ISSN: 0737-5352-32 

SPATIAL AND SEASONAL PATTERNS 

AND LONG TERM VARIABILITY OF 

THE COMPOSITION OF THE HAZE IN THE UNITED STATES: 

An Analysis of Data from the IMPROVE Network

James F. Sisler 

Cooperative Institute for Research in the Atmosphere 

Colorado State University 

Fort Collins, CO 80523 

Contributions by 

William C. Malm 

Kristi A. Gebhart 

Air Resources Division 

National Park Service 

Colorado State University 

Fort Collins, CO 80523 

Principal Investigators 

William C. Malm 

National Park Service 

Air Resources Division 

Colorado State University 

Fort Collins, CO 80523 

Marc L. Pitchford 

Applied Sciences Branch 

ARL/NOAA 

c/o Desert Research Institute 

PO Box 10940 

Las Vegas, NV 89132-0040 

July 1996 
2

DISCLAIMER 

 The assumptions, findings, conclusions, judgements, and views presented herein are those 

of the authors and should not be interpreted as necessarily representing official National Park 

Service policies, and are not necessarily endorsed by the management and sponsors of project 

MOHAVE. 
3

ACKNOWLEDGEMENTS 

 This report is the result of a collaborative effort involving the authors and a number of 

individuals from several institutions. We thank Drs. Thomas Cahill and Robert Eldred of the 

University of California at Davis for their efforts in developing the IMPROVE aerosol monitoring 

network, managing the aerosol database, and providing input to the report concerning aerosol 

sampling and analysis. Also, we thank Dr. Judith Chow and associates at Desert Research Institute, 

University of Nevada Systems at Reno for their input on the analysis of carbonaceous species. We 

also wish to acknowledge Mr. John Molenar and Dr. David Dietrich of Air Resource Specialists in 

Fort Collins, Colorado for developing and managing the IMPROVE visibility monitoring network, 

supplying the optical and relative humidity data, and for their input to this report. 

 We would like to recognize Dr. Marc Pitchford, Chief of Applied Sciences Branch, 

National Oceanic and Atmospheric Administration in Las Vegas, Nevada, for his efforts in 

establishing, designing, and managing the IMPROVE program. We would also like to recognize 

Richard Damberg of the U.S. Environmental Protection Agency's Office of Air Quality Planning 

and Standards in Research Triangle Park, North Carolina, for his review and comments of this 

report. 
ISSN: 0737-5352-32

SPATIAL AND SEASONAL PATTERNS 

AND LONG TERM VARIABILITY OF 

THE COMPOSITION OF THE HAZE IN THE UNITED STATES: 

An Analysis of Data from the IMPROVE Network

APPENDICES

CIRA Cooperative Institute for Research in the Atmosphere 
4

TABLE OF CONTENTS 

Chapter Page

 OVERVIEW AND SUMMARY S-1 

 S.1 Optical and Aerosol Data S-1 

 S.2 Spatial and Seasonal Distribution of Aerosol Concentration and S-4 

 Chemical Composition 

 S.3 Light Extinction and its Relationships to Aerosols S-5 

 S.4 Spatial and Seasonal Distribution of Reconstructed Light Extinction S-5 

 and Species Contributions 

 S.5 Spatial and Seasonal Trends in Visibility in the United States S-9 

 S.6 Temporal Trends and Interrelationships of Aerosol Concentrations S-10 

 S.7 Recommended Future Research S-12 

1 INTRODUCTION 1-1 

 1.1 Objectives of Visibility Monitoring 1-1 

 1.2 Overview of the IMPROVE Monitoring Network 1-3 

 1.3 Background Regarding Visibility Impairment and Aerosols 1-6 

 1.3.1 Relationship Between Visibility and Aerosol Concentrations 1-8 

 1.3.2 Effect of Relative Humidity on Light Scattering 1-9 

 1.4 Organization of the Report 1-9 

 1.5 References 1-9 

2 OPTICAL AND AEROSOL DATA 2-1 

 2.1 Transmissometers 2-1 

 2.2 Integrating Nephelometers 2-2 

 2.3 Particle Sampling System 2-3 

 2.4 Determination of Aerosol Types 2-4 

 2.5 References 2-7 

3 AEROSOL MASS BUDGETS AND SPATIAL DISTRIBUTIONS 3-1 

 3.1 Characteristics of the Regions 3-2 

 3.2 Spatial Trends in Aerosol Concentrations in the United States 3-15 

 3.2.1 PM10 Aerosol 3-15 

 3.2.2 Fine Aerosol 3-16 

 3.2.3 Coarse Aerosol 3-18 

 3.2.4 Fine Sulfate Aerosol 3-18 

 3.2.5 Fine Nitrate Aerosol 3-19 

 3.2.6 Fine Organic Aerosol 3-19 

 3.2.7 Fine Light-Absorbing Carbon Aerosol 3-23 

 3.2.8 Fine Soil Aerosol 3-23 

 3.3 Summary 3-23 

 3.4 References 3-26 
5

4 LIGHT EXTINCTION AND ITS RELATIONSHIP TO AEROSOLS 4-1 

 4.1 Comparison of Reconstructed to Measured Fine Mass 4-3 

 4.2 Extinction Components 4-5 

 4.2.1 Estimating Light Scattering 4-7 

 4.2.2 Estimating Aerosol Absorption 4-9 

 4.3 Aerosol Scattering and Absorption 4-10 

 4.4 Comparison of Reconstructed Extinction and Scattering 4-13 

 4.4.1 Extinction, Scattering, and Absorption Characteristics 4-13 

 at Meadview 

 4.4.2 Comparison of Estimated and Measured Scattering at Meadview 4-15 

 4.4.3 Comparison of Estimated and Measured Extinction at Meadview 4-15 

 and IMPROVE Sites 

 4.4.4 Regression Analysis 4-19 

 4.5 Attribution of Extinction to Aerosol Species 4-21 

 4.5.1 The Attribution Equation 4-21 

 4.5.2 Estimating f(RH) from Average Relative Humidity 4-22 

 4.6 References 4-25 

5 SPATIAL DISTRIBUTIONS OF RECONSTRUCTED LIGHT 5-1 

 EXTINCTION AND LIGHT EXTINCTION BUDGETS 

 5.1 Reconstructing Light Extinction from Aerosol Measurements 5-1 

 5.2 Reconstructed Light Extinction and Light Extinction Budgets 5-3 

 5.2.1 Characteristics of the Region 5-4 

 5.2.2 Spatial Trends in Reconstructed Light Extinction in the 5-22 

 United States 

 5.2.3 Spatial Trends in Visibility in the United States 5-28 

 5.3 Summary 5-29 

6 TEMPORAL TRENDS AND INTERRELATIONSHIPS 6-1 

 OF AEROSOL CONCENTRATIONS 

 6.1 Protocol Induced Trends of Sulfur Concentrations and babs 6-1 

 6.2 Seasonal Trends of Sulfur 6-3 

 6.3 Seasonal Trends of Absorption (babs) 6-5 

 6.4 Long-Term Variability 6-9 

 6.4.1 Bryce Canyon National Park 6-10 

 6.4.2 Rocky Mountain National Park 6-10 

 6.4.3 Guadalupe Mountains National Park 6-17 

 6.4.4 Crater Lake National Park 6-17 

 6.4.5 Great Smoky Mountains National Park 6-24 

 6.4.6 Mesa Verde National Park 6-24 

 6.4.7 Chiricahua National Monument 6-31 

 6.4.8 Grand Canyon National Park - Winter 6-31 

 6.4.9 Grand Canyon National Park - Summer 6-36 

 6.4.10 Grand Canyon National Park - Autumn 6-42 
6

 6.5 Interrelationships of Fine Mass, Sulfur and Absorption 6-42 

 6.6 Daily Scatter Plots 6-46 

 6.6.1 Shenandoah National Park 6-46 

 6.6.2 Glacier National Park 6-46 

 6.6.3 Denali National Park 6-46 

 6.6.4 Bridger Wilderness Area 6-48 

 6.7 Conclusions 6-48 

 6.8 References 6-50 
7

LIST OF FIGURES 

Chapter Page

 OVERVIEW AND SUMMARY 

 S-1 The 42 IMPROVE sites out of 43 included in the report. S-2 

 Denali National Park in Alaska is not shown. 

 S.2a-b Average reconstructed light extinction coefficient (Mm-1) S-6 

 calculated from the aerosol concentrations measured during 

 three years of IMPROVE, March 1992 through February 

 1995. The various panels of this figure show total extinction 

 (including Rayleigh scattering due to air) and the contributions 

 due to the various aerosol components: coarse particles 

 and fine soil, sulfate, organic carbon, nitrate, and absorption. 

 S.2c-d Continued S-7 

 S.2e-f Continued S-8 

 S.3 Average visibility impairment in deciviews calculated from total S-10 

 (Rayleigh included) reconstructed light extinction for three years 

 of IMPROVE, March 1992 through February 1995. 

 

1 INTRODUCTION 

 1.1 The 42 IMPROVE sites out of 43 included in the report. 1-4 

 Denali National Park in Alaska is not shown. 

3 AEROSOL MASS BUDGETS AND SPATIAL DISTRIBUTIONS 

 3.1 Average PM10 mass concentration (in ìg/m3) for each site 3-16 

 in the IMPROVE network. 

 3.2 Average fine mass aerosol concentrations (in ìg/m3) (top) and 3-17 

 fine mass fraction of PM10 (bottom) for each site in the 

 IMPROVE network. 

 3.3 Average coarse particle mass concentration (in ìg/m3

) for 3-18 

 each site in the IMPROVE network. 

 3.4 Average fine sulfate aerosol concentrations (in ìg/m3

) (top) 3-20 

 and sulfate fine mass fractions (bottom) for each site in the 

 IMPROVE network. 

 3.5 Average fine nitrate aerosol concentrations (in ìg/m3

) (top) 3-21 

 and nitrate fine mass fractions (bottom) for each site in the 

 IMPROVE network. 

 3.6 Average fine organic aerosol concentrations (in ìg/m3

) (top) 3-22 

 and organic fine mass fractions (bottom) for each site in the 

 IMPROVE network. 

 3.7 Average fine elemental carbon aerosol concentrations (in ìg/m3

) 3-24 

 (top) and elemental carbon fine mass fractions (bottom) for each 

 site in the IMPROVE network. 

 3.8 Average fine soil aerosol concentrations (in ìg/m3

) (top) 3-25 

 and soil fine mass fractions (bottom) for each site in the 

 IMPROVE network. 
8

4 LIGHT EXTINCTION AND ITS RELATIONSHIP TO AEROSOLS 

 4.1 Map showing the location of monitoring sites and some of the 4-2 

 larger national park units. 

 4.2 Scatter plot of measured and reconstructed fine mass for the 4-4 

 summer Meadview data set. The error bars show the 

 measurement uncertainty. 

 4.3 Scatter plot of measured and reconstructed fine mass for the 4-6 

 IMPROVE data set. 

 4.4 f(RH) for ammonium sulfate is plotted as a function of relative 4-8 

 humidity. 

 4.5 Temporal plot of measured bext, bscat, babs, relative humidity, 4-11 

 and f(RH) for the summer Meadview data set. Units on 

 extinction, scattering, and absorption are Mm-1, while relative 

 humidity is in percent and f(RH) is unitless. Time is in Julian 

 day, and for reference the month and day axis is also included. 

 4.6 Temporal plot of estimated scattering associated with 4-12 

 ammonium sulfate, ammonium nitrate, organics, fine soil, 

 and coarse mass. Units are in Mm-1. 

 4.7 Reconstructed extinction using bscat, babs, and coarse mass 4-14 

 divided by 2 is plotted against measured extinction. Units are 

 in Mm-1. 

 4.8 Reconstructed extinction using bscat, blac, and coarse mass 4-15 

 divided by 2 is plotted against measured extinction. Units are 

 in Mm-1. 

 4.9 Reconstructed bscat using the sum of estimated aerosol species 4-16 

 scattering is plotted against measured bscat. Units are in Mm-1. 

 4.10 Reconstructed bext using the sum of estimated aerosol species 4-16 

 scattering but with coarse mass scattering divided by 2 is plotted 

 against measured bscat. Units are in Mm-1. 

 4.11 Reconstructed bext using blac and the sum of estimated aerosol 4-18 

 scattering from the various aerosol species is plotted against 

 measured bext. Units are in Mm-1. 

 4.12 Reconstructed bext using babs and the sum of estimated aerosol 4-18 

 scattering from the various aerosol species is plotted against 

 measured bext. Units are in Mm-1. 

 4.13 Reconstructed bext using blac and the sum of estimated aerosol 4-19 

 scattering from the various aerosol species is plotted against 

 measured bext for the IMPROVE data set. Units are in Mm-1. 

 4.14 Dependence on average site relative humidity of the relative 4-23 

 correction factor for sulfate (FTs) for the 39 IMPROVE sites 

 with relative humidity measurements. 

 4.15 Spatial variation in annual average relative humidity 4-24 

 [NOAA, 1978]. 
9

5 SPATIAL DISTRIBUTIONS OF RECONSTRUCTED LIGHT EXTINCTION 

 AND LIGHT EXTINCTION BUDGETS 

 5.1 Three-year averages of total reconstructed light extinction 5-4 

 coefficient (Mm-1) for each of the reported sites in the 

 IMPROVE network in the United States. 

 5.2 Three-year averages of reconstructed sulfate light extinction 5-23 

 coefficient in Mm-1 (top) and sulfate fraction in percent of 

 aerosol light extinction (bottom), for each of the sites in the 

 IMPROVE network reported for the United States. 

 5.3 Three-year averages of reconstructed nitrate light extinction 5-24 

 coefficient in Mm-1 (top) and nitrate fraction in percent of 

 aerosol light extinction (bottom), for each of the sites in the 

 IMPROVE network reported for the United States. 

 5.4 Three-year averages of reconstructed organic carbon light 5-25 

 extinction coefficient in Mm-1 (top) and organic carbon 

 fraction in percent of aerosol light extinction (bottom), 

 for each of the sites in the IMPROVE network reported for the 

 United States. 

 5.5 Three-year averages of reconstructed absorption coefficient in 5-26 

 Mm-1 (top) and absorption fraction in percent of aerosol 

 light extinction (bottom), for each of the sites in the 

 IMPROVE network reported for the United States. 

 5.6 Three-year averages of reconstructed light extinction due to 5-27 

 coarse material in Mm-1 (top) and percent of aerosol 

 extinction (bottom), for each of the sites in the IMPROVE 

 network reported for the United States. 

 5.7 Average visibility impairment in deciviews calculated from 5-28 

 total (Rayleigh included) reconstructed light extinction for the 

 three-year period, March 1992 through February 1995, of 

 IMPROVE. 

 5.8 Average winter visibility impairment in deciviews calculated 5-30 

 from total (Rayleigh included) reconstructed light extinction 

 for the three-year period, March 1992 through February 1995, 

 of IMPROVE. 

 5.9 Average spring visibility impairment in deciviews calculated 5-31 

 from total (Rayleigh included) reconstructed light extinction for 

 the three-year period, March 1992 through February 1995, of 

 IMPROVE. 

 5.10 Average summer visibility impairment in deciviews calculated 5-31 

 from total (Rayleigh included) reconstructed light extinction 

 for the three-year period, March 1992 through February 1995, 

 of IMPROVE. 

 5.11 Average autumn visibility impairment in deciviews calculated 5-32 
10

 from total (Rayleigh included) reconstructed light extinction 

 for the three-year period, March 1992 through February 1995, 

 of IMPROVE. 

6 TEMPORAL TRENDS AND INTERRELATIONSHIPS OF AEROSOL 

 CONCENTRATIONS 

 6.1 Time line of absorption (babs) at five sites that demonstrate a 6-4 

 clear change before and after the IMPROVE network was 

 initiated. 

 6.2 Time lines of sulfur concentration at seven sites that 6-6 

 demonstrate a range of seasonal behavior with the strongest 

 seasonality at the top and the weakest at the bottom. 

 6.3 Time lines of absorption at six sites that demonstrate a range of 6-8 

 seasonal behavior with the weakest seasonality at the top and the 

 strongest at the bottom. 

 6.4a Monthly statistics for fine mass concentration (ng/m3

) at 6-11 

 Bryce Canyon National Park in the autumn. 

 6.4b Monthly statistics for sulfur concentration (ng/m3) at 6-12 

 Bryce Canyon National Park in the autumn. 

 6.4c Monthly statistics for absorption (ng/m3

) at Bryce Canyon 6-13 

 National Park in the autumn. 

 6.5a Monthly statistics for fine mass concentration (ng/m3

) at Rocky 6-14 

 Mountain National Park in the winter. 

 6.5b Monthly statistics for sulfur concentration (ng/m3) at Rocky 6-15 

 Mountain National Park in the winter. 

 6.5c Monthly statistics for absorption (ng/m3

) at Rocky Mountain 6-16 

 National Park in the winter. 

 6.6a Monthly statistics for fine mass concentration (ng/m3) at 6-18 

 Guadalupe Mountains National Park in the autumn. 

 6.6b Monthly statistics for sulfur concentration (ng/m3

) at 6-19 

 Guadalupe Mountains National Park in the autumn. 

 6.6c Monthly statistics for absorption (ng/m3) at Guadalupe 6-20 

 Mountains National Park in the autumn. 

 6.7a Monthly statistics for fine mass concentration (ng/m3

) at Crater 6-21 

 Lake National Park in the winter. 

 6.7b Monthly statistics for sulfur concentration (ng/m3) at Crater 6-22 

 Lake National Park in the winter. 

 6.7c Monthly statistics for absorption (ng/m3

) at Crater Lake 6-23 

 National Park in the winter. 

 6.8a Monthly statistics for fine mass concentration (ng/m3

) at Great 6-25 

 Smoky Mountains National Park in the autumn. 

 6.8b Monthly statistics for sulfur concentration (ng/m3) at Great 6-26 

 Smoky Mountains National Park in the autumn. 

 6.8c Monthly statistics for absorption (ng/m3) at Great Smoky 6-27 

 Mountains National Park in the autumn. 
11

 6.9a Monthly statistics for fine mass concentration (ng/m3) at 6-28 

 Mesa Verde National Park in the summer. 

 6.9b Monthly statistics for sulfur concentration (ng/m3) at Mesa 6-29 

 Verde National Park in the summer. 

 6.9c Monthly statistics for absorption (ng/m3) at Mesa Verde 6-30 

 National Park in the summer. 

 6.10a Monthly statistics for fine mass concentration (ng/m3

) at 6-32 

 Chiricahua National Monument in the summer. 

 6.10b Monthly statistics for sulfur concentration (ng/m3

) at Chiricahua 6-33 

 National Monument in the summer. 

 6.10c Monthly statistics for absorption (ng/m3) at Chiricahua National 6-34 

 Monument in the summer. 

 6.11a Monthly statistics for fine mass concentration (ng/m3

) at 6-35 

 Grand Canyon National Park in the winter. 

 6.11b Monthly statistics for sulfur concentration (ng/m3

) at Grand 6-37 

 Canyon National Park in the winter. 

 6.11c Monthly statistics for absorption (ng/m3

) at Grand Canyon 6-38 

 National Park in the winter. 

 6.12a Monthly statistics for fine mass concentration (ng/m3) at 6-39 

 Grand Canyon National Park in the summer. 

 6.12b Monthly statistics for sulfur concentration (ng/m3

) at 6-40 

 Grand Canyon National Park in the summer. 

 6.12c Monthly statistics for absorption (ng/m3

) at Grand Canyon 6-41 

 National Park in the summer. 

 6.13a Monthly statistics for fine mass concentration (ng/m3) at 6-43 

 Grand Canyon National Park in the autumn. 

 6.13b Monthly statistics for sulfur concentration (ng/m3

) at Grand 6-44 

 Canyon National Park in the autumn. 

 6.13c Monthly statistics for absorption (ng/m3) at Grand Canyon 6-45 

 National Park in the autumn. 

 6.14 Matrix scatter plots of absorption (babs) sulfur (S) and 6-47 

 gravimetric fine mass (FM) for the four seasons at 

 Shenandoah National Park. Assuming an absorption 

 efficiency of 10 m2

/gm all units are ìg/m3. 

 6.15 Matrix scatter plots of absorption (babs) sulfur (S) and 6-47 

 gravimetric fine mass (FM for the four seasons 

 at Glacier National Park. Assuming an absorption 

 efficiency of 10 m2

/gm all units are ìg/m3. 

 6.16 Matrix scatter plots of absorption (babs) sulfur (S) and 6-48 

 gravimetric fine mass (FM) for the four seasons 

 at Denali National Park. Assuming an absorption 

 efficiency of 10 m2

/gm all units are ìg/m3. 

 6.17 Matrix scatter plots of absorption (babs) sulfur (S) and 6-49 

 gravimetric fine mass (FM) for the four seasons at Bridger 

 Wilderness. Assuming an absorption efficiency of 10 m2

/gm 

 all units are ìg/m3

. 
12

LIST OF TABLES 

Chapter Page

 OVERVIEW AND SUMMARY 

 S.1 IMPROVE and NPS/IMPROVE protocol sites according S-3 

 to region. 

1 INTRODUCTION 

 1.1 IMPROVE and NPS/IMPROVE protocol sites according 1-5 

 to region. 

2 OPTICAL AND AEROSOL DATA 

 2.1 Filter media and analysis techniques used to determine 2-3 

 concentrations of particulate matter species from IMPROVE 

 sampler modules. 

 2.2 The formulae and assumptions applied to IMPROVE sampler 2-5 

 measurements to derive the principal fine aerosol species, 

 reconstructed fine mass, and coarse mass. The brackets 

 indicate the mass concentration of the aerosol species or element. 

3 AEROSOL MASS BUDGETS AND SPATIAL DISTRIBUTIONS 

 3.1 Measured fine and coarse aerosol concentration (in ìg/m3

) for 3-3 

 the 21 regions in the IMPROVE network. 

 3.2 Measured fine aerosol mass budgets (in percent) for the 21 3-7 

 regions in the IMPROVE network. 

4 LIGHT EXTINCTION AND ITS RELATIONSHIP TO AEROSOLS 

 4.1 Summary statistics for aerosol mass concentrations for the 4-4 

 summer Meadview data set. The number of valid data points 

 is 97. 

 4.2 Summary statistics for aerosol mass concentrations for the 4-5 

 IMPROVE data set. The number of valid data points is 5108. 

 4.3 Summary statistics for optical variables for the summer 4-10 

 Meadview data set. The numbers reported are associated 

 with the scattering, absorption, or extinction associated 

 with each variable. Units on scattering, absorption, and 

 extinction are in Mm-1, while relative humidity is in percent 

 and f(RH) factors have no units. Rbext1 and Rbext2 refer 

 to reconstructed extinction using blac and babs, respectively 

 and Rbscat is reconstructed scattering. Units on scattering, 

 absorption, and extinction are in Mm-1, while relative 

 humidity is in percent and f(RH) factors have no units. 

 The number of valid data points is 82. 
13

 4.4 Summary statistics for optical variables for the IMPROVE 4-13 

 data set. The numbers reported are associated with the 

 scattering, absorption, or extinction associated with each 

 variable. Units on scattering, absorption, and extinction 

 are in Mm-1, while relative humidity is in percent and f(RH) 

 factors have no units. Rbext1 and Rbext2 refer to reconstructed 

 extinction using blac and babs, respectively. The number 

 of valid data points is 1642. 

 4.5 Results of a factor analysis on various extinction, scattering, 4-20 

 and absorption variables for the summer Meadview data set. 

 4.6 Results of a three-step ordinary least square (OLS) regression 4-21 

 with various optical variables as dependent and independent 

 variables. 

 4.7 Parameters of the best-fit quadratic equation relating the 4-24 

 relative humidity light extinction correction factors (FT) 

 to average site relative humidity (F = b0 + b2(1/(1-rh))2

 

 + b3(1/(1-rh))3 + b4(1/(1-rh))4). 

5 SPATIAL DISTRIBUTIONS OF RECONSTRUCTED LIGHT EXTINCTION 

 AND LIGHT EXTINCTION BUDGETS 

 5.1 Parameters of the best-fit quadratic equation relating the relative 5-3 

 humidity light extinction correction factors (FT) to average site 

 relative humidity (F = bo + b2(1/(1-rh))2

 + b3(1/(1-rh))3 + b4

 (1/(1-rh))4). 

 5.2 Seasonal and annual averages of reconstructed total light 5-6 

 extinction coefficient (Mm-1) for the 21 regions in the 

 IMPROVE network. Also shown are the light scattering 

 coefficients resulting from fine and coarse aerosols, light 

 absorptions for carbonaceous aerosol, percentage of total 

 extinction resulting from aerosols, and the average region 

 relative humidity. 

 5.3 Seasonal and annual averages of reconstructed aerosol 5-10 

 light extinction coefficient (Mm-1) for the 21 regions in the 

 IMPROVE network. Also shown are the light extinction 

 coefficients (Mm-1) resulting from sulfate, nitrate, organic 

 carbon, light absorption, and coarse particles/fine soil. 

 5.4 Seasonal and annual averages of percentage contributions 5-14 

 to the reconstructed aerosol light extinction coefficient 

 (light extinction budget) for the 21 regions in the IMPROVE 

 network for sulfate, nitrate, organic carbon, absorption, and 

 coarse particle/fine soil. 
14

6 TEMPORAL TRENDS AND INTERRELATIONSHIPS OF AEROSOL 

 CONCENTRATIONS 

 6.1 Sites and time periods for IMPROVE and SFU 6-2 

 6.2 Seasonal statistics (in ng/m3) for particulate sulfate at seven 6-7 

 sites. 

