

for absorption by gaseous NO2. NO2 is not available at most IMPROVE sites, and is not 

included in analysis presented here. 

IMPROVE data are available on the IMPROVE Web site 

(http://vista.cira.colostate.edu/improve/) and the Visibility Information 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 designation indicating that extinction was 

calculated using the original/old IMPROVE algorithm, and RHR2 indicates that extinction was 

calculated using the revised/new IMPROVE algorithm. 

VISTAS chose to use the new IMPROVE algorithm 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 geographic source areas most 

likely to contribute to visibility impairment on the 20% worst visibility days at the Class I areas. 

Back trajectory analyses use interpolated measured or modeled meteorological fields to estimate 

the most likely central path over geographical areas that provided air to a receptor 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 for 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 Hybrid-Single Particle Lagrangian Integrated 

Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric 

Administration’s (NOAA) Air Resources Laboratory (ARL). HYSPLIT uses archived 3-

dimensional meteorological fields generated from observations and short-term forecasts. 

HYSPLIT can be run to generate forward or backward 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 Assimilation 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 horizontal 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 trajectories 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 

2-5 

Appendix B.1 - 17
actual vertical motion measurements (represented by model data), surfaces of constant entropy, 

or surfaces of constant pressure, depending on the meteorological conditions at a given location 

and time. The impact of receptor height (or end height) on an individual trajectory is also 

important. Low-ending trajectories represent air parcels nearer to ground level and high-ending 

trajectories may better represent boundary layer flow above the local terrain. Specific back 

trajectory analyses performed by ARS for 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 

2-6 

Appendix B.1 - 18
3-1 

3.0 VISIBILITY CONDITIONS 

 The Regional Haze Rule sets a 60 year timeline 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. Analyses include comparisons of the old and new 

IMPROVE algorithms, determination of baseline (2000-2004) conditions, determination of the 

glide slope between baseline and natural conditions, and determination of geographic source 

areas using back trajectory analysis. 

3.1 COMPARISON OF OLD AND NEW IMPROVE ALGORITHM 

One of the most compelling reasons for developing 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 GRSM1 site comparing measured light scattering 

(nephelometer) and reconstructed light scattering calculated using the revised (New) and original 

(old) IMPROVE algorithms. A stacked bar chart for the most recent year of comparable record, 

in this case 2004 at the GRSM1 site, 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 aerosol light scattering is calculated using daily 

f(RH) based upon measured RH, as opposed to the IMPROVE method of using 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 reconstructed extinction, coarse mass 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 VISTAS nephelometer site in Appendix A for both 

the total available data set and each quarter separately. 

The GRSM1 site has one of the longest periods of nephelometer data in the VISTAS 

region, and has been in operation since 1993. In general, 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 the data and generally slightly lower correlation.

Figures 3-3 and 3-4 present comparisons of the average 20% worst and best days in the 

2000-2004 baseline period at VISTAS and neighboring sites calculated using both the revised 

and original IMPROVE algorithm. For the worst 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 comparison of the 20% worst days in 2002 at the GRSM1 site 

Appendix B.1 - 19
calculated using both the new and old IMPROVE algorithm. The 20% best and worst days are 

very similar using either the original and revised algorithms. In the case of GRSM1, 9/23 was a 

worst day in 2002 using the old algorithm, but this one day is traded for 7/22 when extinction is 

calculated using the new algorithm. Other days change in magnitude, but the same days are still 

counted among the 20% worst days. Additional plots comparing extinction calculated using the 

old and new IMPROVE algorithms for 20% best and worst days in 2002 at all VISTAS sites are 

available in Appendix B. 

3-2 

Appendix B.1 - 20
Old Algorithm

y = 0.71x + 14.70

R2 = 0.89

0

50

100150200250300

0 50 100 150 200 250 300

24-hr Nephelometer bsp (Mm-1)

24-hr Aerosol b sp (Mm-1)

New Algorithm

y = 0.98x + 5.97

R2 = 0.87

0

50

100150200250300

0 50 100 150 200 

24-hr Nephelometer bsp (

24-hr Aerosol bsp (Mm-1)

Figure 3-1. Comparison of measured light scattering (nephelometer) and light scattering calculated using the originaIMPROVE algorithms for the GRSM1 site, 1993-2004. 

3-3 
0

50

100150200250

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

Extinction (Mm-1)

Amm SO4 Amm NO3 POM Soil CM Sea Salt 

Figure 3-2. Stacked bar chart comparing extinction calculated using the new IMPROVE algorithm(left), the old IMP(right), and measured light scattering (grey line) in 2004. 

3-4 
0

50

100

150

200

250

300

SWAN1ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm-1)

CM

Soil

EC

POM

Amm NO3

Amm SO4

Rayleigh

Figure 3-3. Stacked bar chart comparing 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

60

SWAN1ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm-1)

CM

Soil

EC

POM

Amm NO3

Amm SO4

Rayleigh

Figure 3-4. Stacked bar chart comparing 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.

3-5

Appendix B.1 - 23
GRSM1

Old and New IMPROVE Algorithm (oia/nia) Extinction by Species

20% Worst Days (2002)

0

50

100150200250300350400

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

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 

Extinction (Mm-1)

Rayleigh Amm SO4 Amm NO3 POM LAC Soil CM Sea S

Figure 3-5. Stacked bar chart comparing 20% worst days in 2002 at the GRSM1 site calculated using both the new (IMPROVE algorithm. 

3-6 
3- 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 baseline period average (2000-2004) contain at least 

3 complete years of data. RHR guidance requirements for IMPROVE aerosol data completeness 

include the following conditions: 

• Individual samples must contain all species required for the calculation of light 

extinction (sulfate, nitrate, organic carbon, elemental carbon, 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 least 75% of all possible daily samples 

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

• The baseline period (2000-2004) must contain 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 filter 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 the data, the resulting complete years are 

eligible for use in calculation of baseline conditions and tracking progress under the RHR. 

Further details can be found in the RHR 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 for the 2000-2004 baseline years for IMPROVE 

Appendix B.1 - 25
sites in and around the VISTAS region. After routine RHR data substitutions, five IMPROVE 

monitoring sites in the VISTAS states (CHAS1, SAMA1, COHU1, SHRO1 and SWAN1), and 

two regional site (BRET1 and MING1) did not achieve 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 had 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 

FL EVER1 0 1 1 1 1 4 

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 

NC SHRO1* 0 0 0 1 1 2 

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 1 

NonVISTAS 

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 chart of the VISTAS data substitution methods, and details for 

these methods are described below. The starting data set was the RHR2 IMPROVE data using 

the new IMPROVE algorithm. This data set includes the routine surrogate and patched data 

3- 8 

Appendix B.1 - 26
substitutions 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 missing individual samples 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- 9 

Appendix B.1 - 27
3.2.1.1 Carbon Substitutions 

 The first substitution method relied on using a surrogate for carbon mass measurements 

when the C module data was not available. Hydrogen (H) is measured on the A module filter, 

and is assumed to be primarily associated with organic carbon and inorganic compounds such as 

ammonium sulfate. Organic carbon (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 inorganic 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 Carolina during the second quarter between 2000-2004 

for OC vs. organic H, and EC vs. OC. Statistics were calculated and applied 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 Organic 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 identification 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 characteristics to be used as a donor site. 

Figure 3-8 presents a sample inter-site mass 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 (GRSM1) 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- 10

Appendix B.1 - 28
For BRET1 substitutions, chloride/chlorine was not available from the GFP SEARCH 

site. In this case, sea salt could not be calculated from the GFP data, so seasonal averages of sea 

salt from the BRET1 site were used as substitutes. 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 required some degree of substitution, where a 2 

indicates a substituted year, a 1 indicates the year was already complete under RHR guidelines, 

and dashes indicate the year did not meet RHR guidelines and no additional substitutions were 

made. The table also lists sites that were selected 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 the 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 the SAMA1 site depicting 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 bar 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 and not substituted. In the case of SAMA1, 

substitutions were made for the years 2001 and 2004. In 2001, data from the CHAS1 site was 

scaled and substituted for about 33% of monitored 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 required 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 included in Appendix C. Additional charts and tables for all VISTAS 

substituted sites are included in Appendix D. 

3- 11

Appendix B.1 - 29
Q2

r2 = 0.68

0

10

20

30

40

50

0 10 20 30 40 50

AmmSO4 (μg/m3)

AmmSO4 (μg/m3)

Q2

r2 = 0.62

0

1

2

3

4

5

012345

AmmNO3 (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 Mass Between SHRO1, NC (y-axis) and GRSM1, 

TN (x-axis).

3- 12

Appendix B.1 - 30
 

Table 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- 13

Appendix B.1 - 31
SAMA1 (CHAS1 donor)

Baseline Extinction, New IMPROVE Algorithm

20% W > 105

0

50

100

150

200

J-02

F-02

M-02

A-02

M-02

J-02

J-02

A-02

S-02

O-02

N-02

D-02

Extinction (Mm-1)

0 ■ ■ ■ ■■■■■■■■■■■■■■■■■■■ ■ ■ ■■ ■■■■■■■■■■ ■■■ ■

50

100

150

200

J-01

F-01

M-01

A-01

M-01

J-01

J-01

A-01

S-01

O-01

N-01

D-01

Extinction (Mm-1)

20% W > 101

0

50

100

150

200

J-00

F-00

M-00

A-00

M-00

J-00

J-00

A-00

S-00

O-00

N-00

D-00

Extinction (Mm-1)

20% W > 109

0

50

100

150

200

J-03

F-03

M-03

A-03

M-03

J-03

J-03

A-03

S-03

O-03

N-03

D-03

Extinction (Mm-1)

0 ■■■■■■■ ■ ■■■■■■■■■■■■■■■■■■■■ ■■■■■■■■■■■■■

50

100

150

200

J-04

F-04

M-04

A-04

M-04

J-04

J-04

A-04

S-04

O-04

N-04

D-04

Extinction (Mm-1)

20% W > 117

Figure 3-9. 2000-2004 annual bar charts 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 indicates the threshold above 

which days are counted in the 20% worst days for that year. 

3- 14

Appendix B.1 - 32
3.3 EXTINCTION TRENDS 

10-year extinction trends for the period 1995-2004 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 provisions of the 1990 Clean Air Act Amendments and 

under the NOx SIP call for ozone. Theil slopes were calculated to determine the trend, and pvalues were calculated using Mann-Kendall trend analysis to determine the significance of each 

slope. Lower p-values indicate higher confidence levels in the computed slopes. 

Figure 3-10 is a map indicating 10-year annual average 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 showed decreasing trends (using pvalue<0.20 as indicator ). No sites 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 sites are included on the plot for reference. 

Additional trend plots for other regional groupings 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 trends and blank diamonds indicate insignificant trends (pvalue<0.20). No sites showed increasing trends. 

3- 15

Appendix B.1 - 33
10 Year Temporal Trends

All Days

0

20

40

60

80

100

120

140

1995199619971998199920002001200220032004

TBext (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

60

1995199619971998199920002001200220032004

TBext (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

300

1995199619971998199920002001200220032004

TBext (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 trend plots for a regional grouping of sites. Slopes and p-values are 

indicated in the legend. 

3- 16

Appendix B.1 - 34
3.4 BASELINE (2000-2004) AND NATURAL (2064) VISIBILITY CONDITIONS 

Baseline (2000-2004) visibility conditions were determined using the new revised 

IMPROVE algorithm to select the 20% worst and 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 visibility for the 20% worst days is estimated by 

assuming that fine particle concentrations for natural background are normally distributed and 

the 90th percentile of the annual distribution represents 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 92nd percentile of a normal 

distribution would be a better representation of the mean of the 20% worst days than the 90th

percentile16. EPA’s guidance did not estimate the contributions of individual fine particle 

components to natural background visibility on the 20% worst days. Following development of 

the new IMPROVE algorithm, a workgroup 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 each 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’ WinHaze Visual Air Quality Modeler (Ver. 2.9.6). 

Figure 3-12 presents a spit image depicting simulated baseline conditions and 2018 projected 

conditions at the GRSM1 site. There is a discernable difference in visibility between these time 

periods. Figure 3-13 depicts the estimated natural 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 contribution to aerosol extinction (Rayleigh not included), and 2064 natural 

condition estimates generated using the revised calculations 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 and 3-17 indicate species contribution to mass 

(μg/m3

) on the 20% worst and best extinction days. 

Ammonium sulfate is the largest contributor to visibility impairment on the 20% haziest 

days in the baseline 2000-2004 period (69-74%) at all the IMPROVE sites in the VISTAS region 

except Everglades National Park in Florida, where Ammonium sulfate is a close second to 

Particulate Organic Material, POM (40 and 45%, respectively). Particulate Organic Material 

(also referred to as organic carbon) is the second largest contributor to aerosol extinction at all 

other sites, contributing to between 13 and 18% of aerosol extinction on the worst days. 

Baseline conditions for 20% worst days at the inland sites (182.2 - 241.4 Mm-1) average higher 

3- 17

Appendix B.1 - 35
than conditions measured at the coastal sites (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 the 20% worst days, but for the 20% best days it contributes to between 2 

and 7% of the aerosol extinction at the VISTAS coastal sites. 

Ammonium sulfate, CM and POM are the largest contributors 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. Ammonium 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 are most likely to occur in the summer, and 

the 20% best days occur most frequently during the winter. For coastal ROMA1 site, the 20% 

haziest days can occur year round, with the summer days being dominated by ammonium sulfate, 

while the worst days that occur between October and February have large contributions from 

ammonium sulfate, POM and sometimes ammonium nitrate. 

3- 18

Appendix B.1 - 36
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 GRSM1 site simulated using WinHaze Visual Air 

Quality Modeling program. 

3- 19

Appendix B.1 - 37
Table 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. 

SO4

Amm. 

NO3

POM LAC Soil CM Sea 

Salt 

bext

(Mm-1) dV VR (km) 

bext

(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 11.0 

VISTAS 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 10.9 

VISTAS 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 11.5 

non-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- 20

Appendix B.1 - 38
Table 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. 

SO4

Amm. 

NO3

POM LAC Soil CM Sea 

Salt 

bext

(Mm-1) dV VR (km) 

bext

(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 5.9 

VISTAS 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 5.0 

VISTAS 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 4.1 

non-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- 21

Appendix B.1 - 39
Average Extinction for 20% Worst Days

New IMPROVE Algorithm (nia)

2000-2004

0

50

100

150

200

250

300

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (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

60

SWAN1ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Extinction (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- 22

Appendix B.1 - 40
0

5

10

15

20

25

30

35

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Mass (μg/m3

)

Sea Salt

CM

Soil

EC

POM

Amm NO3

Amm SO4

Figure 3-16. Contributions to mass (μg/m3) 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

12

SWAN1

ROMA1

OKEF1

EVER1

CHAS1

SAMA1

DOSO1

SHEN1

JARI1

LIGO1

SHRO1

GRSM1

COHU1

SIPS1

MACA1

BRIG1

BRET1

MING1

HEGL1

UPBU1

CACR1

VISTAS coastal VISTAS inland non-VISTAS

Mass (μg/m3

)

Sea Salt

CM

Soil

EC

POM

Amm NO3

Amm SO4

Figure 3-17. Contributions to mass (μg/m3) for the average of the 20% best visibility days in 

2000-2004 at VISTAS and neighboring Class I areas. 

3- 23

Appendix B.1 - 41
GRSM1

Baseline Extinction, New IMPROVE Algorithm

20% W > 142

0

100

200

300

400

J-02

F-02

M-02

A-02

M-02

J-02

J-02

A-02

S-02

O-02

N-02

D-02

Extinction (Mm-1)

20% W > 140

0

100

200

300

400

J-01

F-01

M-01

A-01

M-01

J-01

J-01

A-01

S-01

O-01

N-01

D-01

Extinction (Mm-1)

20% W > 139

0

100

200

300

400

J-00

F-00

M-00

A-00

M-00

J-00

J-00

A-00

S-00

O-00

N-00

D-00

Extinction (Mm-1)

20% W > 139

0

100

200

300

400

J-03

F-03

M-03

A-03

M-03

J-03

J-03

A-03

S-03

O-03

N-03

D-03

Extinction (Mm-1)

20% W > 130

0

100

200

300

400

J-04

F-04

M-04

A-04

M-04

J-04

J-04

A-04

S-04

O-04

N-04

D-04

Extinction (Mm-1)

 Figure 3-18. Bar charts by year indicating speciation of all data collected at the GRSM1 site. 

The red line indicates the threshold above which days are counted in the 20% worst days. 

3- 24

Appendix B.1 - 42
ROMA1

Baseline Extinction, New IMPROVE Algorithm

20% W > 92

0

50

100

150

200

250

300

J-02

F-02

M-02

A-02

M-02

J-02

J-02

A-02S-02

O-02

N-02D-02

Extinction (Mm-1)

20% W > 102

0

50

100

150

200

250

300

J-01

F-01

M-01

A-01

M-01

J-01

J-01

A-01S-01

O-01

N-01D-01

Extinction (Mm-1)

20% W > 133

0

50

100

150

200

250

300

J-00

F-00

M-00

A-00

M-00

J-00J-00

A-00S-00

O-00

N-00D-00

Extinction (Mm-1)

20% W > 96

0

50

100

150

200

250

300

J-03

F-03

M-03

A-03

M-03

J-03J-03

A-03S-03

O-03

N-03D-03

Extinction (Mm-1)

20% W > 116

0

50

100

150

200

250

300

J-04

F-04

M-04

A-04

M-04

J-04

J-04

A-04

S-04

O-04

N-04D-04

Extinction (Mm-1)

 Figure 3-19. Bar charts by year indicating speciation of all data collected at the ROMA1 site. 

The red line indicates the threshold above which days are counted in the 20% worst days. 

3- 25

Appendix B.1 - 43
y

0

50

100

150

200

250

300

350

400

450

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 10/30

2000 2001 2002 2003 2004

Extinction (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

80

1/8 1/12 1/15 1/22 1/26 1/29 2/12 3/11 3/29 4/5 4/8 4/12 4/15 4/22 4/26 10/9 11/14 11/20 12/14 12/23 1/7 1/10 1/19 1/22 1/31 2/6 2/15 2/24 2/27 3/14 3/20 4/13 5/25 6/30 9/1 9/4 9/25 10/7 10/10 10/16 10/25 11/30 12/15 12/21 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 12/22 1/30 2/2 2/5 2/17 2/26 3/19 3/31 4/9 4/18 5/12 5/21 7/2 10/6 10/15 10/27 11/5 11/20 11/26 12/2 12/5 12/8 12/14 12/17 1/1 1/7 1/16 1/25 1/31 2/6 2/12 2/15 2/27 3/7 3/31 4/6 6/23 9/18 10/15 11/11 11/14 11/23 11/26 11/29 12/2 12/8 12/11 12/23

2000 2001 2002 2003 2004

Extinction (Mm-1)

Sea Salt

CM

Soil

LAC

POM

Amm NO3

Amm SO4

Rayleigh

Figure 3-21. Contributions to light extinction (Mm-1) for the 20% best visibility days in 2000-

2004 at the GRSM1 site. 

3- 26

Appendix B.1 - 44
y

0

50

100

150

200

250

300

350

400

1/1 2/12 2/23 5/3 7/1 7/8 7/12 7/15 7/19 8/12 8/16 8/19 8/26 9/15 10/15 10/18 11/5 11/8 12/8 12/11 1/1 1/4 2/21 3/2 4/10 5/13 5/16 5/19 5/28 5/31 6/15 7/9 7/12 7/15 8/8 8/23 8/26 8/29 9/19 9/22 9/28 10/31 11/9 11/18 12/3 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 12/7 1/9 2/20 2/26 3/25 5/27 5/30 6/5 6/11 6/20 6/23 6/26 7/11 7/29 8/19 8/25 9/6 9/9 9/12 9/21 10/3 10/6 10/12 10/21 1/10 1/13 1/22 2/24 3/13 4/9 5/6 5/12 5/27 6/20 7/11 7/14 7/20 7/23 7/26 8/4 8/19 9/3 9/24 9/30 10/3 10/27 10/30 12/5

2000 2001 2002 2003 2004

Extinction (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

80

1/5 1/15 1/29 2/26 4/22 4/26 6/14 6/17 6/28 7/29 8/2 8/23 9/9 9/12 9/18 10/9 10/24 12/23 12/26 1/10 1/31 2/12 3/14 4/25 5/1 6/27 6/30 7/3 8/2 8/5 9/10 9/16 10/7 10/10 10/13 10/22 11/3 11/15 11/24 11/27 12/6 12/18 12/24 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 12/19 1/24 2/5 2/23 3/31 4/9 5/21 5/24 6/14 6/17 7/14 7/23 7/26 8/16 9/3 9/15 9/27 11/5 11/14 11/17 11/20 12/11 12/20 12/23 1/7 2/12 2/15 2/27 3/1 3/4 3/7 3/31 4/15 6/14 6/17 7/29 8/1 8/13 9/21 11/2 11/5 11/11 11/26 12/2 12/11 12/14 12/20

2000 2001 2002 2003 2004

Extinction (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- 27

Appendix B.1 - 45
3.5 GLIDEPATHS 

The RHR guidance requires reasonable progress to be tracked using the Haze Index 

(deciviews), which is determined as a logarithmic 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 natural 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 algorithm for the GRSM1 and ROMA1 sites are 

presented in Figures 3-24 and 3-25 and for other Class I areas in Appendix H. Natural 

conditions for the glidepath using the original IMPROVE equation are EPA default calculations, 

and natural conditions for the path using the revised 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 coastal sites and thus a higher rate of improvement is 

required in each decade to achieve a uniform rate of progress 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

35

1986-19901988-19921990-19941992-19961994-19981996-20001998-20022000-2004

200620082010201220142016201820202022202420262028203020322034203620382040204220442046204820502052205420562058206020622064

Deciviews (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 natural background conditions (NB2). 

3- 28

Appendix B.1 - 46
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

35

1986-19901988-19921990-19941992-19961994-19981996-20001998-20022000-2004

200620082010201220142016201820202022202420262028203020322034203620382040204220442046204820502052205420562058206020622064

Deciviews (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 using both the original IMPROVE 

algorithm using EPA default natural conditions (Default NB) and the new IMPROVE algorithm 

using the revised estimate of natural background conditions (NB2). 

The glidepath defined using deciview does not provide information regarding the relative 

contributions of individual species to overall visibility. This information is useful because some 

species (sulfate and nitrate) originate from largely anthropogenic sources, while others (organic 

carbon, elemental carbon) originate from a mixture 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 charts for total extinction and for ammonium 

sulfate, ammonium nitrate and POM extinction at the GRSM1 site, and 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, translates to a curved line 

when converted to total extinction in Mm-1. The dV contribution can not be constructed for 

individual species, so linear approximations in Mm-1 are used for the species specific glidepaths 

to approximate progress goals. 2018 projected extinction values were provided by ENVIRON 

based on CMAQ modeling of the 2018 Base G1 inventory. Natural conditions presented here do 

not use the revised NC2 numbers that became available in August, 2007 but differences between 

the original set provided by IMPROVE, and the revised set were minimal. Plots for each site in 

and around the VISTAS region are included in Appendix I. 

3- 29

Appendix B.1 - 47
For total extinction, the projected 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 projected value of 54 Mm-1 in 2018. POM again needs little 

improvement to reach natural conditions, and projected 2018 values are on the glidepath. For 

ammonium nitrate, projected 2018 values are lower than the baseline emissions, slightly above 

the glidepath, but still low, with projected 2018 values of 5 Mm-1. 

0

50

100

150

200

250

20002002200420182064

Extinction (Mm-1)

Total Bext

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-26. Glidepath depicting total bext (Mm-1) for 20% worst days at the GRSM1 site. 

3- 30

Appendix B.1 - 48
0

20

40

60

80

100

120

140

160

180

200

20002002200420182064

Extinction (Mm-1)

Ammonium Sulfate

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-27. Glidepath depicting Ammonium Sulfate for 20% worst days at the GRSM1 site. 

0

1

2

3

4

5

6

7

8

9

10

20002002200420182064

Extinction (Mm-1)

Ammonium Nitrate

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-28. Glidepath depicting Ammonium Nitrate for 20% worst days at the GRSM1 site. 

3- 31

Appendix B.1 - 49
0

5

10

15

20

25

30

35

40

20002002200420182064

Extinction (Mm-1)

Particulate Organic Matter

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-29. Glidepath depicting Particulate Organic Material for 20% worst days at the 

GRSM1 site. 

0

20

40

60

80

100

120

140

160

180

200

20002002200420182064

Extinction (Mm-1)

Total Bext

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-30. Glidepath depicting total bext (Mm-1) for 20% worst days at the ROMA1 site. 

3- 32

Appendix B.1 - 50
0

20

40

60

80

100

120

140

20002002200420182064

Extinction (Mm-1)

Ammonium Sulfate

Baseline

Glide Slope

2018 Base G

Natural Conditions

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

0

1

2

3

4

5

6

7

8

20002002200420182064

Extinction (Mm-1)

Ammonium Nitrate

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-32. Glidepath depicting Ammonium Nitrate for 20% worst days at the ROMA1 site. 

3- 33

Appendix B.1 - 51
0

5

10

15

20

25

30

35

20002002200420182064

Extinction (Mm-1)

Particulate Organic Matter

Baseline

Glide Slope

2018 Base G

Natural Conditions

Figure 3-33. Glidepath depicting Particulate Organic Material for 20% worst days at the 

ROMA1 site. 

3- 34

Appendix B.1 - 52
3.6 ATTRIBUTION ANALYSIS 

An attribution analysis was performed to allow 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 comparisons 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 before 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 hourly point for back trajectories 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 presented in Figure 3-34. Back trajectory maps 

for all VISTAS sites are included in Appendix J. 

Figure 3-34. Back trajectories for the 20% Worst extinction days at the Great Smoky Mountains 

National Park (GRSM1) IMPROVE monitoring site. 

3.6.2 Residence Time Maps

Residence time maps were constructed using 2000-2004 back trajectories. 20% worst 

extinction days were determined using the substituted IMPROVE RHR2 (new IMPROVE 

algorithm, updated 3/06) data set. Each hourly point for back trajectories with end dates 

3- 35

Appendix B.1 - 53
corresponding to the 20% worst visibility days was counted and summed into 1⁄4 degree 

horizontal grid cells of latitude and longitude. The percent of hourly points 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 reaching the IMPROVE monitors. A residence time map 

for the 20% worst measured extinction days at the GRSM1 site for trajectories with end heights 

of 100m is presented in Figure 3-31. 100m and 500m residence time Maps for all VISTAS and 

regional sites are included 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 haze 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 for 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 generated extinction-weighted residence time data for each 

IMPROVE 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 these data 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 trajectories 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 extinction components including 

3- 36

Appendix B.1 - 54
ammonium sulfate (AmmSO4), ammonium nitrate (AmmNO3), particulate organic material 

(POM), Light Absorbing Carbon (LAC), Soil, and 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 removal processes. These charts 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 international emissions was used to represent 

species mass attributable to international sources. 

Figure 3-36 and 3-37 present stacked 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 ROMA1 site. For this comparison all sulfate and 

nitrate were assumed to be fully neutralized by particulate NH4

+

 (similar to IMPROVE 

monitoring network assumptions). Negative numbers 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 computed as the difference between total measured 

mass and international modeled mass. 

International attribution at the GRSM1 site ranges 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, attribution was between 0 and 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- 37

Appendix B.1 - 55
GRSM1

Domestic and International Aerosol Mass by Species

20% Worst Days (2002)

0

5

10

15

20

25

30

35

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

International

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

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 3-36. Stacked bar charts of mass at the GRSM1 site depicting domestic and international 

contributions to the 20% worst days in 2002. 

GRSM1

Domestic and International Aerosol Mass by Species

20% Best Days (2002)

0

2

4

6

8

10

12

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

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 12/22

Mass (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 domestic and international 

contributions to the 20% best days in 2002. 

3- 38

Appendix B.1 - 56
ROMA1

Domestic and International Aerosol Mass by Species

20% Worst Days (2002)

0

5

10

15

20

25

30

35

40

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

International

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 12/7

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 3-38. Stacked bar charts of mass at the ROMA1 site depicting domestic and international 

contributions to the 20% worst days in 2002. 

ROMA1

Domestic and International Aerosol Mass by Species

20% Best Days (2002)

0

2

4

6

8

10

12

14

16

18

20

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

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 12/19

Mass (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. 

3- 39

Appendix B.1 - 57
Appendix B.2 

Justification for New IMPROVE Equation 
Appendix B.2 - 1
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Appendix B.3 

Trends in Speciated PM2.5 and 

Visibility Across Monitoring 

Networks in the Southeastern US 
1 

1 Trends in Speciated PM2.5 and Visibility across Monitoring 

2 Networks in the Southeastern US 

3 

4 Patricia F. Brewer 

5 VISTAS, 2090 US Highway 70, Swannanoa, NC 28778 

6 Joseph P. Adlhoch 

7 Air Resource Specialists, Inc., 1901 Sharp Point Dr. Suite E, Fort Collins, CO 80525-4429 

8 

9 ABSTRACT 

10 

11 Trends in fine particulate matter less than 2.5 microns in diameter (PM2.5) and visibility in the 

12 Southeastern U.S. were evaluated for sites in the Interagency Monitoring of Protected Visual 

13 Environments (IMPROVE), Speciated Trends Network (STN), and Southeastern Aerosol 

14 Research and Characterization Study (SEARCH) networks. These analyses are part of the 

15 technical assessment by VISTAS, the regional planning organization for the Southeastern states, 

16 in support of State Implementation Plans for the regional haze rule. 

17 

18 At all VISTAS IMPROVE sites, ammonium sulfate and organic carbon are the largest and 

19 second largest contributors, respectively, to light extinction on both the 20% haziest and 20% 

20 clearest days. Ammonium nitrate, elemental carbon, soils, and coarse particles make 

21 comparatively small contributions to PM2.5 mass and light extinction on most days at the Class I 

22 areas. At Southern Appalachian sites, the 20% haziest days occur primarily in the late spring to 

23 fall, while at coastal sites, the 20% haziest days can occur through out the year. Levels of 

24 ammonium sulfate in Class I areas are similar to those in nearby urban areas, and generally 

25 higher at the interior sites than the coastal sites. Concentrations of organic carbon, ammonium 

26 nitrate and, sometimes, elemental carbon, tend to be higher in the urban areas than in nearby 

27 Class I areas, although differences in measurement methods complicated comparisons between 

28 networks. Results support regional controls of sulfur dioxide for both regional haze and PM2.5

29 implementation and suggest that controls of local sources of organic carbon, elemental carbon, or 

Appendix B. 3 - 1
30 nitrogen oxides might also be considered for urban areas that are not attaining the annual 

31 National Ambient Air Quality Standard for PM2.5. 

32 

33 IMPLICATIONS 

34 

35 State Implementation Plans for the first phase of implementation of the Regional Haze Rule are 

36 due in December 2007. This paper describes the pollutant contributions on the haziest and 

37 clearest days in Class I areas in the southeastern U.S. and compares fine particle composition in 

38 Class I areas to those in nearby urban areas. Several of the urban areas are also nonattainment 

39 areas for the national ambient air quality standard for fine particulate matter. States are using the 

40 results of the analyses described in this paper to design more effective emissions control 

41 strategies for regional haze and related air quality issues. 

42 

43 INTRODUCTION 

44 

45 Visibility is a term that refers to the color, contrast, and outline of objects viewed in a landscape. 

46 Particulate matter and gases in the atmosphere scatter or absorb light and reduce visibility by 

47 reducing the clarity and distance that can be discerned by the human eye. Regional haze 

48 describes the cumulative visibility impact from numerous air emission sources across a broad 

49 geographic region. 

50 

51 The 1977 Amendments to the Clean Air Act set a national visibility goal to prevent “any future, 

52 and remedy any existing, impairment of visibility in mandatory Class I Federal areas” which 

53 results from manmade air pollution. In 1999 the U.S. Environmental Protection Agency (EPA) 

54 promulgated regional haze regulations to address visibility at 156 designated Class I areas 

(national parks greater than 6000 acres and wilderness areas greater than 5000 acres)155 . The 

56 regional haze rule calls for States to establish “reasonable progress goals” for each Class I area to 

57 improve visibility on the 20% haziest days and to prevent visibility degradation on the 20% 

58 clearest days. States are to evaluate their contributions to visibility impairment at Class I areas 

59 both within and outside the State and to develop long-term control strategies to reduce emissions 

60 of air pollutants that impair visibility. The national goal is to return visibility to natural 

Appendix B. 3 - 2
61 background levels by 2064. Using the period 2000 to 2004 as the baseline period, States are to 

62 evaluate progress in improving visibility by 2018 and every 10 years thereafter. State 

63 Implementation Plans for the first phase of the regional haze regulation are due in December 

64 2007. The southeastern states illustrated in Figure 1 are working together through a regional 

65 planning organization, Visibility Improvement – State and Tribal Association of the Southeast 

66 (VISTAS) to conduct the technical analyses to support the states’ regulatory planning for 

67 regional haze and related air quality issues. 

68 

69 The National Ambient Air Quality Standard for particulate matter was revised in 1997 to set 

annual and 24-hr limits (15.0 and 65 μg/m370 , respectively) for particles with diameters less than 

71 2.5 microns (PM2.5). In December 2004, several counties in the southeastern states were 

72 designated as not attaining the annual PM2.5 standard. These counties include several urban 

73 areas near Class I areas, for example: Atlanta, GA; Birmingham, AL; Charleston, WV; 

74 Louisville, KY; and Chattanooga and Knoxville, TN (PM2.5 design value for years 2002-2004 in 

these cities ranged between 17.5 and 15.7 μg/m3)

 2 75 . By April 2008, affected states are required 

76 to submit a State Implementation Plan demonstrating control strategies to attain the annual PM2.5

77 standard by April 2010. 

78 

79 This paper summarizes current fine particle and visibility conditions at Class I areas in the 

80 southeastern U.S, and compares fine particle loadings at Class I areas with nearby urban areas in 

81 the region. These analyses are being used by VISTAS states to better understand source 

82 contributions to PM2.5 and regional haze in the southeastern United States and to design effective 

83 emissions control strategies for State Implementation Plans. 

84 

85 METHODS 

86 

87 Fine Particle Measurements 

88 Fine particle mass and components are being measured at several sites in the southeastern U.S. 

89 The locations of the 18 Class I areas in the VISTAS region (plus Breton Wildlife Refuge located 

90 on the Louisiana coast of the Gulf of Mexico), and the locations of monitoring sites included in 

91 analyses described in this paper are illustrated in Figure 1. 

Appendix B. 3 - 3
92 

93 The Interagency Monitoring of Protected Visual Environments (IMPROVE) network monitors 

fine particle concentrations at Class I areas throughout the U.S.394 including 15 of the 18 Class I 

95 areas in the VISTAS region. IMPROVE collects fine particle samples on filters for 24 hours 

96 every third day. EPA and the State and Local Agencies monitor fine particle components at 

97 Speciated Trends Network (STN) sites in both urban and rural areas using filter samples 

collected every third or every sixth day.498 The Southeastern Aerosol and Research 

99 Characterization Study (SEARCH) monitors fine particle concentrations at 4 paired urban and 

rural sites (8 sites total) in Alabama, Georgia, and the Gulf Coast of Florida and Mississippi.5100 

101 SEARCH filter samples are collected once every third day (daily in Atlanta, GA). IMPROVE 

102 monitoring began between 1988 and 1993 for several Class I areas in the VISTAS states; 

103 monitors were installed at additional sites during 2000. SEARCH operations began in 1999, 

104 while STN sites reported in this paper began monitoring in 2001 or 2002. 

105 

106 The three networks use similar methods for sample collection and analysis, but differences 

107 between programs complicate inter-comparison of monitoring results. All three networks collect 

108 PM2.5 on Teflon, nylon, and quartz filters. For all networks, the Teflon filters are weighted 

109 gravimetrically to define PM2.5. IMPROVE uses X-Ray Fluorescence (XRF), and Proton Elastic 

110 Scattering Analysis (PESA) to analyze elements. SEARCH uses XRF and STN uses Energy 

111 Dispersive X-Ray Fluorescence (EDXRF) to analyze elements. IMPROVE and STN define 

112 “fine soils” as a weighted sum of aluminum (Al), calcium (Ca), iron (Fe), silicon (Si), and 

113 titanium (Ti). SEARCH defines “major metal oxides” (similar to fine soils) as a somewhat 

114 different weighted sum of these elements and potassium (K). 

115 

116 IMPROVE uses a sodium carbonate denuder in the nylon filter sample inlet to remove acidic 

117 gases. SEARCH uses both sodium carbonate and citric acid denuders upstream of its 

118 Teflon/nylon/glass fiber filter packs to remove acidic gases and ammonia (NH3), respectively. 

119 Some STN samplers use a magnesium oxide denuder to remove acidic gases prior to the nylon 

120 filter. All three networks analyze the nylon filters using ion chromatography and report sulfate 

121 (SO4), chloride (Cl), and nitrate (NO3). (Valences are omitted for brevity.) SEARCH includes 

122 non-volatilized NO3 from the Teflon filter in reported NO3. STN and SEARCH both report 

Appendix B. 3 - 4
123 ammonium (NH4). SEARCH uses a citric acid coated cellulose filter behind the Teflon filter to 

124 capture volatilized NH4; STN does not. 

125 

126 IMPROVE infers NH4 concentrations from the combined concentrations of SO4 and NO3, by 

127 assuming both are fully neutralized by NH4 as ammonium sulfate, (NH4)2SO4 and ammonium 

128 nitrate, NH4NO3. To test this assumption, for 13 of 15 IMPROVE sites in the VISTAS region, 

129 the nylon filters have been analyzed for NH4 (beginning 1997 for Dolly Sods, Shenandoah, and 

130 Great Smoky Mountains and since October 2002 for 10 additional Southern Appalachian and 

131 Coastal sites). 

132 

133 The quartz filters are used to analyze carbon. SEARCH uses a carbon denuder upstream of its 

134 quartz filter to remove organic gases from the sample. IMPROVE uses a backup quartz filter on 

135 a small fraction of samples to estimate the extent of adsorbed organic gases. The STN data 

reported on the EPA Air Quality System website6136 do not include a correction factor for carbon 

137 artifacts. However, EPA has evaluated carbon on backup filters and now recommends a 

correction factor for each of the STN monitor brands7138 . The separation of particulate organic 

139 carbon (OC) from elemental carbon (EC) is operationally defined. IMPROVE and SEARCH use 

140 Thermal Optical Reflectance (TOR) while STN uses Thermal Optical Transmittance (TOT) for 

141 carbon analysis. The temperature of separation of EC is higher for TOT than for TOR. Using 

142 the TOT method, STN reports higher OC values and lower EC values than IMPROVE and 

143 SEARCH. All three networks use a mass multiplier of 1.4 to account for species associated 

144 with carbon. 

145 

146 IMPROVE uses a second Teflon filter to collect particles with diameters less than 10 microns 

147 (PM10). SEARCH uses a separate sampler to collect PM10. STN does not measure PM10. 

148 Coarse mass is calculated as the difference between PM10 and PM2.5 and can be calculated for 

149 IMPROVE and SEARCH but not for STN sites. The difference between mass measured 

150 gravimetrically on the PM2.5 filter and the reconstructed mass is reported as an unidentified 

151 component. The unidentified component may be positive or negative and is the net result of 

152 positive and negative artifacts on the filters, unmeasured species, and measurement errors. 

153 

Appendix B. 3 - 5
Data for the years 2000 – 20038, 9 154 for IMPROVE sites in the VISTAS region are reported here. 

Data were compared to SEARCH10 for the years 2000 – 2003 and to STN6155 for the years 2002 – 

156 2003. Data completeness requirements outlined by the EPA for tracking progress under the 

Regional Haze Rule11 157 (valid data for at least 75% of days in a year and for at least 50% of the 

158 days in each quarter) were adopted and applied to IMPROVE, SEARCH, and STN data sets. 

159 Table 1 lists the years determined to have complete data sets for IMPROVE sites under these 

160 requirements. All SEARCH and STN sites used in this analysis met these same requirements for 

161 the time periods analyzed. 

162 

163 Calculating Light Extinction 

164 Light extinction is a measure of visibility that can be estimated from the measured (or modeled) 

165 concentrations of the components of fine particle mass. Total light extinction consists of both 

166 scattering of light by particles and absorption of light by gases (e.g., nitrogen dioxide) and 

167 particles (e.g., light-absorbing carbon). A particle’s chemical composition, size, shape, and 

168 mixture with other chemical components affect the efficiency of light extinction. Particles in the 

169 size range of 0.1 to 1.0 microns are most efficient at scattering light. Ammonium sulfate, 

170 (NH4)2SO4, and ammonium nitrate, NH4NO3, particles are hygroscopic and absorb increasingly 

171 more water at higher relative humidity. The acidity of SO4 particles also affects water 

172 absorption; fully neutralized ammonium sulfate, (NH4)2SO4, absorbs less water than the more 

173 acidic ammonium bisulfate (NH4HSO4) or sulfuric acid (H2SO4). Some organic carbon species 

174 absorb water but the amounts are variable and are less well defined. 

175 

EPA guidance for calculating light extinction11 176 , which is based on the protocol defined by the 

IMPROVE program12-15 177 was followed for this analysis. The extinction (scattering or absorption) 

178 efficiency of each species is multiplied by the measured species mass. Particle components are 

179 assumed to act as if in an external mixture (single chemical species per particle) even though 

180 particles actually occur in internal mixtures of different chemical species (chemical interactions 

181 could alter extinction efficiency). Sulfate is assumed to be fully neutralized. The extinction 

efficiencies for ammonium sulfate and ammonium nitrate are assumed to be equal (3 m2182 /g) and 

less than that of organic carbon mass (4 m2183 /g). Scattering enhancement factors are included for 

184 ammonium sulfate and ammonium nitrate to account for increased scattering at higher relative 

Appendix B. 3 - 6
185 humidity. Monthly average “climatologically-representative” enhancement factors appropriate 

186 for each Class I area are used in the calculation. The measured organic carbon is multiplied by a 

187 factor of 1.4 to account for other chemical species associated with carbon in the total organic 

carbon mass. A value of 10 inverse megameters (Mm-1 188 ) is added to account for extinction due to 

189 scattering from molecules in a clean atmosphere (Rayleigh conditions). The full extinction 

190 equation is: 

191 

bext 192 = 3 fs(RH) [(NH4)2SO4] + 3 fn(RH) [NH4NO3] + (1) 

193 4 [OCM] + 1 [SOIL] +0.6 [Coarse] + 10 [LAC] + 10 

194 

Where bext = calculated extinction, in units of inverse megameters (Mm-1 195 ) 

196 f(RH) = scattering enhancement factor based on relative humidity 

197 OCM = Organic Carbon Mass defined as 1.4 times measured Organic Carbon 

198 LAC = Light Absorbing Carbon (typically represented by measured Elemental Carbon) 

199 

200 Uncertainties in the calculation of light extinction include: 

201 • measurement methods for fine particle mass and composition 

202 • the factor used to calculate total organic carbon mass 

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

204 components of coarse mass 

205 • the lack of contribution by unidentified fine mass 

206 • the assumed scattering enhancement factors for relative humidity 

207 

208 The IMPROVE Steering Committee is currently reviewing the assumptions used to calculate 

209 light extinction and will make recommendations for modifications of the above assumptions 

based on the most recent data16, 17 210 . This paper does not attempt to address the accuracy of the 

211 current assumptions or potential modifications to these assumptions. 

212 

213 RESULTS 

214 

215 Visibility and Fine Particle Trends in VISTAS Class I areas 

Appendix B. 3 - 7
216 Eight of the 15 IMPROVE sites in the VISTAS region have complete data sets for the four year 

217 period 2000-2003 (see Table 1), as defined by EPA’s Guidance for Tracking Progress Under the 

Regional Haze Rule11 218 . Three sites had valid data for 2001-2003, but not for 2000. There are 

219 only two years of valid data for St. Marks, FL, and Swanquarter, NC and only one year of valid 

220 data for Cohutta, GA and Shining Rock, NC. For the latter four sites, contributions to light 

221 extinction (Figures 2-3) may be skewed by smaller sample sizes and seasonal bias in available 

222 data. All IMPROVE, STN, and SEARCH sites used in quarterly analyses (Figures 4-8) meet 

223 EPA’s data completeness criteria for the illustrated quarters of 2002 (at least 50% of days in the 

224 quarter have valid data.) Breton, LA had no years meeting the minimum annual criteria, but data 

225 do meet the quarterly criteria for first and third quarters of 2002. 

226 

227 Ammonium sulfate, (NH4)2SO4, is the largest contributor to light extinction at the IMPROVE 

228 sites in the VISTAS region on both the 20% haziest and 20% clearest days in the period 2000 – 

229 2003 (Figures 2 and 3). Ammonium sulfate is also the largest contributor to fine particle mass 

230 at the IMPROVE sites on the 20% haziest days and the 20% clearest days (not shown). 

231 Extinction from (NH4)2SO4 on the 20% haziest days is higher in the interior and northern tier of 

sites (116-161 Mm-1) than at the coastal and southern IMPROVE sites (44-85 Mm-1 232 ). On the 

20% clearest days, extinction from (NH4)2SO4 (11-24 Mm-1 233 ) shows smaller spatial variation. 

234 The exception is Shining Rock, NC, which on the 20% clearest days shows the lowest extinction 

from (NH4)2SO4 (6 Mm-1 235 ) of all the VISTAS IMPROVE sites. Shining Rock, at 1621 m 

236 elevation, may be influenced on the clearest days by regional transport of air masses above the 

237 boundary layer. Because only one year (2003) at Shining Rock met the data completeness 

238 criteria established for these analyses, these results may be skewed compared to the other sites, 

239 but are not inconsistent with the site’s topography. Other sites with missing data show similar 

240 (NH4)2SO4 contributions to nearby sites with complete records. 

241 

242 Organic carbon mass (OCM) is the second largest contributor to fine particle mass and light 

243 extinction at VISTAS Class I areas on the 20% haziest days (Figure 2). On the 20% clearest 

244 days, OCM is the second largest contributor to light extinction at all the coastal sites and most 

245 interior sites (Figure 3). On the 20% clearest days, OCM can exceed (NH4)2SO4 mass at some 

coastal IMPROVE sites and NH4NO3 246 ,mass at all sites. Due to the hygroscopic nature of 

Appendix B. 3 - 8
247 (NH4)2SO4 and NH4NO3, these components still contribute more to extinction on the 20% 

248 clearest days than OCM. 

249 

250 Ammonium nitrate, EC, and fine soil have small contributions to fine particle mass and light 

251 extinction on most days. At coastal sites, fine soil has a larger relative contribution to extinction 

252 and mass than at Southern Appalachian sites. Coarse mass is higher at coastal sites than at 

253 Southern Appalachian sites, possibly due to the presence of sea salt. Due to the low extinction 

254 efficiency assigned to coarse mass, the contribution of this component to light extinction is low 

255 at all sites. 

256 

257 The fractional contribution of NH4NO3 to extinction is higher on the clearest days than on the 

haziest days, even though the absolute contribution is similar between the clearest (1-9 Mm-1 258 ) 

and haziest (2-12 Mm-1 259 ) days. In the southeastern U.S., NH4NO3 formation is generally limited 

by availability of NH3 in the atmosphere18 260 . Ammonia preferentially reacts with SO4 to neutralize 

261 the ion’s two negative charges. Until SO4 particles are fully neutralized, little NH3 is available to 

262 react with nitric acid (HNO3) and form NH4NO3. Ammonium nitrate formation is dependent on 

263 temperature and relative humidity. Because NH4NO3 volatilizes at higher temperatures, 

264 NH4NO3 concentrations in VISTAS Class I areas are lowest during the summer months and 

265 highest during the winter months. 

266 

267 For the Southern Appalachian and interior IMPROVE sites, the 20% haziest days are most likely 

268 to occur May through October. The 20% clearest days occur most frequently in the cooler 

269 winter months. For the southern and coastal IMPROVE sites, the 20% haziest days occur year 

270 round. Light scattering measured by nephelometer at Great Smoky Mountains, Mammoth Cave, 

271 and Shenandoah National Parks is under estimated by the IMPROVE default assumptions on the 

haziest days and over estimated on the clearest days19 272 . Alternative assumptions that increase the 

273 OCM multiplier and reflect greater extinction efficiency for SO4, NO3, and OCM at greater mass 

concentrations16, 17 274 result in slightly greater extinction on the 20% haziest days and slightly lower 

275 extinction on the 20% clearest days. They do not change the conclusion that the haziest days at 

276 Class I areas in the VISTAS region are dominated by SO4 with significant contributions from 

277 OCM. 

Appendix B. 3 - 9
278 

279 The relative importance of components of fine particle mass varies seasonally and spatially, as 

280 illustrated in Figure 4 for Mammoth Cave, Great Smoky Mountains, and Everglades National 

281 Parks, and Okefenokee and Cape Romain National Wildlife Refuges for the year 2002. 

282 Ammonium sulfate is highest in the summer months at the Southern Appalachian and interior 

283 sites but less variable seasonally at the southern and coastal sites. Note that NH4NO3 is elevated 

284 in the winter months (quarters 1 and 4) at Mammoth Cave compared to other sites. Note also 

285 that fine soil is the largest component of PM2.5 mass at Everglades in the summer quarter of 2002 

286 while SO4 mass is least in the summer quarter at Everglades. Fine soil is episodically elevated 

287 at the Florida IMPROVE sites (and to a lesser extent at other IMPROVE sites) during the 

summer months due to fine dust transported from Africa (Saharan Desert20 288 ). When air masses 

289 are influenced by transport from the Atlantic Ocean and African continent, SO4 mass is low. The 

290 impact of a single large fire in May 2002 near Okefenokee, GA, is illustrated in the elevated 

291 average OCM for the second quarter at Okefenokee. 