 6.3 Seasonal statistics for absorption (in ng/m3

) at six sites. 6-9 
15

LIST OF APPENDICES 

APPENDIX 1 Long term stacked timelines of fine mass concentrations (FM), sulfur 

concentrations (S), and concentrations of light-absorbing mass (babs) by site 

in ng/m3

. 

APPENDIX 2 Long term seasonal statistics of fine mass concentrations, sulfur 

concentrations, and concentration of light-absorbing mass by site in ng/m3

. 

APPENDIX 3 Matrix scatter plots of light-absorbing mass concentrations (babs), fine mass 

concentrations (FM), and sulfur concentrations (S) by site in ìg/m3. 

Copies of appendices available upon request to National Park Service office at 970/491-8292. 
OVERVIEW AND SUMMARY

 This report describes data for the three year period, March 1992 through February 1995, of the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) measurement program. 

IMPROVE is a cooperative visibility monitoring effort between the United States Environmental 

Protection Agency, (EPA) federal land management agencies, and state air agencies. 

 The objectives of IMPROVE are: 

 (1) To establish current background visibility in Class I areas; 

 (2) To identify chemical species and emission sources responsible for existing man-made 

visibility impairment; and 

 (3) To document long-term trends. 

 The design of the IMPROVE monitoring network was resource and funding limited so that it was not 

practical to place monitoring stations at all 156 mandatory Class I areas where visibility is an important 

attribute. Instead, the IMPROVE Steering Committee selected a set of sites that were representative of the 

Class I areas. For the first IMPROVE report, published in the spring of 1993, data for 36 sites was 

summarized. In the intervening time the IMPROVE network has evolved; two sites were dropped, some 

sites were downgraded to the measurement of a subset of the variables measured at a fully complemented 

site, and other sites have been added. There are currently a total of 58 IMPROVE sites with various 

configurations of optical and aerosol monitoring equipment. For this report, the 43 IMPROVE sites that are 

fully configured as aerosol monitoring sites with data for the three-year period, March 1992 through 

February 1995, are utilized. However, only 26 of the sites have optical monitoring equipment (e.g., 

transmissometers or nephelometers to measure visibility-related parameters). Figure S.1 shows a map of 

the United States indicating the locations of the 43 monitoring sites analyzed in this report. On the basis of 

regional similarities, the sites were grouped into 21 regions, listed in Table S.1. 

S.1 Optical and Aerosol Data

Aerosol monitoring in the IMPROVE network is accomplished by a combination of particle sampling 

and sample analysis. The sampler was designed specifically for IMPROVE. It collects four simultaneous 

samples: one PM10 sample (particles less than 10 ìm in diameter) on a Teflon filter and three PM2.5

samples on Teflon, nylon, and quartz filters. The IMPROVE sampler is programmed to collect two 24-

hour duration samples per week (i.e., 26 per season, 104 per year). The PM10 filter is used to determine 

total PM10 mass. The PM2.5 Teflon filter is used to measure
PROVE sites out of 43 included in the report. Denali National Park in Alaska is not shown. 
S-3

Table S.1 IMPROVE and NPS/IMPROVE protocol sites according to region. 

Alaska (AKA)

!Denali NP (DENA) 

Appalachian Mountains (APP)

!Great Smoky Mountains NP (GRSM) 

!Shenandoah NP (SHEN) 

!Dolly Sods WA (DOSO) 

Boundary Waters (BWA)

!Boundary Waters Canoe Area (BOWA) 

Cascade Mountains (CAS)

!Mount Rainier NP (MORA) 

Central Rocky Mountains (CRK)

!Bridger WA (BRID) 

!Great Sand Dunes NM (GRSA) 

!Rocky Mountain NP (ROMO) 

!Weminuche WA (WEMI) 

!Yellowstone NP (YELL) 

Coastal Mountains (CST)

!Pinnacles NM (PINN) 

!Point Reyes NS (PORE) 

!Redwood NP (REDW) 

Colorado Plateau (CPL)

!Bandelier NM (BAND) 

!Bryce Canyon NP (BRCA) 

!Canyonlands NP (CANY) 

!Grand Canyon NP (GRCA) 

!Mesa Verde NP (MEVE) 

!Petrified Forest NP (PEFO) 

Florida (FLA)

!Chassahowitzka NWR (CHAS) 

!Okefenokee NWR (OKEF) 

Great Basin (GBA)

!Jarbidge WA (JARB) 

!Great Basin NP (GRBA) 

Lake Tahoe (LTA)

!D.L. Bliss State Park (BLISS) 

!South Lake Tahoe (SOLA) 

Mid Atlantic (MAT)

!Edmond B. Forsythe NWR (EBFO) 

Mid South (MDS)

!Upper Buffalo WA (UPBU) 

!Sipsey WA (SIPS) 

!Mammoth Cave NP (MACA) 

Northeast (NEA)

!Acadia NP (ACAD) 

!Lye Brook WA (LYBR) 

Northern Great Plains (NGP)

!Badlands NM (BADL) 

Northern Rocky Mountains (NRK)

!Glacier NP (GLAC) 

Sierra Nevada (SRA)

!Yosemite NP (YOSE) 

Sierra-Humboldt (SRH)

!Crater Lake NP (CRLA) 

!Lassen Volcanoes NP (LAVO) 

Sonoran Desert (SON)

!Chiricahua NM (CHIR) 

!Tonto NM (TONT) 

Southern California (SCA)

!San Gorgonio WA (SAGO) 

Washington, D.C. (WDC)

!Washington, D.C. (WASH) 

West Texas (WTX)

!Big Bend NP (BIBE) 

!Guadalupe Mountains NM (GUMO) 

NP = National Park NM = National Monument WA = Wilderness Area 

 NWR = National Wildlife Refuge NS = National Seashore 
S-4

 total fine aerosol mass, individual chemical species using Proton Induced X-ray Emission (PIXE) and 

Proton Elastic Scattering Analysis (PESA), and light-absorption coefficient using the Laser Integrating 

Plate Method (LIPM). The nylon filter is used to measure nitrate and sulfate aerosol concentrations 

with Ion Chromatography (IC). Finally, the quartz filters are analyzed for organic and elemental 

carbon using the Thermal Optical Reflectance (TOR) method. 

 

 Transmissometers are employed to measure the light-extinction coefficient at 15 of the IMPROVE 

sites, and 11 sites have integrating nephelometers, which measure the scattering coefficient. 

Transmissometers measure the light transmitted through the atmosphere over a distance of one to 

fifteen kilometers. The light transmitted between the light source (transmitter) and the light monitoring 

component (receiver) is converted to the path-averaged light extinction coefficient (bext), which is the 

sum of scattering (bscat) and absorption (babs). Integrating nephelometers measure the scattering of light 

over a defined band of visible wavelengths from an enclosed volume of air and represents a point 

measurement of scattering. By combining the absorption coefficient from the particle sampler with the 

scattering coefficient from the nephelometer the extinction coefficient can be reconstructed at the 11 

nephelometer sites. Relative humidity was measured continuously at the transmissometer and 

nephelometer sites.

 

 S.2 Spatial and Seasonal Distribution of Aerosol Concentration and 

Chemical Composition 

 Fine aerosol concentrations are highest in the eastern United States (in the Appalachian 

Mountains, Mid South, Mid Atlantic and in Washington, D.C.). Concentrations are also 

relatively high in southern California. The lowest concentrations occur in the Great Basin in 

Nevada, the Colorado Plateau in the four corners states, Wyoming, and in Alaska. 

 

 The largest single component of the fine aerosol in the East is sulfate, while in the Pacific 

Northwest it is organics, and in southern California it is nitrate. In general, the largest mass fractions 

of the fine aerosol are sulfate and organics. Of the 21 regions in the IMPROVE network, organic 

carbon is the largest single component in 10 regions (Alaska, Cascades, Colorado Plateau, Central 

Rockies, Pacific Coastal Mountains, Great Basin, Northern Rockies, Sierra Nevada, Sierra-Humboldt, 

and Lake Tahoe). Sulfate is the largest single component of fine aerosol in seven regions, primarily in 

the East (Appalachian Mountains, Florida, Northeast, Mid South, Mid Atlantic, Washington D.C., and 

West Texas). The contributions of organic carbon and sulfate are approximately equal in three regions 

(Boundary Waters, Sonoran Desert, and Northern Great Plains). Soil is the next largest contributor, 

followed by nitrate and light-absorbing carbon. Nitrate is the largest component of fine aerosol in 

southern California only. 

 

 With few exceptions, average fine mass concentrations, as well as the sulfate, organic carbon, and 

light-absorbing carbon components of fine mass, are highest in summer. Soil concentrations are 

highest in spring or summer. Nitrate concentrations are generally highest in winter or spring. 

 
S-5

S.3 Light Extinction and its Relationship to Aerosols 

Two unique data sets were used to explore the relationship between optical extinction, absorption, 

scattering, and various aerosol species. The Measurement of Haze and Visual Effects (MOHAVE) special 

study provided, at one monitoring site, independent optical measurements of bext, bscat, and babs, and the 

various aerosol species. This data set provided for a variety of ways for exploring absorption and scattering 

efficiencies. The second data set, IMPROVE, provides for the first time, an opportunity to explore the 

relationship between measured extinction (as opposed to scattering) and aerosol species over the whole 

western United States. These are the first data sets where extinction was directly measured as opposed to 

estimated by summing bscat and absorption as derived from "elemental" carbon measurements. 

 The most surprising outcome of the analysis relates to estimates of absorption. It has been known for 

some time that, at remote nonurban locations, babs as derived from the LIPM, was about twice the absorption 

as estimated from elemental carbon derived from thermal optical reflectance techniques (blac). Although 

there may be alternative interpretations, the most straightforward explanation of the relationships between 

bext, bscat, babs, and blac is that babs is a more accurate predictor of absorption than blac. If this is the case, then 

absorption is on average at about 30% of the non-Rayleigh extinction budget, as opposed to about 10% as 

conventional wisdom would have dictated. 

 An examination of the hygroscopic nature of organics lead to the conclusion that organics are not 

hygroscopic to weakly hygroscopic. However, it is estimated that they have about a 4.0 m2

/g rather than a 

3.0 m2

/g mass scattering efficiency. 

 Another result of the babs analysis is that a significant amount of babs is linked to light absorption by 

soil. Of fine mass absorption, 15-20% is soil related, while elemental and organic carbon contribute about 

equal amounts of absorption. 

S.4 Spatial and Seasonal Distribution of Reconstructed Light Extinction and Species 

Contributions 

 The light-extinction coefficient (bext) is calculated from the measured aerosol species' concentrations by 

multiplying the concentration of a given species by its light-extinction efficiency, and summing over all 

species. Since sulfates and nitrates were assumed to be hygroscopic, their light-extinction efficiencies 

increase with relative humidity; therefore, extinction efficiencies for soluble species must be adjusted 

according to the seasonal and annual average relative humidity at each site. 

 Figures S.2a through S.2f summarize the spatial distribution of reconstructed light extinction (in Mm1

), as well as the contributions to the total extinction from coarse particles and fine soil, sulfate, organics, 

nitrate, and light-absorbing carbon, averaged over three years of IMPROVE (March 1992 through February 

1995). 

 Reconstructed light extinction varies throughout the United States in a way analogous to fine aerosol 

concentrations. The greatest light extinction occurs in the eastern United States and in 
S-6

S.2(a) Total light extinction bext (Mm-1) 

S.2(b) Extinction due to coarse particles and fine soil (Mm-1) 

Figure S.2 Average reconstructed light extinction coefficient (Mm-1) calculated from the aerosol 

concentrations measured during three years of IMPROVE, March 1992 through 

February 1995. The various panels of this figure show total extinction (including 

Rayleigh scattering due to air) and the contributions due to the various aerosol 

components: coarse particles and fine soil, sulfate, organic carbon, nitrate, and 

absorption. 
S-7

S.2(c) Extinction due to sulfate (Mm-1) 

S.2(d) Extinction due to organic carbon (Mm-1) 

 

Figure S.2 Continued. 
S-8

S.2(e) Extinction due to nitrate (Mm-1) 

S.2(f) Extinction due to light absorption (Mm-1) 

Figure S.2 Continued. 
S-9

southern California, while the least light extinction occurs in the nonurban west (e.g., the Great Basin of 

Nevada and the Colorado Plateau) and in Alaska. However, since relative humidity (and hence the lightscattering efficiency of sulfate and nitrate) is higher in the East than in the West, the difference between 

eastern and western light extinction is even more pronounced than the difference in aerosol concentrations. 

 Fine aerosols are the most effective in scattered light and are the major contributors to light extinction. 

 In most cases, the sulfate component of fine aerosol is the largest single contributor to light extinction. 

This is because sulfate, being hygroscopic, generally has a higher light extinction efficiency than other 

species due to associated liquid water. This is especially true in the eastern United States, where relative 

humidity is high. In the Appalachian Mountains (Shenandoah and Great Smoky Mountains), sulfate 

accounts for 2/3 of the total aerosol light extinction throughout the year, and 3/4 of the total in summer. 

Sulfate is the largest single contributor to light extinction in 14 of the 21 regions, and is comparable with 

organics as the most significant contributor in three additional regions (Northern Rockies, Central Rockies, 

and Sierra-Humboldt). Organic carbon is the largest single contributor to light extinction in three of the 21 

regions (Great Basin, Sierra Nevada, and Lake Tahoe) and is a major contributor in the two previously 

mentioned regions. Smaller contributions come from wind-blown dust (coarse particles and fine soil) and 

nitrate. Nitrate is the single largest contributor to light extinction only in southern California. 

 Generally, reconstructed light extinction is highest in summer and lowest in winter; however, there are 

many exceptions to this general rule. Higher extinction occurs in summer generally because of elevated 

sulfate and carbonaceous aerosol concentrations. Also, higher average RH's occur in the East during the 

summer, which increases extinction.

S.5 Spatial and Seasonal Trends in Visibility in the United States 

 To show the effect on visibility of aerosol extinction, the deciview (dv) scale is applied to the total 

(Rayleigh included) reconstructed aerosol extinction (see Chapter 1). By utilizing the dv scale, the effect of 

light extinction on visibility is portrayed in a way that is approximately linear with respect to perceived 

visual air quality. 

 Because higher extinction coefficients lead to higher dv numbers, the geographic trends in visibility 

follow the trends in reconstructed extinction. Pristine or Rayleigh conditions correspond to a dv of zero. 

 Figure S.3 shows isopleths of deciviews averaged over three years of IMPROVE, March 1992 through 

February 1995. The smallest dv or best visibility is reported at Denali NP with 8 dv. A broad region, which 

includes the Great Basin, most of the Colorado Plateau, and portions of the Central Rockies, has visibility 

impairment of less than 11 dv. Moving in any direction from this region generally results in increasing dv. 

West of the Sierra Range and including southern California one finds dv values in excess of 15, with a 

maximum value of 19 dv at Point Reyes and San Gorgonio. The northwest United States and all of the 

eastern half of the United States have an excess of 15 dv of impaired visibility. The region east of the 

Mississippi and south of the Great Lakes has impairment in excess of 20 dv, with the Appalachian, Mid 

South 
S-10

 and Florida regions exceeding 24 dv. The highest annual dv is reported in Washington D.C. at 29 

dv, followed by Sipsey Wilderness at 28 dv. 

Figure S.3 Average visibility impairment in deciviews calculated from total (Rayleigh included) 

reconstructed light extinction for three years of IMPROVE, March 1992 through 

February 1995. 

 The general spatial trend noted above for the annual average dv generally holds true for each 

season's average dv as well. Specifically, the least impairment occurs in all or part of the Great 

Basin, Colorado Plateau, and Central Rockies, with gradients of increasing dv in any direction. The 

best visibility occurs during the winter and the worst in the summer. Visibility impairment in the 

spring and autumn are comparable. 

S.6 Temporal Trends and Interrelationships of Aerosol Concentrations

 The IMPROVE aerosol monitoring network, established in March 1988, initially consisted of 

36 sites instrumented with aerosol sampling modules A through D. Many of the IMPROVE sites 

are successors to sites where aerosol monitoring with stacked filter units (SFU) was carried out as 

early as 1979. The IMPROVE sites that can be paired with antecedent SFU sites have an almost 

unbroken record of fine mass and sulfur from as early as 1979, and babs from 1983. Table S.2 lists 

the sites and time periods that IMPROVE or SFU samplers were operated. These data provide an 

excellent opportunity to look for evidence of temporal trends in aerosol concentrations. Two 
S-11

distinct temporal trends are considered here: seasonal, and long-term trends of statistical measures 

such as maxima, minima, percentiles, and standard deviations. 

Table S.2 Sites and time periods for IMPROVE and SFU. 

Acronym Full Name SFU Start SFU End IMPROVE 

Start 

IMPROVE 

End 

ACAD Acadia NP 9/21/85 11/28/87 3/01/88 Present

ARCH Arches NP 9/28/79 11/28/87 3/01/88 5/92 

BAND Bandelier NM 10/02/82 2/09/85 3/01/88 Present

BIBE Big Bend NP 7/27/82 11/28/87 3/01/88 Present

BRCA Bryce Canyon NP 9/21/79 12/02/87 3/01/88 Present

BRLA Brooklyn Lake 3/01/91 7/31/93 7/31/93 Present

CANY Canyonlands NP 9/21/79 11/28/87 3/01/88 Present

CHIR Chiricahua NM 6/8/82 5/31/86 3/01/88 Present

CRLA Crater Lake NP 10/12/82 11/28/87 3/01/88 Present

CRMO Craters of the Moon 7/17/82 3/29/86 5/12/92 Present

DENA Denali NP & 9/10/86 11/25/87 3/01/88 Present

DEVA Death Valley NP 6/01/82 3/29/86 10/18/93 Present

GLAC Glacier NP 9/28/82 12/5/87 3/01/88 Present

GICL Gila NF 10/1/79 8/31/81 3/28/94 Present

GRBA Great Basin NP 10/12/82 3/29/86 5/00/88 Present

GRCA Grand Canyon NP 8/03/79 11/28/87 3/01/88 Present

GRSA Great Sand Dunes 9/15/80 8/31/81 5/04/88 Present

GRSM Great Smoky Mtns 1/31/84 11/28/87 3/01/88 Present

GUMO Guadalupe Mtns NP 2/19/83 12/02/87 3/01/88 Present

LAVO Lassen Volcanic NP 6/29/82 5/29/84 3/01/88 Present

MEVE Mesa Verde NP 10/30/82 12/05/87 3/01/88 Present

MORA Mount Rainier NP 7/23/83 12/16/87 3/01/88 Present

PEFO Petrified Forest NP 7/30/79 11/25/87 3/01/88 Present

ROMO Rocky Mountain NP 9/21/79 12/02/87 9/15/90 Present

SAGU Saguaro NM 7/2/85 8/31/88 3/1/88 Present

SALM Salmon NF 9/01/90 11/13/93 11/09/93 Present

SHEN Shenandoah NP 7/13/82 11/28/87 3/01/88 Present

TONT Tonto NM 8/3/79 11/29/83 3/01/88 Present

VOYA Voyageurs NP 7/13/85 Present 3/01/88 Present

YELL Yellowstone NP 9/29/79 12/05/87 3/01/88 Present

YOSE Yosemite NP 9/25/82 10/28/87 3/01/88 Present

NP = National Park NM = National Monument NF = National Forest
S-12

 A hallmark of sites impacted by sulfate pollution is a distinct seasonal trend of sulfate 

concentrations manifested by high concentrations during the summer and lowest during the winter. 

Sulfate seasonality is attributed to many factors with seasonal changes in meteorology and 

photochemistry being the most influential. Sites that demonstrate the most sulfate seasonality are in 

the East and southern California, while sites in the intermountain west have little or no seasonality. 

Absorption also demonstrates a clear seasonal trend at many sites and tends to be highest during the 

summer and early autumn. The seasonality of absorption, unlike sulfate seasonality, is driven by 

seasonal changes of emissions. In the West, where the absorption seasonality is strongest, 

controlled burning and wildfires have a strong influence, while in the East the seasonality is less 

pronounced. 

 Demonstrated long-term trends fall into three categories: increases, decreases, and variable. 

Sites that demonstrate decreases are at Crater Lake and Rocky Mountain National Parks, where 

absorption dropped dramatically, and at Guadalupe Mountains National Park where sulfur is 

decreasing in the autumn. A clear demonstration of decreased sulfur concentrations as a result of 

emission reductions is in the desert southwest at Chiricahua National Monument. Two sites where 

increases have been observed are at Grand Canyon National Park in the autumn, where the 25th 

percentile of sulfur concentrations have increased steadily since 1980, and at Great Smoky 

Mountains National Park, where autumn concentrations of sulfur and absorption have increased. 

Other sites that demonstrate little or variable changes in sulfur concentrations are at Bryce Canyon, 

Rocky Mountain, and Crater Lake National Parks. Variable or little change in absorption was noted 

at Grand Canyon in the winter and Chiricahua in the summer. 

 The most notable observation from a national perspective is the lack of a clear uniform trend of 

sulfur concentration or absorption. There are local success stories related to emission controls, and 

there are failures most likely associated with increased local emissions or long-range transport. The 

bulk of the sites show little or variable trends in the long run. 

 The matrix scatter plots demonstrate correlations ranging between slight to strong between 

gravimetric fine mass, babs, and sulfur. Some of the strongest correlations are between fine mass 

and babs, even though light-absorbing material is a small fraction of fine mass suggesting an internal 

mixture of carbon with the primary constituents of the fine mass. The exceptions to this are sites in 

the eastern United States where sulfur is a large fraction of the fine mass; here sulfur shows strong 

correlations with fine mass indicative of strong sources. Weak correlations are usually manifested 

by 'fan shaped' scatters, some with hard edges, which suggest multiple sources with variable ratios 

of babs or sulfur. 

S.7 Recommended Future Research

 There are a number of uncertainties raised by the work described in this report that deserve 

additional study. 
S-13

 Organic Aerosol Measurement. The measurement of organic mass is still responsible for the 

most uncertainty in estimates of how various aerosol species affect visibility. Adjustments are made 

 to the organic carbon mass to correct for the adsorption of organic aerosols on the filter. However, 

this adjustment often results in negative concentrations. This area needs to be considered in future 

studies. Also, the mass fractions of hydrogen and carbon in organics are based on an assumption of 

the hydrocarbon type. Future research should evaluate these fractions on the basis of the most 

common organic molecules in the samples. 

 Light-Absorbing Carbon Measurement. The work reported here suggests that babs estimated 

from LIPM is a more accurate measure of absorption than that derived from elemental carbon 

measurements. The difference between the estimates is significant at about a factor of two. 

 Hygroscopicity of Aerosols. The relative humidity correction terms applied to sulfate and 

nitrate need to be reevaluated. The sulfate and nitrate RH factors are based on ammonium sulfate. 

Specific curves should be developed for ammonium nitrate, which has a different deliquescence 

point than sulfate. Also, acidic sulfates (e.g., sulfuric acid and ammonium bisulfate) have higher 

water contents and higher light scattering efficiencies than ammonium sulfate. Furthermore, the 

hygroscopicity of organics is not currently well understood. Basic research is required in this area. 

Until such research is available, alternative assumptions regarding organic hygroscopicity should be 

tested. 

 Long-Term Trends. The analysis of long-term trends of fine mass concentrations, sulfur 

concentrations, and absorption as presented here is based on descriptive statistics and inspection. A 

major point of contention is the fact that two protocol changes occurred in the middle of the data 

record (SFU vs IMPROVE samplers; and, 72 hour vs 24 hour duration samples). No IMPROVE 

and SFU samplers were operated concurrently side by side, nor were any 72-hour duration samples 

collected concurrently with 24-hour duration samples. Therefore, any bias in the data due to 

protocol changes should be revealed in the data; moreover, since the protocol changes were system 

wide any bias should be systematic. If there is a bias in the data then long- term trends, if any, 

could be masked or exaggerated. A detailed statistical analysis across all sites needs to be carried 

out to look for and quantify systematic changes in the data behavior that can be attributed to 

protocol changes. This understanding is required for correct interpretation of long-term trends. 

 In addition to the above refinements in the analyses conducted in this report, additional data 

analysis is recommended. For example, back trajectory analysis and spatial/temporal pattern 

analysis of episodes is recommended to determine the source region contributions to elevated 

concentrations. Also, the cleanest days should be studied to determine the source areas and 

meteorological causes of clean air.
S-2
1-1

CHAPTER 1 

INTRODUCTION

 This report is the second in a series of periodic reports that describe the data collected by the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network. The 

objectives of this report are threefold: 

(1) To describe the spatial and temporal variation of visibility, as measured by the lightextinction coefficient, and the chemical composition of the visibility-degrading aerosol1

for three years of operation of the network: March 1992 through February 1995. 

 

 1An aerosol is a suspension of fine and coarse solid and liquid particles in air. Particles, 

especially fine particles less than 2.5 ìm, scatter light and degrade the visual information content 

of a scene (e.g., contrast, color, line, and texture). Fine particles consist of different chemical 

species either within the same particle (internally mixed) or in different particles (externally mixed). 