292 

293 Network Measurement Intercomparison and Source Implications 

294 VISTAS is using the year 2002 as the base year for emissions inventories and air quality 

modeling for both the regional haze and PM2.5 State Implementation Plans21 295 . To evaluate the 

296 relative contributions to PM2.5 mass at Class I and nearby urban areas, quarterly average 

297 components of PM2.5 mass in 2002 were compared at the IMPROVE, nearby STN, and 

298 SEARCH sites. Average components of PM2.5 mass for the third quarter of 2002 (July, August, 

299 September) are illustrated for selected sites in the Southern Appalachian area in Figure 5 and in 

300 the Gulf and Atlantic coastal areas in Figure 6. Average particulate concentrations for the same 

301 sites during the first quarter of 2002 (January, February, March) are illustrated in Figures 7 and 

302 8. Birmingham, AL; Louisville, KY; Chattanooga and Knoxville, TN; and Atlanta, GA 

303 (illustrated in Figures 5 and 6) are PM2.5 nonattainment areas. 

304 Sulfate. Sulfate measurement methods are comparable across the three networks and 

305 measurement uncertainty is lower for SO4 than for other components of PM2.5. Sulfate values in 

quarter 3 (July, August, September) are very similar (6-9 μg/m3306 ) across IMPROVE, STN, and 

307 SEARCH sites in the southern Appalachians (Figure 5) and are generally higher than SO4 values 

Appendix B. 3 - 10
(2-6 μg/m3

308 ) across the coastal sites (Figure 6). Sulfate levels in urban areas are generally within 

1 μg/m3309 of those in nearby rural and Class I areas. Larger differences between Shining Rock and 

310 Charlotte, NC, and between Shenandoah and Roanoke, VA on the winter days are at least 

311 partially attributable to difference in elevation that lead to differences in air masses trajectories 

312 and influences from regional transport between these sites. Large differences in SO4 between 

Cape Romain and Charleston, SC, and Okefenokee and Savannah, GA (2 μg/m3 313 during the 

314 summer quarter) indicate local as well as regional source contributions in these southern coastal 

315 areas. Sulfate is attributable primarily to utility and industrial sources and these are broadly 

316 distributed across a large geographic region. Sulfate levels in the southern Appalachian and 

interior urban sites are lower in winter (2-4 μg/m3317 , Figure 7) than summer, reflecting lower rates 

318 of chemical production of SO4 from sulfur dioxide, SO2, in the winter (due to low winter 

319 temperatures and low availability of ozone, hydrogen peroxide, and the hydroxyl radical.) 

Sulfate levels at the coastal areas are fairly similar in winter (2-4 μg/m3320 , Figure 8) and summer 

321 and do not show as strong seasonal patterns as the interior sites. Rao, et al. compared annual 

322 average constituents of PM2.5 mass for the period March 2001 to February 2002 at 13 paired 

IMPROVE-STN sites nationwide7323 . Quarterly average SO4 values reported here show greater 

324 variability between urban-rural pairs than reported by Rao, et al. for annual average SO4 (0.0-0.5 

μg/m3325 ) for eight paired IMPROVE-STN sites in the eastern U.S. Greater rural-urban variability 

326 in this analysis is attributable to a combination of seasonal variability, smaller geographic scope, 

327 and local as well as regional contributions to SO4 at these sites. 

328 

329 Nitrate. Nitrate measurement uncertainty is higher than that for SO4 and is attributable to 

330 differences in measurement methods between the networks. SEARCH measurements are likely 

331 to have the highest accuracy because the citric acid denuder reduces the positive NO3 artifact due 

332 to reaction of NH3 gas onto the Teflon filter. Nitrate values are low at all sites during quarter 3 

333 but are higher at urban sites than at IMPROVE sites. In contrast to SO4, NO3 values are higher 

during winter months (less than 1 to greater than 2 μg/m3

334 across IMPROVE sites, Figure 7) than 

summer months (less than 1 μg/m3335 ). Because NH4NO3 formation is generally limited by NH3 in 

336 the southeastern U.S., lower SO4 values during the winter months mean more NH3 is available to 

337 react with nitric acid gas, HNO3. Also NH4NO3 volatilizes at higher temperature so less is 

338 present in particulate phase during the warmer summer months. As a result, NH4NO3 has a 

Appendix B. 3 - 11
339 larger contribution to mass and light extinction on the 20% clearest days, which tend to occur 

340 during the winter months, than on the haziest days. 

341 

342 Ammonium. In Figures 5 through 8, NH4 is illustrated separately from SO4 and NO3, using 

343 inferred NH4 for IMPROVE and measured NH4 for STN and SEARCH. Inferred NH4 values at 

344 IMPROVE sites are similar to measured NH4 at nearby SEARCH sites. STN sites generally 

345 have lower measured NH4 values than IMPROVE and SEARCH. 

346 

347 NH4 measurements in all three networks are subject to positive and negative artifacts. The 

348 SEARCH NH4 measurements likely have greater accuracy and fewer artifacts than the 

349 IMPROVE and STN measurements due to the use of a citric acid coated backup filter behind the 

350 nylon filter, specifically designed to capture volatilized NH4. In special studies at Great Smoky 

351 Mountains National Park, the potential loss of NH4 off the nylon filter was evaluated using a 

352 denuder/filter pack system with a phosphorous acid-coated denuder after the nylon filter to 

capture NH3

22 353 . Results indicated that for summertime samples at Great Smoky Mountains, an 

354 average of 18% (with a range of 10-35%) of the NH4 volatilized off the nylon filter. Losses of 

355 NH4 off the routine IMPROVE nylon filter may be of similar magnitude. 

356 

357 The validity of the assumption used in the IMPROVE equation, Eq. 1, that SO4 is fully 

358 neutralized as (NH4)2SO4 was tested by measuring NH4 on the nylon filters for routine 

359 IMPROVE samples from 13 sites in the VISTAS region. Molar ratios of NH4 to SO4 were used 

360 to describe the degree of neutralization of SO4 by NH4. A molar ratio of 2.0 indicates fully 

361 neutralized (NH4)2SO4 , while a molar ratio of 1.0 implies NH4HSO4 (or mix of forms with 

362 average molar ratio of 1.0), and a molar ratio of 0.0 indicates H2SO4. Because the more acidic 

363 forms of SO4 aerosol absorb more water and scatter more light than (NH4)2SO4, three separate 

364 curves have been developed to describe the light extinction enhancement factor for water 

365 absorption, f(RH), for (NH4)2SO4, NH4HSO4, and H2SO4. The f(RH) curve for H2SO4 is much 

366 steeper than the f(RH) curve for (NH4)2SO4 and NH4SO4, so if the molar ratio of NH4 to SO4 is 

367 less than 1.0 (indicating a greater fraction of H2SO4), light extinction would be greater than that 

368 calculated by the IMPROVE equation, Eq. 1. Monthly average molar ratios for interior and 

369 coastal IMPROVE sites are always less than 2.0, frequently less than 1.5, and sometimes below 

Appendix B. 3 - 12
370 1.0. Some but not all sites show seasonal variation in molar ratios with the lowest ratios in the 

371 fall and summer and higher ratios in the spring. Molar ratios are generally lower at the coastal 

372 sites than at the interior sites. Given the likely underestimate in NH4 values reported for 

373 IMPROVE nylon filters, these monthly average molar ratios of NH4 to SO4 should be interpreted 

374 as lower limits. 

375 

376 For comparison, monthly average molar ratios for NH4 to SO4 were calculated for SEARCH 

377 sites using NH4 collected on both the nylon filter and the backup filter. For the SEARCH sites, 

378 the monthly average molar ratio is higher than at nearby IMPROVE sites and frequently close to 

379 2.0 (example IMPROVE and SEARCH sites are illustrated in Figure 9.) Values are lower at 

380 coastal SEARCH sites than at urban interior SEARCH sites but rarely fall below 1.4. There is 

381 much less seasonal variation in the SEARCH values than seen in the IMPROVE data. It is 

382 difficult to separate differences due to loss of NH4 from IMPROVE filters from those attributable 

383 to spatial differences in NH3 emissions. We can conclude, based on SEARCH NH4

384 measurements, that SO4 may not be fully neutralized in the coastal areas and possibly interior 

385 sites in the summer, and that light extinction by SO4 may be slightly underestimated using the 

386 IMPROVE equation, Eq. 1. Improved methods for measuring NH4 at the IMPROVE sites are 

387 required to verify this conclusion. 

388 

389 Organic Carbon mass. The greatest uncertainties and the largest differences in measured mass 

390 are related to organic carbon mass, OCM. STN reported values for OCM do not include a 

391 correction for background carbon on the quartz filter, however OC correction factors have been 

estimated for each of the STN monitors7392 . Quarterly average OCM values for STN sites 

393 illustrated in Figures 5-8 include the appropriate carbon correction factors. Corrected quarterly 

394 average OCM values are 20-45% lower than reported OCM values in quarters 1 and 3 of 2002. 

Quarterly average STN OCM values after correction are 1-5 μg/m3395 greater than OCM values at 

396 nearby IMPROVE sites. These differences are partially attributable to differences in TOR vs 

397 TOT analytical methods and partially attributable to differences in source strengths between 

398 urban and rural sites. STN OCM values are greater than SEARCH OCM values in Birmingham, 

399 AL for both quarters 1 and 3 and in Atlanta for quarter 3, while SEARCH OCM is higher than 

400 STN OCM in Atlanta, GA for quarter 1 (Figure 5). While local-scale variations in emissions 

Appendix B. 3 - 13
401 sources may contribute to differences in measured OCM in Birmingham and Atlanta (the 

402 SEARCH and STN monitors are not collocated), differences between analytical methods are 

403 likely the major contributors to OCM differences. 

404 

405 Measurement of total carbon on quartz filters is comparable across the three networks, but the 

406 TOT method used by STN applies a higher temperature of separation than the TOR method 

407 (used by IMPROVE and SEARCH). More of the total measured carbon is reported by STN as 

408 OC and less as EC than that reported by IMPROVE and SEARCH. Because analytical 

409 laboratories differ subtly in application of the TOR or TOT methods, there is not currently a 

410 single consistent correlation factor that can be used to compare OC reported by STN with that 

reported by IMPROVE and SEARCH23 411 . 

412 

413 Another source of uncertainty in the reported OCM is the accuracy of the assumption that a 

414 common OC multiplier of 1.4 can be used at all sites to account for the mass of other 

415 components of organic carbon compounds (e.g. hydrogen, oxygen, nitrogen, etc.). Currently 

416 IMPROVE, SEARCH, and STN consistently use an OC multiplier of 1.4. Several studies and 

417 reviews have recommended that a higher multiplier would be appropriate for organic carbon in 

urban areas influenced by fossil fuels (~1.6 +/- 0.2) 24 418 . Multipliers of 2.1 (+/- 0.2) and 2.1-2.4 

419 (+/- 0.2) are recommended for carbon from vegetative emissions and biomass burning, 

respectively24, 25 420 . If a carbon mass multiplier of 1.6 were applied to OC for measurements in 

421 urban areas and a multiplier of 1.8 were applied for rural sites, the differences in OCM between 

422 IMPROVE sites and urban STN and SEARCH sites would be less than illustrated in Figures 5-8. 

423 

424 Sources contributing to OCM vary spatially for both biogenic sources (e.g. vegetative emissions, 

425 wildfire) and anthropogenic sources (e.g. automotive, truck, or construction equipment, 

426 residential wood smoke, prescribed fire or land-clearing emissions). Chemical Mass Balance 

427 analyses use chemical profiles as indicators for gasoline, diesel, wood smoke, meat cooking, and 

vegetation to evaluate the relative contributions of different sources of primary organic carbon26 428 . 

Analysis of the 14 429 C isotope is used to identify the percentage of a carbon sample attributed to 

430 fossil fuel combustion compared to recent carbon sources (e.g. wood burning or vegetative 

emissions). Analyses of PM2.5 samples from SEARCH sites by Zheng, et al.26 431 and Edgerton, et 

Appendix B. 3 - 14
al.27 432 indicate higher proportions of recent carbon in rural areas than in urban areas and higher 

433 contributions from fossil carbon in urban areas than in rural areas. At both urban and rural areas, 

434 primary carbon sources dominate in the winter, while secondary carbon sources are more 

435 important in the summer. Wood burning contributes to the majority of the recent carbon in the 

436 winter, but it is difficult to separate the contributions of wood smoke due to anthropogenic 

437 activities (land clearing, prescribed fire, and residential wood burning) from those due to 

438 wildfire. 

439 

At Great Smoky Mountains, analysis of 14 440 C isotope in PM10 samples indicates that 55-85% of 

441 the total organic carbon mass measured in spring, summer, and fall is modern carbon (from 

442 biomass burning or secondary conversion from biogenic emissions) and 15-40% is attributable to 

fossil carbon28 443 . Using PM2.5 samples, fossil carbon was found to have a larger contribution in a 

444 nearby urban area, Chattanooga, TN, than in the Great Smoky Mountains, but still less than 

modern carbon for most samples29 445 . To develop a better understanding of the sources of carbon, 

446 VISTAS supported high-volume PM2.5 filter samplers at Mammoth Cave, Great Smoky 

447 Mountains, and Shenandoah National Parks, Cape Romain Wilderness Area, and Millbrook, NC 

448 (suburban location outside Raleigh, NC) for the period April 2004-May 2005. Twenty-four 

449 hour samples were collected on quartz filters every third day. Chemical Mass Balance analyses 

and analyses of 14 450 C isotope are underway and will provide better characterization of fossil fuel 

451 versus biogenic influences at these sites. 

452 

453 Elemental Carbon. Elemental carbon values are higher at urban STN sites than at nearby 

454 IMPROVE sites, despite the fact that the TOT method yields lower EC than the TOR method. 

455 EC values are also higher at urban SEARCH sites than at rural SEARCH and IMPROVE sites. 

456 Emissions inventories indicate that industrial point sources and fires are generally localized 

457 sources of EC while vehicles and equipment engines are more regionally dispersed sources. 

458 

459 Total Carbon Mass. Total carbon mass, TCM, (calculated as 1.4 times OC, plus EC) can be 

460 compared between networks to eliminate the effect of difference between the TOR and TOT 

461 analytical methods. Because EC is such a small fraction of TCM, the range for TCM between 

462 urban STN sites and IMPROVE sites illustrated in Figures 5-8 is the same as the range for OCM, 

Appendix B. 3 - 15
1-5 μg/m3

463 . These differences in quarterly average TCM between rural and urban sites are 

similar to those reported by Rao, et al.7 464 for annual average TCM for IMPROVE-STN pairs in the 

465 eastern U.S. 

466 

467 Fine Soil. Measurement methods for fine soils are comparable across the three networks, 

468 although SEARCH uses a slightly different calculation of component elements. Fine soil is 

469 higher at the southern and coastal sites than at the southern Appalachian sites, especially during 

470 the summer months. Construction, road dust, and agriculture are major anthropogenic sources 

471 of fine soil. Saharan dust is a major contributor to fine soil mass at the southern and coastal 

472 sites, especially Everglades, while Asian dust is seen in fine soil at northern tier sites such as 

Shenandoah in spring and early summer months20 473 . Because fine soil has low extinction 

474 efficiency, its average contribution to light extinction is generally low. 

475 

476 Coarse Mass. Coarse mass is higher at coastal IMPROVE sites than Southern Appalachian 

477 IMPROVE sites or SEARCH sites. Sea salt is an unspeciated component of coarse mass and 

478 likely contributes to higher values of coarse mass at coastal areas. 

479 

480 DISCUSSION 

481 

482 Implications for Future Monitoring 

483 The IMPROVE, STN, and SEARCH monitoring networks have similar, but differing objectives 

484 and differing analytical methods that complicate inter-comparison across networks. Additions of 

485 an acid denuder before the nylon filter to collect NH3, a backup nylon filter to collect volatilized 

486 NH4, and an acid denuder after the nylon filter to collect volatilized NH3 would improve NO3

487 and NH4 measurements at IMPROVE sites. Given that these methods are labor intensive and 

488 expensive to implement in the routine network, we recommend additional intensive studies in 

489 different seasons and IMPROVE locations to better understand NO3 and NH4 chemistry in the 

490 southeastern U.S. 

491 

492 Collated IMPROVE and STN monitors are providing information on comparability of the 

493 measurements in the two networks, and EPA is already evaluating changes to the STN protocol, 

Appendix B. 3 - 16
494 particularly for carbon, to be more similar to that used by IMPROVE. Studies currently 

495 underway in VISTAS will assist in definition of contributions of primary vs secondary 

496 carbonaceous species and fossil fuel vs recent carbon sources in the southeastern U.S. This 

497 information, combined with other studies, can be used to improve estimates of the OCM 

498 multiplier appropriate to the urban and rural areas in the southeastern U.S. 

499 

500 Implications for Atmospheric Modeling 

VISTAS is modeling air quality in 200221 501 as the base year for regional haze and PM2.5 State 

502 Implementation Plans. A critical component of this analysis is to demonstrate that the 

503 performance of the atmospheric model meets established performance goals. Model 

504 performance is constrained by measurement uncertainty as well as uncertainties in emissions 

505 inventories and representation of chemical and transport processes in the model. Differences in 

506 network measurement methods contribute to measurement uncertainty, especially for organic 

507 carbon mass, and these differences need to be considered when evaluating model performance 

508 across the networks. Sulfate measurements are comparable across the networks. SEARCH NO3 

509 measurements are likely more accurate than IMPROVE and STN measurements, but all three 

510 networks report fairly low NO3 concentrations. Because NO3 concentrations are low, model 

performance can tolerate larger bias and error than for SO4 and OCM components21 511 . Given the 

512 likely underestimate in NH4 from the IMPROVE nylon filters, we cannot recommend using the 

513 measured NH4 mass from IMPROVE nylon filters as a better estimate of NH4 for IMPROVE 

514 sites than inferred NH4 based on measured SO4 and NO3. For STN sites, OCM values should be 

515 corrected for background carbon before comparison to modeled OCM values. Model 

516 performance evaluation should also consider that OC reported by STN is defined by TOT 

517 analysis and is likely at least 20% higher than OCM reported by SEARCH and IMPROVE based 

518 on TOR analysis. Consideration should be given to using a higher carbon mass multiplier than 

519 1.4 for all three networks (in the range between 1.6-2.0, possibly selecting a lower multiplier for 

520 urban areas than rural areas) to account for chemical species associated with carbon. 

521 

522 CONCLUSIONS 

523 Fine particle mass and light extinction are dominated by ammonium sulfate in the southeastern 

524 U.S. Ammonium sulfate levels are generally higher in the northern tier of the VISTAS states 

Appendix B. 3 - 17
525 and lower at the coastal and southern sites. Regional SO2 controls should be effective in 

526 reducing fine particulate sulfate and its contribution to light extinction and PM2.5 mass in the 

527 VISTAS states. Levels of organic carbon, elemental carbon, and ammonium nitrate are 

528 generally higher in urban areas than rural and Class I areas. A combination of local and regional 

529 sources is likely contributing to these fine particle components. Improved understanding of the 

530 relative contributions of forest fire, land clearing, or residential wood burning will be necessary 

531 to determine what reductions in carbon from wood burning are feasible at urban and Class I 

532 areas. Fossil carbon sources are important contributors to OCM in urban areas, suggesting 

533 greater opportunity for reduction in OCM in response to anthropogenic emissions controls than 

534 at Class I areas. Because secondary carbonaceous species from biogenic emissions are major 

535 contributors to OCM in the southeastern U.S., particularly in the summer months, even after 

536 anthropogenic controls, OCM will likely remain a major component of PM2.5 and visibility in 

537 the VISTAS region. 

538 

539 ACKNOWLEDGEMENTS

540 

541 The work reported in this paper was supported by the VISTAS regional planning organization 

542 through its funding agency the Southeastern Air Resource Managers, Inc. and grants from the 

543 Environmental Protection Agency. The authors thank the VISTAS Data Workgroup for 

544 technical direction and are especially grateful to Dr. Ivar Tombach for technical review and 

545 editorial recommendations. 

546 

547 REFERENCES 

548 

549 1. Regional Haze Regulations; Final Rule. Federal Register, 1999, 64, 35713-35774. 

550 2. U.S. Environmental Protection Agency. PM2.5 Air Quality Trends Update 2002-2004.

551 www.epa.gov/airtrends/pdfs/pm25dv20050824.pdf. Data retrieved September 2005. 

552 3. Malm, W.C.; Pitchford, M.L; Scruggs, M.; Sisler, J.F.; Ames, R.; Copeland, S.; Gebhart, K.; 

553 Day, D.E. Spatial and Seasonal Patterns and Temporal Variability of Haze and Its 

Appendix B. 3 - 18
554 Constituents in the United States. Report III. Cooperative Institute for Research in the 

555 Atmosphere, May 2000. 

556 4. U.S. Environmental Protection Agency. Particulate Matter (PM2.5) Speciation Guidance, 

557 Final Draft. 1999. http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/specfinl.pdf 

558 5. Hansen, D.A.; Edgerton, E.S.; Hartsell, B. E.; Jansen, J.J.; Kandasamy, K.; Hidy, G. M.; 

559 Blanchard, C. L. The Southeastern Aerosol Research and Characterization Study: Part 1 - 

560 Overview; J. Air & Waste Manage. Assoc. 53, 1260-1471, 2003. 

561 6. Aerometric Information Retrieval System Database. U. S. Environmental Protection Agency 

562 http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebhome.htm. Data retrieved June 2005. 

563 7. Rao, V., Frank, N., Rush, A., Dimmick, F. Chemical Speciation of PM2.5 in Urban and 

564 Rural Areas in National Air Quality and Emissions Trends Report, 2003. 

565 http://www.epa.gov/air/airtrends/aqtrnd03 

566 8. IMPROVE website, http://vistas.cira.colostate.edu/improve/ Data retrieved June 2005. 

567 9. VIEWS website, http://vista.cira.colostate.edu/views/ Data retrieved June 2005. 

568 10. SEARCH website, http://www.atmospheric-research.com/studies/SEARCH/ Data retrieved 

569 June 2005. 

570 11. U.S. Environmental Protection Agency. Guidance for Tracking Progress under the Regional 

571 Haze Program; EPA-54/B-03-004, 2003. 

572 12. Malm, W.C.; Sisler, J.F.; Huffman, D.; Eldred, R.A.; Cahill, T.A. Spatial and seasonal trends 

573 is particle concentration and optical extinction in the United States. J. Geophys. Res. 99, 

574 1347-1370, 1994. 

575 13. Malm, W.C.; Molenar, J.V.; Eldred, R.A.; Sisler, J.F. Examining the relationship among 

576 atmospheric aerosols and light scattering and extinction in the Grand Canyon area, J. 

577 Geophys. Res. 101, 19,251-19,265, 1996. 

578 14. Malm, W.C.; Day, D.E. Optical properties of aerosols at Grand Canyon National Park. 

579 Atmos. Environ. 34, 3373-3391, 2000. 

580 15. Malm, W.C.; Day, D.E.; Kreidenweis, S.M. Light Scattering Characteristics of Aerosols as a 

581 Function of Relative Humidity: Part I -- A Comparison of Measured Scattering and Aerosol 

Appendix B. 3 - 19
582 Concentrations Using the Theroretical Models. J. Air & Waste Manage. Assoc 50, 686-700, 

583 2000. 

584 16. Hand, J. L., and Malm, W. C. Review of the IMPROVE Equation for Estimating Ambient 

585 Light Extinction Coefficients. 

586 http://www.vista.cira.colostate.edu/improve/Publications/Graylit/016_IMPROVEeqReview/I

587 MPROVEeqReview.htm. 

588 17. Ryan, P. A., Lowenthal, D., Kumar, N. Light Scattering in Light Extinction Reconstruction. 

589 J. Air & Waste Manage. Assoc., this issue, 2005. 

590 18. Blanchard, C.L. and Hidy, G.M. Effects of Changes in Sulfate, Ammonia, and Nitric Acid 

591 on Particulate Nitrate Concentrations in the Southeastern United States. J. Air & Waste 

592 Manage. Assoc. 53,283-290, 2003. 

593 19. Air Resource Specialists, Inc. Analysis of Visibility and Particulate Data Within the 

594 VISTAS Region. May 2003. ftp://ftp.air-resource.com/VISTAS. 

595 20. Tombach, I. and Brewer, P. Natural Background Visibility and Regional Haze Goals in the 

596 Southeastern United States. J. Air & Waste Manag. Assoc., this issue, 2005. 

597 21. Morris, R. E., Kou, B. Guenther, A., McNally, D. E., Tesche, T.W., Tonnessen, G., Boylan, 

598 J. W., and Brewer, P. Model Sensitivity Evaluation for Organic Carbon Using Two Multi599 pollutant Air Quality Models that Simulate Regional Haze in the Southeastern United States 

600 Atmos. Environ., In press, 2005. 

601 22. Yu, X.-Y., Lee, T., Ayres, B., Kreidenweis, S. M., Malm, W., and Collett, J.L. Loss of fine 

602 particle ammonium from denuded nylon filters. Submitted to Atmos. Environ., 2005. 

603 23. Watson, J. Visibility: Science and Regulation. J. Air & Waste Manage. Assoc. 52, 626-713. 

604 2002. 

605 24. Turpin, B. J.; Lim, H-J. Species Contributions to PM2.5 Mass Concentrations: Revisiting 

606 Common Assumptions for Estimating Organic Mass. Aerosol Sci. Tech., 35, 602-610, 2001. 

607 25. McMeeking G. R.; Kreidenweis, S. M.; Carrico, C. M.; Lee, T.; Collett, J. L., Jr.; Malm, W. 

608 C. Observations of smoke-influenced aerosol during the Yosemite Aerosol Characterization 

609 Study: Size distributions and chemical composition, J. Geophys. Res., 110, D09206, 

610 doi:10.1029/2004JD005389, 2005. 

Appendix B. 3 - 20
611 26. Zheng,, M.; Cass, G.R.; Schauer, J.J.; Edgerton, E.S. Environ. Sci. Tech. 36, 2361-2371, 

612 2002. 

613 27. Edgerton, E. S., Hartsell, B. E., and Jansen, J. J. Natural Sources of PM2.5 and PMcoarse

614 Observed in the SEARCH network. Regional and Global Perspectives on Haze: Causes, 

615 Consequences and Controversies - Visibility Specialty Conference, Air and Waste 

616 Management Association, Asheville, NC, October 25-29, 2004. 

617 28. Tanner, R.L., Parkhurst, W. J., and McNichol, A.P. Fossil Sources of Ambient Aerosol 

Carbon based on 14 618 C measurements. Aerosol Sci. Tech., 38(S1), 133-139, 2004. 

619 29. Tanner, R. L., Zheng, M., Lin, K., Schauer, J. J., and McNichol, A.P. Source Contributions 

620 to the Organic Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural 

621 Differences. Presentation to AAAR International Specialty conference, Particulate Matter: 

622 Supersites Program and Related Studies”, Atlanta, GA, Feb 7-11, 2005. 

 623 

Appendix B. 3 - 21
Brewer, 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 X 

Appendix B. 3 - 22
Brewer, 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. 

Appendix B. 3 - 23
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. 

Appendix B. 3 - 24
Appendix B. 3 - 25
0

50

250

200

150

100

Extinction (Mm-1)

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Shining Rock, NC

Cohutta, GA

Saint Marks, FL 

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Appendix B. 3 - 26
Extinction (Mm-1)

0

60

50

40

30

20

10

Dolly Sods, WV

Shenandoah, VA

James Rvier Face, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Linville Gorge, NC

Shining Rock, NC

Cohutta, GA

Saint Marks, FL 

Swan Quarter, NC

Cape Romain, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Appendix B. 3 - 27
5.0

0.0

20.0

15.0

10.0

Q1

Q2

Q3Q4

Q1Q2Q3Q4

Q1

Q2

Q3

Q4

Q1Q2

Q3

Q4

Q1Q2

Q3Q4

g/m3

μ

Mammoth

Cave, KY

Okefenokee,

GA

Everglades

FL

Cape 

Romain, SC

Great Smoky 

Mtn., TN

Appendix B. 3 - 28
5.0

0.0

25.0

20.0

15.0

10.0

g/m3 μ Fine Particle Mass -

Shenandoah, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Shining Rock, NC

Atlanta, GA

Birmingham, AL

Louisville, KY

Knoxville, TN

Atlanta, GA

Charlotte, NC

Roanoke, VA

Cohutta, GA

D

ll

S

d

WV

Birmingham, AL

Appendix B. 3 - 29
Swan Quarter, NC

Cape Romain, SC

Tampa, FL

St. Marks, FL

Charleston, SC

Okefenokee, GA

Savannah, GA

Chassahowitzka, FL

Everglades, FL

Pensacola, FL

Breton Island, LA

Gulfport, MS

5.0

0.0

25.0

20.0

15.0

10.0

g/m3 μ Fine Particle Mass -

Appendix B. 3 - 30
8.0

4.0

0.0

16.0

12.0

g/m3 μ Fine Particle Mass -

Shenandoah, VA

Mammoth Cave, KY

Sipsey, AL

Great Smoky Mtns, TN

Shining Rock, NC

Atlanta, GA

Birmingham, AL

Louisville, KY

Chattanooga, TN

Atlanta, GA

Charlotte, NC

Roanoke, VA

Cohutta, GA

Dolly Sods

WV

Birmingham, AL

Appendix B. 3 - 31
8.0

4.0

0.0

16.0

12.0

Swan Quarter, NC

Cape Romain, SC

Tampa, FL

St. Marks, FL

Charleston, SC

Okefenokee, GA

Chassahowitzka, FL

Everglades, FL

Pensacola, FL

Breton Island, LA

Gulfport, MS

g/m3 μ Fine Particle Mass -

Appendix B. 3 - 32
2.5

2.0

1.5

1.0

0.5

0.0

Jan-02

Mar-02

May-02

Jul-02

Sep-02

Nov-02

Jan-03

Mar-03

May-03

Jul-03

S

Molar Ratio

Birmingham, AL

Great Smoky Mtn, TN

Gulfport, MS

Mammoth Cave, KY

SEARCH

IMPROVE Cap

Appendix B. 3 - 33
Appendix B.4 

Natural Background Visibility and 

Regional Haze Goals in the 

Southeastern United States 
1 Natural Background Visibility and Regional Haze 

2 Goals in the Southeastern United States 

3 Ivar Tombach

4 Consultant, 753 Grada Ave., Camarillo, CA 93010 

5 Patricia Brewer

6 VISTAS, 2090 U.S. 70 Highway, Swannanoa, NC 28778 

7 ABSTRACT 

8 The goal of the regional haze mitigation program in the United States is to attain “natural 

9 conditions” in national parks and wilderness areas by 2064. Results of research investigations on 

10 background concentrations of sea salt and biogenic organic matter, of episodic Saharan and 

11 Asian dust, and of carbon from natural fires were reviewed to provide a basis for making site12 specific estimates of what the concentrations of atmospheric fine particulate matter components 

13 might be under natural conditions in the southeastern United States. Based on this review, rough 

14 estimates were made of potential contributions of these aerosol components to natural 

15 background visibility. Natural organic particles were the dominant influence on the rate of 

16 visibility improvement required to reach natural conditions at an inland, mountainous location 

17 and organic particles and sea salt were the dominant influences on the rate at a coastal location. 

18 African dust also had a large episodic effect, but the current regulatory approach is not designed 

19 to address episodic background variations. Insufficient data exist to quantify the contributions of 

20 wildfires with any detail, although global air pollution modeling provides insight and their 

21 emissions can be locally dominant. Conservative regional refinements to the default natural 

22 background estimates do not greatly alter the region-wide rates of reduction of ambient 

23 particulate matter concentrations that will be needed to accomplish the first phase of the regional 

24 haze program. However, refinements at specific Class I areas may have considerable influence 

25 on defining the nature (magnitude and spatial and temporal distribution) of local emission 

26 reduction efforts there.

27 INTRODUCTION 

Appendix B.4 - 1
2

The United States Environmental Protection Agency’s Regional Haze Rule128 prescribes that 

29 States are to make efforts to improve visibility in 156 national parks and wilderness areas (“Class 

30 I areas”) at such rates that "natural conditions" would be achieved in each Class I area by 2064. 

31 A "reasonable rate of progress" corresponds to linear improvement in visibility, as characterized 

32 in units of deciviews, between current conditions during the base period of 2000-2004 and 

33 natural conditions at the end point of 2064. 

34 To provide a starting point for the regional haze planning process, the U.S. EPA has 

35 issued guidance for estimating natural visibility conditions based on assumed natural background 

concentrations of the chemical components of particulate matter.236 Recognizing that these 

37 “default” concentration estimates may not be appropriate for every Class I area, the guidance 

38 document also provides a State with the option of developing a "refined" estimate of natural 

39 conditions that reflects local circumstances. 

40 This paper discusses the attributes of the default approach, identifies issues that might 

41 suggest consideration of a refined method, and provides a survey of the current scientific 

42 understanding of background and natural particulate matter (PM) component concentrations. 

43 (Note that the concept of achieving “natural conditions” by a regulatory program in the United 

44 States is an idealistic one, in that it assumes that the only pollutants ultimately transported into 

45 the U.S. across its borders will be natural ones and ignores potential contributions from 

46 anthropogenic sources outside the country. Although the issue of transboundary transport is 

47 important in the implementation of U.S. regional haze policy, it will not be addressed here. 

48 Rather, we focus on the scientific issues involved in estimating natural conditions in the absence 

49 of transboundary anthropogenic contributions.) 

50 The discussion here focuses on the geographic region encompassed by the VISTAS 

51 Regional Planning Organization, essentially the southeastern United States. VISTAS (Visibility 

52 Improvement – State and Tribal Association of the Southeast) is an association of ten States, the 

53 Eastern Band of Cherokee Indians, and local air pollution control agencies. The VISTAS States 

54 and the Class I areas within them are shown in Figure 1. 

55 Of importance to VISTAS is whether the limitations in the default concentration 

56 estimates are significant, particularly for the development of emission control strategies designed 

57 to reach a first phase goal by 2018 -- i.e., could the decisions that VISTAS will be making about 

58 initial emission management strategies be affected by uncertainties in the natural background 

Appendix B.4 - 2
3

59 concentration estimates? And, as a corollary, will it be worthwhile to consider refining the 

60 estimates for some components of particulate matter using the new information that has become 

61 available since the default natural concentration estimates were developed? 

62 Fortuitously, the global climate change research community has carried out substantial 

63 research on global background and natural aerosols in the past decade. Their interest is to 

64 separate the natural and anthropogenic contributions by particulate matter to global radiation 

65 balance, a focus that neatly parallels the regional haze interests here. Consequently, the 

66 information base now available for estimating natural background concentrations greatly exceeds 

67 that of the pre-1990 research upon which the EPA’s default guidance is based. 

68 To address the questions raised and to take advantage of the new data, this paper reviews 

69 the basis for the default concentration estimates, discusses two technical issues concerning 

70 estimating light extinction from those concentrations, surveys relevant research findings that 

71 provide insight into natural conditions PM component concentrations in the Southeast, and 

72 discusses the implications of this new information on control strategy planning. 

73 BACKGROUND -- THE DEFAULT ESTIMATES OF NATURAL CONDITIONS 

74 In September 2003, the U.S. EPA issued Guidance for Estimating Natural Visibility Conditions 

Under the Regional Haze Rule,

2

75 to provide initial guidance for estimating natural conditions. 

76 That document recommends that natural extinction levels be estimated from the default 

77 component concentrations by using the formula established by IMPROVE for estimating current 

78 light extinction from measurements of the concentrations of the main components of fine 

79 particulate matter. (IMPROVE stands for Interagency Monitoring of Protected Visual 

80 Environments, the national visibility-related PM component monitoring program.) The 

81 IMPROVE formula estimates light extinction from the concentrations of fine ammonium sulfate, 

82 ammonium nitrate, particulate organic matter, light absorbing carbon, and fine soil, plus coarse 

83 particles: 

bext 84 = 3f(RH)[SULFATE] + 3f(RH)[NITRATE] + 4[POM] + 1[SOIL] 

85 + 0.6[CM] + 10[LAC] + 10, (1)

where the concentrations, indicated by square brackets, are in μg/m3; bext is in Mm-1 86 ; and the 

87 numerical coefficients before each term, which represent extinction efficiencies (light extinction 

per unit mass), are in m288 /g. SULFATE is assumed to be in the form of ammonium sulfate; 

Appendix B.4 - 3
4

89 NITRATE is assumed to be ammonium nitrate; and POM is calculated as 1.4[OC] where [OC]

90 represents the organic carbon concentration in particulate organic matter [POM]. (The 

91 IMPROVE program labels POM as OMC and, as with POM here, uses that label to describe only 

92 the fine fraction.) CM represents coarse matter, particles between 2.5 and 10 μm diameter. Light 

93 absorbing carbon (LAC) is sometimes interchanged with elemental carbon (EC). Although the 

94 two are not exactly equivalent, we will use the terms interchangeably here. The constant 10 at the 

95 end approximates Rayleigh scattering by air; it is the correct value at an altitude of about 1500 m 

96 (5000 ft). A humidity-dependent enhancement factor, f(RH), is used to account for the increased 

97 light scattering by sulfates and nitrates as the relative humidity increased. Monthly-average 

98 "climatologically-representative" values of f(RH) for each Class I area have been published by 

the U.S. EPA and are to be used for the purposes of the Regional Haze Rule.299 

100 Much of the Regional Haze Rule describes visibility in terms of a haze index (HI), in 

101 units of deciviews (dv). The haze index is determined via the formula

102 HI = 10 ln(bext/10), (2)

using the bext (in Mm-1 103 ) derived in eq 1. 

104 Estimates of natural background concentrations of the particulate matter components in 

105 eq 1 were developed in 1990 by Trijonis for the National Acid Precipitation Assessment Program 

(NAPAP).3106 He estimated annual average concentrations of chemical components of particulate 

107 matter for both the eastern United States and the mountain/desert regions of the western United 

108 States. The East was defined as extending up to one tier of states west of the Mississippi River, 

109 thus the VISTAS domain falls entirely within that region. 

110 The U.S. EPA has adopted Trijonis’ estimates of natural background concentrations, after 

adjusting his values to make them consistent with current IMPROVE measurement conventions.2111 

112 The resulting default concentrations for the East are listed in Table 1, along with Trijonis’ 

113 estimates of the errors in his values. The last two columns list the dry extinction efficiencies (the 

114 numerical coefficients in eq 1) for each component and the estimated contribution of each 

115 component to light extinction under “dry” conditions (relative humidity ≤ 36%), as calculated 

116 using eq 1 with f(RH) = 1. 

117 Note that particulate organic matter is overwhelmingly the largest contributor to natural 

118 background extinction, in contrast to the present situation in the East where sulfates usually 

119 dominate light extinction. 

Appendix B.4 - 4
5

120 Trijonis’ error factors show that there are large uncertainties in his concentration 

121 estimates, typically by a factor of two or so. These uncertainties reflect the limited information 

122 he had available for developing his estimates and the result of assigning of a single set of 

123 numbers to characterize concentrations throughout the year over the large geographic area of the 

124 eastern United States. 

125 Trijonis arrived at his estimates for the eastern United States as follows, using the 

126 information that was available to him in 1990: 

127 • Fine organic particulate matter (POM) – He made ad hoc adjustments to 

128 measurements then available for the rural East (i.e., he assumed that 1/3 of the 

average measured concentration of 4 μg/m3129 is natural), which were supported by 

130 measurements of organic aerosol concentrations in remote areas (which suggest a 

background of 1.5 to 2.25 μg/m3131 in the northern hemisphere). (Note that there is a 

132 difference between background concentrations in remote areas today and 

133 concentrations under natural conditions. The former can include global background 

134 from anthropogenic emissions and thus may differ from the natural value.) 

135 • Fine sulfates – He estimated that 3% of sulfur emissions in the East are due to natural 

136 sources (based on the NAPAP emission inventory), considered rural measurements in 

137 the southern hemisphere, and considered background estimates made for central 

138 Alaska. 

139 • Fine elemental carbon – He estimated that 2.8% of EC emissions are due to wildfires 

140 and prescribed burning (from fire emissions information in the NAPAP emission 

141 inventory) and considered that global background EC concentrations are very small. 

• Fine ammonium nitrate – He considered nitrate levels (about 0.1 μg/m3142 ) in tropical 

143 forests and observed that the NAPAP emission inventory indicated that natural NOx

144 emissions are 4.5 times as great as those of gaseous sulfur (on a molar basis) but that 

145 atmospheric nitrate production should be relatively less that that of sulfur. 

146 • Fine soil -- Measurements suggested an average fine soil concentration of about 1 

μg/m3147 in the East. He arbitrarily assumed that half of that amount is natural. 

148 • Coarse matter – He used the lower end of the range of coarse mass concentration 

149 measurements at "remote" sites in the U.S. 

Appendix B.4 - 5
6

150 Thus, Trijonis based his estimates on the limited base of data that was available at the 

151 time and interpreted it with a healthy dose of intuition, so the large error factors he assigned to 

152 his own estimates are not surprising. 

153 Figure 2 displays the annual average estimates of bext that result from applying the default 

154 concentrations of Table 1 together with the EPA’s site-specific, climatologically-representative 

155 f(RH) at four of the Class I areas in the VISTAS region, and identifies the contribution of each 

156 particulate matter component (and any water associated with it) to that extinction. The bars in the 

157 figure are labeled with the IMPROVE abbreviations for the names of the Class I areas the 

158 monitors represent – GRSM = Great Smoky Mountains National Park; MACA = Mammoth 

159 Cave National Park; ROMA = Cape Romain Wilderness within the Cape Romain National 

160 Wildlife Refuge; and EVER = Everglades National Park. 

161 The extinction coefficient and haze index values that result from the application of eqs 1 

162 and 2 (using the concentrations in Table 1 and site-specific, climatologically-representative 

163 monthly f(RH) values) are intended to represent default estimates of the average natural 

164 conditions light extinction during each month, though based on annually constant default 

165 concentrations. Since the Regional Haze Rule specifies that reasonable progress in mitigating 

166 regional haze is to consist of improvement on the 20% of the days that are most hazy and of no 

167 deterioration on the 20% of the days that are least hazy (clearest), the natural conditions 

168 extinction value that is most relevant is the one that applies to the 20% of the days that are the 

169 most hazy. 

Ames and Malm4170 developed a statistical procedure to estimate the average of the 

background extinction on the 20% haziest days (which they assumed is represented by the 90th 171 

172 percentile of extinction values) from the annual average default values. They assumed that the 

haze index under natural conditions would be normally distributed, in which case the 10th 173 and 

90th 174 percentile values can be related to the mean and standard deviation of the distribution. The 

175 mean values were calculated using Trijonis' default concentrations. To estimate the standard 

176 distribution of natural conditions, they adjusted IMPROVE-measured frequency distributions of 

177 today's haze indices at each Class I area by reducing actual sulfate and nitrate concentrations 

178 until, on average, they matched the Trijonis default values in the East and the West. Measured 

179 concentrations of organic carbon, elemental carbon, fine soil, and coarse matter were not reduced 

Appendix B.4 - 6
7

180 similarly, under the assumption that at Class I areas they represent local concentrations that 

181 already derive largely from natural sources. 

182 From such synthetic frequency distributions at several Class I areas, Ames and Malm 

183 estimated one representative value of the standard deviation for the East and another one for the 

West, enabling calculation of 10th and 90th 184 percentile estimates. They concluded that, for the 

East, the 90th 185 percentile value for any given Class I area is 3.84 dv greater than the annual mean 

186 haze index (in deciviews) that is calculated from the default concentrations and f(RH). Based on 

this procedure, Appendix B of EPA's guidance document2187 presents default estimates of the haze 

188 index that are intended to represent the 20% haziest and 20% clearest days under natural 

189 conditions at each Class I area. 

190 SOURCES OF NATURAL BACKGROUND PARTICULATE MATTER 

191 Sources of gas and particulate emissions that contribute to natural particulate matter can be 

192 grouped into two classes depending on their variability during the year – essentially steady or 

193 episodic. Since haze calculations under the Regional Haze Regulations are based on 24-hour 

194 averages, diurnal variations are not of import to us here. 

195 Considering potential contributors, relatively steady-state natural sources (i.e., ones that 

196 vary slowly throughout the year or within a season, though they may reflect diurnal variability 

197 and even flare up at times) include the following: 

198 • Oceans and tidal zones– sources of sea salt (affected by the amount of wind-raised 

199 spray) and also sulfates and organic carbon (both primary and secondary). The 

200 primary on-land effect occurs within tens of kilometers to, at most, a few hundred 

201 kilometers of the shoreline. 

202 • Forests and vegetation – sources of organic carbon (both primary and secondary), 

203 coarse organic matter (from plant detritus), sulfates, and nitrates. Emissions result 

204 from plant photosynthesis and respiration and also from decaying vegetation. 

205 Ammonia produced in forests plays a role in the formation of ammonium sulfate, 

206 ammonium nitrate, and even organic particles. 

207 • Lightning – source of nitrates (relatively steady from day to day in some regions and 

208 seasons). 

Appendix B.4 - 7
8

209 • The earth's crust – source of fine and coarse soil, including sulfates and nitrates, due 

210 to wind and natural disturbances. 

211 • Volcanoes – sources of sulfates and nitrates (both primary and secondary) and of soil 

212 particles. The main volcano currently affecting the VISTAS region is Popocatepetl in 

213 southern Mexico, whose SO2 emissions alone were estimated by the BRAVO Study

to average 3500 tonnes (3800 tons) per day.5214 These emissions are from the ultimate 

215 "tall stack" at 5465 m (18050 feet) above sea level (before plume rise), and thus can 

216 be transported very long distances along different pathways than lower-elevation 

217 emissions. 

218 The principal episodic natural sources include: 

219 • Windstorms – sources of soil particles, which can be transported long distances, even 

220 intercontinentally (examples are documented North African and Asian dust impacts 

221 on the United States). 

222 • Wildfires – sources of organic carbon and, to a lesser degree, elemental carbon and 

223 soil, which can also be transported long distances (an example is the observed effect 

224 of transported Central American biomass burning emissions on the U.S. South, 

225 although most of this burning is man-caused). 

226 If EPA’s default natural concentration values are valid, the contributions of all of these 

227 steady-state and episodic sources should already be reflected in those default concentration 

228 estimates, at least on average. At any specific location and time, however, conditions could 

229 deviate considerably from the default values. 

230 Subsequent to the development of the default values, natural background concentrations 

231 over the entire globe have been estimated by applying air quality models. As examples, such 

232 simulations have produced estimates for the southeastern U.S. of concentrations of OC and EC 

from natural sources, biomass burning, and all sources, with the Grantour model;6233 of 

concentrations of all components, with the GEOS-CHEM model;7, 8 234 and of concentrations of 

235 anthropogenic and biogenic secondary organic aerosol (SOA, the secondary fraction of POM), 

with the MOZART-2 model.9236 Specifically, the most recent application of the GEOS-CHEM 

model8237 yielded the following estimates of annual average natural background concentrations in 

the U.S. East: ammonium sulfate, 0.11 μg/m3; ammonium nitrate, 0.03 μg/m3238 ; organics, 1.17 

Appendix B.4 - 8
9

μg/m3, and elemental carbon, 0.04 μg/m3239 . These can be compared with the default values in 

240 Table 1, with the caveat that the GEOS-CHEM modeling values include effects of worldwide 

241 “climatological” (i.e., multi-year average) fire emissions, both natural or anthropogenic, and thus 

242 may overstate the natural portion of carbon concentrations alone. 

243 Such global modeling could provide finer spatial resolution than the East-West split of 

244 the default values and could be used to develop seasonal or monthly estimates of the natural 

245 background. As uncertainties in emission inventories, particularly for natural emissions, are 

246 being reduced, global modeling is improving in accuracy and its results should be considered in 

247 parallel with the default estimates of Trijonis when establishing refined natural background 

248 values. 

249 We conducted a review of the scientific understanding of the emissions and ambient 

250 concentrations of all the source types mentioned above, to evaluate which of those source types 

251 are most likely to cause appreciable impact to natural conditions concentrations in any part of the 

VISTAS region.10 252 An important consideration was whether the scientific information base is 

253 strong enough to develop credible quantitative estimates of aerosol concentrations resulting from 

254 each of the above emissions categories. The potential impact of each emission category on 

255 natural visibility in the VISTAS region, and the credibility and importance associated with each 

256 impact relative to the default conditions, was estimated. 

257 As a result of that review, contributions of the following four categories of sources were 

258 considered for potential inclusion in refined estimates of natural conditions and thus have been 

259 investigated further: 

260 • organic and elemental carbon from wildfires, 

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

262 • oceanic sea salt and POM, and 

263 • long range transport of windblown dust from Asia and North Africa. 

264 Impacts from other source categories mentioned earlier were considered to be too small 

265 to be important or our understanding of them at this time was too limited to enable development 

266 of credible concentration estimates. 

Appendix B.4 - 9
10

267 In addition, the statistical procedure used by the EPA to derive extinction under natural 

268 conditions for the average 20%-haziest days and the empirical factor used to calculate POM 

269 concentrations from measured OC concentrations were considered worthy of review. 

270 Alternative assumptions for extinction efficiencies in eq 1 would also affect calculated 

271 light extinction under both current and natural background conditions. These alternatives are not 

discussed in this paper and the reader is referred to recent reviews of the topic.11 - 13 272 

273 The rest of this paper discusses the following issues: (1) statistical estimation of 

274 extinction under natural conditions for the average of the haziest 20% days, (2) the choice of the 

275 scaling factor for OC mass, and (3) current knowledge about the four categories of sources listed 

276 above. Included are initial estimates (sometimes crude) of the potential magnitudes of these 

277 issues on natural background concentrations and their potential impacts on developing 

278 reasonable progress goals. The questions then addressed in conclusion are whether such effects 

279 are substantial enough at some locations to materially affect the slope of the required "reasonable 

280 progress" line, and might there be areas where additional effort to refine natural background 

281 estimates would be justified? 