 Significant chemical species found in particles include sulfates, nitrates, organics and elemental 

carbon, and soil dust. The sulfates, nitrates, and some hygroscopic organics absorb water from the 

atmosphere, thereby increasing significantly the light-scattering particle size and mass.

(2) To provide a first estimate of the apportionment of visibility impairment to the 

fundamental chemical species, such as sulfates, nitrates, organics and elemental carbon, 

and soil dust. 

(3) To document the long-term trends (or lack of trends) of aerosol mass and its principal 

aerosol species. 

1.1 Objectives of Visibility Monitoring

 The primary objectives of IMPROVE are the following: 

(1) To establish current background visibility levels in Class I areas; 

(2) To identify chemical species and emission sources responsible for existing man-made 

visibility impairment. 

(3) To document long-term trends for assessing progress toward the national visibility goal. 

 By measuring visibility routinely over a network and over a sufficiently long period of time, 

the first and third objectives of IMPROVE can be met. The monitoring also meets a portion of the 

second objective: the identification of the chemical composition of the visibility-degrading aerosol. 

 
1-2

 Each of these IMPROVE objectives are discussed in greater detail below. 

 Establish Current Visibility. This is necessary for two reasons. First, visibility levels 

monitored at a Class I area, when compared to surrounding area visibility or area estimates for 

natural levels, may be sufficient to indicate man-made impairment. Second, knowledge of existing 

visibility levels is required to model the anticipated visibility effects of proposed emission sources, 

because increments of pollution are more noticeable in clear conditions. 

 Establishment of present visibility levels requires monitoring that is appropriate for both 

surface and elevated layer impairment distributions. Optical monitoring systems, such as the 

transmissometer, are appropriate for surface haze monitoring, while scene monitoring with 

photography is the only practical way to routinely monitor elevated layers. 

 Visibility changes with time: diurnal, seasonal, and yearly variations all exist. Though five to 

eight years of data would be considered ideal for establishing present seasonal and annual averaged 

conditions, a minimum of one year is a reasonable compromise if that year is typical from a 

meteorological and source activity point of view. 

 Source Identification. Identification of chemical species and emission sources responsible for 

man-made visibility impairment is necessary to protect Class I areas, as called for by Congress. 

Monitoring is the principal means of gathering information needed to identify the contribution to 

impairment by emission sources. Even to distinguish man-made from natural impairment, which is 

fundamental to the national visibility goals, requires information derived from monitoring data. 

 Aerosol and scene monitoring are the primary sources of emission source identification 

information. Photography of a plume emanating from its source and impacting a Class I area is 

sufficient to indicate impairment. Furthermore, photographs can be evaluated to indicate the 

density or intensity of the visible plume. Unfortunately, most visibility impairment does not lend 

itself to this simple type of source attribution. Often sources are not visible from any line of sight 

that includes the Class I area, or their plumes disperse to a haze layer before reaching it. 

 Visibility impacts are often caused by aerosols formed over time from gaseous pollutants that 

are emitted without visibly noticeable plumes. Characteristics of the aerosol that are responsible for 

the haze provide valuable information that can be used in conjunction with other information to 

help identify the responsible emission sources. It is possible to statistically relate measured optical 

data to corresponding aerosol composition data to estimate the relative importance of the various 

major components of the aerosol. The result, known as an extinction budget, should narrow the list 

of possible sources responsible for large impacts. For example, if organic carbon is shown to be 

responsible for 75% of the extinction coefficient, the major sources responsible must emit organic 

carbon or precursor gases that form organic aerosols. 

 Another related approach for source identification using aerosol data is known as receptor 

modeling. Instead of using only the major aerosol components that are directly responsible for the 

impairment, receptor models use relative concentrations of trace components that can more 

specifically identify the influence of individual sources (or source types). 
1-3

 Long-Term Trends. With the establishment of a long-term goal of no man-made visibility 

impairment in protected areas, Congress imposed the responsibility to show progress towards 

meeting that goal. Trends monitoring is an ideal approach for tracking the visibility conditions of 

Class I areas. 

 Optical and scene monitoring conducted to establish present visibility levels (described above), 

if conducted in perpetuity, can provide the data required to determine long-term visibility trends. 

Alternatively, tracking levels of ambient aerosols and the key aerosol species will reveal the 

effectiveness of emission control programs. In either case, in order to determine the effectiveness 

of individual concurrent emission reduction programs, it is necessary to conduct aerosol monitoring 

to support extinction budget analysis as described above. 

1.2 Overview of the IMPROVE Monitoring Network

 The design of the IMPROVE monitoring network was resource and funding limited so that it 

was not practical to place monitoring stations at all 156 mandatory Class I areas where visibility is 

an important attribute. Instead, the IMPROVE Steering Committee selected a set of sites that were 

representative of the Class I areas. For the first IMPROVE report, published in the spring of 1993, 

data for 36 sites was summarized. In the intervening time the IMPROVE network has evolved; two 

sites were dropped, some sites were downgraded to the measurement of a subset of the variables 

measured at a fully complemented site, and other sites have been added. There are currently a total 

of 58 IMPROVE sites with various configurations of optical and aerosol monitoring equipment. 

For this report, only the 43 IMPROVE sites that are fully configured as aerosol monitoring sites 

with data for the three-year period, March 1992 through February 1995, are utilized. However, 

only 26 of the sites have optical monitoring equipment (e.g., transmissometers or nephelometers to 

measure visibility-related parameters). 

 View monitoring at all aerosol monitoring sites was routinely done for the first five years of the 

IMPROVE program. View monitoring is used to document the range of visibility conditions for a 

particular scene. Due to resource considerations, five years of scene monitoring was judged to 

adequately document the range of visibility. Now, view monitoring is only carried out at selected 

sites with less than five years of data. View monitoring is accomplished by automated 35-mm 

camera systems. These systems provide three color slides per day to document the appearance of a 

selected scene at each of the IMPROVE sites. The slides are used to interpret measurements, to 

communicate perceived visual conditions, and, if needed, to derive quantitative estimates of light 

extinction by microdensitometry. 

 Figure 1.1 shows a map of the United States indicating the locations of the 43 monitoring sites 

analyzed in this report. On the basis of regional similarities, the sites were grouped into 21 regions, 

listed in Table 1.1. 
 IMPROVE sites out of 43 included in the report. Denali National Park in Alaska is not shown.
1-5

Table 1.1 IMPROVE and NPS/IMPROVE protocol sites according to region. 

Alaska (AKA)

•Denali NP (DENA) 

Appalachian Mountains (APP)

•Great Smoky Mountains NP (GRSM) 

•Shenandoah NP (SHEN) 

•Dolly Sods WA (DOSO) 

Boundary Waters (BWA)

•Boundary Waters Canoe Area (BOWA) 

Cascade Mountains (CAS)

•Mount Rainier NP (MORA) 

Central Rocky Mountains (CRK)

•Bridger WA (BRID) 

•Great Sand Dunes NM (GRSA) 

•Rocky Mountain NP (ROMO) 

•Weminuche WA (WEMI) 

•Yellowstone NP (YELL) 

Coastal Mountains (CST)

•Pinnacles NM (PINN) 

•Point Reyes NS (PORE) 

•Redwood NP (REDW) 

Colorado Plateau (CPL)

•Bandelier NM (BAND) 

•Bryce Canyon NP (BRCA) 

•Canyonlands NP (CANY) 

•Grand Canyon NP (GRCA) 

•Mesa Verde NP (MEVE) 

•Petrified Forest NP (PEFO) 

Florida (FLA)

•Chassahowitzka NWR (CHAS) 

•Okefenokee NWR (OKEF) 

Great Basin (GBA)

•Jarbidge WA (JARB) 

•Great Basin NP (GRBA) 

Lake Tahoe (LTA)

•D.L. Bliss State Park (BLISS) 

•South Lake Tahoe (SOLA) 

Mid Atlantic (MAT)

•Edmond B. Forsythe NWR (EBFO) 

Mid South (MDS)

•Upper Buffalo WA (UPBU) 

•Sipsey WA (SIPS) 

•Mammoth Cave NP (MACA) 

Northeast (NEA)

•Acadia NP (ACAD) 

•Lye Brook WA (LYBR) 

Northern Great Plains (NGP)

•Badlands NM (BADL) 

Northern Rocky Mountains (NRK)

•Glacier NP (GLAC) 

Sierra Nevada (SRA)

•Yosemite NP (YOSE) 

Sierra-Humboldt (SRH)

•Crater Lake NP (CRLA) 

•Lassen Volcanoes NP (LAVO) 

Sonoran Desert (SON)

•Chiricahua NM (CHIR) 

•Tonto NM (TONT) 

Southern California (SCA)

•San Gorgonio WA (SAGO) 

Washington, D.C. (WDC)

•Washington, D.C. (WASH) 

West Texas (WTX)

•Big Bend NP (BIBE) 

•Guadalupe Mountains NM (GUMO) 

 

 NP = National Park 

 NM = National Monument 

 WA = Wilderness Area 

 NWR = National Wildlife Refuge 

 NS = National Seashore 
1-6

 The routine IMPROVE monitoring approach now involves aerosol, and optical monitoring. 

Aerosol monitoring measures the mass concentration (in micrograms per cubic meter, ìg/m3

) and 

the chemical composition of the particles. Optical monitoring measures the light-extinction 

coefficient (bext) using a transmissometer or the light-scattering coefficient (bscat) using a 

nephelometer. 

 Aerosol monitoring in the IMPROVE network is accomplished by a combination of particle 

sampling and sample analysis. The sampler employed was designed specifically for the program. 

It collects four simultaneous samples: one PM10 sample (particles less than 10 micrometers, ìm, in 

diameter) on a Teflon filter and three PM2.5 samples (particles less than 2.5 ìm in diameter) on 

Teflon, nylon, and quartz filters. Each of the four samples is collected by a separate subsystem (or 

module) including everything from the inlet to the pump with only the support structure and 

controller/timer in common. The particle size segregation for the PM10 module is accomplished by 

a wind insensitive inlet with a 10 ìm cutoff, while the PM2.5 segregation is produced by passing 

the sampled air through a cyclone separator. Constant sample flow is maintained by a critical 

orifice in each module. The IMPROVE sampler is programmed to automatically collect two 24-

hour duration samples per week. 

 Only mass analyses are conducted on the PM10 samples. The PM2.5 samples are analyzed for 

mass, elements, ions (including particulate nitrate sampled through a denuder), organics and 

elemental carbon, and optical absorption. 

 At many sites in the IMPROVE network, long-path transmissometers are employed for optical 

measurements. These instruments measure the amount of light transmitted through the atmosphere 

over a known distance, usually 0.5 to 10 kilometers, between the light source (transmitter) and the 

light-monitoring component (receiver). Transmission measurements are converted electronically to 

the path-averaged, light-extinction coefficient (bext). At other sites nephelometers are used that 

measure the light-scattering coefficient (bscat) from an enclosed volume of air. 

 In addition to the aerosol and optical monitoring, those sites that have optical monitoring have 

temperature and relative humidity instruments. Liquid water is a component of the hygroscopic 

sulfate, nitrate, and possibly organic carbon fractions, but it is not quantified by any of the filter 

sampling techniques. Relative humidity measurements are used to estimate the amount of liquid 

water associated with these particles. 

1.3 Background Regarding Visibility Impairment and Aerosols

 Visibility is usually characterized either by visual range (the greatest distance that a large dark 

object can be seen) or by the light-extinction coefficient (the attenuation of light per unit distance 

due to scattering and absorption by gases and particles in the atmosphere). Under certain assumed 

conditions these two measures of visibility can be shown to be inversely related to each other. 

Visual range functions well as an aid in military operations and transportation safety. Issues of 
1-7

concern for such use include: the minimum distance required to land an aircraft, the distance to the 

first appearance of a military target or an enemy aircraft or ship, and safe maneuvering distances 

under impaired visibility conditions. Because of the use of familiar distance units, the simple 

definition, and the ability of any sighted person to characterize visual conditions with this parameter 

without instruments, visual range is likely to remain the most popular measure of atmospheric 

visibility. 

 Extinction coefficient is used most by scientists concerned with the causes of reduced 

visibility. There are direct relationships between the concentrations of the atmospheric constituents 

and their contribution to the extinction coefficient. Apportioning the extinction coefficient to 

atmospheric constituents provides a method to estimate the change in visibility caused by a change 

in constituent concentrations. This methodology, known as extinction budget analysis, is important 

for assessing the visibility consequences of proposed pollutant emission sources, or for determining 

the extent of pollution control required to meet a desired visibility condition. Interest in the causes 

of visibility impairment is expected to continue and the extinction coefficient will remain important 

in visibility research and assessment. 

 Neither visual range nor extinction coefficient is linear with humanly perceived changes caused 

by uniform haze (i.e., as opposed to elevated haze layers and plumes). For example, a given change 

in visual range or extinction coefficient can result in a scene change that is either unnoticeably 

small or very apparent depending on the baseline visibility conditions. Presentation of visibility 

measurement data or model results in terms of visual range or extinction coefficient can lead to 

misinterpretation by those who are not aware of the nonlinear relationship. 

 To rigorously determine the perceived visual effect of a change in extinction coefficient 

requires the use of radiative transfer modeling to determine the changes in light from the field of 

view arriving at the observer location, followed by the use of psychophysical modeling to determine 

the response to the light by the eye-brain system. Results are dependent not only on the baseline 

and changes to atmospheric optical conditions, but also on the characteristics of the scene and its 

lighting. The complexity of employing such a procedure and the dependence of the results on nonatmospheric factors prevent its widespread use to characterize perceived visibility changes resulting 

from changes in air quality. 

 Parametric analysis methods have been used to suggest that a constant fractional change in 

extinction coefficient or visual range produces a similar perceptual change for a scene regardless of 

baseline conditions. Simplifying assumptions eliminate the need to consider the visibility effects of 

scene and lighting conditions. Using the relationship of a constant fractional change in extinction 

coefficient to perceived visual change, a new visibility index called deciview (dv) is defined as: 

dv = 10ln(b ext/0.01 km –1) (1.1) 
1-8

where extinction coefficient is expressed in km-1 [Pitchford and Malm, 1994]. A one dv change is 

about a 10% change in extinction coefficient, which is a small but perceptible scenic change under 

many circumstances. The deciview scale is near zero for pristine atmosphere (dv = 0 for Rayleigh 

condition at about 1.8 km elevation) and increases as visibility is degraded. Like the decibel scale 

for sound, equal changes in deciview are equally perceptible. 

1.3.1 Relationship Between Visibility and Aerosol Concentrations

 Visibility is degraded by light scattered into and out of the line of sight and by light absorbed 

along the line of sight. Light extinction (the sum of light scattering and absorption) is usually 

quantified using the light-extinction coefficient (bext), which may be thought of as the atmospheric 

concentration of light-extinction, cross-sectional area. Light extinction has units of 1/length. 

 The light-extinction coefficient (bext) is the sum of the light-scattering coefficient (bscat) and the 

light-absorption coefficient (babs). Light scattering results from the natural Rayleigh scatter (bRay) 

from air molecules (which causes the blue sky) and the scattering caused by suspended particles in 

the atmosphere (aerosols). Particle scatter (bsp) can be caused by natural aerosol (e.g., wind-blown 

dust and fog) or by man-made aerosols (e.g., sulfates, nitrates, carbonaceous aerosols, and other fine 

and coarse particles). Light absorption results from gases (bag) and particles (bap). Nitrogen 

dioxide (NO2) is the only major light-absorbing gas in the lower atmosphere. Its strong 

wavelength-dependent scatter causes yellow-brown discoloration if present in sufficient quantities. 

Soot (elemental carbon) is thought to be the dominant light-absorbing particle in the atmosphere. 

Thus, the total light extinction is the sum of its components: 

The particle light-scattering coefficient (bsp), in turn, is composed of the contributions from 

individual species. Fine particles are much more efficient at light scattering (per unit mass) than 

larger particles. Thus, it makes sense to divide the contributions to bsp into the contributions from 

various species of fine and coarse particles. In this study, we specifically evaluated the following 

components of fine particles (those with diameters less than 2.5 ìm): sulfate (SO), nitrate (NO), 

organic carbon, elemental carbon (soot), and soil. In addition to these chemical species, the effect 

of water associated with sulfates, nitrates, and some organics need to be considered in the overall 

assessment of light extinction. Finally, the coarse fraction of PM10 (those with diameters between 

2.5 and 10 ìm) are separately considered. 

 The light-extinction coefficient can be written with a number of assumptions, as the sum of the 

products of the concentrations of individual species and their respective light-extinction 

efficiencies: 

ext Ray i

Ci b = b + ∑ β (1.3) 

where âi is the light-extinction efficiency (m2

/g) of species i, Ci is the atmospheric concentration of 

 bext = bscat + babs = bRay + bsp + bag + bap (1.2) 
1-9

species i (ìg/m3), and the summation is over all light-interacting species (i.e., sulfates, nitrates, 

organic carbon, elemental carbon, other fine particles, coarse particles, and NO2). The above units, 

when multiplied, yield units for bext of 10-6

 m

-1 or (106

 m)-1, or as we prefer to label it here, inverse 

megameters (Mm-1). 

1.3.2 Effect of Relative Humidity on Light Scattering

 Sulfates, nitrates, and some organics can combine with water in the vapor phase to form 

solutions. Thus, at some humidity conditions, considerable water may be associated with these 

species. Although the overall light-scattering efficiency is on the order of 3 m2

/g for these 

solutions, if the light-scattering efficiency is stated in terms of the mass of dry sulfate (SO4

=

), the 

efficiency must be larger than 3 m2

/g to account for the additional mass (and volume) of the 

associated water. In addition, the associated cations (H+

 and NH4

+

) must also be included. As a 

result, light-scattering efficiency per unit of dry sulfate can be much larger than 3 m2

/g. This 

hygroscopic effect can be described by the following equation: 

wet RH dry β = kf β (1.4) 

where âwet is the light extinction efficiency of the wet sulfate, nitrate, and/or organic solution, k is 

the ratio in molecular weight of the neutralized species (e.g., ammonium sulfate or ammonium 

nitrate) to the anion (sulfate, nitrate), fRH is a factor that accounts for the liquid water associated 

with the aerosol at the given relative humidity (RH), and âdry is the light-extinction efficiency of the 

dry particle. 

1.4 Organization of the Report

 This report is divided into six chapters. Chapter 2 summarizes the optical and aerosol 

measurement techniques and details the assumptions for determining the chemical composition of 

the aerosol species. The spatial and seasonal patterns of aerosol mass and chemical composition 

are summarized in Chapter 3. Chapter 4 discusses the theory of light extinction in detail and 

specifies the assumptions used to reconstruct light extinction from aerosol measurements. Using 

reconstructed light extinction, Chapter 5 discusses the spatial and seasonal patterns of reconstructed 

light extinction. Chapter 6 discusses the long-term temporal trends of two key aerosol species, 

sulfur and light-absorbing carbon. 

1.5 References

Pitchford, M.L., and Malm, W.C. Development and applications of a standard visual index, 

Atmospheric Environment, 28(5):1049-1054, 1994. 
1-4
2-1

CHAPTER 2 

OPTICAL AND AEROSOL DATA

 Monitoring of protected visibility areas is conducted on two complementary fronts: 1) optical 

monitoring of visibility in these areas; and 2) monitoring the concentration and composition of the 

aerosols in these areas. For optical monitoring, two measurements are possible, extinction (bext) 

measured by transmissometers and scattering (bscat) measured by nephelometers. The IMPROVE 

particulate monitors provide measurements of PM10 mass and PM2.5 mass. Chemical and elemental 

analysis of the PM2.5 fraction is carried out to identify the fine aerosol species. What follows is a 

brief description of the IMPROVE monitoring instruments, their operating characteristics, and the 

data derived from them. 

2.1 Transmissometers

 Transmissometers are calibrated to measure the irradiance, at a wavelength of 550 nm, of a 

light source after the light has traveled over a finite atmospheric path. The transmittance of the path 

is calculated by dividing the measured irradiance at the end of the path by the calibrated initial 

intensity of the light source. Bouger's law is applied to calculate the extinction. Because of the 

relatively clean atmospheres found in the western United States, path lengths of a few kilometers 

are required to achieve the necessary sensitivity to resolve extinctions near the Rayleigh limit. 

 The transmissometers used in this study are the OPTEC, Inc., LPV-2 instruments, which have 

been in use since 1986. Their use in remote locations such as national parks is discussed by 

Molenar et a1. [1989], while their use in urban settings is presented by Dietrich et al. [1989]. Data 

processing algorithms that incorporate corrections for interferences are thoroughly discussed by 

Molenar and Malm [1992]. Basically, there are five checks the data must pass to be incorporated 

into a validated data set. They are: 

 1) relative humidity must be less than 90%; 

 2) maximum extinction cannot exceed a threshold value based on photometer sensitivity and 

path length; 

 3) variability in extinction readings taken over a period of one hour cannot exceed a 

 threshold value; 

 4) rate of change of hourly average extinction measurements cannot exceed a threshold 

 value; and 

 5) isolated data points. (By definition any hourly average data point passing the above four 

criteria but falling in between two hourly average data points that have failed the criteria is 

referred to as "isolated." It is conservatively assumed that it has also been affected by 
2-2

interferences.) 

 Molenar et al. [1989] discuss the inherent uncertainties associated with the measurement. The 

accuracy of the transmission measurement, as determined by field and laboratory calibrations, is 

better than 1%. However, the accuracy of the derived extinction is dependent on the accuracy of 

the transmission measurement in field conditions. The transmission calculation is determined from 

an absolute (as opposed to relative) measurement of irradiance of a light source of known intensity 

that is located some known distance from the receiver. The measurement is made through optics 

that are exposed to the ambient atmosphere but are assumed to be free of dust or other films, which 

tend to build up on the optical surfaces. The uncertainties associated with these parameters 

contribute to the overall uncertainty of the measurement. For a typical 5 km path length the 

estimated uncertainty is about 4 Mm-1. 

2.2 Integrating Nephelometers

 Integrating nephelometers measure the scattering of light over a defined band of visible 

wavelengths from an enclosed volume of air. Historically, integrating nephelometers used in most 

major field studies have underestimated scattering because of: 

 1) modification of the ambient aerosol by heating when a large fraction of the sampled aerosol 

is hygroscopic; 

 2) inlet, sampling train, and optical chamber design that limits the size of particles that make it 

into the sampling chamber; 

 3) optical geometry that causes a truncation of the true scattering volume; and 

 4) electronics that display large nonlinear drifts in zero and span values. 

 The OPTEC NGN-2 ambient integrating nephelometer was developed to minimize these 

limitations of integrating nephelometry. The instrument, which measures light scattering at an 

effective wavelength of 550 nm, is described in some detail by Molenar et al. [1989]. It is an "open 

air" design that has minimal heating characteristics, and because it is open air it tends to allow a 

wider spectrum of particles to pass through the instrument. However, the cutpoint of the instrument 

has not been characterized. It is also designed with solid-state electronics that are very stable over 

wide temperature and humidity shifts. It still has an inherent limitation of an abbreviated 

acceptance angle in that it only samples light scattered between 5 and 175o

. Calibration of the 

instrument and data validation and processing algorithms are also discussed in detail in Molenar 

and Malm [1992]. 

 Unlike transmissometers, where an uncertainty in transmittance leads to an additive error in 

extinction, uncertainties in nephelometer calibration lead to a multiplicative error in measured 

scattering. Typical uncertainties for the OPTEC instrument are on the order of 5-10% [Molenar 

and Malm, 1992]. 
2-3

2.3 Particle Sampling System

 The standard IMPROVE sampling module consists of: 1) a size selective inlet; 2) a cyclone to 

provide a particle size cutoff based on the flow rate; 3) collection substrates; 4) a critical orifice that 

provides the proper flow rate for the desired particle size cutoff; and 5) a vacuum pump that 

produces the flow. The system is described in some detail by Malm et al. [1994] and Eldred et al.

[1988] and is only briefly described here. 

 The sampling system consists of four independent sampling modules. Three modules (denoted 

A, B, and C) employ a cyclone with a flow rate of 22.7 l/min that allows for collection of fine 

particles less than 2.5 ìm in diameter [John et al., 1988]. The fourth module (D) is a PM10

sampler with a wind insensitive size selective inlet that collects particles less than 10 ìm in 

diameter. Table 2.1 summarizes the substrates used and aerosol species measured on each filter. 

Table 2.1 Filter media and analysis techniques used to determine concentrations of particulate 

matter species from IMPROVE sampler modules. 

Module Filter Media Analyses 

A Teflon gravimetric analysis for mass < 2.5 ìm dia. 

1

LIPM for optical absorption 

2

PIXE for elements Na to Pb 

3

PESA for H 

B nylon (denuded) ion chromatography for NO3 and SO4

C quartz 4TOR for organic and light-absorbing C 

D Teflon gravimetric analysis for mass < 10 ìm dia. 

 1LIPM - Laser Integrating Plate Method 2PIXE - Particle Induced X-ray Emission 3PESA - Proton Elastic Scattering 4TOR - Thermal Optical Reflectance 

 Gravimetric mass (channel A fine mass, channel D PM10 mass) is measured as the difference 

between the weight of the substrates before and after sampling, using an electromicrobalance. The 

channel A Teflon substrates are analyzed for sulfur and other elements by Particle Induced X-ray 

Emission (PIXE), and simultaneously for hydrogen by Proton Elastic Scattering Analysis (PESA) 

[Cahill et al., 1986]. 
2-4

 The coefficient of light absorption for fine particles, babs, is also determined from the channel A 

Teflon substrates using a Laser Integrating Plate Method (LIPM) [Cahill et al., 1986]. This 

involves direct measurement of the absorption of a laser beam by a sample over the area of the 

sample. 