282 POTENTIAL REFINEMENTS TO THE DEFAULT VALUES

283 Statistical Estimation of the Haziest 20% of Days 

Ames and Malm4284 assumed that the frequency distribution of haze index values over a year is 

normally distributed. For a normal distribution, the haze indices at the 10th and 90th 285 percentiles of 

286 the haze distribution under natural conditions can be calculated using the following formula: 

287 HI = <HI> ± 1.28σ, (3) 

288 where HI is the haze index (in deciviews), <HI> is the annual mean haze index for the Class I 

area of interest (from the EPA guidance document2

289 ), and σ is the standard deviation of the 

assumed normal distribution of HI. Using the plus sign produces the 90th 290 percentile value and 

using the minus sign produces the 10th 291 percentile value. The term 1.28σ represents the distance 

from the mean to the 10th and 90th 292 percentiles. 

293 Using the synthesized natural concentrations frequency distribution described earlier, 

294 Ames and Malm estimated σ to average 3 in the East and 2 in the West under natural conditions, 

295 so the formula for the East becomes simply 

Appendix B.4 - 10
11

296 HI = <HI> ± 3.84. (4) 

However, since the HI frequency distribution from the 80th to the 100th 297 percentiles is not 

uniform in a normal distribution, the HI value at the midpoint (90th 298 ) percentile of that range does 

299 not actually represent the mean of the 20% haziest days that is prescribed by the Regional Haze 

300 Regulations. Rather, the mean value for that range in a normal distribution is represented by the 

haze index at the 92nd percentile.14 301 Consequently, the 1.28 factor should be 1.42 if the 

302 distribution is normal, in which case the formula for the East becomes 

303 HI = <HI> ± 4.26. (5) 

304 Thus, for the East, the haze indices for the average of the haziest 20% of the natural days should 

305 be 0.42 dv greater than the default values that are presented in the EPA's guidance document 

306 (and those for the clearest 20% of the natural days should be 0.42 dv less), if a normal 

307 distribution is assumed. 

308 

309 Mass Concentration of Particulate Organic Matter 

310 Trijonis estimated the natural background concentration of organic carbon (OC) in the East to 

average 1 μg/m3311 . This estimate should include both emissions from natural fires and biogenic 

312 emissions from vegetation. To account for the oxygen, hydrogen, nitrogen, and other elements 

313 that are contained in POM, he multiplied his OC value by 1.5; to conform with current 

314 IMPROVE practice, the EPA has used a factor of 1.4 for the same adjustment for Table 1. The 

315 global aerosol research community generally appears to use a factor of 1.6 to 1.8 (see, for 

316 example, references 15 and 16). Considering individual species, the known range of the 

multiplier spans from 1.17 for large saturated hydrocarbons to 3.75 for oxalic acid.17 317 

318 A comprehensive review of experimental and theoretical research results by Turpin and 

Lim18 319 led to the conclusions that the appropriate multiplier for ambient OC will vary depending 

320 on the origin and age of the aerosol and that the value of 1.4 does not accurately represent a non321 urban aerosol. Rather, they concluded that the appropriate factor for a non-urban aerosol is 2.1 ± 

322 0.2. They also concluded that the appropriate value for an urban aerosol is 1.6 ± 0.2, which 

323 approximates the 1.5 and 1.4 factors used by Trijonis and the EPA and IMPROVE. Turpin and 

324 Lim’s value for an aerosol heavily impacted by wood smoke is 2.4 ± 0.2. 

Appendix B.4 - 11
12

325 It thus seems reasonable that the OC multiplier that would best represent natural 

326 conditions most of the time would be about 2.1 (Turpin and Lim’s value for a non-urban 

327 aerosol), while 2.4 could represent natural conditions during episodic fire events. Using the 2.1 

factor, the estimated mass concentration of POM in Table 1 would be 2.1 μg/m3328 and the 

extinction contribution would become 8.4 Mm-1 329 , which increases the overall dry extinction of 

9.09 Mm-1 to 11.89 Mm-1, an increase of 2.8 Mm-1 330 or 31%. This corresponds to an increase of 

331 2.7 deciviews under dry conditions. 

332 A different multiplicative factor than 2.1 is needed for deducing current POM 

333 concentrations from IMPROVE OC measurements, though, because today's organic aerosol may 

334 include an urban component or reflect smoke from intentional burning of vegetation. For 

335 example, ratios of POM concentration to OC concentration in Class I areas have been found to 

be 1.8 and 2.0 in recent research studies.19, 20 336 A recent evaluation of the literature recommends a 

value of 1.8 for IMPROVE monitoring sites.11 337 

338 Concentrations of POM mentioned in the next few sections will be based on the factor of 

339 1.4, unless otherwise indicated. If the factor used by the investigator to scale measured or 

340 modeled OC concentrations is different or if the model directly presents POM concentrations, 

341 such a situation will be identified. The implications of changing to a different factor for natural 

342 conditions will be addressed after we have surveyed results of scientific research concerning the 

343 contributions of the sources listed earlier.

344 

345 Carbon from Wildfires

346 Wildfires can affect air pollutant concentrations over large geographic areas. Effects of large 

347 wildfires in northwestern Canada have been observed on air pollutant concentrations in 

Tennessee21 348 and transport and impact of smoke from wildfires and biomass burning in southern 

349 Mexico and Central America have been observed in the central and southeastern portions of the 

United States.22, 23 350 

351 The contributions of natural fires to background carbon concentrations have just begun to 

352 be quantified. They are, of course, highly episodic at any given location. In the VISTAS domain, 

using a combination of chemical mass balance (CMB) modeling and 14 353 C analyses of particulate 

354 matter, average wood smoke contributions to POM were estimated to lie between 0.8 and 2.4 

μg/m3 at a rural location near Atlanta, Georgia.24 355 These values would also include the 

Appendix B.4 - 12
13

356 contribution of intentional burning of vegetation, and thus are likely to overstate the natural 

357 contribution. 

Another estimate is provided by the global air pollution model GEOS-CHEM,7, 8 358 which 

359 was described earlier. The mean concentration of EC in the East, attributable almost entirely to 

biomass fires (natural and anthropogenic, worldwide), was estimated to be 0.04 μg/m3360 , twice the 

361 natural default EC value in Table 1. The modeled estimate for POM from the same fires and 

from all other natural sources is 1.17 μg/m3362 for the East, which is slightly less than the 

corresponding natural default value of 1.4 μg/m3 363 for POM in Table 1. 

364 Both the default POM concentration in Table 1 and the upper bound GEOS-CHEM value 

365 cited above lie in the range determined by the measurements at the rural location near Atlanta, so 

366 there is no inconsistency between the three sources of information. 

Another type of bounding estimate is provided by 14 367 C analyses at Great Smoky 

Mountains National Park during two recent summers.25, 26 368 Those measurements, which are 

369 described in the next section, found average modern carbon concentrations of 2.45 and 3.42 

μg/m3370 , respectively. Modern carbon would be derived naturally from wildfires and as a result of 

371 emissions from vegetation (discussed below) and from anthropogenic agricultural and prescribed 

372 biomass burning. It is likely that most of the modern carbon at Great Smoky Mountains in 

373 summer is attributable to biogenic emissions from vegetation, thus the natural burning 

374 contribution to carbon at these times will necessarily be substantially less than 2.45 or 3.42 

μg/m3375 (or the corresponding POM concentration will be substantially less than 3.43 or 4.79 

μg/m3376 , if the 1.4 factor is used).

377 The average seasonal regional contributions of natural wildfires throughout the Southeast 

378 need to be better quantified before default concentrations can be adjusted to reflect their 

379 contributions. 

380 As an example of a potential approach that could be used for this purpose, the total 

381 current biomass-burning contribution could be measured by creating and using local biomass382 burning particle signatures analogous to those developed for the Intercontinental Transport and 

Chemical Transformation (ITCT) mission from measurements in a forest fire plume in Utah.27 383 

384 Once the total biomass burning contribution is quantified, the anthropogenic and natural biomass 

385 burning contributions could be separated based on their proportions in (rapidly improving) 

386 emission inventories for wildfires, prescribed burning, and agricultural burning. 

Appendix B.4 - 13
14

387 Alternatively, the contribution of biomass-burning smoke could be estimated by 

388 speciation of organic matter in particulate matter samples and then using organic species tracers, 

14 389 C dating, and CMB analysis of the data to deduce source types. VISTAS is currently carrying 

390 out such a study; one year of measurements at five locations was completed in May 2005 and 

391 sample and data analyses are now under way. 

392 

393 Emissions of POM from Forests and Other Vegetation

394 Forest vegetation is the principal global source of atmospheric organic carbon in particles 

395 (both primary and secondary), with global biogenic organic emissions exceeding anthropogenic 

emissions by as much as an order of magnitude.28, 29 396 In the United States, biogenic sources 

account for 1/2 to 3/4 of the hydrocarbon emissions.29, 30 397 The tropospheric burden of fine 

398 particulate carbon in low latitude regions of the globe is dominated by long-range transport from 

the tropics.31 399 

400 A visible manifestation of biogenic organic aerosol is the blue haze often observed over 

401 forested regions, which results from oxidative particle formation, probably from biogenic 

402 terpenes. It is unclear, though, to what extent such secondary terpenoid aerosols contribute to the 

total biogenic organic aerosol burden.32 403 

404 The largest biogenic emissions occur in the tropics, where there is a combination of high 

405 temperatures, high biomass density, and ample sunlight. During summer in the southeastern 

406 United States, though, the emission flux (emissions rate per unit area of land) of biogenic 

hydrocarbon emissions is estimated to be as large as at that in the tropics30 407 , such as in the 

Amazon basin.9, 29 408 

409 Studies at several locations on the globe have concluded that the formation of biogenic 

410 organic aerosol begins with the formation of minute particles (a few nanometers in size) through 

411 nucleation involving sulfuric acid, ammonia, water, and possibly some volatile organic 

412 compounds. These particles grow to optically-important sizes via condensation of both inorganic 

413 and photochemically-oxidized organic vapors. Aspects of the formation process in different parts 

of the world are described in several articles.33 - 40 414 

415 The exact role of VOCs in the nucleation process is a subject of debate, although the fact 

that biogenic VOCs are much more reactive than anthropogenic VOCs41 416 suggests that they play a 

417 role from the outset. Also, the acidity of seed particles appears to enhance the formation of 

Appendix B.4 - 14
15

SOA,42-44 418 which may mean that current organic particle formation rates from natural emissions 

419 are greater than they would be under natural conditions because of the presence today of global 

420 anthropogenic inorganic acidic pollution. 

421 Concentrations of POM have been found to be quite high in tropical forests. 

422 Measurements of submicrometer (< 1 μm) particulate matter in an equatorial forest in the Ivory 

423 Coast (west coast of Africa) found the average particulate carbon concentration (i.e., before 

multiplication by a factor such as the 1.4 used by EPA and IMPROVE) to be 9 μg/m3424 during the 

wet (growing) season.31 425 Since biomass burning, which would have contributed carbonaceous 

426 particles during the dry season, did not take place during the wet season, the measured 

427 particulate carbon was attributed to natural emissions from the forest. 

428 Measurements made by low-flying aircraft over a tropical Amazon Basin forest in Brazil 

resulted in similar conclusions.45 429 The measurements were made during the early to middle "dry" 

430 season, when the forest was moist during the measurements and biomass burning did not yet 

431 appear to be an important source. Their sampler used for carbonaceous particles did not have a 

432 specific size cut, although it is likely that most of the particles sampled were in the fine size 

433 fraction. The average particulate organic carbon concentration in the mixed layer was 740 

nanomoles C/m3, which works out to 8.9 μg/m3434 of carbon (or a POM concentration of 12.5 

μg/m3435 , if a factor of 1.4 is used). 

436 In another study in the Amazon Basin, Absolute Principal Component Analysis (APCA) 

437 of five years of field measurements at two remote locations yielded that biogenic particles 

accounted for 57-63% of the total PM2.5 mass during the wet (non-burning) season.46 438 The 

439 average biogenic particle concentrations during those seasons were estimated to be 3.9 and 5.5 

μg/m3440 at the two sites. The biogenic material was considered to be composed principally of 

441 organic carbon compounds plus a small amount of sulfur compounds. 

442 These measured concentrations are consistent with concentrations simulated with the 

MOZART-2 model9443 , which estimated that SOA concentrations in the Amazon basin were as 

much as 10 μg/m3444 in summer. 

445 These tropical data show that the potential concentrations of biogenic POM can be quite 

446 large in and near forests. The question is how to translate these tropical research results into 

447 biogenic POM concentrations that would apply in the VISTAS region. Because of the cooler 

448 climate, it is likely that the average concentrations over the year will be lower in forests in the 

Appendix B.4 - 15
16

449 much of the VISTAS region than in the tropics. As noted above, though, several analyses have 

450 concluded that summertime (June to September) biogenic VOC emission fluxes in the Southeast 

(except near the coast) are comparable to those in the Amazon basin.9, 29 451 

452 In a more northerly setting, research carried out on formation of POM in remote forests in 

453 Finland provides further insight. Overall PM2.5 mass concentrations observed during the spring 

there were lowest (2.6 μg/m3454 ) when the air mass was polar or arctic, and higher concentrations 

455 were observed when the air came from other directions that reflected warmer air and impacts 

from distant anthropogenic sources.47 456 

457 Carbon concentrations in June, measured by high-volume sampler (needed to collect 

458 sufficient sample for speciation of the organic carbon compounds) at the same location in 

Finland, were 3.2 μg/m3 for OC and 0.19 μg/m3459 for elemental carbon (or, more rigorously, black 

carbon).48 460 Direct comparison with the values in Table 1 is not possible, though, because the 

461 particle size range collected by the hi-vol is much broader than that of PM2.5. 

Estimates of natural SOA in the southeastern U.S. have been made via the 14 462 C 

measurements and CMB analyses that were cited earlier.26 463 Based on measurements made during 

464 one month of each quarter during one year at a rural, predominantly agricultural, location near 

Atlanta, Georgia, the average natural SOA concentration was 1.6 μg/m3465 , the peak concentration 

observed was about 2.5 μg/m3466 in July, and the concentration in winter (January) was zero. Data 

from another 14C study,49 467 this time in Nashville, Tennessee, allow us to conclude that biogenic 

468 OC concentrations there during a one-month period from mid-June to mid-July ranged from 

about 2 to about 5 μg/m3, which correspond to POM concentrations of 2.8 to 7 μg/m3469 or more. 

Two 14C studies at Great Smoky Mountains National Park in August 200225 470 and the 

summer of 200426 471 concluded that averages of 83% and 90% of the sampled POM concentrations 

472 were of modern origin, respectively. These translate into average concentrations of modern OC 

during those periods of 3.42 and 2.45 μg/m3473 , which are considered most likely to be mainly SOA 

derived from biogenic VOC emissions.26 474 (The corresponding POM concentrations are 4.78 and 

3.43 μg/m3475 , respectively, calculated with a factor of 1.4.) 

476 Simulation with the MOZART-2 model finds current fine SOA concentrations 

(attributable to both biogenic and anthropogenic emissions) to be around 2 μg/m3477 throughout the 

inland South in June and September, and 1 μg/m3478 or less in March and December. These values 

479 are somewhat less than the average POM concentrations (sum of both primary and secondary) 

Appendix B.4 - 16
17

measured by IMPROVE samplers in these regions,50 480 but not greatly so, which suggests that they 

481 are not far off the mark. These modeled values may thus provide an indication of the upper 

482 bound to the typical biogenic contribution alone in the Southeast today. 

483 The information given here suggests that biogenic POM concentrations in forested 

portions of the VISTAS region could reach 4 μg/m3484 or more in summer. This value is 

485 comparable to currently observed summertime levels of organic carbon in forested areas in the 

southeastern U.S.50 486 Since biogenic contributions will be less the rest of the year, the annual 

average impact is likely in the range of 1 to 2 μg/m3487 . Such a biogenic contribution alone is 

comparable to the 1.4 μg/m3488 average default concentration for all organics, while the 

489 summertime peak is many times the default average. 

490 

491 Oceanic Emissions of Sea Salt and Organics

492 Sea Salt. Sea salt contributes to ambient particulate matter near the ocean. Over the ocean in 

493 remote areas it accounts for about half of the submicrometer (< 1 μm) aerosol in the boundary 

layer and for about 3⁄4 of the aerosol that is larger than 1 μm and smaller than 10 μm.16 494 The 

495 concentration will vary with the state of the ocean; average concentrations measured in the 

remote southern hemisphere ocean during the ACE 1 research study were 0.6 μg/m3496 for the 

submicrometer sea salt and about 9.5 μg/m3 for the salt larger in sizes between 1 and 10 μm.16 497 

498 The chemical composition of sea salt, which is a mixture of NaCl and other constituents, 

499 is complex and variable. In particular, sea salt loses chloride as it ages because exposure to 

500 atmospheric H2SO4 and HNO3 and certain non-acidic gases results in the release of gaseous HCl. 

501 Consequently, the mass scattering efficiency of aged sea salt differs from that of fresh sea salt 

(whose scattering is similar to that of pure NaCl).51 502 Being hygroscopic, sea salt has the potential 

503 to contribute disproportionately to haze when the humidity is high. 

504 Although IMPROVE measures elemental Na and Cl, and hence estimating the amount of 

505 sea salt at Class I areas is conceptually feasible, its effect is not included in the EPA/IMPROVE 

506 formula for estimating light extinction (eq 1). An assessment of the effect of this omission, using 

507 IMPROVE data taken at 59 sites from 1993 to 1999, led to the conclusion that including the 

508 mass concentration of NaCl consistently improved the comparison between reconstructed fine 

mass concentrations and measured PM2.5 at all sites.52 509 Fine NaCl accounted for 1% to 33% of the 

510 reconstructed fine mass concentrations (the 33% was at Point Reyes National Seashore, 

Appendix B.4 - 17
18

511 California). In the VISTAS region, NaCl accounted for 5% of the fine mass concentration at 

512 Chassahowitzka National Wildlife Refuge (NWR) in Florida, 3% at the more inland Okefenokee 

513 NWR in Georgia, and 1-2% elsewhere. (Another VISTAS coastal site, Cape Romain NWR in 

514 South Carolina, was not included in the analysis.) 

515 The average reconstructed PM2.5 concentrations between 1996 and 1998 at 

Chassahowitzka and Okefenokee were 9.8 and 9.7 μg/m3, respectively.50 516 We can thus infer from 

517 the percentages above that the average fine sea salt concentration at those two locations is in the 

range of 0.3 to 0.5 μg/m3518 , which corresponds to 13 to 22% of the default natural fine mass 

519 concentrations there. Such concentrations are comparable to the default natural concentration of 

520 soil. Further, since the sea salt size distribution approximates that of soil (see, for example, a 

521 comparison of size distributions in reference 30), the extinction impact of comparable amounts 

522 of fine soil and dry fine sea salt can be expected to be comparable. Sea salt is hygroscopic 

523 however, the effect of which will be addressed later in this section. 

524 In an experiment in which the composition of particulate matter arriving at Miami from 

525 over the Atlantic ocean was studied for 23 years, the annual mean sea salt concentration in TSP 

(calculated from the measured Na concentration and the Na/Cl ratio of seawater) was 8.67 μg/m3526 

for those periods when the flow arrived from over the ocean.53 527 The highest values were in the 

528 winter (attributed to stronger winds creating more sea spray then) and lower than average 

529 concentrations were found from May through September. The minimum daily concentration 

measured during every month of the year exceeded 4.5 μg/m3530 . 

531 The size cut point of the sampler used for these measurements is not known, but the 

investigator estimated that 1/3 to 1/2 of the sampled mass was in the fine (PM2.5) range.53 532 If the 

533 same fraction applied to the sea salt, that would suggest an annual average fine sea salt 

contribution in the range of 3 to 4.5 μg/m3534 . The corresponding estimate of minimum daily fine 

sea salt concentration (on days with onshore flow) would be in the range of 1.5 to 2.2 μg/m3535 . 

536 These concentrations are greater than the highest averages inferred above from analyses of 

IMPROVE samples.52 537 A possible reason for the discrepancy is that the Miami concentrations 

538 only reflect those measured at the shoreline during the times when airflow came from over the 

539 ocean, while the IMPROVE data represent all wind directions. Also, the Miami measurements 

540 were taken much closer to the shoreline than the IMPROVE measurements at Okefenokee, and 

Appendix B.4 - 18
19

541 thus they could be influenced by sea spray from breaking surf, although the investigator states 

that the concentrations were not strongly influenced by salt spray from the surf zone.53 542 

543 Sea salt concentrations from IMPROVE measurements at Cape Romain and Okefenokee 

were also calculated by Paine and colleagues.54 544 Their estimate of current sea salt concentrations 

545 included both NaCl and Na2SO4, where the sulfate was assumed to have replaced chloride that 

546 was lost due to chemical reactions of NaCl with acidic gases and other substances in the 

547 atmosphere. Because such reactants might not be present under natural conditions, they also 

548 calculated the concentration of NaCl alone. Considering the latter concentration, which is the 

lower one, they estimated annual average fine sea salt concentrations to be 1.3 μg/m3549 at Cape 

Romain and 0.85 μg/m3 at Okefenokee. The Cape Romain figure of 1.3 μg/m3550 is consistent with 

the slightly higher range of 1.5 to 2.0 μg/m3551 that was deduced above from measurements at 

552 Miami during onshore flow alone. The Okefenokee value is somewhat larger than the 0.3 to 0.5 

μg/m3553 range inferred above for Okefenokee from data in reference 52. 

Another study24 554 estimated the fine sea salt concentrations along the coast of the Gulf of 

Mexico (the southern boundary of the VISTAS domain), to be in the range of 0.4 to 0.5 μg/m3555 at 

556 the coast of the Gulf of Mexico. This range of concentrations is lower than the ranges cited 

557 above, but the investigators point out that the Gulf may generate less sea salt spray than the more 

558 highly energetic ocean along the Atlantic coastline. 

559 Meaningful sea salt concentrations can extend well inland from the shoreline. Particulate 

560 matter measurements by low-flying aircraft (at about 150 m altitude) in the Amazon Basin of 

Brazil found that that, assuming that aerosol Na+561 was derived exclusively from sea salt, the 

average concentration of sea salt larger than 1 μm in size in the mixed layer was about 4 μg/m3562 

near the coast, 1.5 μg/m3 250 km inland, and less than 1 μg/m3 more than 400 km inland.45 563 

564 Without further information on size distribution, one cannot say how much of this material 

565 would show up as PM2.5 and how much would be coarser. A corresponding description for 

566 submicrometer sea salt was not possible because concentrations were below the detection 

567 method for the analytical method used. 

Studies of the light scattering efficiency and hygroscopic growth of sea salt particles51, 55 568 

569 don't, unfortunately, provide information on the scattering of light by sea salt particles in the 

570 actual size range found in the atmosphere. Over the Indian Ocean, an extinction efficiency of 1.0 

to 1.2 m2/g was estimated for submicrometer (< 1 μm diameter) sea salt at 55% RH,15 571 which is in 

Appendix B.4 - 19
20

the same range as the 1.0 m2572 /g used by IMPROVE for dry fine soil particles. The estimate for 

supermicrometer (> 1 μm diameter) particles was 5.1 to 6.0 m2573 /g at 55% RH, which is well 

above the 0.6 m2574 /g used by IMPROVE for dry coarse particles, even after allowance is made for 

575 hygroscopic growth, although some of the difference may be explained by the difference 

576 between the 1 μm lower size cuts used for this work and the 2.5 μm size cut of IMPROVE. 

577 In the absence of better information, the light scattering efficiency of fine dry sea salt is 

sometimes taken to be the same as the 1 m2578 /g used by IMPROVE for fine soil, and that of the 

coarse fraction of dry sea salt is taken to be the 0.6 m2579 /g scattering efficiency of coarse 

particulate matter. This is the approach used by Paine et al.,54 580 who also deduced a humidity 

581 scaling function f(RH) for fine sea salt based on measurements made over the Atlantic Ocean 

582 during the ACE 2 experiment. The resulting function is similar to the EPA/IMPROVE f(RH) for 

583 sulfates and nitrates, except that hygroscopic growth begins at a lower relative humidity for sea 

584 salt than for sulfate and nitrate and the increase above 90% RH is not as rapid for sea salt. The 

585 f(RH) curves for fine sea salt and sulfate/nitrate are almost identical from 60% RH to 90% RH. 

586 Humidity-related growth can also be expected for the coarse fraction of sea salt, though not with 

the same f(RH) curve as for the fine sea salt.56 587 

588 Using their empirical f(RH) curve and their lower sea salt concentration estimates at Cape 

589 Romain and Okefenokee, Paine et al. calculated seasonal and annual average extinction impacts 

of fine sea salt concentrations.45 590 They concluded that the contribution of natural background fine 

sea salt to extinction was 4.3 Mm-1 at Cape Romain and 3.0 Mm-1 591 at Okefenokee. Using the data 

592 in Table 1 and the same annual f(RH) that Paine et al. used, we calculate that the annual default 

particulate matter extinctions without sea salt are 11.4 Mm-1 at Cape Romain and 11.6 Mm-1 593 at 

594 Okefenokee. Adding the above sea salt contribution to these values increases the estimated 

595 annual-average natural extinction due to particulate matter by 38% at coastal Cape Romain and 

596 26% at the more-inland Okefenokee. 