 Extract from the channel B nylon substrates are analyzed by Ion Chromatography (IC) for 

sulfate and nitrate ions from which the sulfate and nitrate compounds can be estimated [Cahill et 

al., 1986; Malm et al., 1994]. 

 

 The channel C quartz substrates are analyzed by Thermal Optical Reflectance (TOR) 

combustion for organic and elemental carbon [Chow et al., 1993]. Because carbon derived from 

TOR analysis will be explored in some detail in Chapter 4, a more complete description of the 

analysis scheme is presented than for the other analytic procedures. 

 TOR involves: 1) heating a sample through a series of temperature increases or steps (in a pure 

helium atmosphere to which oxygen is added in the later stages to enable the volatilization of 

elemental carbon); 2) converting the carbon evolved at each step into CO2, using an oxidizer 

(MnO2 at 912o

C); and 3) reducing the CO2 to methane, which is then quantified by passage through 

a flame ionization detector. Over the mid range of the TOR heating (between about 130oC and 

550oC), charring of the sample occurs, due to pyrolysis of organic particles; this is monitored as a 

decrease in the reflectance from the sample surface. When the reflectance reaches a minimum, 2% 

oxygen is added to the atmosphere. This allows the elemental carbon in the sample, including the 

char produced by pyrolysis of organic matter, to oxidize and the reflectance of the sample increases 

as the char is removed. All carbon measured up to the point where the reflectance reattains its 

initial value is traditionally interpreted as organic carbon. Carbon evolved beyond this point is 

reported as elemental carbon. Overall, the peaks in the carbon evolution from the sample are 

operationally defined as O1 (25oC-140oC), O2 (140oC-230oC), O3 (230oC-450oC), and O4 (450oC550o

C). At 1100 seconds and at 550oC, 2% oxygen is introduced. The carbon evolved between 

1100 seconds and when the sample reflectance returns to its initial value is referred to as pyrolized 

carbon (OP). The remainder of the carbon evolved at 550oC and 2% oxygen is labeled as E1. 

Temperatures are then ramped up to 800o

C in two steps. The evolved carbon is labeled as E2 

(550o

C-700oC) and E3 (700oC-800oC). Traditionally, O1, O2+O3+O4+OP, E1, and E2+E3 are 

referred to as OCLT, OCHT, ECLT, and ECHT, respectively. Organic carbon (OC) is assumed to 

be the sum of OCLT and OCHT. High temperature carbon, often referred to as elemental carbon or 

light-absorbing carbon (LAC), is the sum of ECLT and ECHT. 

2.4 Determination of Aerosol Types

 The fine aerosol species at most continental sites are classified into five major types: sulfates, 

nitrates, organics, light-absorbing carbon, and soil. Methods for apportionment of measured mass 

to the various aerosol species are detailed in Malm et al. [1994] and only a summary will be 

presented here. The major aerosol types are composites of the elements and ions measured in 

IMPROVE samplers, and their concentrations or masses are calculated from the masses of the 
2-5

measured elements and ions according to their presumed or probable composition and are 

summarized by Table 2.2. The convention used here to denote the mass concentration of a 

measured element, ion, or species is to enclose its symbol in brackets ([ ]). 

 In the West, most sulfur is in the form of ammonium sulfate. In the East, or other environments 

where ammonia can be limited, it is recognized that acidic species such as ammonium bisulfate and 

sulfuric acid are not uncommon. However, for a first approximation, all elemental sulfur is 

interpreted as being in the form of ammonium sulfate, and ammonium sulfate concentrations are 

estimated by multiplying elemental sulfur concentrations by 4.125. For simplicity, ammonium 

sulfate is referred to as sulfate. 

Table 2.2. The formulae and assumptions applied to IMPROVE sampler measurements to derive 

the principal fine aerosol species, reconstructed fine mass, and coarse mass. The 

brackets indicate the mass concentration of the aerosol species or element. 

 

 SPECIES FORMULA ASSUMPTIONS 

 SULFATE 4.125[S] All elemental S is from sulfate. All 

sulfate is from ammonium sulfate. 

 NITRATE 1.29[NO3] Denuder efficiency is close to 100%. 

All nitrate is from ammonium nitrate. 

EC (elemental 

carbon) 

[ECLT] + [ECHT] All high temperature carbon is 

elemental. 

OMC (organic mass 

from carbon) 

1.4{[OCLT]+[OCHT]} Average organic molecule is 70% 

carbon. 

SOIL (fine soil) 2.2[Al]+2.19[Si] 

+1.63[Ca]+2.42[Fe] 

+1.94[Ti] 

[Soil K]=0.6[Fe]. FeO and Fe2O3 are 

equally abundant. A factor of 1.16 is 

used for MgO, Na2O, H2O, CO2. 

RCFM (reconstructed 

fine mass) 

[SULFATE]+[NITRATE] 

+[LAC]+[OMC]+[SOIL] 

Represents dry ambient fine aerosol 

mass for continental sites. 

CM (coarse mass) [PM10] - [PM2.5] Consists only of insoluble soil particles. 

 

 

 Assuming, as is the case for sulfate, that the collected nitrate ion is associated with fully 

neutralized ammonium nitrate aerosol (NH4NO3). The mass of ammonium nitrate is estimated by 

using a multiplication factor of 1.29 and is referred to as simply nitrate. 
2-6

 Organic mass (organics) concentration is estimated by: 

[ ] [ ][ ] OMC = 1.4( OCLT + OCHT ) (2.1) 

The factor of 1.4 assumes that organic mass contains a constant fraction of carbon by weight 

[Watson et al., 1988]. 

 Light-absorbing carbon concentration, usually thought of as elemental carbon, is defined as the 

sum of E1+E2+E3 or more conventionally as: 

[ ][ ] LAC = ECLT + ECHT (2.2) 

where ECLT and ECHT are the low and high temperature elemental carbon concentrations. 

 Soil mass concentration is estimated by summing the elements predominantly associated with 

soil, plus oxygen for the normal oxides (Al2O3, SiO2, CaO, K2O, FeO, Fe2O3, TiO2), plus a 

correction for other compounds such as MgO, Na2O, water, and carbonate. The final equation for 

fine soil is: 

[ ] [ ] [] [ ] [ ] [] SOIL = 2.20 Al + 2.49 Si +1.63 Ca + 2.42 Fe +1.94 Ti (2.3) 

 Components of these factors were confirmed in comparisons of local resuspended soils and 

ambient aerosols in the western United States [Cahill et al., 1981; Pitchford et al., 1981]. 

 The sum of the above five composites should provide a reasonable estimate of the ambient fine 

mass concentration measured in the atmosphere (RCFM). The equation for RCFM concentration is 

therefore: 

[ ][ ][ ][ ][ ][ ] RCFM = SULFATE + NITRATE + LAC + OMC + SOIL (2.4)

 Coarse mass (CM) is estimated gravimetrically by subtracting fine mass (PM2.5) concentration 

from total aerosol mass (PM10) concentration: 

[ ][ ][ ] CM = PM10 − PM 2.5 (2.5) 

In the IMPROVE program additional chemical analysis is not carried out on the coarse fraction. 

However, it is known that in rural or remote areas of the country the primary constituent of coarse 

mass is naturally occurring wind-blown dust along with some vegetative material [Noll et al., 1985; 

Noll, 1991]. 

 The self consistency and overall quality of the aerosol measurements are assured by redundancy 

and intercomparisons between independently measured species. A detailed description of 
2-7

validation and quality assurance procedures is available in Malm et al. [1994], Sisler et al. [1993], 

and Eldred et al. [1988]. In the most general sense, validation is a matter of comparing chemicallyrelated species that have been measured in different channels. Fortunately, the design of the 

IMPROVE sampler allows for redundancy between certain channel A measurements and channel B 

and C measurements of the ions and carbons enabling quality control checks. For example, in the 

IMPROVE network, it was found that elemental sulfur mass times three agrees well with the sulfate 

ion measured in channel B; validating the assumption that concentrations of sulfate aerosols can be 

estimated by channel A PIXE analysis [Sisler et al., 1993]. However, when comparing measured 

fine mass to RCFM, two complicating factors must be dealt with. First, a large portion of the 

nitrates (≥50%) are presumed to volatilize from the channel A teflon filter; and second, it is 

presumed that there is residual water on the filters due to the soluble species. 

2.5 References

Cahill, T.A., R.A. Eldred, and P.J. Feeney, Particulate monitoring and data analysis for theNational 

Park Service 1982-1985, University of California, Davis, 1986. 

Cahill, T.A., L.L. Ashbaugh, R.A. Eldred, P.J. Feeney, B.H. Kusko, and R.G. Flocchini, 

Comparisons between size-segregated resuspended soil samples and ambient aerosols in the 

western united States, In Atmospheric Aerosol: Source/Air Quality Relationships, Am. Chem. Soc. 

Symp. Ser., 167, edited by E. Macias, Washington, D.C., 1981. 

Chow, J.C., J.G. Watson, L.C. Pritchett, W.R. Pierson, C.A. Frazier, and R.G. Purcell, The DRI 

 thermal/optical reflectance carbon analysis system: description, evaluation, and applications 

in U.S. air quality studies, Atmospheric Environment, 27(A), (8), 1185-1201, 1993. 

Dietrich, D.L., J.D. Molenar, J.F. Faust, Transmissometer extinction measurements in an urban 

environment, In Visibility and Fine Particles, C.V. Mathai, Ed., AWMA, Pittsburgh, pages 374-

383, 1989. 

Eldred, R.A., T.A. Cahill, M. Pitchford, and W.C. Malm, IMPROVE-a new remote areaparticulate 

monitoring system for visibility studies, Proc. APCA (Air Pollution Control Assoc.) Ann. Mtg., 81, 

1-16, 1988. 

John, W., S.M. Wall, and J.L. Ondo, A new method for nitric acid and nitrate aerosol measurement 

using the dichotomous, Atmospheric Environment, 22, 1627-1635, 1988. 

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

 in particle concentration and optical extinction in the United States, J. Geo. Res., 99(D1), 

1347-1370, 1994. 

Molenar, J.F. and W.C. Malm, Ambient optical monitoring techniques, presented at the Conference 
2-8

on Visibility and Fine Particles, Vienna, Austria, September, 1992. 

Molenar, J.F, D.L. Dietrich, and R.M. Tree, Application of a long range transmissometer to 

measure the ambient atmospheric extinction coefficient in remote pristine environments, In 

Visibility and Fine Particles, C.V. Mathai, Ed., 374-383. AWMA, Pittsburgh, 1989. 

Noll, K.E., Collection and characteristics of atmospheric coarse particles, Final Report, Dept. of 

 Environmental Engineering, Illinois Institute of Technology, Chicago, 1991. 

Noll, K.E., A. Pontius, R. Frey, and M. Gould, Comparison of atmospheric coarse particles at an 

 urban and non-urban site, Atmospheric Environment, 19(11): 1931-1943, 1985. 

Pitchford, M., R.G. Flocchini, R.G. Draftz, T.A. Cahill, L.L. Ashbaugh, and R.A. Eldred, Silicon 

 in submicron particles in the southwest, Atmospheric Environment, 15, 321-333, 1981. 

Sisler, J.F., D. Huffman, D.A. Latimer, W.C. Malm, and M.L. Pitchford, Spatial and temporal 

 patterns and the chemical composition of the haze in the United States: An analysis of data 

from the IMPROVE network, 1988-1991, 1993. 

Watson, J.G., J.C Chow, L.C. Pritchett, W.R. Pierson, C.A. Frazier, Purcell, R.G., I. Olmez, The 

 1987-88 Metro Denver brown cloud study, Desert Reserch Institute, Doc 8810 1F2, Desert 

Resert Institute, Reno, NV, 1988. 
3-1

CHAPTER 3

AEROSOL MASS BUDGETS AND SPATIAL 

DISTRIBUTIONS

This chapter discusses the observed spatial and temporal variations in aerosol concentration and 

chemical composition throughout the United States on the basis of the IMPROVE measurements [Sisler 

et al., 1993] for the three-year period, March 1992 through February 1995.

Aerosol concentrations and chemical composition vary because of a number of factors, including 

the spatial distribution of natural and anthropogenic emission sources and meteorological conditions. 

The highest aerosol concentrations tend to occur in significant urban or industrialized areas where 

emission densities are high. Also, concentrations are highest when atmospheric dilution is minimal such 

as what occurs in stagnation periods or periods of limited mixing. In addition, since sulfate and nitrate 

aerosols are formed from SO2 and NOx emissions and chemical reactions in the atmosphere, these 

aerosols are highest when photochemistry is strongest.

For example, concentrations of sulfates tend to be highest in areas of significant sulfur dioxide 

(SO2) emissions such as the eastern United States where SO2 is emitted from coal-fired stationary 

sources, and in the Southwest due to copper smelters, power plants, and SO2 emissions from Mexico. 

Organic carbon concentrations tend to be highest in regions such as the Pacific Northwest and

Southeast due in part to forests and forest-product industries, which cause organics to dominate fine 

aerosol mass in the Pacific Northwest. Nitrates tend to be most prevalent in California where both NOx

emissions from motor vehicles and industry are high.

Spatial and temporal variations in aerosol composition and concentrations can be qualitatively

examined through the use of annual and seasonal mass budgets. Mass budgets are the contribution of 

individual aerosol species to the reconstructed fine particle mass [Sisler et al., 1993]. Mass budgets 

are calculated by dividing the average concentration of each species by the average reconstructed fine 

particle mass for each region and time period of interest.

In this chapter, the observed spatial and seasonal trends in aerosol concentrations and chemical 

composition from the three-year period, March 1992 through February 1995, of the IMPROVE 

network are presented. There are 58 sites in the IMPROVE network that are fully instrumented for 

aerosol monitoring (channels A-D). Only 43 sites with data for this three-year period are summarized in 

this report. Since the last IMPROVE report [Sisler et al., 1993] five sites have been downgraded to 

channel A only or were discontinued and are not summarized here. The downgraded sites are at 

Everglades and Voyageurs National Parks. The discontinued sites are Arches, Isle Royale, and Hawaii 

Volcanoes National Parks. 
3-2

The 43 IMPROVE sites are grouped into 21 regions according to their relative location,

climatology, similarities in concentrations, and seasonal trends. Since the last IMPROVE report, three 

new regions have been introduced, the Mid-South, Mid-Atlantic, and Lake Tahoe region, while one 

region was dropped, Hawaii. Average concentrations and chemical composition are calculated on the 

basis of measurements for each region. Tables 3.1 and 3.2 show the mass concentrations of fine and 

coarse aerosol and the chemical composition (mass budgets) of the fine aerosol for each of the 21 

regions in the United States. These concentrations and mass budgets are averaged over the entire 

three-year period to provide the annual average and over the three years for each of the four seasonal 

averages. 

First, the characteristics of each of the regions (in alphabetic order) are discussed, followed by the 

spatial and temporal trends of the fine and coarse mass concentrations and the constituents of the fineparticle mass. 

3.1 Characteristics of the Regions

Alaska. The Alaska region has only one monitoring site, Denali National Park. The average 

concentrations of fine and coarse aerosols over the three-year period were 1.8 and 3.3 ìg/m3, 

respectively. The fine aerosol concentration was the lowest measured anywhere in the United States 

during this period. Both fine and coarse aerosol concentrations are largest in summer and smallest in 

autumn. Organics are the largest contributor of fine particle mass (52%), followed by sulfate (28.6%), 

soil (10.2%), light-absorbing carbon (4.6%), and nitrate (3.3%). The concentrations of organics and 

light-absorbing carbon are largest in summer, perhaps due to the prescribed burning and forest fires that 

usually occur during that season.

Appalachian Mountains. This region has five sites of which three are reported here: Great Smoky 

Mountains and Shenandoah National Parks, both initiated in March 1988, and Dolly Sods Wilderness 

Area in West Virginia, initiated in September 1991. The other two sites, Shinning Rock in North 

Carolina and James River Face in Virginia have less than one year of data. 

The average concentrations of fine and coarse aerosol for this region were 11.3 ìg/m3 and 4.8

ìg/m3

, respectively. Both fine and coarse aerosol concentrations are maximum in summer and minimum 

in winter. Sulfate is by far the largest component of the fine particle mass. At 59.9%, it is more than 

twice that of the next largest contributor, organics (26%). Other contributors include nitrate (5.5%), soil 

(4.7%), and light-absorbing carbon (3.7%). Except for nitrate and light-absorbing carbon, which have 

their maximum concentrations in the winter and autumn, respectively, all other species have maximum 

concentrations in summer. The seasonal variation in sulfate concentrations is particularly strong with 

summer concentrations more than three times the winter concentrations at 11.2 ìg/m3. 
3-3

Table 3.1. Measured fine and coarse aerosol concentration (in micro-g/m3) for the 21 regions in the 

IMPROVE network.

Season Fine 

Mass

Sulfate Nitrate Organics Elemental 

Carbon

Soil Coarse Mass

Alaska

Spring 2.0 0.8 0.1 0.8 0.1 0.3 2.8

Summer 2.4 0.5 0.0 1.6 0.1 0.2 2.9

Autumn 1.4 0.4 0.0 0.7 0.1 0.1 1.9

Winter 1.5 0.5 0.1 0.7 0.1 0.1 1.7

ANNUAL 1.8 0.5 0.1 1.0 0.1 0.2 2.3

Appalachian Mountains

Spring 10.3 6.0 0.8 2.7 0.4 0.5 5.5

Summer 16.8 11.2 0.3 3.7 0.4 1.2 6.3

Autumn 10.6 6.3 0.5 3.0 0.5 0.3 4.3

Winter 7.1 3.2 0.9 2.3 0.4 0.2 3.3

ANNUAL 11.3 6.7 0.6 2.9 0.4 0.6 4.8

Boundary Waters

Spring 5.4 2.8 0.5 1.5 0.2 0.4 3.4

Summer 5.2 1.9 0.1 2.8 0.2 0.2 3.8

Autumn 4.3 1.7 0.5 1.7 0.2 0.2 3.0

Winter 5.5 2.1 1.4 1.5 0.2 0.2 2.6

ANNUAL 5.1 2.1 0.6 1.9 0.2 0.3 3.2

Cascade Mountains

Spring 5.6 1.6 0.3 3.0 0.4 0.3 4.0

Summer 6.3 2.4 0.4 2.9 0.4 0.3 3.7

Autumn 5.3 1.3 0.2 3.2 0.5 0.2 3.9

Winter 3.5 0.5 0.1 2.3 0.4 0.1 2.5

ANNUAL 5.2 1.5 0.2 2.8 0.4 0.2 3.5

Central Rocky Mountains

Spring 3.3 0.9 0.2 1.1 0.1 0.9 5.0

Summer 4.0 1.0 0.2 1.8 0.2 0.8 5.7

Autumn 3.1 0.8 0.2 1.4 0.2 0.5 3.9

Winter 2.1 0.6 0.2 1.0 0.1 0.2 2.8

ANNUAL 3.1 0.8 0.2 1.3 0.2 0.6 4.4
3-4

Table 3.1 Continued

Season Fine 

Mass

Sulfate Nitrate Organics Elemental 

Carbon

Soil Coarse Mass

Colorado Plateau

Spring 3.5 1.1 0.2 1.2 0.1 0.9 4.2

Summer 3.9 1.3 0.2 1.5 0.2 0.7 5.3

Autumn 3.4 1.1 0.2 1.5 0.2 0.4 4.0

Winter 2.6 0.8 0.2 1.1 0.2 0.2 3.0

ANNUAL 3.3 1.1 0.2 1.3 0.2 0.6 4.1

Florida

Spring 11.0 6.0 0.6 3.3 0.6 0.6 6.8

Summer 11.8 4.9 0.5 3.0 0.4 3.1 10.1

Autumn 8.8 4.5 0.4 2.9 0.6 0.4 6.4

Winter 8.9 3.9 0.6 3.3 0.8 0.2 6.3

ANNUAL 10.1 4.8 0.5 3.1 0.6 1.1 7.4

Great Basin

Spring 3.2 0.7 0.2 1.2 0.1 1.0 5.4

Summer 4.2 0.8 0.2 1.7 0.2 1.4 7.1

Autumn 3.0 0.6 0.1 1.5 0.2 0.6 4.6

Winter 2.1 0.4 0.2 1.1 0.2 0.2 2.6

ANNUAL 3.1 0.7 0.2 1.4 0.2 0.8 4.9

Lake Tahoe

Spring 5.7 0.9 0.4 2.6 0.6 1.1 6.8

Summer 5.4 1.1 0.3 2.6 0.6 0.8 5.4

Autumn 6.8 0.8 0.4 3.9 1.1 0.6 5.2

Winter 8.0 0.4 0.5 5.0 1.5 0.6 9.2

ANNUAL 6.4 0.8 0.4 3.5 0.9 0.8 6.6

Mid Atlantic

Spring 10.1 5.6 1.2 2.4 0.5 0.4 10.1

Summer 13.9 8.3 0.7 3.5 0.6 0.9 11.4

Autumn 10.5 5.3 1.1 3.1 0.7 0.4 8.6

Winter 11.1 4.5 2.2 3.3 0.8 0.3 7.2

ANNUAL 11.4 5.9 1.3 3.1 0.6 0.5 9.1
3-5

Table 3.1 Continued

Season Fine 

Mass

Sulfate Nitrate Organics Elemental 

Carbon

Soil Coarse Mass

Mid South

Spring 11.8 6.3 1.2 3.3 0.5 0.6 5.3

Summer 15.3 8.3 0.4 3.9 0.5 2.2 8.9

Autumn 11.0 5.8 0.7 3.6 0.6 0.4 5.6

Winter 10.3 4.5 1.8 3.1 0.6 0.3 4.5

ANNUAL 12.1 6.2 1.0 3.5 0.5 0.9 6.0

Northeast

Spring 5.7 3.0 0.4 1.6 0.3 0.3 4.4

Summer 8.4 4.8 0.3 2.6 0.3 0.3 4.5

Autumn 5.7 3.0 0.4 1.8 0.3 0.2 4.0

Winter 5.6 2.5 0.7 1.9 0.4 0.2 4.0

ANNUAL 6.4 3.4 0.5 2.0 0.3 0.2 4.2

Northern Great Plains

Spring 4.7 1.9 0.6 1.3 0.2 0.7 5.6

Summer 4.5 1.7 0.2 2.0 0.2 0.5 5.6

Autumn 4.2 1.3 0.5 1.7 0.2 0.5 5.5

Winter 4.7 1.7 1.2 1.3 0.2 0.2 3.1

ANNUAL 4.5 1.7 0.6 1.6 0.2 0.5 5.0

Northern Rocky Mountains

Spring 4.7 1.0 0.2 2.6 0.3 0.6 5.0

Summer 5.2 1.0 0.1 3.0 0.3 0.8 8.2

Autumn 7.4 1.0 0.3 4.7 0.6 0.7 6.9

Winter 5.3 1.1 0.6 2.9 0.5 0.3 2.8

ANNUAL 5.7 1.0 0.3 3.3 0.4 0.6 5.8

Pacific Coast

Spring 4.2 1.4 0.7 1.5 0.2 0.3 9.3

Summer 4.2 1.8 0.7 1.4 0.2 0.3 8.9

Autumn 5.4 1.4 0.8 2.5 0.4 0.4 8.2

Winter 4.7 0.8 1.6 1.9 0.3 0.1 6.0

ANNUAL 4.6 1.3 0.9 1.8 0.2 0.3 8.2
3-6

Table 3.1 Continued

Season Fine 

Mass 

Sulfate Nitrate Organics Elemental 

Carbon

Soil Coarse Mass

Sierra-Humboldt

Spring 3.1 0.7 0.3 1.4 0.2 0.7 3.7

Summer 3.8 0.8 0.2 2.1 0.3 0.6 4.1

Autumn 3.3 0.6 0.2 1.8 0.2 0.4 3.0

Winter 1.9 0.3 0.2 1.0 0.3 0.1 1.8

ANNUAL 3.1 0.6 0.2 1.6 0.2 0.5 3.2

Sierra Nevada

Spring 4.6 1.2 0.6 1.9 0.2 0.7 5.3

Summer 6.8 1.6 0.5 3.7 0.4 0.7 6.2

Autumn 4.9 1.0 0.5 2.5 0.3 0.6 5.1

Winter 1.9 0.3 0.2 1.1 0.1 0.1 3.3

ANNUAL 4.5 1.0 0.4 2.3 0.2 0.5 5.0

Sonoran Desert

Spring 4.6 1.5 0.3 1.5 0.2 1.1 6.4

Summer 5.0 1.9 0.2 1.7 0.2 1.0 6.6

Autumn 4.3 1.6 0.2 1.7 0.3 0.5 4.9

Winter 3.1 1.1 0.2 1.3 0.2 0.3 4.0

ANNUAL 4.3 1.5 0.2 1.6 0.2 0.7 5.5

Southern California

Spring 12.5 1.8 6.1 3.2 0.5 0.9 8.9

Summer 12.0 2.4 4.2 4.0 0.6 0.7 11.1

Autumn 7.5 1.3 2.5 2.2 0.4 1.1 11.8

Winter 3.4 0.6 1.3 1.1 0.2 0.2 2.9

ANNUAL 9.0 1.5 3.7 2.6 0.4 0.7 8.4

Washington D.C.

Spring 17.1 8.0 2.6 4.2 1.4 0.9 7.6

Summer 23.0 13.9 1.3 5.1 1.4 1.3 6.9

Autumn 18.6 7.9 2.4 5.4 2.0 0.9 7.4

Winter 18.4 5.9 3.9 5.9 1.9 0.8 7.8

ANNUAL 19.2 9.0 2.5 5.2 1.7 1.0 7.4

West Texas

Spring 5.3 2.1 0.2 1.7 0.2 1.1 7.6

Summer 7.0 2.7 0.3 1.8 0.2 2.0 7.8

Autumn 4.6 2.0 0.2 1.4 0.2 0.7 6.8

Winter 3.8 1.6 0.3 1.3 0.2 0.4 5.4

ANNUAL 5.2 2.1 0.2 1.5 0.2 1.1 6.9
3-7

Table 3.2 Measured fine aerosol mass budgets (in percent) for the 21 regions in the

IMPROVE network.

Season Sulfate Nitrate Organics Elemental 

Carbon

Soil

Alaska

Spring 38.1 3.7 38.0 4.7 15.5

Summer 19.4 1.8 65.8 4.9 8.1

Autumn 26.5 3.1 51.9 7.9 10.7

Winter 34.0 5.6 46.7 7.2 6.5

ANNUAL 28.6 3.3 52.0 5.9 10.2

Appalachian Mountains

Spring 58.4 7.4 25.9 3.8 4.6

Summer 66.8 1.9 22.0 2.3 7.0

Autumn 58.9 5.0 28.5 4.3 3.2

Winter 46.0 12.6 32.6 5.9 2.9

ANNUAL 59.9 5.5 26.0 3.7 4.9

Boundary Waters

Spring 51.8 9.1 28.1 3.6 7.5

Summer 36.2 2.2 53.2 3.8 4.6

Autumn 40.2 11.3 38.4 4.7 5.4

Winter 38.9 26.0 27.1 3.9 4.0

ANNUAL 41.8 11.9 37.0 4.0 5.4

Cascade Mountains

Spring 27.8 5.4 53.1 7.5 6.2

Summer 38.4 5.6 45.6 6.4 4.0

Autumn 24.1 3.6 59.4 9.4 3.6

Winter 15.4 3.8 66.6 10.9 3.3

ANNUAL 28.1 4.7 54.6 8.2 4.4

Central Rocky Mountains

Spring 27.8 7.3 33.4 3.8 27.7

Summer 24.2 4.1 46.3 4.9 20.5

Autumn 27.1 5.4 45.5 5.8 16.2

Winter 27.6 8.2 47.8 6.3 10.0

ANNUAL 26.5 5.9 42.9 5.1 19.6
3-8

Table 3.2 Continued

Season Sulfate Nitrate Organics Elemental 

Carbon

Soil

Colorado Plateau

Spring 30.0 6.2 33.6 4.2 26.0

Summer 34.5 4.8 37.7 4.8 18.1

Autumn 33.1 5.2 43.1 6.0 12.6

Winter 33.1 9.2 42.9 7.2 7.6

ANNUAL 32.6 6.1 39.1 5.4 16.7

Florida

Spring 54.0 5.5 30.1 5.3 5.1

Summer 41.9 3.9 25.0 3.2 25.9

Autumn 51.0 4.9 33.3 6.5 4.4

Winter 43.8 7.3 37.5 8.8 2.8

ANNUAL 47.4 5.3 30.9 5.7 10.7

Great Basin

Spring 22.2 5.6 37.3 4.2 30.7

Summer 19.8 3.7 41.1 3.6 31.9

Autumn 20.9 4.0 48.8 5.9 20.4

Winter 20.9 8.1 53.7 8.1 9.2

ANNUAL 21.1 5.0 44.3 5.1 24.5

Lake Tahoe

Spring 16.3 7.6 46.4 10.1 19.6

Summer 20.6 5.8 48.3 10.2 15.0

Autumn 11.9 6.1 57.3 15.5 9.2

Winter 5.3 6.5 62.6 18.3 7.2

ANNUAL 13.0 6.5 54.3 13.9 12.3

Mid Atlantic

Spring 55.6 11.8 23.7 4.8 4.1

Summer 59.5 5.3 25.0 4.0 6.2

Autumn 50.0 10.0 29.1 6.8 4.0

Winter 40.5 19.8 29.8 7.1 2.9

ANNUAL 51.8 11.3 26.8 5.6 4.4
3-9

Table 3.2 Continued

Season Sulfate Nitrate Organics Elemental 

Carbon

Soil

Mid South

Spring 53.3 9.8 27.6 4.5 4.8

Summer 54.2 2.7 25.2 3.3 14.5

Autumn 52.4 6.1 32.6 5.2 3.7

Winter 43.1 17.9 30.3 5.7 3.0

ANNUAL 51.3 8.4 28.6 4.5 7.2

Northeast

Spring 53.5 7.8 28.7 4.8 5.3

Summer 57.7 3.6 30.8 4.1 3.8

Autumn 52.1 7.4 31.2 6.1 3.2

Winter 45.0 12.4 33.1 6.4 3.0

ANNUAL 52.9 7.2 30.9 5.2 3.8

Northern Great Plains

Spring 41.1 12.9 28.4 3.3 14.3

Summer 37.6 3.4 45.0 3.6 10.4

Autumn 31.8 11.3 39.7 4.3 12.8

Winter 37.5 25.2 27.9 4.0 5.4

ANNUAL 37.2 13.3 35.1 3.8 10.7

Northern Rocky Mountains

Spring 20.9 4.4 55.0 6.7 13.1

Summer 18.7 2.5 58.5 5.8 14.6

Autumn 14.0 4.3 63.7 8.4 9.6

Winter 20.0 11.6 53.9 9.5 5.0

ANNUAL 17.9 5.6 58.4 7.7 10.4

Pacific Coast

Spring 33.2 17.8 36.3 4.5 8.2

Summer 41.5 15.3 33.5 3.6 6.0

Autumn 25.2 15.3 46.3 6.5 6.7

Winter 17.2 33.8 40.3 6.3 2.5

ANNUAL 29.0 20.2 39.6 5.3 5.9
3-10

Table 3.2 Continued

Season Sulfate Nitrate Organics Elemental Soil

Sierra-Humboldt

Spring 20.7 8.0 43.8 5.5 21.9

Summer 19.7 4.1 54.8 6.6 14.8

Autumn 18.4 6.8 54.2 6.9 13.7

Winter 16.5 9.1 53.6 13.1 7.7

ANNUAL 19.1 6.5 51.7 7.4 15.2

Sierra Nevada

Spring 25.3 13.2 41.1 4.3 16.0

Summer 23.6 7.0 54.0 5.8 9.7

Autumn 21.4 10.1 51.4 5.7 11.4

Winter 17.8 10.5 57.8 7.0 7.0

ANNUAL 22.8 9.8 50.3 5.5 11.6

Sonoran Desert

Spring 31.5 6.8 32.5 4.3 24.9

Summer 38.0 4.3 33.7 4.4 19.6

Autumn 37.9 4.4 39.6 6.1 12.0

Winter 34.0 7.8 42.8 7.3 8.2

ANNUAL 35.4 5.7 36.6 5.3 17.1

Southern California

Spring 14.3 49.2 25.3 3.8 7.4

Summer 19.9 35.3 33.5 5.0 6.2

Autumn 17.2 33.5 28.8 5.3 15.3

Winter 16.5 38.9 33.1 6.5 5.0

ANNUAL 17.0 40.8 29.3 4.7 8.2

Washington D.C.

Spring 46.6 15.1 24.9 8.0 5.5

Summer 60.4 5.6 22.1 6.1 5.7

Autumn 42.7 12.8 29.0 10.8 4.8

Winter 32.0 21.5 32.1 10.2 4.3

ANNUAL 46.6 12.9 26.8 8.6 5.1

West Texas

Spring 40.2 4.3 31.5 3.6 20.4

Summer 38.6 4.4 25.6 2.4 29.1

Autumn 44.1 4.2 31.6 3.9 16.2

Winter 42.2 7.1 34.1 5.0 11.5

ANNUAL 40.8 4.8 30.0 3.5 20.9
3-11

Boundary Waters. This region in northern Minnesota is monitored at Boundary Waters Canoe Area in 

the Superior National Forest, which began monitoring in August 1991. Previously, this region was 

represented by two sites, Isle Royale National Park, which was discontinued in July 1991, and

Voyageurs National Park, which has been downgraded to channel-A only.

The average fine and coarse aerosol concentrations were 5.1 and 3.7 ìg/m3, respectively. The 

highest fine and coarse aerosol concentrations occurred during summer, but there was not as strong a 

seasonal variation as in Alaska and the Appalachian Mountains. In this region, sulfate was the largest 

fraction of fine particle mass (41.8%), followed closely by organics (37%), and more distantly by nitrate 

(11.9%), soil (5.4%), and light-absorbing carbon (4%). 

Cascade Mountains. This region in the states of Washington and Oregon has two monitoring sites 

out of the four reported here. Mount Rainier National Park, initiated in March 1988, is southeast of 

Seattle, and the Columbia River Gorge on the Hood River National Forest, east of Portland, began 

monitoring in June 1993. The other two sites, Three Sisters Wilderness Area on the Willamette 

National Forest and Snoqualamie Pass on the Snoqualamie National Forest, were implemented in July 

1993 but were not fully operational until September 1994. 

Here the average fine and coarse aerosol concentrations are 5.2 and 3.5 ìg/m3, respectively. 

Fine and coarse aerosol concentrations reach their maxima in summer and minima in winter. Sulfate and 

nitrate concentrations have strong seasonal variations, with maxima for sulfate in summer and nitrate in 

winter. This seasonal variation could be, in part, the result of seasonal variations in mixing and in 

photochemistry. In this region, organics are the single most significant contributor (54.6%) to fine 

particle mass. Sulfate (28.1%) is about half the contribution of organics. Nitrate contributes 4.7%, 

followed by light-absorbing carbon (8.2%) then soil (5.4%).

Central Rocky Mountains. The measurements in this region were made at five locations in the 

mountainous Class I areas of Colorado and Wyoming, including the Bridger and Weminuche 

Wilderness Areas, Rocky Mountain and Yellowstone National Parks, and Great Sand Dunes National 

Monument. Fine and coarse aerosol concentrations in this region averaged 3.1 and 4.4 ìg/m3 over 

the three-year period. Like many of the other regions, concentrations, especially of sulfate, organics, 

light-absorbing carbon, and coarse aerosol, were highest in summer and lowest in winter. The largest 

contributor to fine particle mass in this region was organics (42.9%), followed by sulfate (26.5%), soil 

(19.6%), nitrate (5.9%), and light-absorbing carbon (5.1%). 

Colorado Plateau. This region in the Four Corners' states of the Southwest is the most intensively 

monitored in the IMPROVE network. There are six sites, most of them within the so-called Golden 

Circle of National Parks: Bandelier, Bryce Canyon, Canyonlands, Grand Canyon, Mesa Verde, and 

Petrified Forest National Parks. A seventh site, Arches National Park, was discontinued in May 1992. 

 This region is of particular concern to the Grand Canyon Visibility Transport Commission as required 

by Congress in the 1990 amendments to the Clean Air Act [Stensvaag, 1991].

In this region, fine and coarse aerosol concentrations averaged 3.3 and 4.1 ìg/m3, respectively. 

Fine and coarse aerosol concentrations here were greatest in summer and minimum in winter. 

Concentrations of sulfate and organics were also greatest in summer and smallest in winter. However, 

nitrate and light-absorbing carbon were both largest in winter. Here organics (39.1%) and sulfate 

(32.6%), contribute the most followed by soil (16.7%), nitrate (6.1%), and light-absorbing carbon 
3-12

(5.4%).

Florida. Previously, this region had its monitoring site at Everglades National Park, which has now 

been downgraded to a channel-A only site. This region is now represented by two sites at

Chassahowitzka Wildlife Refuge on the Gulf Coast north of Tampa, and Okefenokee Wilderness Area 

on the Georgia-Florida border. Monitoring at these two sites began in April 1993 and September 

1991, respectively. Only Chassahowitzka is reported here for the three-year averages. 

The fine and coarse aerosol concentrations averaged 10.1 and 7.4 ìg/m3, their concentrations 

were highest in summer. Fine and coarse aerosol concentrations were smallest in winter. Sulfate was 

found to be the largest contributor to fine particle mass (47.4%), followed by organics (30.9%), soil 

(10.7%), light-absorbing carbon (5.7%), and nitrate (5.3%).

Great Basin. The Great Basin of Nevada has two sets of measurements at Jarbidge Wilderness 

Area in northeastern Nevada and Great Basin National Park, which began monitoring in March 1988 

and May 1992, respectively. Here the fine and coarse aerosol concentrations averaged 3.1 and 4.9

ìg/m3

. The fine mass concentration was the lowest of any of the regions in the lower 48 states. 

Perhaps this is due to the fact that this site is relatively remote from high emission density areas and is 

generally well ventilated. Both fine and coarse aerosol concentrations, as well as all of the fine aerosol 

components, except nitrate and light-absorbing carbon, experienced largest concentrations in the

summer and lowest concentrations in the winter. The largest single contributors to fine particle mass at 

this region were organics (44.3%) and soil (24.6%). Sulfate was a smaller contributor (21.1%), 

followed by light-absorbing carbon (5.1%) and nitrate (5%). 

Lake Tahoe. Two sites are monitored for this region: one site is in Bliss State Park in southern 

California and a bit east of the lake and began sampling in March 1989. The other is close to the urban 

area of Lake Tahoe and sampling started in November 1990. Fine and coarse aerosol concentrations 

averaged 6.4 and 6.6 ìg/m3, respectively; there is a modest seasonality with highest concentrations 

occurring in the winter, and the least for fine aerosols in the summer and for coarse aerosol in the 

autumn. Sulfate, nitrate, organics, and light-absorbing carbon have strong seasonal trends with sulfate 

concentrations being more than twice as high in the summer than in the winter; however, nitrates, 

organics, and light-absorbing carbon have winter maxima at least twice their summer concentrations. 

The largest contributor to fine aerosol is organics (54.3%), followed by light-absorbing carbon (13.9%), 

sulfate (13%), soil (12.3%), and nitrate (6.5%).

Mid Atlantic. This new region is represented by the Edmond D. Forsyth Wildlife Refuge west of 

Atlantic City, New Jersey and began monitoring in September 1991. Fine and coarse aerosol

concentrations averaged 11.4 and 9.1 ìg/m3, respectively. A moderate seasonality is evident with the 

highest fine and coarse aerosol concentrations occurring in the summer, and the least in the spring and 

winter, respectively. Sulfate, organics, and fine soil are the fine aerosol constituents that follow the 

seasonal trend for fine aerosol mass. Nitrate peaks in the winter at three times its summer

concentration, and light-absorbing carbon peaks in the winter as well but only shows a small seasonality. 

 Sulfate comprises the bulk of the fine aerosol mass (51.8%) followed by organics (26.8%), nitrate 

(11.3%), light-absorbing carbon (5.8%), and soil (4.4%).

Mid South. Three sites are monitored for this new region: Upper Buffalo Wilderness Area in north 

central Arkansas initiated in December 1991, Mammoth Cave National Park in Kentucky initiated in 

September 1991, and Sipsey Wilderness Area in northern Alabama initiated in March 1992. The 
3-13

average concentration of fine and coarse aerosol was 12.1 and 6.0 ìg/m3, respectively. Outside of 

Washington D.C., which is an urban site, this region has the highest average concentration of fine 

aerosol. A modest seasonality is evident for fine and coarse aerosols with the minima occurring in the 

winter and the maxima the summer. All fine aerosol constituents except nitrate and light-absorbing 

carbon follow the seasonality of fine aerosol. Nitrate has its maximum concentrations in the winter, 

while light-absorbing carbon is fairly constant between seasons. Sulfate (51.3%) composes the bulk of 

fine aerosol followed by organics (28.6%), nitrate (8.4%), soil (7.2%), and light-absorbing carbon 

(4.5%).

Northeast. The northeastern United States is represented by measurements at two sites: Acadia 

National Park on the coast of Maine, which began monitoring in March 1988, and Lye Brook 

Wilderness Area in southern Vermont, which began in September 1991. Here fine and coarse aerosol 

concentrations averaged 6.4 and 4.2 ìg/m3. Although fine and coarse aerosol concentrations were 

both largest in summer, there was not a strong seasonal variation. Sulfate, organics, and soil

concentrations were also largest in summer. Nitrate concentrations reached their maximum in winter. 

The contributors to fine particle mass included sulfate (52.9%), organics (30.9%), nitrate (7.2%), lightabsorbing carbon (5.2%), and soil (3.8%). 

Northern Great Plains. Only one set of measurements was made in this region, at Badlands 

National Monument in South Dakota. Here fine and coarse aerosol concentrations averaged 4.5 and 

5.0 ìg/m3, respectively. The maximum concentrations for fine mass occurred in the winter and spring 

and was least in the autumn. The maximum for coarse mass occurred in the spring and summer and was 

least during the winter. Sulfate (37.2%) and organics (35.1%) each contributed to fine mass about 

equally, followed by nitrate (13.3%), soil (10.7%), and light-absorbing carbon (3.8%).

Northern Rocky Mountains. This region has measurements made at Glacier National Park in 

Montana, close to the Canada border. Fine aerosol and coarse aerosol concentrations averaged 5.5

ìg/m3

 each. There were no strong seasonal variations except for nitrate, which showed a strong winter 

peak, and coarse mass, which peaked in the winter. Organics are by far the largest contributor to fine 

particle mass (58.4%) followed by sulfate (17.9%), soil (10.4%), light-absorbing carbon (7.7%), and 

nitrate (5.6%).

Pacific Coast. This region includes three Class I areas along and near the coast of northern 

California: Pinnacles National Monument, Point Reyes National Seashore, and Redwoods National 

Park. In this region, the fine and coarse aerosol concentrations over the three-year period averaged 4.6 

and 8.2 ìg/m3. There was no strong seasonal variation in concentration, except for sulfate that had 

maxima and minima in summer and winter, and nitrate that showed the opposite trend, with maxima and 

minima in winter and summer, respectively. One would expect sulfate to reach its maximum

concentration in summer because of photochemistry. Nitrate would be expected to reach its peak 

during the colder months of winter because of the extreme thermal volatility of ammonium nitrate. 

Organics in this region are the largest single component of fine aerosol (39.6%), followed by sulfate 

(29%), nitrate (20.2%), soil (5.9%), and light-absorbing carbon (5.3%).

Sierra-Humboldt. The region further north in the Sierra Nevada and Humboldt Mountain Ranges 

was measured with sites at Crater Lake National Park in Oregon and Lassen Volcanoes National Park 

in northern California. This region is relatively remote from high emission density areas. Its fine and 

coarse aerosol concentrations were relatively low, at 3.1 and 3.2 ìg/m3, respectively. Summer 

concentrations were generally about twice those during the winter. Organics contributed most of the 
3-14

fine particle mass (51.7%), followed by sulfate (19.1%), soil, (15.2%), light-absorbing carbon (7.4%), 

and nitrate (6.5%).

Sierra Nevada. The Sierra Nevada Mountains in California was monitored at two sites: Yosemite 

and Sequoia National Parks. Yosemite National Park has been monitored since March 1988. Sequoia 

National Park had channel A and D since March 1992 but was not fully instrumented until July 1993. 

Average fine and coarse aerosol concentrations were 4.5 and 5.0 ìg/m3. There was a strong 

moderate variation, with maximum concentrations in summer and minimum concentrations in winter. 

The only exception was nitrate, which was relatively constant throughout the year. Organics contributed 

more than twice what sulfate contributed (50.3% and 22.8%, respectively). Soil was the next largest 

contributor (11.6%), followed by nitrate (9.8%), and light-absorbing carbon (5.5%). 

Sonoran Desert. This region in southeastern Arizona was monitored at two sites: Chiracahua and 

Tonto National Monuments and were initiated in March 1988. The three-year average of fine and 

coarse mass concentrations in this region were 4.3 and 5.5 ìg/m3, respectively. These concentrations 

were highest in summer and lowest in winter. The sulfate, organics, and soil components of fine particle 

mass also had maxima and minima in these seasons. The contributions to fine particle mass were 

distributed nearly equally between sulfate (35.4%) and organics (36.6%), followed by soil (17.1%), 

nitrate (5.7%), and light-absorbing carbon (5.3%). 

Southern California. Measurements in this region were made in San Gorgonio National

Monument, east of the Los Angeles metropolitan area. Fine and coarse aerosol concentrations were 

highest of any western United States site (9.0 and 8.4 ìg/m3); concentrations were only higher in the 

eastern United States. Like many sites in the IMPROVE network, concentrations were highest in 

summer and lowest in winter. This trend was also observed for nitrate: actually nitrate was highest in 

spring and lowest in winter, but concentrations in summer were twice those in winter. This site was the 

only site in the IMPROVE network in which nitrate was a larger contributor to fine particle mass than 

either sulfate or organic carbon. The contributions were nitrate (40.8%), organics (29.3%), sulfate 

(17%), soil (8.2%), and light-absorbing carbon (4.7%). 

Washington, D.C. This is a single monitoring site in the nation's capital. Fine and coarse aerosol 

concentrations were higher here than anywhere in the IMPROVE network. They averaged 19.2 and 

7.4 ìg/m3 over the three-year period. There was a moderate seasonal variation in fine aerosol

concentrations; in spring they ranged from 17.1 to 23 ìg/m3 in summer. However, the sulfate and 

nitrate components varied significantly by season: sulfate concentrations were largest in summer and 

smallest in winter, while nitrate concentrations were largest in winter and smallest in summer. The sulfate 

behavior could be caused by the seasonal variation in photochemistry. The nitrate behavior may be due 

to the extreme volatility of nitrate in warm weather. Over the entire three-year period, fine particle mass 

was constituted of sulfate (46.6%), organics (26.8%), nitrate (12.9%), light-absorbing carbon (8.6%), 

and soil (45.1%).

West Texas. Two measurement sites in west Texas were included: Big Bend and Guadalupe 

Mountains National Parks. Both sites are near the Mexico border in southwestern Texas and have 

operated since March 1988. The fine and coarse aerosol concentrations averaged 5.2 and 6.9 ìg/m3

over the last three years. Minimum concentrations generally occurred during winter, while maximum 

concentrations occurred in summer. The only exception was light-absorbing carbon, which remained 

constant. The contributions to fine particle mass averaged 40.8% for sulfate, 30% for organics, 20.9% 

for soil, 4.8% for nitrate, and 3.5% for light-absorbing carbon.
3-15

In general, the following observations can be made. With few exceptions, aerosol concentrations 

are highest in summer and lowest in winter. This is consistent with the fact that sulfate formation rates, 

natural organic carbon emissions, and mixing into mountainous regions are all maximum in summer and 

minimum in winter. With the notable exception of southern California where nitrate is dominant, sulfate 

and organics are the two principal components of the fine particle mass throughout the United States. 

Sulfate's contribution is much higher in the eastern United States than in the western United States and in 

Alaska. 

3.2 Spatial Trends in Aerosol Concentrations in the United States

Because of the relatively large number of IMPROVE aerosol monitoring sites in the western United 

States, isopleth maps of the average aerosol concentrations measured over the three-year period from 

March 1992 through February 1995 could be drawn. Figures 3.1 through 3.8 show isopleth maps of 

the three-year average aerosol concentrations (PM10, fine mass, coarse mass, sulfate, nitrate, organics, 

light-absorbing carbon, and soil). These figures provide us with information on how aerosol

concentrations and mass budgets vary over the United States.

3.2.1 PM10 Aerosol

Figure 3.1 shows isopleths of the PM10 aerosol mass concentration measured during this three-year 

period. The highest concentrations occur in the eastern United States. With the exceptions of the 

Northern Great Plains states, almost all the area east of Colorado and New Mexico has concentrations 

in excess of 10 ìg/m3. The highest concentrations are in Washington D.C. at 22 ìg/m3, followed by 

Florida and the Mid South, which experienced concentrations in excess of 18 ìg/m3. Outside of 

California and the Northern Rockies the least amount of PM10 concentrations occur in the western 

United States, where there is a large swath extending from Oregon, northern California, Nevada, Utah, 

Wyoming, into northern Arizona and northern New Mexico and western Colorado, where the

concentration of PM10 is less than 8.5 ìg/m3. The lowest concentration in the lower 48 states occurs 

at Bridger Wilderness Area in Wyoming with only 5.7 ìg/m3 on average, the least was recorded at 

Denali National Park in Alaska at 4.2 ìg/m3. The strongest gradient is between northern California 

and Utah and the coastal regions of California, where concentrations vary form 6.4 ìg/m3 to an excess 

of 15 ìg/m3.
3-16

Figure 3.1 Average PM10 mass concentration (in micro-g/m3) for each site in the IMPROVE

network.