597 

598 Oceanic POM. Turning to organic particles from the ocean, mass concentrations of fine organic 

599 particles at the northeastern tip of Puerto Rico (and thus exposed to the easterly trade winds 

600 coming over the ocean) and also during ship sampling away from the coast there were found to 

be in the range of 0.3 to 0.4 μg/m3 during a February to June study.57 601 Black (elemental) carbon 

602 was not found in the samples, which indicated that the aerosol was relatively free of combustion 

Appendix B.4 - 20
21

603 products. Consequently, a substantial fraction of this organic material may be associated with 

natural oceanic emissions.57, 58 604 

605 As another indication of background levels of oceanic carbon, average POM 

606 concentrations (defined as 1.6 x OC) in marine air masses over the Indian Ocean south of the 

equator were found to be 0.07 to 0.13 μg/m3 in submicrometer particles and 0.14 to 0.56 μg/m3 607 

in supermicrometer particles during the February-March monsoon season.15 608 These 

609 concentrations again represent values obtained for oceanic air masses, with the lowest 

610 concentrations associated with transport from the south. According to the investigators, these 

611 values are comparable with what has been measured in remote areas of the Atlantic and Pacific 

612 Oceans. In contrast, in the North Atlantic during the phytoplankton bloom periods, 

submicrometer OC concentrations in clean marine air at a coastal site averaged 0.9 μg/m3613 (which 

corresponds to a POM concentration of about 1.4 μg/m3, using the same 1.6 factor).59 614 

615 It is not clear how to relate open-ocean POM concentrations to concentrations of natural 

616 POM of oceanic origin at coastal IMPROVE sites such as Cape Romain. Available data aren’t 

617 sufficient to give insight into variability due to temperature or season. Nor do the oceanic 

618 concentrations reflect enhanced POM emissions from breaking waves at the shoreline and 

619 biological tidal zone POM formation, both of which are likely to be very site-specific. 

As a first guess, if one assumes 0.4 μg/m3620 for natural fine POM over the ocean and 

621 assumes that the wind blows onshore from the ocean half of the time (a good assumption at Cape 

622 Romain), then the annual average contribution of fine oceanic POM at Cape Romain might be 

0.2 μg/m3623 . This corresponds to an increase in the default POM concentration by about 15% and 

the default light extinction by 0.8 Mm-1 624 . The oceanic POM contribution would be smaller at 

625 more inland non-coastal sites such as Okefenokee. 

626 

627 Long Range Transport of Windblown Dust

628 To estimate the average fine soil concentration, Trijonis elected to "arbitrarily assume" (his 

words) that half of the current average measured fine soil concentrations are natural.3 629 The limited 

data available to him indicated that average rural fine soil concentrations were about 1 μg/m3630 in 

631 both the East and the desert/mountainous West, which he divided by two to arrive at his natural 

background estimate of 0.5 μg/m3632 for both regions. Recent measurements and data analyses 

633 allow us to assess the applicability of this estimate to the VISTAS region. 

Appendix B.4 - 21
22

634 

635 IMPROVE Fine Soil Measurements in the VISTAS Region. IMPROVE measurements of fine 

636 soil concentrations at nine locations throughout the VISTAS region during 1996-1998 produce a 

composite annual average fine soil concentration of 0.62 μg/m3.

50 637 The annual average values are 

least at the northern sites (0.38 μg/m3 at Dolly Sods Wilderness, West Virginia and 0.42 μg/m3638 at 

Shenandoah National Park, Virginia) and greatest at the southern sites (0.77 μg/m3639 at 

Okefenokee NWR, 0.80 μg/m3 at Sipsey Wilderness, Alabama, and 1.1 μg/m3640 at 

641 Chassahowitzka NWR). The measured soil values in the southern part of the VISTAS region 

approximate the 1 μg/m3642 assumed by Trijonis as representative of the current fine soil 

643 concentration in the East, but the current measured values in the northern portion are closer to his 

default value of 0.5 μg/m3644 for natural conditions alone. Thus the default soil concentration may 

645 overstate natural fine soil concentrations in the northern portion of the VISTAS region. 

646 The soil concentrations measured by IMPROVE vary greatly by season. Winter 

647 concentrations are relatively uniform throughout the VISTAS domain, in the vicinity of 0.2 to 

0.3 μg/m3, while summer concentrations range from around 0.5 μg/m3 to around 2 μg/m3648 as one 

goes from north to south in the VISTAS region.50 649 As we discuss below, long-range transport of 

650 dust appears to be a major contributor to the large spatial variability in the summer. Such dust 

651 has two origins – Asia (including the Gobi Desert) and Africa (especially the Sahara Desert). 

652 

653 Asian Dust Contributions to IMPROVE Samples. The elemental composition of IMPROVE 

654 filter samples taken throughout the United States from 1989 to 1999 was evaluated by VanCuren 

and Cahill to determine the origins of the soil elements in those samples.60 655 Using cluster analysis 

656 of known Asian air masses to develop signatures for the Asian aerosols, they concluded that dust 

657 transported across the Pacific Ocean from Asia affects most of the U.S. during most of the year. 

658 Satellite images have demonstrated that Asian aerosols can easily cross North America and reach 

659 the western Atlantic Ocean. 

Asian dust impacts are greatest in the western U.S.61 660 and subside toward the East Coast, 

661 and are generally greater in the northern part of the country than they are to the south 

662 (presumably because westerly flow is more prevalent in the northern latitudes). This geographic 

663 distribution is not universal, however. A large Asian dust episode in mid April 2001 (discussed 

664 further below) had its greatest impacts along the central West Coast, the Rocky Mountains, and 

Appendix B.4 - 22
23

665 in the Southeast, while the impacts along the northern boundary of the United States were 

smaller, though still significant.62 666 

667 The frequency of appearance of Asian dust in the United States is greatest in the spring 

668 and summer months, with the most noticeable seasonal variation in northern states and, 

669 generally, a flatter temporal distribution in the southeastern U.S. 

Focusing now on the VISTAS region, graphs in the Van Curen and Cahill paper60 670 show 

671 that, on those days when Asian dust was a dominant contributor to fine soil concentrations at 

Shenandoah National Park, the average concentration of that dust exceeded 0.6 μg/m3672 during 

673 every month from March through October and was less the rest of the year. The average 

contribution during Asian dust events during that 7-month period was about 0.7 μg/m3674 and the 

peak monthly average was about 1.2 μg/m3675 , in August. Episodic concentrations (24-hr average) 

676 were much higher. 

677 As an extreme example, which occurred after the period analyzed by VanCuren and 

678 Cahill, the Asian dust event of mid April 2001 (the largest such event ever observed in the U.S.) 

caused 24-hr PM10 concentrations to increase by 20-30 μg/m3679 (!) at the Shenandoah and 

Okefenokee IMPROVE samplers.62 680 This was followed by two additional episodes during the 

681 first half of May. All three episodes also significantly impacted particulate matter concentrations 

682 in the northeastern U.S. Interestingly, the trajectories to the Northeast for the first May episode 

683 first passed through the VISTAS region. They entered the VISTAS states from the northwest, 

684 circled clockwise through the VISTAS region, and then headed northeast to the northeastern 

U.S.63 685 

686 Based on VanCuren’s and Cahill’s analysis, Asian dust events occurred at Shenandoah 

687 during every month of the year, with frequencies between about 20 to 36% of the time in the 

688 spring months, 16% of the time in October, and less than 10% of the time in the remaining 

689 months. Combining these concentrations and frequencies of episodes, we estimate the average 

690 Asian dust concentration (averaged over all days, irrespective of whether Asian dust impact 

691 occurred on any given day) at Shenandoah from March through October is in the range of 0.1 to 

0.2 μg/m3692 . The Asian dust statistics at Dolly Sods Wilderness are similar to those at Shenandoah. 

693 Farther to the south, frequencies of Asian dust events were highest during summer at 

694 Everglades National Park, Florida (averaging around 20% from May through September). The 

695 average concentrations during those events were smaller than at Shenandoah (averaging about 

Appendix B.4 - 23
24

0.5 μg/m3696 from March to September), which suggests an average Asian dust contribution there 

of around 0.1 μg/m3697 over the late spring and summer. 

698 The fine soil concentrations measured by IMPROVE in the Appalachian region (which 

includes Shenandoah and Great Smoky Mountains National Parks) average 0.45 μg/m3699 over the 

year and peak at 0.61 μg/m3 over the summer season.50 700 The episodic fine dust concentrations at 

701 Shenandoah that were attributed by VanCuren and Cahill to Asian dust events (and were 

702 described above) are comparable to or greater than these measured averages. This implies that 

703 Asian dust plays a dominant role in Shenandoah soil concentrations at times from spring into 

fall, and its average 0.1 to 0.2 μg/m3704 contribution accounts for roughly 20 to 40% of the 

705 measured fine soil concentration during that period. 

706 At southeastern IMPROVE sites (encompassing Chassahowitzka, Okefenokee, and Cape 

Romain), the average fine soil concentration in 1996-1998 was 0.81 μg/m3707 and the summer 

average was 1.86 μg/m3.

03 708 As expected, the influence of Asian dust is much less noticeable here 

709 than at the more northerly locations. Rather, as discussed next, African dust transport plays a 

710 major role in the summertime peak. 

711 

712 African Dust Contributions to IMPROVE Samples. Dust transported across the Atlantic Ocean 

713 from the Sahara Desert is another contributor to fine soil concentrations in the VISTAS region. 

714 African dust impacts, which can be identified by their elemental signature, occur less frequently 

715 in the VISTAS region than those from Asian dust but the magnitudes of the African 

contributions are much larger whenever those impacts occur.60 716 

717 African dust impacts tend to be greatest along the Caribbean and diminish toward the 

718 north. Specifically, at the Everglades, African dust events occurred regularly from June through 

719 September over an 11-year period, with the soil composition on more than 40% of the days in 

July dominated by African dust.60 720 The mean 24-hr average African fine soil dust contribution 

there during events occurring in June through August was between 7 and 9 μg/m3721 . The pattern is 

722 similar at Okefenokee, but with somewhat lower frequency and concentrations than at the 

Everglades. Farther north, the average African dust event contributed 4.5 μg/m3 723 at Shenandoah 

724 and Dolly Sods, but those events were infrequent and occurred almost exclusively in August. 

725 IMPROVE measurements at the three southeastern monitoring sites, Chassakhowitzka, 

Okefenokee, and Cape Romain do indeed show an average soil concentration of 1.86 μg/m3726 in 

Appendix B.4 - 24
25

the summer, compared to about 0.6 μg/m3 in the spring and autumn.50 727 The high frequency of 

728 occurrence and the high concentration impacts of African dust events there imply that such dust 

729 is the dominant cause of that summertime peak, which suggests that African dust alone accounts 

for most of the summer increase, perhaps roughly 1 μg/m3730 of fine soil, averaged over the 

731 summer. This amount alone, without considering other natural sources, is well in excess of the 

0.5 μg/m3732 average natural concentration estimated by Trijonis. 

Perry and colleagues64 733 used IMPROVE samples from 1988 through 1995 to estimate the 

734 impacts of transported North African dust at various locations in the Southeast. They concluded 

735 that 57% of the samples taken at the Everglades in July showed an African dust impact greater 

than 3 μg/m3736 . Corresponding percentages at other VISTAS locations were 27% at Okefenokee, 

737 19% at Chassahowitzka, 16% at Sipsey and Great Smoky Mountains, 14% at Dolly Sods, and 

738 8% at Shenandoah. A surprising 23% of the observations at Mammoth Cave National Park, 

Kentucky in July contained African dust above 3 μg/m3739 , which is reinforced by an even higher 

740 37% at Upper Buffalo Wilderness in Arkansas (outside the VISTAS region). Further outside the 

741 VISTAS region, 19% of the observations at Big Bend National Park in western Texas in July 

contained more than 3 μg/m3742 of African dust and the corresponding percentage was 76% at the 

743 Virgin Islands. Percentages of African dust impact were less in other months, with the period of 

744 impact concentrated on June through August in the southern locations in the VISTAS region and 

745 including only July at the northern locations. 

746 In contrast, the period of impact at the Virgin Islands covered the nine months from 

747 February through October. As a result, Perry et al. found the annual mean fine soil concentration 

at the Virgin Islands was 3.25 μg/m3, versus values in the vicinity of 1 μg/m3748 that were given 

749 earlier for the Southeast, and the summertime fine soil concentrations there were a substantial 

7.58 μg/m3750 , about four times as large as the highest concentrations in the VISTAS region. 

751 From 23 years of measurements of the composition of particulate matter transported over 

752 the Atlantic Ocean onto the coast at Miami, the mean concentration of soil dust in TSP during 

onshore flow was 4.72 μg/m3.

53 753 The maximum concentrations occurred from June through 

August. The concentration during onshore flow averaged 16.3 μg/m3754 in July. The investigator 

755 estimated that one-third to one-half of these concentrations is in the PM2.5 range. Based on 

756 correlation with climate conditions (e.g., drought cycles) in Africa and satellite observations of 

757 dust cloud transport, this soil dust is attributed to African emissions 

Appendix B.4 - 25
26

758 The BRAVO Study also found African dust at the Big Bend National Park, Texas, 

IMPROVE site during July-October 1999.65 759 Based on analyses of speciated particulate matter 

measurements taken during that study,66 760 we estimated that the average African dust contribution 

761 at Big Bend National Park in July (averaged over all days, not just Saharan dust events) was 1.4 

μg/m3 and in the first half of August it was 0.9 μg/m3762 . Although Big Bend is well west of the 

763 VISTAS domain, on several days the African dust there still accounted for more than 80% of the 

measured fine soil concentration and contributed over 2.5 μg/m3764 . 

765 African dust contributions over the VISTAS region can be expected to range between 

766 those reported at Shenandoah, Everglades, and Big Bend. Although precise quantification is not 

767 possible from the information provided here, it is reasonable to conclude that African dust 

768 contributes substantially to fine soil concentrations at most VISTAS locations during the summer 

769 (specifically, June through August). As a broad average for the region, daily impacts during 

African dust events during those months may average 3 to 5 μg/m3770 and such events might occur 

771 roughly 20% of the time. Thus, a rough estimate of the average Saharan fine dust contribution 

during the summer could be in the range of 0.6 to 1.0 μg/m3772 -- more in Florida and a bit less at 

773 Shenandoah and Dolly Sods. 

774 These analyses of IMPROVE and BRAVO data provide strong evidence that the natural 

775 contribution to fine soil during the summer is substantially greater than Trijonis’ average natural 

soil estimate of 0.5 μg/m3776 for the year. If African dust is the only natural contributor, then the 

777 foregoing data suggest that a summertime natural conditions estimate would be in the range of 

0.6 to 1 μg/m3, out of a measured summertime average range of 0.5 to 2 μg/m3778 for all fine soil at 

779 IMPROVE sites. Other natural sources will further increase the natural contribution, with Asian 

dust alone adding another 0.1 to 0.2 μg/m3780 . Thus, at least for the summertime, it appears that 

781 natural sources account for the overwhelming majority of the measured fine soil concentration in 

782 the VISTAS area, not just the half postulated by Trijonis, and the concentration of natural soil 

during that season far exceeds the default annual estimate of 0.5 μg/m3783 . 

784 

785 Implications of Transported Dust on Natural Background Extinction. Because of its episodic 

786 nature, it is difficult to suggest an approach for dealing with the contributions of Asian and 

787 African dust to the natural background. The annual average impact is relatively small, but its 

monthly contribution can be substantial during the summer months (a range of 0.6 to 1.0 μg/m3788 

Appendix B.4 - 26
27

789 was estimated above) and the episodic contribution can be much larger at times. Some of the 

average effect is probably reflected in Trijonis’ average estimate of 0.5 μg/m3790 , but the 

791 seasonality, episodicity, and geographic variation is not. Since the scattering efficiency of fine 

soil is assumed to be 1 m2792 /g, the average summertime impact translates to an extinction 

increment of 0.6 to 1.0 Mm-1 793 . Some portion of this amount could be added to the default natural 

794 conditions estimate for specific sites for those months. The effect during the rest of the year is 

795 substantially smaller. 

796 IMPLICATIONS FOR MANAGING REGIONAL HAZE 

797 The discussion above has identified a variety of potential refinements to the default estimates of 

798 natural background concentrations of PM components and light extinction in the VISTAS 

799 region. The question to be addressed at this point is whether such refinements should be pursued 

800 by VISTAS. Developing refined estimates could be worthwhile if they might materially affect 

801 the slope of the "reasonable progress" line and perhaps affect the soundness of the emission 

802 management strategies that are pursued. On the other hand, from a practical point of view, 

803 developing and justifying refined estimates at this time may not be worthwhile if their impact on 

804 the first round of emissions management steps (which have a 2018 target) is so small that it does 

805 not materially affect the emissions management choices. Even in the latter case, there is still 

806 merit in applying the best science available, even at this point, because the process of doing so 

807 will lay the groundwork for the increasing importance of scientifically sound natural background 

808 estimates to making technically and financially responsible emissions management policy 

809 decisions in the future. 

810 This issue is illustrated schematically in Figure 3. Changing the natural conditions 

811 estimate changes the slope of the line required to achieve progress toward the natural conditions 

812 goal. But the difference in the 2018 regional haze goal, in deciviews, is much smaller than the 

813 change in the 2064 natural background estimate. 

814 Note that, because of the nature of the refinements discussed here and the limitations in 

815 the default estimates, it is likely, but not always necessary, that a natural conditions refinement 

816 will increase the estimated natural extinction as illustrated in Figure 3. A refinement that 

817 decreases a default estimate is not precluded, however. Also, some of the refinements under 

818 consideration here for natural conditions (such as for the OC mass multiplier and sea salt) also 

Appendix B.4 - 27
28

819 imply parallel adjustments to corresponding estimates of current conditions, and therefore the 

820 baseline at the top of the figure may also be subject to adjustment. 

821 To put some numbers into this discussion, it will be useful to recap the estimated impacts 

822 resulting from each of the natural contributions discussed in this paper: 

• Adjusting the 90th percentile extinction to the 92nd 823 percentile, to more properly 

824 represent the mean of the haziest 20% of the days in a normal distribution, would 

825 increase the default haze index by 0.42 dv. (The impact on the extinction coefficient, 

in Mm-1 826 will vary depending on the extinction level.) 

827 • Increasing the organic carbon multiplier to 2.1 would increase the default natural 

extinction by 2.8 Mm-1 828 . An even greater multiplier would be appropriate for organic 

829 carbon that originates from fires. Since the biogenic contribution to organics is likely 

830 greater on a seasonal basis than the fire contribution, the 2.1 value seems appropriate 

831 for the Southeast, instead of a higher value. One should also consider increasing the 

832 present day organic carbon multiplier, but probably to a lesser value (say, 1.8) 

833 because of the anthropogenic carbon content of the ambient aerosol today. 

834 • Preliminary information on wildfire impacts from global modeling results suggests 

835 that overall POM and elemental carbon concentrations for the East are about the same 

836 as the default values. Presumably, Trijonis' estimates included average wildfire 

837 impacts. The global modeling is able to improve on the default averages by providing 

838 insight into spatial and seasonal variability, however. 

839 • The contribution of vegetation to POM in forested areas is harder to quantify. The 

840 potential impact within forests in the deep South during the warmer months appears 

841 to be several times the default estimate of POM concentration and extinction and the 

842 annual average impacts are comparable to the default values. If so, the impact on 

background extinction in such areas could be as much as 10 Mm-1 843 during the warmest 

844 months. More research is needed to ascertain the actual impact, which will depend on 

845 the degree to which a given location is surrounded by forest and affected by forest 

846 emissions. As an initial estimate for locations entirely in forested areas, adding 1 

μg/m3 of POM (for an extinction increase of 4 Mm-1 847 ) during spring and summer 

848 seems conservative, or a corresponding annual average impact in the range of 1/3 to 

Appendix B.4 - 28
29

1/2 of that amount (i.e., 0.3 to 0.5 μg/m3, or 1.2 to 2 Mm-1 849 ) may be appropriate. If 

850 geographic distribution is considered, the annual value in the northern part of the 

851 VISTAS domain and at high elevation locations is likely to be smaller than that 

852 farther south and in the lowlands. These estimates have high uncertainty. 

853 • Sea salt has a major impact that can be estimated with fair confidence, at least near 

854 the coast. The impact will vary with distance from the coast. Annual average fine 

fraction concentration impacts of around 1.3 μg/m3855 at the coast and perhaps half that 

856 at inland sites in Florida are suggested by current research results. It is unlikely that 

857 much of this is reflected in Trijonis' estimates because of the limited monitoring data 

858 that was available to him. The extinction impact of such an increase in fine 

concentrations will be 1.3 Mm-1 859 or more (depending on relative humidity) at the coast 

860 and less inland. There will also be a smaller coarse fraction impact. Sea salt is not 

861 considered in the current IMPROVE algorithm for estimating visibility, so the same 

862 impact should also be included in estimates of current extinction. 

863 • POM from the sea may also affect coastal areas. An average mass concentration 

impact of 0.2 μg/m3864 , as suggested earlier, translates into an extinction impact of 0.8 

Mm-1 865 . Because few measurements are available except over open ocean, and coastal 

866 values are dependent on the nature of the shoreline, this estimate has high uncertainty. 

867 • In the northern portion of the VISTAS region, the average soil concentrations already 

868 approximate the default background values, which suggests that the default values 

there may be too high by, perhaps, 0.2 to 0.3 μg/m3869 (and thus that the default 

extinction there may be too large by 0.2 to 0.3 Mm-1 870 ). Counterbalancing this, the 

871 default concentrations may be too low at some locations, but this has not been 

872 established. 

• The 0.1 to 0.2 μg/m3873 of transported Asian dust during spring and summer would 

impact the extinction by 0.1 to 0.2 Mm-1 then, and by perhaps 0.1 Mm-1 874 when 

875 averaged over the full year. The question is whether that Asian dust is already 

876 reflected in the default estimates, since it may have appeared in the measurement data 

877 on which Trijonis based his estimates. 

Appendix B.4 - 29
30

878 • African dust has a very large episodic impact during the summer. If seasonal average 

879 natural conditions concentrations were to be estimated, the summertime mass 

concentration of soil could be increased by about 0.6 to 1.0 μg/m3880 over the annual 

881 default values. (If different adjustments are made for different geographic areas, one 

882 might use an even larger increase in the southern portion of the VISTAS region and a 

883 smaller one in the northern portion.) The result would be to increase natural 

background extinction by 0.6 to 1.0 Mm-1 884 during the summer months. One might 

885 compensate with a small decrease during the rest of the year, or argue that the 

886 episodic summer impacts were not reflected in the limited data base that Trijonis had 

available. On an annual basis a net increase of 0.2 to 0.3 μg/m3 (0.2 to 0.3 Mm-1 887 ) 

888 might be added to the default soil value. 

889 Table 2 summarizes the two suggested refinements to the method of estimating natural 

890 conditions and the potential effects of the seven types of natural sources that are itemized in the 

891 bulleted discussions above. The last column of the table indicates the authors’ assessment of 

892 whether a refinement of a default method or concentration is warranted, based on the magnitude 

893 of the expected impact, whether it might already be included in the default concentrations, and 

894 the degree of scientific understanding of the impact (or the compensating degree of 

895 conservativeness in the estimated impact). Note that the suggested refinements are within the 

896 typical factor of two error estimates by Trijonis, as listed in Table 1. 

897 Of course, it is unlikely that all of the refinements discussed above and summarized in 

898 Table 2 should be applied simultaneously at every location. As already mentioned, some of the 

899 contributions may already be included in the default estimates (possibly Asian dust, for 

900 example). Also, some sources will not be applicable at some locations, e.g., sea salt in the inland 

901 mountains or, perhaps, forest organics at the seashore. 

902 One should also recognize that our conclusions are based on relatively uncritical review 

903 of the literature. Measurement methods may be biased, measurements may be unrepresentative, 

904 or scientific hypotheses may change with more information, Consequently, much of what is 

905 involved in our assessment here has some degree of uncertainty and is itself subject to 

906 refinement. In this light, any values that we assign to potential refinements should be taken as 

907 approximations. 

Appendix B.4 - 30
31

908 To assess the significance of refinements that might realistically be considered for 

909 specific locations, we consider next examples of some of their effects on the regional haze 

910 progress goals at two specific locations -- Great Smoky Mountains National Park (inland, 

911 mountainous) and Cape Romain National Wildlife Refuge (Atlantic coastal). 

912 

913 Implications at Great Smoky Mountains National Park 

914 The average current extinction at Great Smoky Mountains National Park, at the eastern end of 

915 Tennessee, can be approximated by the most recent five years (1998-2002) for which IMPROVE 

particulate matter component concentration data were available on the VIEWS web site67 916 on 10 

917 October 2004. At the GRSM IMPROVE site adjacent to the park the average 20% haziest-days’ 

haze index over those five years (using the EPA-prescribed protocols for the calculations68 918 ) is 

29.54 dv, which is equivalent to an extinction coefficient of 191.9 Mm-1 919 . From EPA's natural 

conditions guidance document,2920 the long-term goal is default natural conditions estimated at 

11.44 dv (31.4 Mm-1 921 ) for the 20% haziest days. 