3.2.2 Fine Aerosol

Figure 3.2 shows isopleths of the average fine aerosol concentrations measured during the threeyear period. Note the strong gradient in fine particle concentrations from southern California, a local 

maximum of 9 ìg/m3 to minima of 2.7 to 3.1 ìg/m3 observed in southern Oregon, Nevada, southern 

Utah, western Colorado, and Wyoming. This is a factor of three variations in average fine aerosol 

concentration. Also, note that fine aerosol concentrations increase again as one moves to the eastern 

United States with maxima of about 13.5 ìg/m3 in Shenandoah and Great Smoky Mountains National 

Parks and over 19 ìg/m3 in Washington D.C. Thus, from the minima in the western United States to 

the maxima in the East, there is a factor of six variations in average concentration. Average fine aerosol 

concentrations in Denali National Park of 1.8 ìg/m3 are lower than any measured in the lower 48 

states. There is a factor of 10 variations between the average measured in Alaska and that measured in 

Washington D.C.

The lower map in Figure 3.2 shows isopleths that depict the fraction of PM10 that is fine aerosol 

(PM2.5). Almost all of the country outside of the intermountain west has a fine mass fraction of PM10

that exceeds 50%. East of the Mississippi and south of the Great Lakes there is a broad region that 

exceeds 65%. The highest values encompass a region that covers the Ohio Valley, parts of the Mid 

South, West Virginia, Shenandoah, and Washington D.C., where fine mass fraction is greater than 70%. 

The smallest fine mass fraction occurs in the Great Basin Region, central Utah, and portions of

Colorado, where less than 40% of PM10 is fine mass. 
3-17

Figure 3.2 Average fine mass aerosol concentrations (in micro-g/m3) (top) and fine mass fraction of 

PM10 (bottom) for each site in the IMPROVE network. 
3-18

3.2.3 Coarse Aerosol

Figure 3.3 shows isopleths of the three-year average coarse aerosol concentrations. There are a 

few local maxima from 7.4 to 10.3 ìg/m3 that are noticeable near Los Angeles, San Francisco, and 

Washington D.C. The lowest coarse aerosol concentrations occur in the swath from the Pacific 

Northwest through Nevada to southern Utah. Concentrations in this region average around 4 ìg/m3. 

Throughout the United States coarse aerosol concentrations are generally in the factor-of-two range 

from 4 to 8 ìg/m3. The patterns in the eastern United States, with the exception of Washington D.C., 

shows a steady north-south trend of increasing coarse aerosol concentrations. Coarse aerosol

concentrations in Alaska are not significantly lower than in the lower 48 states. There is approximately a 

factor-of-three range from the lowest average concentrations measured in Oregon and Utah and the 

highest measured in Washington D.C.

Figure 3.3 Average coarse particle mass concentration (in micro-g/m3) for each site in the

IMPROVE network.

3.2.4 Fine Sulfate Aerosol

The average sulfate component of the fine aerosol measured over the three-year period is shown in 

Figure 3.4. Since sulfate is one of the two major components of fine particle mass, it is not surprising to 

observe similar gradients across the United States to what was observed for total fine particle mass. 

There is a strong gradient from high concentrations in California urban areas to low concentrations in 

southern Oregon and Nevada. There is also a strong gradient from the relatively low concentrations in 

the West to those in the East. There is a factor of 15 variations from the lowest concentration measured 

in Nevada to the highest concentration measured in Washington, D.C. This gradient is most likely 

indicative of the strong regional gradient in SO2 emission density. The eastern United States has a 

concentration of power plants that burn high sulfur coal, while the western United States has relatively 
3-19

low SO2 emission densities. A relative maximum in sulfate concentration is observed in southern

Arizona, which is near copper smelters that emit large quantities of SO2. The lower map in Figure 3.4 

shows that sulfate constitutes as little as 17% of fine particle mass in southern California to as much as 

61% of total fine mass in Shenandoah National Park. In the Golden Circle of parks in the Four 

Corners' states, sulfate is 31 to 35% of the fine particle mass.

In the eastern United States sulfate is the largest single component of fine particle mass. In the 

Boundary Waters, Sonoran Desert, and West Texas regions, sulfate is tied with organic carbon as the 

largest component of fine particle mass. Sulfate is the second largest component of fine mass in all other 

regions studied except southern California and the Great Basin (where sulfate is the third largest 

component).

3.2.5 Fine Nitrate Aerosol

Figure 3.5 shows isopleth maps of the nitrate concentration and nitrate mass fraction of fine 

aerosol, averaged over the three-year period. Note that the highest concentration of 3.7 ìg/m3 was 

measured in San Gorgonio Wilderness, just east of the Los Angeles metropolitan area. Other high 

concentrations occur in Washington, D.C. (2.5 ìg/m3), and near the San Francisco area (1.3 ìg/m3). 

 There is a strong gradient from the high concentrations in the California urban areas to the minima of 

0.1 ìg/m3 measured in Oregon, Nevada, Wyoming, and Colorado. There is a long swath of low 

nitrate concentrations extending from Oregon, Nevada, and Idaho into Utah, Wyoming, Colorado and 

into southern Arizona and southern New Mexico (<0.2 ìg/m3). Nitrate mass fractions are typically 4 

to 12% except in California where they are 30% and higher. In the north central part of the United 

States and the mid-Atlantic region nitrates constitute over 12% of the fine aerosol mass. Nitrates 

generally reach their maxima in the winter when colder temperatures favor the formation of ammonium 

nitrate aerosol from nitric acid vapor. Nitrate is the largest single component of fine aerosol mass in 

southern California at San Gorgonio Wilderness Area.

3.2.6 Fine Organic Aerosol

Figure 3.6 shows isopleth maps of the organic carbon mass fraction of the fine aerosol

concentration, averaged over the three-year period. There is a significant spatial gradient from the 

Pacific Northwest, with average concentrations of 2.0 to 3.0 ìg/m3 to the intermountain region of 

Wyoming, Colorado, Utah, and Arizona of 1.2 ìg/m3 or less. In the eastern United States, 

organics range generally from 2.0 to 4 ìg/m3. In Alaska, organic aerosol concentrations are the lowest 

at 1 ìg/m3. 

Except in the northwestern United States, where organics are over half of the fine particle mass, 

organics generally constitute between 25 to 40% of the fine particle mass. Moreover, organics are the 

largest single component of fine particle mass in most of the regions in the United States. Exceptions 

include the Mid South and eastern United States where sulfate is the dominant component and southern 

California, where nitrate is the dominant component.
3-20

Figure 3.4 Average fine sulfate aerosol concentrations (in micro-g/m3) (top) and sulfate fine mass 

fractions (bottom) for each site in the IMPROVE network.
3-21

Figure 3.5 Average fine nitrate aerosol concentrations (in micro-g/m3) (top) and nitrate fine mass 

fractions (bottom) for each site in the IMPROVE network.
3-22

Figure 3.6 Average fine organic aerosol concentrations (in micro-g/m3) (top) and organic fine mass 

fractions (bottom) for each site in the IMPROVE network.
3-23

3.2.7 Fine Light-Absorbing Carbon Aerosol

Figure 3.7 shows isopleth maps of the light-absorbing carbon concentration and mass fraction of 

the fine aerosol, averaged over the three-year period. Note that light-absorbing carbon concentrations 

are highest in the Pacific Northwest, the area east of the Mississippi and south of the Great Lakes, and 

southern California, while concentrations are much lower in much of the West (Wyoming, Utah, and 

Nevada). Light-absorbing carbon is the smallest contributor to fine particle mass, constituting generally 

3 to 5% of the fine particle mass. Exceptions to this are the Pacific Northwest and Washington, D.C. 

areas where light-absorbing carbon contributes as much as 8% of the fine particle mass.

3.2.8 Fine Soil Aerosol

Figure 3.8 shows isopleth maps for fine soil. The contribution of soil to the fine aerosol in the 

United States is generally small, except for the elevated concentrations (<1 ìg/m3) in the southern tier 

of the United States. There is a quite noticeable north-south trend of increasing soil concentrations with 

the Northeast being the lowest. Soil contributes approximately 5 to 10% of the fine aerosol mass in the 

East. Except for Florida, all of the area east of the Mississippi, the Pacific Northwest, and parts of 

California, soil contributes less than 10% to fine aerosol mass with much of the intermountain west in 

excess of 20%.

3.3 Summary

The following are the major patterns observed in the three-year period of IMPROVE from March 

1992 through February 1995:

1. Spatial Patterns. Concentrations of fine particles (those most important in determining visibility) are 

highest in the eastern United States and in southern California and lowest in the relatively

unpopulated areas of the West.

2. Major Contributions to Fine Aerosol. The largest single component of the fine aerosol in the East 

is sulfate, while in the Pacific Northwest it is organics, and in southern California it is nitrate. In 

general, the largest mass fractions of the fine aerosol are sulfates and organics. Of the 21 regions in 

the IMPROVE network, organic carbon is the largest single component in ten regions (Alaska, 

Cascades, Colorado Plateau, Central Rockies, Pacific Coastal Mountains, Great Basin, Northern 

Rockies, Sierra Nevada, Sierra-Humboldt, and Lake Tahoe). Sulfate is the largest single

component of fine aerosol in seven regions, primarily in the East (Appalachian Mountains, Florida, 

Northeast, Mid South, Mid Atlantic, Washington D.C., and West Texas). The contributions of 

organic carbon and sulfate are approximately equal in three regions (Boundary Waters, Sonoran 

Desert, and Northern Great Plains). Soil is the next largest contributor, followed by nitrate and 

light-absorbing carbon. Nitrate is the largest component of fine aerosol in southern California only. 

3. Smaller Contributors. After the contributions of organics and sulfate, soil is the next largest, 

followed by nitrate and light-absorbing carbon.
3-24

Figure 3.7 Average fine elemental carbon aerosol concentrations (in micro-g/m3) (top) and

elemental carbon fine mass fractions (bottom) for each site in the IMPROVE network.
3-25

Figure 3.8 Average fine soil aerosol concentrations (in micro-g/m3) (top) and soil fine mass

fractions (bottom) for each site in the IMPROVE network.
3-26

4. Seasonality. With a few exceptions, average fine mass concentrations, organics and sulfate

components of fine mass are highest in summer. Soil concentrations are highest in spring or 

summer. On the other hand, nitrate concentrations are generally highest in winter or spring. Lightabsorbing carbon exhibits relatively little seasonal variation.

5. PM10. The highest concentrations of PM10 occur in a region east of the Mississippi and south of 

the Great Lakes, followed by coastal and southern California. In the East, the high concentrations 

are driven by high fine mass, which contributes as much as 70% of PM10.

3.4 References

Sisler, J.F., Huffman, D., Latimer, D.A., Malm, W.C., Pitchford, M.L. "Spatial and temporal patterns 

and the chemical composition of the haze in the United States: an analysis of data from the IMPROVE 

network, 1988-1991," Report by Cooperative Institute for Research in the Atmosphere (CIRA), 

Colorado State University, Ft. Collins, Co 80523, ISSN: 0737-5352-26, 1993.

Stensvaag, J-M., Clean Air Act 1990 Amendments: Law and Practice, John Wiley & Sons, New 

York, NY., 1991.
4-1

CHAPTER 4 

LIGHT EXTINCTION AND ITS RELATIONSHIP TO 

AEROSOLS 

 In this chapter the relationship between aerosol concentration and measured extinction will be 

explored. Transmissometers are operated at a number of sites, in part, as a quality assurance check 

on apportionment of extinction to aerosol species. It is anticipated that the estimated scattering and 

absorption associated with the various aerosol species should sum to equal the measured extinction. 

 However, White [1990] and Trijonis [1990] have shown that under some conditions this 

assumption may not be true. One difficulty in reconstructing extinction is the accurate estimation 

of absorption. The IMPROVE data set, along with the Measurements of Haze and Visual Effects 

(MOHAVE) special study data set, allow for a unique opportunity to explore the interrelationships 

between aerosol mass and absorption. From these intercomparisons a "best estimate" of scattering 

and absorption efficiencies will be developed for purposes of calculating the contribution of each 

aerosol species to extinction and therefore visibility impairment. 

 In 1991, Congress mandated a regional haze study whose goal was to assess the contribution of 

the Mohave Power Project (MPP), other nearby point sources, and regional emissions to visibility 

impairment in Grand Canyon National Park. The location of monitoring sites, selected national 

parks and wilderness areas, and major urban areas, are shown in Figure 4.1. The MOHAVE study 

was carried out over a period of one year (1992) with two major field intensives during the summer 

and winter months. An objective of MOHAVE was to apportion (or attribute) the haze observed in 

the Grand Canyon region to the various measured aerosol species. One set of measurements made 

during the summer intensive at Meadview, Lake Mead National Recreation Area, employed 

independent measurements of bext, bscat, and babs using optical techniques as well as a full suite of 

aerosol mass concentrations including carbonaceous material. Independent measurements of these 

three variables allow for internal consistency checks on the optical measurements in that absorption 

and scattering should sum to extinction. Furthermore, the sum of aerosol scattering should equal 

measured scattering, and absorption estimated from measured aerosol species should equal 

measured absorption. 

 A second data set consists of measured extinction using transmissometers and aerosol mass 

concentration measurements, including babs, by optical techniques in 18 monitoring sites in western 

national parks. 
4-2

Figure 4.1 Map showing the location of monitoring sites and some of the larger national park units. 

 In many early visibility studies, comparisons were made between bscat, as measured by 

nephelometry, and the various aerosol species to derive or validate scattering budgets, while 

extinction was estimated by summing absorption and scattering [Appel et al., 1985; Ouimette et al.,

1981; Groblicki et al., 1981; Macias et al., 1981]. In many of these early studies the nephelometer 

sampling chamber was warmer than ambient temperatures and therefore underestimated scattering 

due to absorbed water at higher relative humidities. Furthermore, the absorption coefficient was 

not directly validated by independent methods. 

 More recent studies carried out in urban environments included estimates of extinction from 

teleradiometer techniques but utilized nephelometers, which heated the aerosol by about 4o

C 

[Dzubay and Clubb, 1981; Dzubay et al., 1982; Lewis and Dzubay, 1986]. At 90% relative 

humidity (RH) a 1o

C difference between ambient and sampling chamber temperature will cause the 

sampling chamber relative humidity to reduce to about 84% RH. A 4o

C temperature difference 

translates into a chamber RH of 70%, which in turn results in a substantial underestimation of 

scattering from hygroscopic particles. 

 The most recent urban studies at Denver, Phoenix/Tucson, and Tucson [Watson et al., 1988, 

1989; Heisler et al., 1980a,b; Watson et al., 1990a,b; Heisler et al., 1994] employed 

transmissometers to measure extinction [Dietrich et al., 1989] ambient nephelometers to measure 
4-3

scattering [Malm et al., 1994] and integrating plate transmission measurements for absorption 

[Watson et al., 1988, 1989]. These studies also included a full suite of aerosol measurements. For 

the most part, the sum of absorption and scattering equaled measured extinction within 

measurement uncertainty and measured absorption, and scattering could be predicted from aerosol 

measurements. 

 Only one study has explored the relationship between ambient measurements of bext, bscat, and 

babs in nonurban settings [White et al., 1994]. They were able to show that the scattering and 

absorption as measured by optical techniques and the fraction of coarse mass scattering not 

captured by the nephelometer summed to extinction and were consistent with measurements of fine 

and coarse mass. They did not explore the relationship between measured absorption and estimates 

of absorption from aerosol concentrations. 

 

4.1 Comparison of Reconstructed to Measured Fine Mass 

 Table 4.1 contains statistical summaries of the aerosol mass concentrations for the Meadview, 

AZ data set, along with the fraction that each aerosol species contributes to reconstructed fine mass, 

while Figure 4.2 is a scatter plot of reconstructed and measured fine mass. The error bars are 

calculated from reported measurement uncertainties. O1, O2, O3, O4, and OP have been multiplied 

by 1.4 to account for the assumed mass of oxygen and other elements in organic carbon. 

 

 Although water associated with hygroscopic aerosols was not explicitly measured, it is expected 

that a significant amount of water was retained on the filter when the filters were weighed. The 

filters were equilibrated in the laboratory at approximately 50% relative humidity, a value which is 

well above the relative humidity at which ammonium sulfate or other hygroscopic particles dry out 

[Tang et al., 1981]. Therefore, retained water will cause scatter in the data points below, but not 

above, the 1:1 line because measured gravimetric mass includes some water, while reconstructed 

mass does not. Figure 4.2 clearly shows this trend. 

 Measured and reconstructed fine mass accounts for 33% and 31% of measured PM10 mass. 

Sulfates are the largest fraction of reconstructed fine mass at 56%. Soil and organic carbon are 

virtually tied for second at 19% and 16%, respectively, while light-absorbing carbon (LAC) is 3% 

and nitrates are 6%. It is worth noting that the sulfate mass fraction of fine mass is somewhat 

greater than reported by others for studies carried out in the same region: 42% at Zilnez Mesa by 

Macias et al. [1981], 40% at Glen Canyon by Sutherland and Bhardwaja [1990], and 40% at 

Meadview by Vasconcelos et al. [1994]. 

 Table 4.2 is a similar summary of aerosol mass species concentrations for 14 western 

IMPROVE sites, while Figure 4.3 shows a scatter plot of reconstructed and measured fine mass. 

As with the Meadview data set there is more scatter below the 1:1 line suggesting that hygroscopic 

aerosols may have retained water during the weighing procedures. In the case of 

 
4-4

Table 4.1 Summary statistics for aerosol mass concentrations for the summer Meadview data set. 

The number of valid data points is 97. 

Variable Mean 

(ìg/m3) 

Std Dev Minimum

(ìg/m3) 

Maximum

(ìg/m3) 

Fraction of 

reconstructed fine 

mass 

CM 9.60 3.84 3.29 18.14 -- 

FM 4.80 1.66 1.72 10.40 -- 

FMrecon 4.14 1.50 1.26 9.98 -- 

(NH4)2SO4 2.31 0.95 0.93 6.68 0.56 

NH4NO3 0.24 0.19 0.04 0.95 0.06 

O1 0.02 0.11 -0.12 0.51 _0.01 

O2 0.14 0.23 -0.26 1.27 0.03 

O3 0.13 0.17 -0.19 0.59 0.03 

O4 0.16 0.10 -0.03 0.62 0.04 

OP 0.23 0.12 0.00 0.60 0.06 

E1 0.04 0.05 0.00 0.26 0.01 

E2 0.08 0.04 0.00 0.22 0.02 

E3 0.01 0.02 -0.02 0.14 _0.01 

SOIL 0.78 0.33 0.35 2.20 0.19 

OMC 0.67 0.49 -0.25 2.59 0.16 

LAC 0.13 0.08 0.00 0.42 0.03 

Figure 4.2 Scatter plot of measured and reconstructed fine mass for the summer Meadview data 

 set. The error bars show the measurement uncertainty. 
4-5

the IMPROVE sites, organic carbon is the largest fraction of fine mass at 41% with sulfate being 

second at 33%. Nitrates and LAC each contribute about 7% of the fine mass. 

Table 4.2. Summary statistics for aerosol mass concentrations for the IMPROVE data set. The 

number of valid data points is 5108. 

Variable Mean 

(ìg/m3) 

Std Dev Minimum 

(ìg/m3) 

Maximum 

(ìg/m3) 

Fraction of 

reconstructed fine 

mass 

CM 4.81 4.48 0.00 72.43 -- 

FM 4.06 2.36 0.00 23.05 -- 

FMrecon 3.86 2.00 0.00 26.14 -- 

(NH4)2SO4 1.26 0.87 0.00 9.45 0.33 

NH4NO3 0.26 0.43 -0.06 10.15 0.07 

O1 0.22 0.22 0.00 5.12 0.06 

O2 0.32 0.22 0.00 4.11 0.08 

O3 0.45 0.40 0.00 4.64 0.12 

O4 0.28 0.23 0.00 3.06 0.07 

OP 0.30 0.24 0.00 5.09 0.08 

E1 0.11 0.15 0.00 2.42 0.03 

E2 0.14 0.07 0.00 0.56 0.04 

E3 0.03 0.03 0.00 1.37 0.01 

SOIL 0.49 0.44 0.00 7.03 0.13 

OMC 1.57 1.11 0.00 19.95 0.41 

LAC 0.28 0.19 0.00 3.12 0.07 

4.2 Extinction Components

 The total extinction coefficient, bext,t, is the sum: 

, , , .

, ,

,

,

ext g scat g abs g

ext scat abs

ext t ext ext g

b b b

b b b and

b b b where

= +

= +

= +

 (4.1)

bext and bext,g are the extinctions due to particles and gases, respectively. bext is the sum of 

scattering, bscat, and absorption, babs, by particles, while bext,g is the sum of scattering, bscat,g, and 

absorption, babs,g, by gases. All terms are wavelength dependent. Light scattering by gases in the 

atmosphere is described by the Rayleigh scattering theory [vandeHulst, 1981] and will be referred 

to as Rayleigh scattering. The only gas that is normally found in the atmosphere and absorbs light 

is nitrogen dioxide. In most instances, particle scattering and absorption are primarily responsible 

for visibility reduction [Trijonis and Pitchford, 1987]. 
4-6

Figure 4.3 Scatter plot of measured and reconstructed fine mass for the IMPROVE data set. 

 Any particle in the atmosphere, whether it is externally or internally mixed, scatters and/or 

absorbs a specific amount of radiant energy and as such has a quantifiable mass extinction 

efficiency. White [1986] refers to this quantity as the specific extinction efficiency. Summing the 

extinction associated with each particle along some path must equal the total atmospheric extinction 

in that path. Therefore, a fraction of total extinction can be assigned to each particle type, and an 

extinction or scattering "budget" can be calculated. 

 Historically, researchers have invoked a number of assumptions concerning measured aerosol 

distributions. They have calculated or estimated specific mass scattering and absorption 

efficiencies, and used these to form estimates of extinction budgets. Because specific bulk aerosol 

species' concentrations are measured, the implicit assumption is one of externally mixed particles. 

However, under realistic assumptions concerning the microphysical properties of the particles the 

postulation of an external or internal mixture is not important to the estimation of specific mass 

extinction efficiencies. Ouimette and Flagan [1982] have shown that if an aerosol is mixed 

externally or if in an internally mixed aerosol the index of refraction is not a function of 

composition or size, and the aerosol density is independent of volume, then: 

= ∑

i

bext α imi (4.2) 

where ái is the specific mass scattering or absorption efficiency and mi is the mass of the individual 

species. It should be pointed out, however, that as water is absorbed by hygroscopic particles the 
4-7

index of refraction will change and that change will be dependent on the growth and mixture 

models that are assumed. 

 All routine aerosol monitoring programs and most special study visibility characterization 

programs were designed to measure aerosol species such as sulfates, nitrates, elements, and 

carbonaceous material [Heisler et al., 1980a; Malm et al., 1994; Tombach and Thurston, 1994; 

Watson et al., 1990a; and Macias et al., 1981]. They were not designed to determine whether these 

species were internally or externally mixed. Therefore, bext is usually apportioned by assigning 

specific mass extinction efficiencies to each species and calculating the total extinction using 

Equation (4.2). 

 A number of investigators have taken advantage of the form of Equation (4.2) to construct a 

multilinear regression model with bext as the dependent variable and the measured aerosol mass 

concentrations of species I as the independent variables. The regression coefficients are then 

interpreted as specific extinction to mass efficiencies [White and Roberts, 1977; Cass 1979; 

Groblicki et al., 1981]. The use of multivariate regression models to apportion mass concentrations 

to scattering and absorption requires that the model meet a number of limiting assumptions, and 

should be used with caution. White [1986] discusses some of the issues associated with this 

problem. 

 Any apportionment of aerosol mass to extinction is only approximate. The assumptions 

required for extinction-mass relationships implied by Equation (4.2) probably are never exactly 

met. The appropriateness of any apportionment scheme can only be judged within the context of 

whether the model is physically "reasonable," and whether independent apportionment of mass to 

extinction is consistent with measurements of scattering and absorption. 

 The strategy used to examine extinction apportionment is to use Equation (4.2) to examine 

various relationships between measured scattering, extinction, and absorption, and between these 

variables and nominal dry particle extinction efficiencies that have been synthesized from a variety 

of estimates. Scattering associated with absorbed water is prorated among hygroscopic aerosol 

species. Regression analysis will be used to investigate the validity of assumptions utilized in the 

apportionment scheme. 

4.2.1 Estimating Light Scattering

 Because certain aerosols, such as sulfates and nitrates, have an affinity for water, their scattering 

characteristics change as a function of relative humidity (RH). Therefore, aerosol scattering of the 

so-called hygroscopic species as a function of relative humidity must be considered. 

 In general, the higher the RH the greater the scattering of soluble aerosols. The relationship 

between RH and scattering efficiency for ammonium sulfate aerosols with a mass mean diameter of 

0.3 ìm and a geometric size distribution of 1.5 is shown in Figure 4.4. This function, referred to 

as f(RH), is: 

f ( )) RH ) = bscat(RH / bscat(0% (4.3) 

where bscat(0%) and bscat(RH) are the dry and wet scattering, respectively. The aerosol growth was 

calculated following the scheme proposed by Tang [1981]. Ammonium sulfate and ammonium 
4-8

nitrate mass are associated with this function. 

Figure 4.4 f(RH) for ammonium sulfate is plotted as a function of relative humidity. 

 Various functions for the hygroscopicity of organics have been proposed. Assumptions must 

not only be made about the solubility of organics but also on the fraction of organics that are 

soluble. White [1990] discusses this issue. Given the variety of organic species, it is possible that a 

geographic variation in organic species exists with large fractions of soluble species occurring in 

certain parts of the continent and much smaller fractions in other areas. 

 The following equation is used to estimate reconstructed particle scattering: 

() ( )[ ]

() ( )[ ]

() ( )[ ]

( )[ ]

( )[ ] CM

SOIL

f RH OMC

f RH NITRATE

b f RH SULFATE

org

scat

0.6

1

3

3

3

+

+

+

+

=

 (4.4)

The brackets indicate the species concentration, 3 m2

/g is the dry scattering efficiency of sulfates, 

nitrates, and organic carbon, while 1 m2

/g and 0.6 m2/g are the respective scattering efficiencies for 

soil and coarse mass. The efficiencies for fine soil and coarse mass are taken from a literature 

review by Trijonis and Pitchford [1987]. 
4-9

 A dry scattering efficiency of 3 m2/g is a nominal scattering efficiency based on a literature 

review by Trijonis et al. [1988, 1990] and a review by White [1990]. Trijonis' best estimate for 

sulfates and nitrates is 2.5 m2

/g with an error factor of 2, while for organics it is 3.75 m2/g again 

with an error factor of 2. White [1990] took a somewhat different approach in that he reviewed 30 

studies in which particle scattering and mass were measured. He then estimated a high and low 

scattering efficiency by using mass measurements to prorate the measured extinction. For sulfate 

the low estimate was arrived at by assuming that sulfate, nitrates, and organics scatter twice as 

efficiently as all other species, and for the high estimate he assumed that only sulfate was twice as 

efficient. His low and high sulfate mass scattering efficiencies for the rural west were 3.0 and 3.7 

m

2

/g, respectively. For organics, his low estimate assumes that organics and other nonsulfate 

species scatter half as efficiently as sulfates, and for the high estimate he assumes organics are 

three, and sulfates two times as efficient at scattering light as other species. His low and high 

estimates for organic mass scattering coefficients are 1.8 and 4.1 m2

/g. It is worth noting that an 

ammonium sulfate scattering efficiency of 3 m2

/g is also consistent with sulfur particle mass size 

distributions measured at Grand Canyon [Malm et al., 1986]. 

 The validity of using Equation (4.4) and the use of associated specific mass scattering 

efficiencies to estimate particle scattering from bulk measurements of aerosol species are explored 

in the next sections. 

4.2.2 Estimating Aerosol Absorption 

 On channel A, babs is quantified directly by the LIPM analysis and is stated in units of 10-8 

m1

. It can also be estimated using Equation (4.2) in the form of: 

blac = α abs [ ] LAC (4.5) 

where áabs is the absorption efficiency of light-absorbing carbon. blac is used to represent particle 

absorption estimates derived from LAC mass concentrations. Horvath [1993] reviewed a number 

of studies where áabs for soot and black carbon were measured. He also reviewed a number of 

theoretical calculations of áabs where a variety of refractive indices and densities were assumed. 

Measured values of áabs range from a low of 3.8 to a high of 17 m2

/g, while theoretical 

calculations of áabs suggest a value of 8-12 m2

/g. The relationship between LAC and babs will be 

further explored in the following analysis. 

4.3 Aerosol Scattering and Absorption

 Table 4.3 presents the statistical summaries of the scattering or absorption associated with each 

variable for the summer Meadview data. The scattering associated with each species was calculated 

using the efficiencies presented in Equation (4.4) and an absorption efficiency for LAC of 10 m2

/g. 

Rbext1 and Rbext2 are reconstructed extinctions using babs and blac, respectively and Rbscat is 

reconstructed scattering. Also presented in the table are summary statistics for measured bscat, bext, 

ambient relative humidity (RH), and f(RH). 
4-10

Table 4.3. Summary statistics for optical variables for the summer Meadview data set. The 

numbers reported are associated with the scattering, absorption, or extinction associated 

with each variable. Units on scattering, absorption, and extinction are in Mm-1, while 

relative humidity is in percent and f(RH) factors have no units. Rbext1 and Rbext2 refer to 

reconstructed extinction using blac and babs, respectively and Rbscat is reconstructed 

scattering. Units on scattering, absorption, and extinction are in Mm-1, while relative 

humidity is in percent and f(RH) factors have no units. The number of valid data points 

is 82. 

Variable Mean 

(Mm-1) 

Std Dev Minimum 

(Mm-1) 

Maximum 

(Mm-1) 

bext 23.65 5.67 14.08 41.36

Rbext1 17.70 5.45 7.69 36.05

Rbext2 23.27 6.88 10.62 48.94 

bscat 12.44 5.22 4.33 36.83

Rbscat 16.48 5.02 7.51 35.05

babs 6.80 2.07 3.11 14.93

blac 1.23 0.75 0.00 3.08

(NH4)2SO4 7.23 3.15 3.01 20.03

NH4NO3 0.80 0.58 0.12 2.84 

OC 1.88 1.24 -0.53 6.51 

SOIL 0.80 0.34 0.35 2.20

CM 5.76 2.28 1.98 10.89

RH 25.79 13.25 6.08 61.92 

f(RH) 1.06 0.12 1.00 1.63

f(RHc) 1 05 0 11 1 00 1 60
4-11

 The nephelometer chamber relative humidity is estimated from chamber temperature using: 

( )

a c

a c

T T

T T

c a RH RH e

−

=

5210.5

 (4.6)

where RHa, RHc, Ta, and T c are the ambient and chamber relative humidities and temperatures, 

respectively. From RHc the f(RHc) function inside the nephelometer can be estimated. It is also 

summarized in Table 4.3. 

 Because of the low relative humidities during the MOHAVE summer intensive, and because 

the "ambient" nephelometer exhibited minimal heating of the aerosol while in the sampling 

chamber, the f(RHc) within the nephelometer was close to the ambient f(RH). The average f(RH) 

values for ambient and within the nephelometer were 1.06 and 1.05, while the maximum f(RH) 

values were 1.63 and 1.60, respectively. Because the f(RH) values were nearly the same, 

adjustments were not made to measured bscat to account for chamber heating. 

 Figure 4.5 is a temporal plot of measured bext, bscat, babs, ambient RH, and f(RH), while Figure 

4.6 is a temporal plot of scattering associated with each aerosol species. Error bars were not 

included because of the unknown uncertainty in the prescribed efficiencies. The reported error on 

the bext and bscat measurements are about 10%. 

Figure 4.5 Temporal plot of measured bext, bscat, babs, relative humidity, and f(RH) for the summer 

Meadview data set. Units on extinction, scattering, and absorption are Mm-1, while 

relative humidity is in percent and f(RH) is unitless. Time is in Julian day, and for 

reference the month and day axis is also included. 
4-12

Figure 4.6 Temporal plot of estimated scattering associated with ammonium sulfate, ammonium 

nitrate, organics, fine soil, and coarse mass. Units are in Mm-1. 

 Table 4.4 presents similar information for the IMPROVE data set. The scattering associated 

with each species was calculated using the efficiencies presented in Equation (4.4) and an 

absorption efficiency for LAC of 10 m2

/g. Of the 18 IMPROVE sites that have a transmissometer, 

nine sites were chosen to intercompare aerosol and bext measurements. They are Grand Canyon, 

Petrified Forest, Guadalupe Mountains, Yellowstone, Rocky Mountain, Glacier, Pinnacles, and 

Bandelier National Parks and Bridger Wilderness Area. At the other nine sites the transmissometer 

site path is directed over a slanted site path or over a canyon. Thus, the aerosol sampler and 

transmissometer are not sampling the same air masses. 

 The IMPROVE particle sampler collects samples for 24 hours, while the transmissometer and 

RH data is gathered on an hourly basis. Therefore, the transmissometer and RH data is averaged to 

24 hours. There are about 5000 total data points consisting of 24-hr average transmissometer 

extinction, however, because of cloudy or foggy conditions not all 24-hr averages contain 24 data 

points. The analysis is restricted to those data points where there are at least 18 hourly readings for 

the transmissometer. With this restriction there remains 1642 valid readings. 
4-13

Table 4.4 Summary statistics for optical variables for the IMPROVE data set. The numbers 

reported are associated with the scattering, absorption, or extinction associated with 

each variable. Units on scattering, absorption, and extinction are in Mm-1, while 

relative humidity is in percent and f(RH) factors have no units. Rbext1 and Rbext2 refer to 

reconstructed extinction using blac and babs, respectively. The number of valid data 

points is 1642. 

Variable Mean 

(Mm-1) 

Std Dev Minimum 

(Mm-1) 

Maximum 

(Mm-1) 

bext 22.68 10.47 0.00 56.10

Rbext1 15.91 8.20 -0.32 59.70

Rbext2 20.41 10.02 0.92 67.97

babs 6.29 3.47 0.00 24.19

blac 1.79 1.71 -1.35 15.60

(NH4)2SO4 4.76 3.39 0.18 28.52 

NH4NO3 1.23 1.77 -0.24 23.09

OMC 3.87 2.33 -0.95 19.51 

SOIL 0.71 0.62 0.02 4.35

CM 3.55 3.05 0.00 26.08

RH 46.67 15.30 7.79 87.17

f(RH) 1 45 0 44 1 00 3 91

4.4 Comparison of Reconstructed Extinction and Scattering

 The Meadview data set offers a unique opportunity to examine the relationship among 

extinction, scattering and absorption directly without having to unduly rely on estimates of 

aerosol scattering from various species. Extinction, scattering, and absorption are all measured 

optically and thus allow for an independent assessment of the accuracy of these measurements. If 

the validity of these measurements can be established then scattering and absorption, as 

estimated from aerosol measurements, can be independently compared to each of these measures. 

4.4.1 Extinction, Scattering, and Absorption Characteristics at Meadview

 Extinction and scattering measurements are directly compared by using the following 

equation: 

bext = bscat + babs + CMS / 2 (4.7) 

where CMS/2 is half the estimated total coarse mass scattering. Hasan and Lewis [1983] have 

carried out theoretical calculations to show that because of the forward angle truncation error in 

the nephelometer, it underestimates coarse mass scattering by about a factor of two. 

Furthermore, White et al., [1994] were able to show from transmissometer derived total 

scattering and nephelometer measurements of fine and coarse particle scattering that the 

nephelometer underestimates scattering by particles greater than 2.5 ìm by about a factor of 

two. 
4-14

 Equation (4.7) consists of all measured optical variables except for CMS. Figure 4.7 is a 

scatter plot of the left and right side of Equation (4.7) along with the one-to-one line. 

Considering the uncertainty in estimated coarse mass scattering and the nephelometer response

to coarse particles, the agreement is quite good. On the average, bext is only about 1 Mm-1

greater than bscat+babs+ CMS/2. However, Figure 4.7 shows that for the main body of data 

points, bext is underestimated by about 2 Mm-1, while the two largest extinctions are clearly 

overestimated. 

Figure 4.7 Reconstructed extinction using bscat, babs, and coarse mass divided by 2 is plotted 

against measured extinction. Units are in Mm-1. 

 In the above analysis bext was compared to reconstructed bext using the direct measurement of 

absorption, babs, by the laser integrated plate technique (LIPM) as opposed to using absorption 

estimates derived from LAC mass concentrations (blac). Figure 4.8 shows a scatter plot of 

reconstructed and measured extinction when blac is used as an estimate of absorption instead of babs. 

 Using blac, which has been the traditional method of estimating babs, apparently yields 

an underestimation of extinction by about 7-8 Mm-1. Examination of Table 4.4 shows that babs is 

5.6 times larger than blac. 

 Using babs without any adjustments for reconstituting extinction gives a reasonable fit to 

measured extinction suggesting that bext, bscat, and b abs are accurate representations of ambient 

extinction, scattering, and absorption, while blac is not. 
4-15

Figure 4.8 Reconstructed extinction using bscat, blac, and coarse mass divided by 2 is plotted 

against measured extinction. Units are in Mm-1. 

4.4.2 Comparison of Estimated and Measured Scattering at Meadview

 Figure 4.9 is a scatter plot of reconstructed and measured bscat along with the 1:1 line for the 

summer Meadview data using CMS/2. Reconstructed scattering was calculated using Equation 

(4.4). The agreement is quite good. Most data points fall about the 1:1 line with the two highest 

measured values being about 7 Mm-1 greater than the 1:1 line. The close agreement between 

measured and reconstructed scattering gives some confidence that the aerosol species mass 

concentrations have been accurately measured and their associated scattering fairly represented. 

4.4.3 Comparison of Estimated and Measured Extinction at Meadview and IMPROVE Sites 

 Figure 4.10 is a scatter plot of reconstructed and measured extinction using babs. Again, 

reconstructed bscat was calculated using Equation (4.4). The agreement between reconstructed and 

measured extinction is quite good with reconstructed extinction being about 1 Mm-1 lower than 

measured extinction. 

 These results are consistent with the direct comparison between bext, bscat, and babs. The real 

difference between the direct comparison of the optical variables is that the nephelometer scattering 

was corrected for underestimation of large particle scattering. In the reconstructions 
4-16

Figure 4.9 Reconstructed bscat using the sum of estimated aerosol species scattering is plotted 

against measured bscat. Units are in Mm-1. 

Figure 4.10 Reconstructed bext using the sum of estimated aerosol species scattering but with 

coarse mass scattering divided by 2 is plotted against measured bscat. Units are in 

Mm-1. 
4-17

of extinction, this correction was not made because the transmissometer does not have a built-in 

underestimation of large particle scattering. In fact, the 0.6 m2

/g estimate of coarse particle 

scattering was derived from nephelometer measurements and it may be an underestimate of 

ambient coarse particle scattering and is certainly an underestimate of coarse particle absorption. 

Figure 4.10 suggests that the extinction in the 20-25 Mm-1 is somewhat underestimated, which may 

in part be due to an underestimation of coarse particle scattering and/or absorption. 

 The overriding issue, however, is the difference between babs and blac. The Meadview data set 

suggests that babs for the LIPM is a more accurate representation of absorption than blac as derived 

from LAC and that atmospheric absorption calculated using LAC may be a severe underestimate. 

The Meadview data set is small in that it covers about one month of time and is at only one 

location. The IMPROVE data set allows reconstructions of extinction using babs and blac to be 

compared with measured extinction over wide geographic regions and over a period of about four 

years. 

 Figures 4.11 and 4.12 are comparisons between reconstructed and measured extinctions using 

blac and babs, respectively, for the previously identified nine sites. As in the Meadview data set, 

reconstructed extinction is significantly lower than measured extinction when using blac and nearly 

the same when using babs. When using blac, reconstructed extinction is about 30% lower than 

measured extinction and about 10% lower when using babs. 

 For the reconstructed extinctions used in Figures 4.11 and 4.12 organics were not considered to 

be hygroscopic, and they were assumed to have the same dry mass scattering efficiency as sulfates. 

The hygroscopicity of organics was examined by assuming various fractions of organics being 

hygroscopic and assigning a variety of f(RH) curves to those fractions. Nonlinear growth curves 

caused the relationship between reconstructed and measured extinction to degrade as judged by the 

r

2

 value associated with an ordinary least square (OLS) regression between the two variables. 

 The best fit between reconstructed and measured extinction, as judged by r

2

 values, is achieved 

by increasing the dry mass scattering efficiency from 3 m2

/g to about 4 m2/g. A 4 m2/g dry mass 

scattering efficiency is consistent with the density of organics being lower than for sulfates. The 

resulting scatter plot between measured and reconstructed extinction is shown in Figure 4.13. The 

r

2

= 0.63 with data points being nearly equally distributed above and below the 1:1 line. On the 

average, measured extinction is about 1 Mm-1 or 4% greater than reconstructed extinction. This 

difference is well within the uncertainties of the measurements. 

 The choice of scattering efficiencies used to match measured and reconstructed extinction are 

well within the constraints of known physical principles, however, they are by no means unique. 

The one outstanding feature is the need to use babs as derived from LIPM as opposed to blac to bring 

measured and reconstructed extinction into agreement. If blac is assumed to be the true atmospheric 

absorption any choice of growth functions, f(RH), and dry scattering efficiencies that force 

measured and reconstructed extinction to be equal are outside constraints imposed by known 

physical and chemical principles. Furthermore, the overall relationship between measured and 

reconstructed extinction is degraded as judged by r

2

 values between the two quantities. 
4-18

Figure 4.11 Reconstructed bext using blac and the sum of estimated aerosol scattering from the 

various aerosol species is plotted against measured bext. Units are in Mm-1. 

Figure 4.12 Reconstructed bext using babs and the sum of estimated aerosol scattering from the 

various aerosol species is plotted against measured bext. Units are in Mm-1. 
4-19

Figure 4.13 Reconstructed bext using blac and the sum of estimated aerosol scattering from the 

various aerosol species is plotted against measured bext for the IMPROVE data set. 

Units are in Mm-1. 

4.4.4 Regression Analysis

 One can further examine the appropriateness of best estimates of scattering and absorption 

efficiencies using regressional techniques. Typically, the regression equation takes on the form of 

Equation (4.2) with either the extinction or scattering coefficient being the dependent variable and 

the aerosol species the independent variables. The regression coefficients are then interpreted as the 

scattering or absorption to mass efficiencies. 

 One problem with using regressional techniques is collinearity. One way to investigate the 

independence of variables is factor analysis. Table 4.5 presents a factor analysis using varimax 

rotation of the optical and aerosol scattering variables. bext, babs, bscat, SO4,scat, and NO3,scat are all 

loaded into the same factor, while organics (blac) and babs load into a second factor, and fine soil 

scattering (soilscat) and scattering due to the sum of coarse mass and soil (CMscat+soilscat) load into a 

third factor. Therefore, for purposes of regression analysis sulfates and nitrates were combined into 

one variable, coarse mass and soil in a second, babs in a third, and organics in a fourth. 
4-20

Table 4.5. Results of a factor analysis on various extinction, scattering, and absorption variables 

for the summer Meadview data set. 

VARIABLE FACTOR 1 FACTOR 2 FACTOR 3 

bext 0.84663 0.25915 0.36179 

bscat 0.84947 0.33622 0.19281 

SO4,scat 0.84729 0.28517 -0.12735 

NO3,scat 0.67126 -0.42681 0.43896 

OMCscat 0.15304 0.84407 0.13199 

soilscat 0.24992 0.41696 0.62812 

CMscat + soilscat 0.06930 0.09632 0.90461 

LAC 0.25854 0.79690 0.14658 

babs 0.63729 0.62277 0.24615 

Variance explained by each factor 

 FACTOR 1 FACTOR 2 FACTOR 3 

 3.170547 2.362169 1.689319 

 A three-step linear least squares regression was carried out on the following equations: 

( ) () ( )

( ) ( ) () ( )

( )( ) ( ) 4 3 .

4 3 ,

,

8 9 10

4 5 6 7

1 2 3

b a S N a OMS a soilcms

b a S N a OMS a b a soilcms and

b a b a b a soilcms

scat

ext abs

ext scat abs

= + +

= + + +

= + +

 (4.8) 

bscat, babs, and b ext are the measured variables, while soilcms is estimated soil plus coarse mass 

scattering. S4N3 is estimated sulfate plus nitrate scattering, and OMS is estimated scattering 

attributed to organics. A three-step regression optimizes the coefficients for a best fit to all three 

equations simultaneously [Judge et al., 1988, 1985]. 

 Results of the regression is presented in Table 4.6. If the estimates of efficiencies are 

representative, the regression coefficients should equal one except for a10, the soilcms coefficient 

associated with the nephelometer scattering, which should be closer to 0.5. (The nephelometer 

measures about 1⁄2 of the coarse mass scattering.) The coefficients are all surprisingly near one 

except for a10, which is closer to the expected 0.5. The regression coefficients suggest that the 

estimates used for calculating scattering are correct, and more importantly babs, as opposed to blac, is 

the more accurate measure of absorption. 
4-21

Table 4.6 Results of a three-step ordinary least square (OLS) regression with various optical 

variables as dependent and independent variables. 

Dependent 

Variable 

Independent 

Variable 

Estimate Std Error t-value r

2

bext bscat 0.93 0.21 4.4 0.65 

babs 1.06 0.42 2.5 

soilcms 0.65 0.15 4.3 

bext S4N3 0.94 0.16 5.9 0.56 

 OMS 1.10 0.28 3.9 

soilcms 1.01 0.16 6.3 

babs 0.98 0.29 3.3 

bscat S4N3 0.96 0.09 11.3 0.66 

 OMS 1.14 0.21 5.3 

soilcms 0.36 0.10 3.6 

4.5 Attribution of Extinction to Aerosol Species

4.5.1 The Attribution Equation

 Two unique data sets were used to explore the relationship between optical extinction, 

absorption, and scattering, and various aerosol species. The MOHAVE special study provided, at 

one monitoring site, independent optical measurements of bext, bscat, and babs, and the various 

aerosol species. This data set provided for a variety of ways for exploring absorption and scattering 

efficiencies. A second data set, IMPROVE, provides for the first time, an opportunity to explore 

the relationship between measured extinction (as opposed to scattering) and aerosol species over the 

whole western United States. These are the first data sets where extinction was directly measured 

as opposed to estimated by summing bscat and absorption as derived from "elemental" carbon 

measurements. 

 The most surprising outcome of the analysis relates to estimates of absorption. It has been 

known for some time that, at remote nonurban locations, babs as derived from the LIPM, was about 

twice the absorption as estimated from elemental carbon derived from thermal optical reflectance 

techniques (blac). Although there may be alternative interpretations, the most straightforward 

explanation of the relationships between bext, bscat, babs, and blac is that babs is a more accurate 

predictor of absorption than blac. 

 Therefore, absorption estimates will be based on babs, while scattering apportionment will be 

based on Equation (4.4), but with the scattering efficiency for organic mass set equal to 4 m2

/g, and 

forg(RH) set equal to one. The equation used for reconstructing extinction then becomes: 
4-22

() ( )[ ]

() ( )[ ]

( )[ ]

( )[ ]

( )[ ]

abs

ext

b

CM

SOIL

OMC

f RH NITRATE

b f RH SULFATE

+

+

+

+

+

=

0.6

1

4

3

3

 (4.9) 

4.5.2 Estimating f(RH) from Average Relative Humidity 

 One remaining issue for the apportionment of scattering to hygroscopic aerosol species is the 

disparity between the instantaneous effects of relative humidity on scattering and the fact that 

aerosol samples are gathered on a 24-hour period. Light extinction and mass budgets involve 

averaging samples collected over a time interval. The extinction and mass budget represents the 

average contribution of each aerosol species to the average extinction or mass for the time interval. 

When soluble aerosols dominate the mass concentration, the distribution of RH over the interval 

becomes an issue. Failure to consider the distribution of RH can have significant effects on the 

average extinction attributed to the soluble aerosol. 

 Mass budgets, for a particular time interval, are calculated by finding the average concentrations 

of the individual species of fine mass, then dividing each by the sum of the averages. If the aerosol 

data can be time matched with RH data, then light extinction budgets can be calculated in a parallel 

fashion. Specifically, a light extinction for each species and each sample can be calculated. Thus, 

the average light extinction due to each species over the time interval can be estimated. 

 If collocated and time-matched RH data are not available, but reliable estimates of the average 

RH over the time interval are, then a first approximation of an average light extinction for a given 

species can be made. One initial approach would be to apply the RH correction factor associated 

with the average RH to estimate the average extinction due to a soluble species. However, it can be 

demonstrated that for sites where the average RH is high, this approach will seriously underestimate 

the average extinction of a soluble aerosol when the soluble aerosol concentration is independent of 

RH. This is due to the convex and highly nonlinear nature of the aerosol growth curves and the 

subsequent functions, fT(RH). In the case of the f(RH) associated with Tang's growth curve, shown 

in Figure 4.4, Equation (4.9) holds 

f (RH ) f ( ) RH . T ≤ T (4.10)

Moreover, if the distribution of soluble species concentrations are independent of RH, then 

f ( ) RH c f ( ) RH ( ) c T ≈ T (4.11) 

Equality would occur as a limiting value when the sample size increases without bound. 

 In this report, light extinction due to a soluble species at site s is derived using hourly RH values 

less than or equal to 98% and the equation is 
4-23

, , b F c ext = β T s (4.12)

where 

( ). T ,s T RHs F = f (4.13)

Using Equation (4.9), extinction budgets for a time interval may be calculated by replacing fT(RHs)

with FT,s and by using the average concentration of each species over the time interval as the mass 

concentration. 

 Using the data for the collocated sites, Figure 4.14 has the plot of Tang's RH dependent factor, 

as defined by Equation (4.12), versus annual average RH for the 39 IMPROVE sites with RH and 

light extinction measurements. A polynomial curve was fitted to the annual and seasonal data as 

defined by, 

( ) ( ) ( ) ( ) ( ) ( ) 4

4

3

3

2

0 2 F = b + b 100 / 100 − RH + b 100 / 100 − RH + b 100 / 100 − RH (4.14)

Figure 4.14 Dependence on average site relative humidity of the relative humidity correction factor 

for sulfate (FT,s) for the 39 IMPROVE sites with relative humidity measurements. 

 Table 4.7 shows the results of the regressions for Tang's weighted correction factors. The high 

r

2

 values arise from the fact that the noise in the relationship is due primarily to differences in the 

RH distributions between sites. More explicitly, if two sites had the same average RH, their 

weighted factors would be the same if their RH distributions were identical. 
4-24

Table 4.7 Parameters of the best-fit quadratic equation relating the relative humidity light 

extinction correction factors (FT) to average site relative humidity (F = b0 + b2(1/(1-

rh))2

 + b3(1/(1-rh))3 + b4(1/(1-rh))4). 