922 Consider four potential scenarios with different estimates of the natural conditions 

923 endpoint. 

924 • GRSM1: EPA's default natural conditions estimate. Calculate annual average from 

925 Table 1 concentrations and f(RH) from U.S. EPA’s natural conditions guidance 

document.2 Estimate average of 20% haziest days based on estimated 90th 926 percentile, 

927 which adds 3.84 dv to the annual average haze index value. 

• GRSM2: Change OC mass multiplier from 1.4 to 2.1, which adds 2.8 Mm-1 928 to the 

929 annual average. (Also, when computing progress slope, adjust the OC mass multiplier 

930 for present day to 1.8, a value midway between the 1.6 and 2.1 values recommended 

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/m3932 of 

933 biogenic POM from the forests (the lower bound of the range proposed in Table 2) to 

934 the annual default concentrations. This increases the natural background annual 

average light scattering contribution by 1.2 Mm-1 935 . 

936 • GRSM3*: As an alternative to GRSM3, if all of the haziest days are in the growing 

937 season/summer (which pretty much holds true at Great Smoky Mountains), add the 

Appendix B.4 - 31
32

peak effects of African dust (0.6 μg/m3, 0.6 Mm-1 938 ) and the peak season impacts of 

vegetation emissions of biogenic organics (conservatively 1 μg/m3, 4 Mm-1 939 ) to the 

940 natural background of Scenario GRSM2 during summer only. Concentrations during 

941 the other three seasons are taken to be the same as those in GRSM2. 

942 For scenarios GRSM1 through GRSM3, the contribution of each PM component to 

943 annual average light extinction is displayed graphically in Figure 4. The largest changes are 

944 produced by changing the assumed mass concentration of organic matter. Since the annual 

945 average effect of the added dust is small, the annual average of extinction for scenario GRSM3* 

946 would be virtually the same as that of GRSM3. 

947 Table 3 compares current and natural conditions values of light extinction, annual 

averages versus averages of 20% haziest days, and haziest days estimates based on the 90th 948 

percentile of the distribution (the default approach) versus the 92nd 949 percentile (the approach 

950 supported here), for all four scenarios. An assumption implicit in this data tabulation and similar 

951 calculations here is that refinements to the default natural conditions concentrations do not alter 

952 the standard deviation of the frequency distribution of the natural conditions haze index, so that 

953 the statistical adjustment used by the EPA to determine average conditions on the 20% haziest 

days from the annual averages4954 is the same for both the original and the refined conditions.

955 Emissions control strategies are intended to focus on the 20% haziest days rather than the 

956 annual average conditions. The control strategy implications for the four scenarios on the 20% 

957 haziest days are summarized in Table 4. The first data column is the current haze index that 

958 represents the average of the 20% haziest days. It was calculated under the current EPA 

protocol,68 with OC multiplied by 1.4 and the 90th 959 percentile used to define the 20% haziest 

natural days for Scenario GRSM1(90). The same scenario, except that the 92nd 960 percentile was 

used, is labeled GRSM1(92). The remaining scenarios also use the 92nd 961 percentile to define the 

962 20% haziest natural days and have natural OC multiplied by 2.1 and current OC multiplied by 

963 1.8. Both the Current Haze Index and the Natural Background Haze Index columns reflect the 

964 effects of the refinements stated above. The difference between these two columns is the 

965 required improvement over 60 years, so 1/6 of that is required over a decade, as listed in the third 

data column. The corresponding change in bext 966 over the first decade is in the next column. The 

967 next-to-last column gives the percentage reduction in particulate matter concentrations that 

Appendix B.4 - 32
33

968 would be required if all particulate matter components in Equation 1 were reduced by the same 

969 percentage. 

970 Since sulfates are the predominant contributors to haze in the VISTAS region most of the 

971 time, the last column in Table 4 indicates the % reduction in non-natural sulfate concentrations 

that would be needed during the first decade to produce the given bext 972 decrease if only sulfates 

973 were reduced and all other components remained unchanged. These values were calculated using 

974 an annual-average f(RH) of 3.32 (from Table A-2 in Reference 2), i.e., RH = 85%, and a 5-year 

(1998-2002) average ammonium sulfate concentration of 14.25 μg/m3975 on the 20% haziest days, 

of which 0.23 μg/m3976 is assumed uncontrollable natural background (from Table 1) . 

977 The differences between scenarios GRSM1 through GRSM3 are relatively small, whether 

978 equal control of all components is assumed, or only control of sulfates. Note, though, that the 

979 percentage improvements given in the next-to-last column assume (unrealistically) that the 

980 concentrations of all of the chemical components in Table 1 will be reduced by the same 

981 percentage. The difference between a 27.5% and a 25.0% reduction in a given component 

982 concentration will be much more significant if part of that component concentration is natural or 

983 otherwise uncontrollable. For example, Trijonis postulated that fine soil is half natural and half 

984 anthropogenic, so the percentage reductions in the anthropogenic part of the soil need to be twice 

985 as large as the percentages just cited if the specified reduction in soil is to hold. This implies 

986 requiring a 55% reduction of anthropogenic soil emissions under the default assumption versus 

987 50% under the GRSM3 scenario. Similarly, if some of the universe of sources that emit a 

988 specific pollutant is already well-controlled, the percentage reductions required of the other 

989 sources will be greater than the values given in Table 4, in which case the differences between 

990 the required reduction rates may be of economic importance when setting a control strategy. 

991 The required percentage of concentration reduction decreases further when we consider 

992 scenario GRSM3*. This scenario takes advantage of the fact that most of the haziest days at 

993 Great Smoky Mountains occur in the summer, at the same time when the organic emissions from 

994 vegetation and impacts from African dust are the greatest and thus the natural background is 

995 high. In this situation, a smaller degree of emissions control is required to attain the required rate 

996 of progress on the 20% haziest days. 

997 

Appendix B.4 - 33
34

998 Implications at Cape Romain National Wildlife Refuge 

999 A similar analysis, following the same protocols, can be carried out for the Cape Romain 

1000 National Wildlife Refuge, on the Atlantic coast of South Carolina. At the Cape Romain 

1001 IMPROVE site (ROMA), the current extinction on the 20% haziest days can be approximated 

for our purposes here by the values in four recent years, 1999-2002.67 1002 (The data for 1998 are too 

1003 incomplete to define the 20% worst days in that year.) The average 20% haziest days' haze index 

over those four years is 25.89 dv or 133.2 Mm-1 1004 . The haziest days natural conditions target in the 

EPA guideline document2 is 11.36 dv, or 31.1 Mm-1 1005 , for this location.

1006 Similar to the previous analysis, consider five scenarios. The first two scenarios, ROMA1 

1007 and ROMA2, are identical to those at Great Smoky Mountains, albeit with local data. The other 

1008 three scenarios reflect the uniqueness of the coastal location of Cape Romain: 

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

1010 • ROMA2: Adjust the OC mass multiplier for natural conditions to 2.1 and for present 

1011 day data to 1.8, as described in GRSM2. 

• ROMA3: Add 1.3 μg/m31012 of sea salt to Scenario ROMA2. Using the annual average 

f(RH) of 3.15 at Cape Romain, this sea salt contributes 4.1 Mm-1 1013 of light scattering. 

1014 Since the EPA/IMPROVE formula (eq 1) does not consider sea salt, this amount is 

1015 added to both current and natural conditions extinction estimates. 

• ROMA 4: Add 0.8 Mm-1 1016 to natural conditions in Scenario ROMA3 to represent the 

contribution of 0.2 μg/m31017 of oceanic POM. 

• ROMA5: Add an annual average of 0.3 μg/m31018 of African dust, which increases 

natural extinction by 0.3 Mm-1 1019 . (Being farther south than Great Smoky Mountains, 

1020 the African dust impact here is greater and temporally more diffuse, although an 

1021 annual average is still a crude approximation. Furthermore the 20% haziest days at 

1022 Cape Romain are distributed throughout the year and thus are not well correlated with 

1023 the summertime episodicity of African dust, so a seasonal analysis of the type used 

1024 for Great Smoky Mountains is not viable here.) 

1025 Figure 5 shows the effects of these scenarios on the contributions to natural conditions extinction 

1026 by the various components of the particulate matter. The biggest changes result from increasing 

Appendix B.4 - 34
35

1027 the organic carbon multiplier and from adding sea salt. The contributions of oceanic organics and 

1028 African dust are relatively minor. 

1029 Table 5 summarizes the extinction statistics for the various scenarios. Cape Romain is 

1030 less hazy than Great Smoky Mountains, especially on the haziest 20% of the days. Note that the 

1031 increased OC multiplier and the addition of sea salt affect the current extinction estimates as well 

1032 as those of natural conditions. 

1033 Table 6, which is in the same format as Table 4, shows the emissions control strategy 

1034 implications of these four scenarios. The main difference from Table 4 is the smaller percentage 

1035 concentration reductions that are needed because Cape Romain is substantially less hazy on 

1036 average than Great Smoky Mountains, while the default natural conditions goals are virtually 

1037 identical. Also, all of the refinements considered here have a bigger effect on the required 

1038 percentage of reduction than did those considered for Great Smoky Mountains. Otherwise, the 

1039 general conclusions concerning Table 4 also apply to Table 6. 

1040 In overview, Figures 4 and 5 illustrate that most refinements in the annual average 

1041 estimates of concentrations of soil, elemental carbon, and nitrates will have minimal effect 

1042 because the underlying contributions of those components are quite small. Even the episodic 

1043 African dust contribution in the summer in Scenario GRSM3* makes only about a 1% 

1044 contribution to the extinction on the natural conditions 20% haziest days. On the other hand, 

1045 changes in POM and sea salt concentrations can have important impacts on the light extinction 

1046 and deserve first attention if a decision is made to refine the default concentrations. The 

combination of the use of the 92nd 1047 percentile and the 2.1 multiplier for calculating POM from OC 

1048 also has a material effect on the natural conditions extinction estimate. When the needed degree 

1049 of emission control is considered, however, the impacts of some of these refinements to natural 

1050 conditions are diluted because partially-compensating adjustments also need to be made to 

1051 estimates of current extinction. 

1052 CONCLUDING COMMENTS 

1053 This review was prepared to illuminate potential approaches for refining natural conditions 

1054 estimates and to provide some insight into the most promising approaches. The calculations in 

1055 the previous section suggest that there is merit in establishing refined estimates of natural 

1056 conditions at some Class I areas, even at this initial part of the 60-year period. 

Appendix B.4 - 35
36

1057 We have found that conservative regional refinements to the default natural background 

1058 estimates do not greatly alter the region-wide rates of reduction of ambient particulate matter 

1059 concentrations that will be needed to attain the natural conditions goal. However, refinements at 

1060 a specific Class I area may have considerable influence on defining the nature (magnitude and 

1061 spatial and temporal distribution) of local emission reduction efforts to reach the natural 

1062 conditions goal there. 

1063 Three relatively non-controversial refinements that could be applied without extensive 

1064 effort are (1) using a more-appropriate percentile to reflect the average of the haziest 20% of the 

1065 days; (2) applying an organic carbon multiplier adjustment at most or all Class I areas and (3) 

1066 adding the effect of sea salt at Class I areas that are relatively close to the ocean. In the last two 

1067 cases, appropriate corresponding adjustments should also be made to present-day estimates 

1068 before the rate of reasonable progress is calculated. In the case of the OC multiplier, though, the 

1069 present day multiplier probably should not be the same as that for natural conditions. 

1070 African dust is a large contributor episodically during the summer, but its episodicity 

1071 means that creative thought will be needed to weave it into the mandated regional haze 

1072 management process. Compared to other components, on an average basis it is not an important 

1073 contributor to natural light extinction, though. 

1074 POM created in forests and emitted from natural wildfires are two other promising 

1075 contributors, but their effects are not well quantified and including them will be more 

1076 challenging and require more research than the other refinements that have been mentioned. 

1077 Asian dust has most of the complexities of African dust, but it has even smaller impact. 

1078 Note that in this paper we have not considered changes to the numerical coefficients 

1079 (extinction efficiencies) used in eq 1 to calculate light extinction. If the scaling factor for OC is 

1080 increased, then total light extinction calculated via eq 1could possibly become an overestimate 

1081 because of the greater POM concentration, unless the extinction efficiencies of carbon or other 

components in the equation are adjusted. These issues are addressed elsewhere11 - 13 1082 and have 

1083 not been included in our calculations. 

1084 As a closing perspective, one needs to remember that any refinements to the default 

1085 natural conditions estimates will have to be made in a regulatory framework. In such a setting, 

1086 refinements are most likely to be accepted if the scientific community and the regulators have 

1087 confidence in the science supporting the changes and justifying the magnitudes of the changes, 

Appendix B.4 - 36
37

1088 and there is minimal controversy concerning those aspects. This paper addresses the technical 

1089 merits of potential refinements to natural background visibility at VISTAS Class I areas but does 

1090 not make policy recommendations as to whether these refinements should be adopted by the 

1091 VISTAS states as part of their state implementation plans for regional haze. 

1092 ACKNOWLEDGMENTS 

1093 The work reported in this paper was funded by the VISTAS regional planning organization, 

1094 through its funding agency, the Southeastern State Air Resource Managers and grants from the 

1095 Environmental Protection Agency. The ideas presented in this paper are those of the authors and 

1096 do not represent policy decisions by the VISTAS states. We appreciate the comprehensive 

1097 comments by an anonymous reviewer; they have substantially enhanced the quality of this paper. 

1098 REFERENCES

1099 1. Regional Haze Regulations; Final Rule. Federal Register, 1999, 64, 35713-35774. 

1100 2. U.S. EPA. Guidance for Estimating Natural Visibility Conditions under the Regional Haze 

1101 Rule. EPA-454/B-03-005. September 2003. 

1102 3. Trijonis, J. C. Characterization of Natural Background Aerosol Concentrations. Appendix A 

1103 in Acidic Deposition: State of Science and Technology. Report 24. Visibility: Existing and 

1104 Historical Conditions -- Causes and Effects. J. C. Trijonis, lead author. National Acid 

1105 Precipitation Assessment Program, Washington, DC. 1990. 

1106 4. Ames, R. B.; Malm, W. C. Recommendations for Natural Condition Deciview Variability: 

1107 An Examination of IMPROVE Data Frequency Distributions. In (CD-ROM) Proceedings of 

1108 A&WMA/AGU Specialty Conference on Regional Haze and Global Radiation Balance -- 

1109 Aerosol Measurements and Models: Closure, Reconciliation and Evaluation, Bend, OR, 2-5 

1110 October 2001. 

1111 5. Pitchford, M. L.; Tombach, I.; Barna, M.; Gebhart, K. A.; Green, M. C.; Knipping, E.; 

1112 Kumar, N.; Malm, W.; Pun, B.; Schichtel, B. A.; Seigneur, C. Big Bend Regional Aerosol 

1113 and Visibility Observational Study. Final report, 

1114 http://vista.cira.colostate.edu/improve/Studies/BRAVO/reports/FinalReport/bravofinalreport.

1115 htm. September 2004.

1116 6. Liousse, C.; Penner, J. E.; Walton, J. J.; Eddleman, H. A global three-dimensional model 

1117 study of carbonaceous aerosols. J. Geophys. Res., 1996, 101, No. D14, 19411-19432. 

Appendix B.4 - 37
38

1118 7. Park, R. J.; Jacob, D. J.; Chin, M.; Martin, R. V. (2003). Sources of carbonaceous aerosols 

1119 over the United States and implications for natural visibility. J. Geophys, Res., 2003, 108, 

1120 No. D12, doi:10.1029/2002JD003190.

1121 8. Park, R. J.; Jacob, D. J.; Field, B. D.; Yantosca, R. M.; Chin, M. Natural and transboundary 

1122 pollution influences on sulfate-nitrate-ammonium aerosols in the United States: Implications 

1123 for policy. J. Geophys. Res., 2004, 109, D15204, doi:10.1029/2003JD004473. 

1124 9. Lack, D. A.; Tie, X. X.; Bofinger, N. D.; Wiegand, A. N.; Madronich, S. Seasonal variability 

1125 of secondary organic aerosol: A global modeling study. J. Geophys. Res., 2004, 109, No. 

1126 D03203, doi:10.1029/2003JD003418.

1127 10. Tombach, I. On Refining Estimates of Natural Background Light Extinction in the VISTAS 

1128 Region. Report prepared for VISTAS Regional Planning Organization, Forest Park, Georgia, 

1129 4 December 2003. 

1130 11. Hand, J. L.; Malm. W. C. Review of the IMPROVE Equation for Estimating Ambient Light 

1131 Extinction Coefficients. Draft report, 

1132 http://www.vista.cira.colostate.edu/improve/Publications/Graylit/016_IMPROVEeqReview/I

1133 MPROVEeqReview.htm. 25 May 2005. 

1134 12. Lowenthal, D. H.; Kumar, N. Variation of mass scattering efficiencies in IMPROVE. J. Air 

1135 & Waste Manage. Assoc., 2004, 54, 926-934. 

1136 13. Ryan, P; Lowenthal, D.; Kumar, N. Improved light extinction reconstruction in IMPROVE.

1137 J. Air & Waste Manage. Assoc., 2005, 55, this issue 

1138 14. Ryan, P. A. Review of the U.S. Environmental Protection Agency Default Implementation 

1139 Guideline for the Regional Haze Rule. Report 1011119; EPRI: Palo Alto, CA, 2004. 

1140 15. Quinn, P. K.; Coffman, D. J.; Bates, T. S.; Miller, T. L.; Johnson, J. E.; Welton, E. J.; 

1141 Neusüss, C.; Miller, M.; Sheridan, P. J. Aerosol optical properties during INDOEX 1999: 

1142 Means, variability, and controlling factors. J. Geophys. Res., 2002, 107, D19, 8020, 

1143 doi:10.1029/2000JD000037. 

1144 16. Quinn, P. K.; Bates, T. S. Regional aerosol properties: Comparisons of boundary layer 

1145 measurements from ACE 1, ACE 2, Aerosols99, INDOEX, ACE Asia, TARFOX, and 

1146 NEAQS. J. Geophys. Res., 2005, 110, D14202, doi:10.1029/2004JD004755. 

1147 17. Huebert, B. J.; Charlson, R. J. Uncertainties in data on organic aerosols. Tellus, 2000, 52B, 

1148 1249-1255. 

Appendix B.4 - 38
39

1149 18. Turpin, B. J.; Lim, H-J. Species Contributions to PM2.5 Mass Concentrations: Revisiting 

1150 Common Assumptions for Estimating Organic Mass. Aerosol Sci. Tech., 2001, 35, 602-610. 

1151 19. Malm, W. C.; Day, D. E.; Carrico, C.; Kreidenweis, S. K.; Collett, J. L. Jr.; McMeeking, G.; 

1152 Lee, T.; and Carrillo, J. Inter-comparison and closure calculations using measurements of 

1153 aerosol species and optical properties during the Yosemite Aerosol Characterization Study. 

1154 Submitted to J. Geophys. Res., 2005.

1155 20. El-Zanan, H. S,; Lowenthal, D. H.; Zielinska, B.; Chow, J. C.; Kumar, N. Determination of 

1156 the organic aerosol mass to organic carbon ratio in IMPROVE Samples. Chemosphere, 2005, 

1157 60, 485-496. 

1158 21. Wotawa, G.; Trainer, M. The Influence of Canadian Forest Fires on Pollutant Concentrations 

1159 in the United States. Science, 2000, 288, 324-328. 

1160 22. Rogers, C. M.; Bowman, K. P. Transport of smoke from the Central American fires of 1998. 

1161 J. Geophys. Res., 2001, 106, No. D22, 28,357-28368. 

1162 23. Peppler, R. A.; Bahrmann, C. P.; Barnard, J. C.; et al. ARM Southern Great Plains Site 

1163 Observations of the Smoke Pall Associated with the 1998 Central American Fires. Bull. 

1164 Amer. Met. Soc., 2000, 81, 2563-2591.

1165 24. Edgerton, E. S.; Hartsell, B. E.; Jansen, J. J. Natural Sources of PM2.5 and PMcoarse Observed 

1166 in the SEARCH Network. J. Air Waste Manage. Assoc., 2005, 55, this issue. 

1167 25. Fallon, S.; Bench, G. IMPROVE special study: Hi-vol sampling for carbon-14 analysis of 

1168 PM 2.5 aerosols. Draft report, Lawrence Livermore National Laboratory, 24 September 

1169 2004.

1170 26. Tanner, R.; Zheng, M.; Lim, K.; Schauer, J. J.; McNichol, A. P. Source Contributions to the 

1171 Organic Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. 

1172 Paper presented at the AAAR International Specialty Conference, PM: Supersite Program 

1173 and Related Studies. Atlanta, GA. 7-11 Feb. 2005. 

1174 27. Hudson, P. K.; Murphy, D. M.; Cziczo, D. J.; Thomson, D. S.; de Gouw, J. A.; Warneke, C.; 

1175 Holloway, J.; Jost, H-J.; Hübler, G. Biomass-burning particle measurements: Characteristic 

1176 composition and chemical processing. J. Geophys. Res., 2004, 109, D23S27, 

1177 doi:10.1029/2003JD0004398. 

Appendix B.4 - 39
40

1178 28. Artaxo, P.; Gerab, F.; Yamasoe, M. A.; Martins, J. V. Fine mode aerosol composition at 

1179 three long-term atmospheric monitoring sites in the Amazon Basin. J. Geophys. Res., 1994, 

1180 99, No. D11, 22,857-22,868. 

1181 29. Guenther, A.; Hewitt, C. N.; Erickson, D.; et al. A global model of natural volatile organic 

1182 compound emissions. J. Geophys. Res., 1995, 100, D5, 8873-8892. 

1183 30. Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics: From Air Pollution to 

1184 Climate Change. John Wiley & Sons, New York. 1998. 

1185 31. Cahier, H.; Buat-Menard, P.; Fontugne, M,; Ranchier, J. Source Terms and Source Strengths 

1186 of the Carbonaceous Aerosol in the Tropics. J. Atmos. Chem., 1985, 3, 469-489. 

1187 32. Jacobson, M. C.; Hansson, H.-C.; Noone, K. J.; Charlson, R. J. Organic Atmospheric 

1188 Aerosols: Review and State of the Science. Rev. Geophys., 2000, 38, 267-294. 

1189 33. Kavouras, I. G.; Mihalopoulos, N.; Stephanou, E. G. Formation of atmospheric particles from 

1190 organic acids produced by forests. Nature, 1998, 395, 683-686. 

1191 34. Kavouras, I. G.; Mihalopoulos, N.; Stephanou, E. G. Formation and gas/particle partitioning 

1192 of monoterpenes photo-oxidation products over forests. Geophys. Res. Letters, 1999, 26, 55-

1193 58. 

1194 35. Rodhe, H. Foreword: Biogenic aerosol formation in the boreal forest (BIOFOR). Tellus, 

1195 2001, 53B, 323. 

1196 36. Kulmala, M.; Pirjola, L.; Mäkelä, J. Stable sulphate clusters as a source of new atmospheric 

1197 particles. Nature, 2000, 404, 66-69. 

1198 37. Kulmala, M.; Hämeri, K.; Aalot, P. P.; Mäkelo, J. M.; Pirjola, L.; Douglas-Nilsson, E.; 

1199 Buzorius, G.; Rannik, Ü.; Dal Maso, M.; Seidl, W.; Hoffman, T.; Janson, R.; Hansson, H.-C.; 

1200 Viisanen, Y.; Laaksonen, A.; O'Dowd, C. D. Overview of the international project on 

1201 biogenic aerosol formation in the boreal forest (BIOFOR). Tellus, 2001, 53B, 324-343. 

1202 38. Kulmala, M.; Korhonen, P.; Napari, I.; Karlsson, A.; Berresheim, H.; O'Dowd, C. D. Aerosol 

1203 formation during PARFORCE: Ternary nucleation of H2SO4, NH3, and H2O. J. Geophys, 

1204 Res., 2002, 107, No. D19, Article 8111. 

1205 39. Janson, R.; Rosman, K.; Karlsson, A.; Hansson, H.-C. Biogenic emissions and gaseous 

1206 precursors to forest aerosols. Tellus, 2001, 53B, 423-440. 

Appendix B.4 - 40
41

1207 40. Held, A.; Nowak, A.; Birmili, W.; Wiedensohler, A.; Forkel, R.; Klemm, O. Observations of 

1208 particle formation and growth in a mountainous forest region in central Europe. J. Geophys. 

1209 Res., 2004, 109, D23204, doi:10.1029/2004JD005346. 

1210 41. O'Dowd, C. D.; Aalto, P.; Hämeri, K.; Kulmala, M.; Hoffman, T. Atmospheric particles from 

1211 organic vapours. Nature, 2002, 416, 497-498. 

1212 42. Limbeck, A.; Kulmala, M.; Puxbaum, H. Secondary organic aerosol formation in the 

1213 atmosphere via heterogeneous reaction of gaseous isoprene on acidic particles. Geophys. Res. 

1214 Ltrs., 2003, 30, 19, 1996, doi:10.1029/2003GL017738.

1215 43. Iinuma, Y.; Böge, O.; Gnauk, T.; Herrmann, H. Aerosol-chamber study of the α-pinene/O3

1216 reaction: influence of particle acidity on aerosol yields and products. Atmos. Environ., 2004, 

1217 38, 761-773.

1218 44. Gao, S.; Ng, N. L.; Keywood, M.; et al. Particle Phase Acidity and Oligomer Formation in 

1219 Secondary Organic Aerosol. Environ. Sci. Technol., 2004, 38, 24, 6582-6589, 

1220 doi:10.102/es049125k. 

1221 45. Talbot, R. W.; Andreae, M. O.; Andreae, T. W.; Harriss, R. C. Regional Aerosol Chemistry 

1222 of the Amazon Basin During the Dry Season. J. Geophys. Res., 1988, 93, No. D2, 1499-

1223 1508. 

1224 46. Echalar, F.; Artaxo, P.; Martings, J. V.; Yamasoe, M.; Gerab, F. Long-term monitoring of 

1225 atmospheric aerosols in the Amazon Basin: Source identification and apportionment. J. 

1226 Geophys. Res., 1998, 103, No. D24, 31,849-31,864. 

1227 47. Makela, J. M.; Yli-Koivisto, S.; Hiltunen, A.; Seidl, W.; Switlicki, E.; Teinilä, K.; Sillanpää, 

1228 M.; Koponen, K.; Paatero, J.; Rosman, K.; Hämeri, K. Chemical composition of aerosol 

1229 during particle formation events in boreal forest. Tellus, 2001, 53B, 380-393. 

1230 48. Alves, C.; Carvalho, A.; Pio, C. Mass balance of organic carbon fraction in atmospheric 

1231 aerosols. J. Geophys. Res., 2002, 107, D21, 8345, doi:10.1029/2001JD000616. 

1232 49. Lewis, C. W.; Klouda, G. A.; Ellenson, W. D. Radiocarbon measurement of the biogenic 

1233 contribution to summertime PM-2.5 ambient aerosol in Nashville, TN. Atmos. Environ., 

1234 2004, 38, 6053-6061, doi:10.1016/jatmosenv.2004.06.011. 

1235 50. Malm, W. C., Principal Author. Spatial and Seasonal Patterns and Temporal Variability of 

1236 Haze and Its Constituents in the United States: Report III. Cooperative Institute for Research 

1237 in the Atmosphere, Colorado State Univ., Ft. Collins, CO. May 2000. 

Appendix B.4 - 41
42

1238 51. Tang, I. N.; Tridico, A. C.; Fung, K. H. Thermodynamic and optical properties of sea salt 

1239 aerosols. J. Geophys. Res., 1997, 102, No. D19, 23,269-23,275. 

1240 52. Lowenthal, D. H.; Kumar, N. PM2.5 Mass and Light Extinction Reconstruction in IMPROVE. 

1241 J. Air Waste Manage. Assoc., 2003, 53, 1109-1120. 

1242 53. Prospero, J. M. Long-term measurements of the transport of African mineral dust to the 

1243 southeastern United States: Implications for regional air quality. J. Geophys. Res., 1999, 104, 

1244 No. D13, 15,917-15,927. 

1245 54. Paine, R. J.; Heisler, S. L.; Heinold, D. W. Regional Haze Assessments with CALPUFF: 

Application of Refined Procedures. Paper 69470, Proceedings of the 96th 1246 Annual Conference 

1247 and Exhibition of the Air and Waste Management Assoc. San Diego, CA. 22-26 June 2003. 

1248 55. Hansson, H.-C.; Rood, M. J.; Koloutsou-Vakakis, S.; Hämeri, K., Orsini, D.; Wiedensohler, 

1249 A. NaCl Aerosol Particle Hygroscopicity Dependence on Mixing with Organic Compounds. 

1250 J. Atm. Chem., 1998, 31, 321-346.

1251 56. Lowenthal, D.; Kumar, N. Light Scattering from Sea Salt Aerosols in IMPROVE. Submitted 

1252 to J. Air Waste Manage. Assoc., 2005.

1253 57. Novakov, T.; Corrigan, C. E.; Penner, J. E.; Chuang, C. C.; Rosario, O.; Mayol-Bracero, O. 

1254 L. Organic aerosols in the Caribbean trade winds: A natural source? J. Geophys. Res., 1997, 

1255 102, No. D17, 21,307-21313. 

1256 58. Mayol-Bracero, O. L., Rosario, O., Corrigan, C. E., Morales, R., Torres, I.; Pérez, V. 

1257 Chemical characterization of submicrometer organic aerosols in the tropical trade winds of 

1258 the Caribbean using gas chromatography/mass spectrometry. Atmos, Environ., 2001, 35, 

1259 1735-1745. 

1260 59. Cavalli, F.; Facchini, M. C.; Decesari, S.; Mircea, M.; Emblico,L.; Fuzzi, S. Advances in 

1261 characterization of size-resolved organic matter in marine aerosol over the North Atlantic. J. 

1262 Geophys. Res., 2004, 109, D24215, Doi:10.1029/2004JD005137. 

1263 60. VanCuren, R. A.; Cahill, T. A. Asian Aerosols in North America: Frequency and 

1264 Concentration of Fine Dust. Journal of Geophysical Research, 2002, 107, D24, 4804, 

1265 doi:10.1029/2002JD002204. 

1266 61. VanCuren, R. A.; Cliff, S. S.; Perry, K. D.; Jimenez-Cruz, M. Asian continental aerosol 

1267 persistence above the marine boundary layer over the eastern North Pacific; Continuous 

Appendix B.4 - 42
43

1268 aerosol measurements from Intercontinental transport and Chemical transformation 2002 

1269 (ITCT 2K2). J. Geophys. Res., 2005, 110, D09S90, doi:10.1029/2004JD004973. 

1270 62. Jaffe, D., Snow, J.; Cooper, O. (2003). The 2001 Asian Dust Events: Transport and Impact 

1271 on Surface Aerosol Concentrations in the U.S. EOS, 2003, 84, 501-507. 

1272 63. DeBell, L. J.; Vozzella, M.; Talbot, R. W.; Dibb, J. E. Asian dust storm events of spring 2001 

1273 and associated pollutants observed in New England by the Atmospheric Investigation, 

1274 Regional Modeling, Analysis and Prediction (AIRMAP) monitoring network. J. Geophys. 

1275 Res., 2004, 109, D01304, doi:10.1029/2003JD003733. 

1276 64. Perry, K. D.; Cahill, T. A.; Eldred, R. A.; Dutcher, D. D.; Gill, T. E. Long-range transport of 

1277 North African dust to the eastern United States. J. Geophys. Res., 1997, 102, No. D10, 

1278 11,225-11,238. 

1279 65. Al-Harbi, B. H. O. Transport of North African dust to Big Bend, Texas, during the 1999 Big 

1280 Bend regional aerosol and visibility study. M. S. thesis, Department of Atmospheric 

1281 Sciences, Colorado State University, 2003. 

1282 66. Schichtel, B. Personal communication, 2003. 

1283 67. VIEWS web site. http://vista.cira.colostate.edu/views. 

1284 68. U.S. EPA. Guidance for Tracking Progress under the Regional Haze Rule. EPA-454/B-03-

1285 004. September 2003. 

1286 

1287 

Appendix B.4 - 43
44

1288 ABOUT THE AUTHORS 

1289 Ivar Tombach is Technical Advisor to the VISTAS Regional Planning Organization. He is an 

1290 independent air pollution consultant, located at 753 Grada Ave., Camarillo, CA 93010. Patricia 

1291 Brewer is VISTAS’ Technical Coordinator at 2090 U.S. 70 Highway, Swannanoa, NC 28778. 

1292 

1293 

1294 

1295 TABLES 

1296 

1297 Table 1. Annual average default natural background concentration estimates for particle 

1298 components in the East, and their estimated impacts on dry light extinction according to the 

1299 IMPROVE algorithm (Eq 1). 

 1300 

Component Average 

Concentration, 

μg/m3

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 

 1302 

Appendix B.4 - 44
45

1303 Table 2. Summary of suggested refinements to default estimates of natural conditions in the 

1304 VISTAS region and their potential effects, with authors’ assessments of whether refinement of 

1305 the default method or concentration is warranted.

 1306 

Issue/Source PM2.5

Component 

Affected 

Annual Average 

Concentration 

Impact (μg/m3) 

Annual Light 

Extinction 

Impact (Mm-1) 

Refining 

Default 

Warranted? 

Methods Refinements 

Use 92nd %-ile 

instead of 90th

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 

 1307 

Appendix B.4 - 45
46

Table 3. Estimated annual and 20%-haziest days extinction averages (in Mm-1 1308 ) for four 

1309 hypothetical scenarios at Great Smoky Mountains National Park. 

1310 

Scenario Annual Average bext 20% Haziest Days bext 

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 

1316 Table 4. Implications of four natural conditions scenarios on the emissions reductions required 

1317 to decrease haze on the 20% haziest days during the first decade at Great Smoky Mountains 

1318 National Park. 

 1319 

Scenario Current 

HI, dv 

Natural 

Backgnd 

HI, dv 

Improvmnt 

Rate, 

dv/decade 

∆bext 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 

 1322 

Appendix B.4 - 46
47

Table 5. Estimated annual and 20%-haziest days extinction averages (in Mm-1 1323 ) for five 

1324 hypothetical scenarios at Cape Romain National Wildlife Reserve. 

 1325 

Scenario Annual Average bext 20% Haziest Days bext 

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 

1331 Table 6. Implications of five scenarios on the emissions reductions required to decrease haze on 

1332 the 20% haziest days during the first decade at Cape Romain National Wildlife Reserve. 

 1333 

Scenario Current 

HI, dv 

Natural 

Backgnd 

HI, dv 

Improvmnt 

Rate, 

dv/decade 

∆bext 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 

1335 

Appendix B.4 - 47
FIGURES 

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.4 - 48
Appendix B.4 - 49
Appendix B.4 - 50
Appendix B.4 - 51
Appendix B.4 - 52
Appendix B.4 - 53
Appendix B.5 

Carbon Analyses for PM2.5 and Haze 
1

Patricia Brewer1, 

Johann Engelbrecht2, Roger Tanner3, 

Scott Reynolds4, John Calcagni5

1VISTAS

2Desert Research Institute

3Tennessee Valley Authority

4South Carolina Department of Health and Environmental Control

5Environmental Protection Agency 

VISTAS Special Monitoring Studies

Carbon Analyses for PM2.5 and Haze

Appendix B.5 - 1
2

Presentation Objectives

• VISTAS Data Analysis • Special Monitoring Studies

• Carbon Studies: C14 isotope, Positive Matrix 

Factorization, and Chemical Mass Balance

Appendix B.5 - 2
Areas with Poorest Visibility are similar to PM2.5 Non-attainment Areas

Appendix B.5 - 3
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

Appendix B.5 - 4
Average Extinction for 20% Haziest Days

New IMPROVE Algorithm 

2000-2004

VISTAS coastal VISTAS inland non-VISTAS

0

50

300250200150100

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 Salt Coarse Soil ECPOM NH4NO3

(NH4)2SO4Rayleigh

Sulfate dominates the 20% Haziest Days at VISTAS Class I areas

Appendix B.5 - 5
Average Extinction for 20% Best Days

New IMPROVE Algorithm

2000-2004

0

605040302010

SWAN

ROMA

OKEF

EVER

CHAS

SAMA

DOSO

SHEN

JARI

LIGO

SHRO

GRSM

COHU

SIPS

MACA

BRIG

BRET

MING

HEGL

UPBU

CACR

VISTAS coastal VISTAS inland non-VISTAS

Extinction (Mm-1)Sea Salt CMSoil ECPOM NH4NO3 (NH4)2SO4

Rayleigh

Sulfate also dominates the 20% Clearest Days at VISTAS Class I areas

Appendix B.5 - 6
Natural Conditions -- New IMPROVE Algorithm -

0

5040302010

Great Smoky Mtns

Shenandoah

Cape Romain

Okefenokee

Everglades

Breton *

Left = B20%; Center = Average; Right = W20%

*Breton based on 2001Q2-2002Q1 & 2002Q4-2003Q3 

bext (Mm-1)

Sea SaltCMSoilECPOMNH4NO3(NH4)2SO4Rayleigh

Appendix B.5 - 7
8

Monitoring Objectives

 Identify sources of organic carbon

 Use high volume samplers with PM2.5 head and 

quartz filters at 5 sites

 Samples collected April 2004 – May 2005 Analyses by Desert Research Institute  Select 250 samples for carbon analysis (5 sites, 

50 samples per site including field and lab blanks)

 Define carbon mass of each sample Characterize organic composition (GC/MS)  Identify tracers of primary sources of carbon 

(Chemical Mass Balance and Positive Matrix Factorization)

Appendix B.5 - 8
9

Monitoring Objectives

 Identify sources of organic carbon

 For same samples, characterize C14

isotope and ratio of modern to fossil carbon 

 Analyses by Woods Hole Oceanographic 

Institute’s National Oceanographic Sciences Accelerator Mass Spectrometry (NOSAMS)

 Sources of modern carbon: vegetative 

emissions, wood burning, agricultural burning, cooking

 Sources of fossil carbon: gasoline, diesel, 

oil

Appendix B.5 - 9
10

URBAN CLASS 1

Great Smoky Mtns.

Cape Romain

Raleigh

VISTAS Carbon Study Sites

Shenandoah

Mammoth Cave

Appendix B.5 - 10
11

Sources of Organic Carbon: 

Lessons Learned

 Modern carbon fraction seldom <0.60, 

usually >0.70 

 Higher modern carbon at forested sites 

(GRSM and MACA) than coastal (ROMA) or urban (MILL) sites

 Higher total carbon and higher total PM2.5 at 

urban site correspond to lower modern carbon

 No clear seasonal trends in modern carbon

 Hypothesize that greater wood burning in winter 

months and greater vegetative emissions in spring/summer

Appendix B.5 - 11
12

Apr 2004 May 2005

Appendix B.5 - 12
13

Fraction Modern Carbon vs. Aerosol Carbon 

Cape Romain, All Seasons

y = 0.045x + 0.556

R2 = 0.422

1.000.900.800.700.600.500.400.30

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Aerosol C, μg/m3

Fraction Modern Carbon, f

m

Appendix B.5 - 13
14

Appendix B.5 - 14
15

Great Smoky Mtns, All Seasons

y = 0.038x + 0.609

R2 = 0.1362

1.101.000.900.800.700.600.500.400.30

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Aerosol Carbon , μg/m3

Fraction Modern Carbon, f

m

Fraction Modern Carbon vs. Aerosol Carbon 

Appendix B.5 - 15
16

Appendix B.5 - 16
17

Mammoth Cave, All Seasons

1.101.000.900.800.700.600.500.400.30

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Fraction Modern Carbon, f

m

Aerosol Carbon , μg/m3

Fraction Modern Carbon vs. Aerosol Carbon 

Appendix B.5 - 17
18

Appendix B.5 - 18
19

Aerosol Carbon , μg/m3

Fraction Modern Carbon vs. Aerosol Carbon 

Millbrook, All Seasons

1.101.000.900.800.700.600.500.400.30

0.00 2.00 4.00 6.00 8.00 10.00 12.00

Fraction Modern Carbon, f

m

Appendix B.5 - 19
20

Appendix B.5 - 20
21

Shenadoah, All Seasons

y = 0.054x + 0.514

R2 = 0.434

1.101.000.900.800.700.600.500.400.30

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

Aerosol Carbon , μg/m3

Fraction Modern Carbon vs. Aerosol Carbon 

Fraction Modern Carbon, f

m

Appendix B.5 - 21
22

Monitoring Objectives

 Positive Matrix Factorization applied to 

cluster organic compounds. 

 Tracers identified for

 Biogenic Emissions Vegetative Burn Gasoline emissions Diesel emissions Crude Oil (Commercial Marine, seen at ROMA) 

 Analyses incomplete: tracers still need to be 

related back to total carbon to understand relative importance

Appendix B.5 - 22
23

Vegetative burnBiogenic EmissionsDiesel CrudeMotor vehicles

Appendix B.5 - 23
24

Appendix B.5 - 24
25

Appendix B.5 - 25
26

Appendix B.5 - 26
27

Appendix B.5 - 27
28

Monitoring Objectives

 Chemical Mass Balance analyses

 results to date from few samples analyzed by Mei

Zheng, Ga Tech

 Chemical markers (profiles) for primary 

carbon sources:

 Wood Combustion Gasoline emissions Diesel emissions Meat Cooking Vegetative detritus Other Organic Matter (unidentified primary sources 

plus secondary organics) 

Appendix B.5 - 28
29

GT-CMB Results

6.005.004.003.002.001.000.00

ROMA 1-22-04 ROMA 4 10-04 - GRSM1-22 04 - GRSM 4-10-04 MACA 1-22-04 MACA 4-10-04 M LL1-22 I 04 - M LL 4-10-04 I SHEN 1-22 04 - SHEN 4-10-04

g/m3μ

Diesel Gasoline Wood Combustion Meat Cooking Vegetative Detritus Other OM

Appendix B.5 - 29
30

Carbon Source Attribution: 

Lessons Learned

 CMB Results from samples analyzed by 

Mei Zheng, Ga Tech

 In April samples from all sites, unidentified 

component is largest fraction (>70%)

 Likely secondary, likely biogenic 

 Wood combustion is largest identified 

component in Jan samples, all sites

 Gasoline higher in Jan than Apr samples? Diesel at all sites in Apr, not Jan samples?

Appendix B.5 - 30
31

Carbon Source Attribution: 

Lessons Learned

 CMB analyses together with C14 results 

indicate that modern carbon is more important than fossil carbon at Class I areas and urban area sampled.

 Little seasonal variability in modern carbon 

 wood burning vs biogenic

 Still need integration to understand source 

contributions

Appendix B.5 - 31
32

Next Steps 

 Relate organic tracers used as PMF factors to 

larger population of organic compounds 

 Complete CMB organic analyses all samples Relate organic profiles to measured carbon  Integrate results with C14 analyses  Interpret source attribution for organic carbon  Integrate results with SOA production in 

CMAQ/CAMx air quality models

 Finalize project reports  Submit manucript for publication

Appendix B.5 - 32
Appendix B.6 

Continuous Measures of Speciated PM2.5 
1

July 23, 2007

Patricia Brewer1, Scott Reynolds2, 

Roger Tanner3, Eric Edgerton4, Ben Hartsell4,

Jim Renfro5, Hoke Kimball6, John Calcagni7

1VISTAS

2South Carolina Department of Health and Environmental Control

3 Tennessee Valley Authority 

4Atmospheric Research Associates

5National Park Service

6North Carolina Department of Natural Resources

7Environmental Protection Agency

VISTAS Special Monitoring Studies 

Continuous Measures of Speciated PM2.5

Appendix B.6 - 1
2

Presentation Objectives

• Special Monitoring Studies

• Continuous Monitors: PM2.5, Sulfate, 

Nitrate, Total Carbon, Black Carbon

Appendix B.6 - 2
3

Monitoring Objectives

 Use continuous monitors of PM2.5 mass, 

sulfate, nitrate, total carbon and black carbon to characterize daily and spatial trends 

 Data collected Mar 2003 - Dec 2004 Raw data available in AQS Normalized data available from VISTAS Draft VISTAS Report authored by Roger 

Tanner, TVA

Appendix B.6 - 3
URBAN CLASS 1

Great Smoky Mtns.

Cape Romain

Raleigh

VISTAS Focus Sites

Atlanta

Appendix B.6 - 4
5

March 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 1 

areas)

• Meteorology• Alternative Sulfate and Nitrate methods

• Filter-based Speciation: IMPROVE or STN

Focus Sites

Appendix B.6 - 5
6

Lessons Learned from 

Continuous Monitors

• Continuous PM2.5 mass (TEOM) shows 

good agreement to PM2.5 mass from filter

Appendix B.6 - 6
7

PM2.5

 2003

y = 1.3336x + 3.3987

R2 = 0.8542

y = 1.0749x + 2.8358

R2 = 0.9629

y = 0.8383x + 2.4046

R2 = 0.9051

0

5040302010

0 10 20 30 40 50

Filter

Continuous

Mass

Continuous PM2.5 mass shows good agreement to PM2.5 mass from filter

Appendix B.6 - 7
8

Lessons Learned from 

Continuous Monitors

• Aethalometer (BC) has good performance • R&P instruments for S, N, and Total C achieved 

good data capture (>75%) when operating

• Periodic failures R&P instruments

• S, N, TC, BC continuous data needs to be 

normalized to filter-based data to interpret trends.

• Correlations by Site and Quarter 

• Continuous speciated monitors have high upkeep 

and data management 

• Postscript: R&P monitors are being discontinued• Alternative commercial continuous monitors

Appendix B.6 - 8
9

V IS T A S F o c u s S ite T E O M P M 2 5

Q 3 2003

0

6 05 04 03 02 01 0

7 /1 7 /8 7 /1 5 7 /2 2 7 /2 9 8 /5 8 /1 2 8 /1 9 8 /2 6 9 /2 9 /9 9 /1 6 9 /2 3 9 /3 0

micrograms / cubic meter

DK PM MB PM CR PM LR PM

Third Quarter 2003

V IS T A S F o c u s S ite S u lfa te

2003

0

2 01 0

7/1 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 meter

MB SO 4 CR SO4 LR SO 4

MASS

SULFATE

Appendix B.6 - 9
10

V IS T A S F o c u s S ite T o ta l C a rb o n

2003

0

1 0

7/1 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 meter

MB TC CR TC LR TC

Third Quarter 2003

V IS T A S F o c u s S ite T E O M P M 2 5

Q 3 2003

0

6 05 04 03 02 01 0

7 /1 7 /8 7 /1 5 7 /2 2 7 /2 9 8 /5 8 /1 2 8 /1 9 8 /2 6 9 /2 9 /9 9 /1 6 9 /2 3 9 /3 0

micrograms / cubic meter

DK PM MB PM CR PM LR PM MASS

CARBON

Appendix B.6 - 10
11

Lessons Learned from 

Continuous Monitors

• Continuous monitors high scatter 

compared to filter-based data

• Capture same spatial and seasonal 

trends as filter-based data

Appendix B.6 - 11
12

Filter comparisons

Sulfate - IMPROVE

Look Rock Sulfate

Q3 2003

50

25201510

7/1 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

r micrograms / cubic mete

LR SO4fLR SO4

Appendix B.6 - 12
13

Filter comparisons

Sulfate - IMPROVE

Cape Romain Sulfate

Q3 2003

50

25201510

7/1 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

r micrograms / cubic mete

CR SORfCR SO4

Appendix B.6 - 13
14

Filter comparisons

Sulfate – STNRVE 

Millbrook Sulfate

Q3 2003

50

25201510

7/1 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

r micrograms / cubic mete

MB STN SO4MB SO4

Appendix B.6 - 14
15

Sulfate

2003

y = 1.0079x + 0.7062

R2 = 0.6451

y = 0.6538x + 1.2038

R2 = 0.7711

y = 0.5623x + 0.6845

R2 = 0.7775

50

201510

0 5 10 15 20

Filter

Continuous

Sulfate

Appendix B.6 - 15
16

Nitrate

2003

y = 0.322x + 0.1088

R2 = 0.1171

y = 0.9853x + 0.0657

R2 = 0.7019

y = 0.5556x + 0.1306

R2 = 0.3529

1.00.50.0

0.0 0.5 1.0

Filter

Continuous

Nitrate

Appendix B.6 - 16
17

Carbon

2003

y = 0.783x + 0.7507

R2 = 0.6823

y = 1.2608x + 0.4168

R2 = 0.5383

y = 0.4249x + 1.0943

R2 = 0.7599

840

1612

0 4 812 16

Filter

Continuous

Total Carbon

Appendix B.6 - 17
18

Elemental Carbon

2003

y = 0.6704x + 0.7892

R2 = 0.6108

y = 1.0152x + 0.5788

R2 = 0.4042

y = 0.2646x + 0.8859

R2 = 0.591

840

1612

0 4 812 16

Filter

Continuous

Elemental to Black Carbon

Appendix B.6 - 18
19

Normalizing Continuous 

Monitoring Data

• Continuous data was normalized to filter 

data to facilitate comparison to modeled values

• Regressions calculated for each site, each 

quarter 

Appendix B.6 - 19
20

Lessons Learned from 

Continuous 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 other 

seasons

• Consistent with photochemical production

Appendix B.6 - 20
21

Diurnal Pattern of Average Sulfate in Winter (Jan-Mar) 2004 

at Focus Sites 

5.04.03.02.01.00.0

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Hour of Day

4 [SO

= g/m μ ], 3

ROMAGRSMMILL

Appendix B.6 - 21
22

Diurnal Pattern of Average Sulfate in Spring (Apr-Jun) 2004 

at Focus Sites 

8.07.06.05.04.03.02.01.00.0

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Hour of Day

4 [SO

= g/m μ ], 3

ROMAGRSMMILL

Appendix B.6 - 22