Season Intercept T2 T3 T4 r

2

Spring 0.76 0.31 -0.004 -0.004 0.95 

Summer 0.51 0.47 -0.081 0.004 0.95 

Autumn -0.03 0.83 -0.196 0.014 0.93 

Winter 1.19 0.29 -0.033 0.001 0.87 

ANNUAL 0.52 0.53 -0.095 0.006 0.94 

 In the IMPROVE monitoring network there are currently 55 sites operating that have fully 

complemented aerosol samplers (channels A-D); however, only those sites with a year or more of 

aerosol data are reported here. Of these sites, 39 have optical monitoring and hence RH data. Using 

the results of the regressions, annual and seasonal weighted factors were calculated for the 

additional sites by estimating their annual and seasonal average RH from weather service RH

contour maps [NOAA, 1978] (Figure 4.15) or from alternate sources. 

Figure 4.15 Spatial variation in annual average relative humidity [NOAA, 1978]. 
4-25

4.6 References

Appel, B.R., Y. Tokiwa, J. Hsu, L.E. Kothny, E. Hahn, and J.J. Wesolowski, Visibility as related 

 to atmospheric aerosol constituents, Atmos. Environ., 19, 1525-1534, 1985. 

Cass, G.R., On the relationship between sulfate air quality and visibility with examples in Los 

 Angeles, Atmos. Environ., 13, 1069-1084, 1979. 

Dietrich, D.L., J.D. Molenar, J.F. Faust, Transmissometer extinction measurements in an urban 

 environment, In Visibility and Fine Particles, C.V. Mathai, Ed., AWMA, Pittsburgh, 1989. 

Dzubay, T.G., and K.W. Clubb, Comparison of telephotometer measurements of extinction 

 coefficients with scattering and absorption coefficients, Atmos. Environ., 15, 2617-2624, 1981. 

Dzubay, T.G., R.K. Stevens, C.W. Lewis, D.H. Hern, W.J. Courtney, J.W. Tesch, and M.A. 

 Mason, Visibility and aerosol composition in Houston, TX, Environ. Sci. and Tech., 16, 

 514-525, 1982. 

Groblicki, P.J., G.T. Wolff, and R.J. Countess. Visibility-reducing species in the Denver "brown 

 cloud"-I. Relationships between extinction and chemical composition, Atmos. Environ., 15, 

2473-2484, 1981. 

Hasan, H. and C.W. Lewis, Integrating nephelometer response corrections for bimodal size 

 distributions, Aerosol Sci. & Technol., 2, 443-453, 1983. 

Heisler, S.L., R.C. Henry, J.G. Watson, and G.M. Hidy, The 1978 Denver winter haze study 

 volume II. ERT document #P-5417-1. Environmental Research and Technology, Inc., West 

Lake Village, CA, 1980a. 

Heisler, S.L., R.C. Henry, and J.G. Watson, The source of the Denver haze in November and 

 December 1978, Paper 80-58.6, presented at the 73rd Annual Meeting of the Air Pollution 

Control Association, Pittsburgh, PA, 1980b. 

Heisler, S.L., I. Tombach, D. Fitz, J. Watson, and J. Chow, Tucson urban haze study, Final 

 Report, Arizona Dept. Of Environmental Quality, Phoenix, AZ, Doc. #0493-005-804, 1994. 

Horvath, H., Atmospheric light absorption - A review, Atmos. Environ., 27(A), 3, 293-317, 1993. 

Judge, G.G., W.E. Griffiths, R.C. Hill, H. Lutkepohl, and T.C. Lee, "The theory and practice of 

 econometrics", 2nd Edition, New York, John Wiley & Sons, 1985. 

Judge, G.G., R.C. Hill, W.E. Griffiths, H. Lutkepohl, and T.C. Lee, "Introduction to the theory 

 and practice of econometrics", 2nd Edition, New York, John Wiley & Sons, 1988. 

Lewis, C.W., and T.G. Dzubay, Measurement of light absorption extinction in Denver, Aerosol 

 Sci.Tech., 5, 325-336, 1986. 
4-26

Macias, E.S., J.O. Zwicker, and W.H. White, Regional haze case studies in the southwestern 

 United States II: source contributions, Atmos. Environ., 15, 1987-1997, 1981. 

Malm, W.C., T.A. Cahill, K.A. Gebhart, and A. Waggoner, Optical characteristics of atmospheric 

 sulfur at Grand Canyon, Arizona, In Visibility Protection, Research and Polisy Aspects, edited 

by P.S. Bhardwaja, pp. 418-433, 1986. 

Malm, W.C., K.A. Gebhart, J.V. Molenar, T.A. Cahill, R.A. Eldred, and D. Huffman, Examining 

 the relationship between atmospheric aerosols and light extinction at Mount Rainier and North 

Cascades National Parks, Atmos. Environ., 28, 347-360, 1994. 

National Oceanic and Atmosperic Administration, Climatic Atlas of the United States, ESSA, 

 Department of Commerce - 1968, Reprinted by NOAA, October, 1978. 

Ouimette, J.R., and R.C. Flagan, The extinction coefficient of multicomponent aerosols, Atmos. 

 Environ., 16, 2405-2419, 1982. 

Ouimette, J.R., R.C. Flagan, and A.R. Kelso, Chemical species contributions to light scattering 

 by aerosols at a remote arid site, In: E.S. Macias and P.K. Hopke, eds. Amer. Chem. Soc. 

Symposium, Series 167: Atmospheric Aerosol, 1981. 

Sutherland, J.L. and P.S. Bhardwaja, Seasonal fine mass budgets through regression-application 

 to the Glen Canyon SCENES data, In: Transactions of Visibility and Fine Particles, C.V. 

Mathai (ed), Air & Waste Management/EPA International Conference, Pittsburgh, PA, 207-

212, 1990. 

Tang, I.N., W.T. Wong, and H.R. Munkelwitz, The relative importance of atmospheric sulfates 

 and nitrates in visibility reduction, Atmos. Environ., 15, 2463, 1981. 

Tombach, I. and S.A. Thurston, The quality of the SCENES measurements: the roles of data 

 quality goals and evolving technology, In Proc. Aerosols and Atmospheric Optics: Radiative 

Balance and Visual Air Quality, Air & Waste Management Association, Pittsburgh, PA, 1994. 

Trijonis, J.C., and M. Pitchford, Preliminary extinction budget results from the RESOLVE 

 program, edited by P.S. Bhardwaja, Visibility Protection Research and Policy Aspects, Air 

Pollution Control Association, Pittsburgh, Pa., 1987. 

Trijonis, J.C. M. McGown, M. Pitchford, D. Blumenthal, P. Roberts, W. White, E. Macias, R. 

 Weiss, A. Waggoner, J. Watson, J. Chow, and R. Flocchini, RESOLVE Project Final Report: 

Visibility Conditions and Causes of Visibility Degradation in the Mojave Desert of California, 

NWC TP #6869, Naval Weapons Center, China Lake, CA, 1988. 

Trijonis, J.C., R. Charlson, R.B. Husar, W.C. Malm, M. Pitchford, and W. White, Visisbility: 

 Existing and Historical Conditions-Causes and Effects. State of Science and State of 

Technology, Report No. 24. National Acid Precipitation Assessment Program, 1990. 
4-27

vandeHulst, H.C., Light scattering by small particles, Dover Publications, New York, 1981. 

Vasconcelos, L.A., E.S. Macias, and W. White, Aerosol composition as a function of haze and 

 humidity levels in the southwestern U.S., Atmos Environ 28(22), 3679-3691, 1994. 

Watson, J.G., J.C. Chow, L.C. Pritchett, L.W. Richards, D.L. Dietrich, J. Molenar, J. Faust, S.R. 

 Andersen, and C.S. Sloane, Comparison of three measures of extinction in Denver, CO, Paper 

#89-151.4, Presented at the Air Pollution Control Association Annual Meeting, 1989. 

Watson, J.G., J.C. Chow, L.W. Richards, S.R. Anderson, J.E. Houck, and D.L. Dietrich, The 

 1987-88 metro Denver brown cloud air pollution study, Volume I: program plan. DRI 

document 8810.1F1, prepared for the Greater Denver Chamber of Commerce, Denver, CO, by 

Desert Research Institute, Reno, NV, 1988. 

Watson, J.G., J.C. Chow, L.W. Richards, D.L. Haase, C. McDade, L.D. Dietrich, D. Moon, L. 

 Chinkin, and C. Sloane, The 1989-90 Phoenix urban haze study. Volume I: program plan. DRI 

document 8931.1F, prepared for Arizona Department of Environmental Quality, Phoenix, AZ, 

by Desert Research Institute, Reno, NV, 1990a. 

Watson, J.G., J.C. Chow, L.W. Richards, D.L. Haase, C. McDade, D.L. Dietrich, D. Moon, L. 

 Chinkin, and C. Sloane, The 1989-90 pilot Tucson urban haze study. Volume I: program plan. 

DRI document 8931.3F, prepared for Arizona Department of Environmental Quality, Phoenix, 

AZ, by Desert Research Institute, Reno, NV, 1990b. 

White, W.H., On the theoretical and empirical basis for apportioning extinction by aerosols: a 

 critical review, Atmos. Environ., 20, 1659, 1986. 

White, W.H., Contributions to light scattering, In: Acidic Deposition: State of Science and 

 Technology Report 24, J. Trijonis (lead author), National Acid Precipitation Assessment 

Program, Washington, DC, pp85-102, 1990. 

White, W.H. and P.T. Roberts, On the nature and origins of visibility-reducing aerosols in the 

 Los Angeles air basin, Atmos. Environ. 11, 803-812, 1977. 

White, W.H., E.S. Macias, R.C. Ninger, and S. Schorran, Size-resolved measurement of light 

 scattering by ambient particles in the southwestern U.S.A., Atmos. Environ., 28(50), 909-921, 

1994. 
5-1

CHAPTER 5 

SPATIAL DISTRIBUTIONS OF RECONSTRUCTED 

LIGHT EXTINCTION AND LIGHT EXTINCTION 

BUDGETS

 In the previous chapter, a model for reconstructing light extinction was presented. In this 

chapter, this model is used to derive the reconstructed light extinction coefficient for the 43 sites 

examined here. In addition, the relative contribution of various aerosol components to total light 

extinction are combined into a light extinction budget. 

5.1 Reconstructing Light Extinction from Aerosol Measurements

 To review the discussion presented in Chapter 4, the light extinction coefficient is the sum of 

several components: 

ext scat abs Ray sp ag ap b = b + b = b + b + b + b (5.1)

where bext = light extinction coefficient, 

 bscat = light scattering coefficient, 

 babs = light absorption coefficient, 

 bRay = Rayleigh light scattering coefficient, 

 bsp = light scattering coefficient due to particles, 

 bag = light absorption coefficient due to gases, and 

 bap = light absorption coefficient due to particles. 

The Rayleigh scattering coefficient (bRay) is the light scattered by molecules of gas in the natural 

atmosphere (i.e., oxygen and nitrogen, primarily). The Rayleigh scattering coefficient will vary 

with atmospheric pressure. For this report, we assume the Rayleigh scattering coefficient is 10 

Mm-1 (inverse megameters) at all sites. 

 In most instances, bsp and bap are primarily responsible for visibility reduction. The light 

absorption coefficient due to gases (bag) is dominated in the atmosphere by the effect of nitrogen 

dioxide (NO2) gas. For this report, we assume this component is negligible, however, this 

assumption may not be correct at locations close to significant NOx emission sources (e.g., urban 

areas or power plants). 
5-2

 

 The approach used here to estimate scattering assumes externally mixed aerosols. The light 

scattering coefficient can then be calculated (or reconstructed) from aerosol concentrations by 

taking Equation (5.1) and describing the light scattering contributed by aerosol component (i) as the 

product of the aerosol component's concentration (Ci) and its light scattering efficiency (bi). Thus, 

the total light scattering coefficient is simply the sum of the light extinctions of each aerosol 

component: 

ext Ray i

Ci b = b + ∑ β (5.2) 

Equation (5.2) can be cast into the following form for the aerosol components measured as part of 

the IMPROVE program: 

[ ] [ ] []

[ ] [] SOIL CM abs

ext Ray sulfate NITRATE OC

SOIL CM b

b b SULFATE NITRATE OCM

+ + +

= + + +

β β

β β β

 (5.3)

where bext is the total light extinction coefficient (in Mm-1), bRay is the Rayleigh scattering 

coefficient (10 Mm-1), the â's are the light extinction coefficients for each component (in m2/g), and 

the parameters in brackets ([ ]) are the concentrations of the aerosol components (in ìg/m3

). To 

complete the equation for estimating extinction the channel A determination of absorption, babs, is 

used. 

 The values of light scattering efficiency (in m2/g) used in this report are as follows: 

 Sulfates and Nitrates 3 fT(RH) 

 Organic Carbon 4 

 Fine Soil 1 

 Coarse Particles 0.6 

 In this report, we assume that coarse particles and fine soil particles are from a single natural 

source, wind-blown dust. Thus, the scattering calculated for these two components is combined 

into a single category and is reported as coarse scattering. 

 

 The function fT(RH) is a correction factor to account for the liquid water that may be part of 

the hygroscopic aerosol components. These functions are dependent on the relative humidity (RH) 

at the given site. In this report, light extinction, due to a soluble species at site s, is derived using 

hourly RH values less than or equal to 98% and the equation is 

b F c ext,s = β T ,s (5.4)

where 
5-3

( ) T s T RHs F = f , (5.5)

Using Equation (5.3), extinction budgets for a time interval may be calculated by replacing fT(RHs)

with FT,s and by using the average concentration of each species over the same time interval as the 

mass concentration. 

 Using the data for the collocated sites, a polynomial curve was fitted to the annual and seasonal 

data as defined by 

( ) ( ) ( ) ( ) ( ) ( ) 4

4

3

3

2

0 2 F = b + b 100 / 100 − RH + b 100 / 100 − RH + b 100 / 100 − RH (5.6)

Table 5.1 shows the results of the regressions for Tang's weighted correction factors. For those 

sites without collocated optical and RH data the annual and seasonal factors can be calculated. In 

this fashion, all 43 sites are treated the same enabling the same spatial coverage used for aerosol 

mass concentrations. 

Table 5.1 Parameters of the best-fit quadratic equation relating the relative humidity light 

extinction correction factors (FT) to average site relative humidity (F = bo + b2(1/(1-rh))2

+ b3(1/(1-rh))3

 + b4(1/(1-rh))4). 

Season Intercept T2 T3 T4 r

2

Spring 0.76 0.31 -0.004 -0.004 0.95 

Summer 0.51 0.47 -0.081 0.004 0.95 

Autumn -0.03 0.83 -0.196 0.014 0.93 

Winter 1.19 0.29 -0.033 0.001 0.87 

ANNUAL 0.52 0.53 -0.095 0.006 0.94 

5.2 Reconstructed Light Extinction and Light Extinction Budgets 

 Spatial patterns in the reconstructed light extinction are similar to those observed for aerosols 

since reconstructed light extinction is calculated from aerosol concentrations. However, since light 

scattering efficiencies of sulfates and nitrates are larger than other fine aerosols because of 

associated water, and since light-absorbing carbon has a relatively high extinction efficiency, the 

extinction budgets are somewhat different from fine aerosol budgets. 
5-4

 Figure 5.1 shows isopleths of the total reconstructed light extinction coefficient (including 

Rayleigh) for the entire three-year period, March 1992 through February 1995. The highest light 

extinction (>100 Mm-1) occurs in the eastern United States; the highest extinction for a rural site 

occurs at Sipsey Wilderness Area in northern Alabama at 157 Mm-1 followed by Mammoth Cave 

National Park at 148 Mm-1 then Dolly Sods Wilderness Area at 145 Mm-1. The highest extinction 

of 183 Mm-1 is reported at Washington D.C., an urban site. The lowest extinction (<30 Mm-1) 

generally occurs in the intermountain west in the Great Basin and Colorado Plateau regions. The 

lowest extinction for the lower 48 states is at Bridger Wilderness Area at 26 Mm-1. The lowest 

extinction for the entire United States is at Denali National Park in Alaska with an annual 

extinction of 23 Mm-1. Jarbidge Wilderness Area and Great Basin National Park have an annual 

extinction of 28 Mm-1 and 27 Mm-1, respectively.

Figure 5.1 Three-year averages of total reconstructed light extinction coefficient (Mm-1) for each 

of the reported sites in the IMPROVE network in the United States. 

5.2.1 Characteristics of the Regions 

 Tables 5.2, 5.3, and 5.4 summarize the seasonal and annual averages of the reconstructed light 

extinction coefficients for each of the 21 regions in the United States, averaged over three years of 

the IMPROVE monitoring program, March 1992 through February 1995. 

 Table 5.2 shows the breakdown of extinction among fine and coarse particle scattering and 

light absorption. In addition, this table shows the percentage of total light extinction (including 

Rayleigh) that is caused by aerosol light extinction (both scattering and absorption). Also, the 

average relative humidity for each region is reported. Table 5.3 shows the aerosol light extinction 
5-5

as well as the contributions of sulfate, nitrate, organic carbon, light absorption, and coarse particles 

(including fine soil). Table 5.4 shows the aerosol light extinction budgets: the fractions (percent) of 

total aerosol (non-Rayleigh) light extinction contributed by sulfate, nitrate, organic carbon, light 

absorption, and coarse particles (including fine soil). 

 The characteristics of each region, in alphabetic order, are briefly discussed. 

 Alaska. The Alaska region consists only of the measurements at Denali National Park. The 

three-year annual average extinction is 23.2 Mm-1, of which aerosol extinction constituted 57%. 

The seasonal variation is small and varies from a low of 20.9 Mm-1 in the autumn to a high of 26 

Mm-1 in the summer. However, the extinction attributable to nitrate and organics show significant 

seasonal variation. Nitrate extinction ranges from a low of 0.4 Mm-1 in the summer to a high of 0.8 

Mm-1 in the winter. Organics extinction, on the other hand, is highest in the summer at 6.4 Mm-1

and lowest in the winter at 2.8 Mm-1. Sulfate is the largest contributor to aerosol extinction at an 

annual average of 37% and ranges from a seasonal high in the winter of 42.8% to a summer low of 

24%. The next largest contributor is organics at a seasonal average of 29% ranges from a summer 

high of 40.1% to a winter low of 23%. The remaining contributors on an annual basis in order of 

importance are absorption at 17.8%, soil and coarse particles at 12%, and nitrate at 4.3%. 

 Appalachian Mountains. This region consists of three sites, Dolly Sods Wilderness Area in the 

Monongahela National Forest, Shenandoah National Park, and Great Smoky Mountains National 

Park. With an annual extinction of 128 Mm-1 this region is typical of many eastern rural venues. 

The seasonal variation of extinction is about a factor of 2, ranging from 88 Mm-1 in the winter to 

181 Mm-1 during summer. The seasonal variation is almost entirely due to sulfate extinction, which 

varies by a factor of 3 from 44 Mm-1 in the winter to 129 Mm-1 in the summer. Similarly, extinction 

due to organics, which averages 11.7 Mm-1 annually, varies from a winter low of 9.2 Mm-1 to 14.7 

Mm-1 during the summer. Nitrates show a significant variation that is opposed to the variation 

displayed by sulfates and organics. Nitrate extinction is lowest in the summer at 3.8 Mm-1 and in 

the winter it is 12.1 Mm-1. The seasonal variation of sulfates, organics, and nitrates are driven by 

seasonal changes in meteorology and photochemistry. For sulfates and organics this leads to higher 

concentrations during the summer. This coupled with the fact that RH is highest in the summer 

leads to high extinction efficiencies for sulfate aerosols. Nitrates, on the other hand, are quite 

volatile. The lower temperatures during the winter lead to higher concentrations of nitrates. Sulfate 

extinction comprises the largest fraction of aerosol extinction accounting for 68% annually and 

varies from a high during the summer of 75.7% down to 56.2% in the winter. The next highest 

contributor on an annual basis is absorption (12.4%), followed by organics (9.9%), nitrates (6.4%), 

and soil and coarse particles (3.1%). 

 Boundary Waters. This region, in northern Minnesota, is represented by the Boundary Waters 

Canoe Area in the Superior National Forest. Annual average extinction here is about 56 Mm-1 of 

which 82% is due to the ambient aerosol. The seasonal variation is slight, and ranges from a high in 

the winter of 61.3 Mm-1 to as low as 52.7 Mm-1 in the spring. Sulfate contributes the most to 

extinction at 50.9% annually and ranges between 44.1% for the winter and up to 54.8% in the 

spring. Annually, the next largest contributor is organics (16.4%) followed by nitrate (14.5%), 

absorption (13.4%), and soil and coarse particles (4.8%). 
5-6

Table 5.2 Seasonal and annual averages of reconstructed total light extinction coefficient (Mm-1) 

for the 21 regions in the IMPROVE network. Also shown are the light scattering 

coefficients resulting from fine and coarse aerosols, light absorptions for carbonaceous 

aerosol, percentage of total extinction resulting from aerosols, and the average region 

relative humidity. 

Season Total 

Extinction 

Fine 

Scattering 

Coarse 

Scattering 

Absorption Percent 

Aerosol 

Relative 

Humidity 

Alaska

Spring 23.4 8.9 2.0 2.5 57 56 

Summer 26.0 10.6 1.9 3.5 62 64 

Autumn 20.9 7.9 1.3 1.7 52 72 

Winter 21.2 8.3 1.1 1.7 53 68 

ANNUAL 23.2 9.3 1.6 2.4 57 65 

Appalachian

Spring 107.8 79.2 3.8 14.8 91 67 

Summer 180.7 147.6 4.9 18.1 94 76 

Autumn 124.9 97.6 2.9 14.5 92 74 

Winter 88.3 65.4 2.2 10.8 89 74 

ANNUAL 128.0 100.0 3.4 14.6 92 73 

Boundary Waters

Spring 52.7 33.6 2.5 6.7 81 62 

Summer 54.0 34.9 2.5 6.6 81 76 

Autumn 53.3 35.7 2.0 5.5 81 79 

Winter 61.3 43.7 1.8 5.9 84 77 

ANNUAL 56.0 37.6 2.2 6.2 82 74 

Cascade Mountains

Spring 70.4 48.1 2.8 9.6 86 82 

Summer 87.4 65.0 2.5 10.0 89 83 

Autumn 69.6 47.0 2.5 10.1 86 86 

Winter 45.3 27.1 1.6 6.5 78 91 

ANNUAL 70.5 49.1 2.3 9.0 86 86 

Central Rocky Mountains

Spring 33.9 15.8 3.9 4.3 71 65 

Summer 34.0 13.7 4.3 6.1 71 49 

Autumn 29.5 12.3 2.9 4.4 66 55 

Winter 22.9 8.6 1.9 2.4 56 58 

ANNUAL 29.8 12.2 3.2 4.3 66 57 
5-7

Table 5.2 Continued 

Season Total 

Extinction 

Fine 

Scattering 

Coarse 

Scattering 

Absorption Percent 

Aerosol 

Relative 

Humidity 

Colorado Plateau

Spring 30.9 12.7 3.4 4.8 68 48 

Summer 33.6 13.8 3.9 5.9 70 44 

Autumn 31.6 13.5 2.8 5.3 68 48 

Winter 29.3 13.5 2.0 3.8 66 61 

ANNUAL 31.4 13.4 3.0 4.9 68 50 

Florida

Spring 115.3 85.5 4.7 15.2 91 70 

Summer 112.1 78.8 9.1 14.1 91 75 

Autumn 104.5 77.5 4.2 12.7 90 77 

Winter 102.0 72.0 4.0 16.0 90 75 

ANNUAL 110.6 80.5 5.5 14.6 91 74 

Great Basin

Spring 28.3 9.8 4.2 4.2 65 50 

Summer 32.1 10.9 5.6 5.5 69 34 

Autumn 27.9 10.3 3.4 4.2 64 49 

Winter 23.7 9.3 1.8 2.7 58 64 

ANNUAL 27.9 10.1 3.7 4.1 64 49 

Lake Tahoe

Spring 45.1 17.2 5.2 12.8 78 53 

Summer 42.6 15.9 4.1 12.6 77 42 

Autumn 52.6 21.1 3.8 17.7 81 48 

Winter 62.0 25.2 6.1 20.7 84 57 

ANNUAL 50.3 19.7 4.7 15.8 80 50 

Mid Atlantic

Spring 92.5 61.6 6.5 14.3 89 72 

Summer 128.2 93.0 7.7 17.6 92 76 

Autumn 88.5 55.8 5.6 17.1 89 68 

Winter 90.2 57.0 4.6 18.6 89 66 

ANNUAL 98.8 66.0 5.9 16.9 90 71 

Mid South

Spring 123.8 92.2 3.7 17.9 92 68 

Summer 163.9 128.1 7.5 18.2 94 78 

Autumn 126.5 97.1 3.8 15.7 92 74 

Winter 127.0 98.0 3.0 16.0 92 77 

ANNUAL 137.0 105.6 4.5 17.0 93 74 
5-8

Table 5.2 Continued 

Season Total 

Extinction 

Fine 

Scattering 

Coarse 

Scattering 

Absorption Percent 

Aerosol 

Relative 

Humidity 

Northeast

Spring 61.7 41.3 2.9 7.4 84 69 

Summer 102.7 80.0 3.0 9.7 90 79 

Autumn 79.7 58.9 2.6 8.2 87 79 

Winter 65.8 45.0 2.6 8.2 85 74 

ANNUAL 77.3 56.1 2.7 8.4 87 75 

Northern Great Plains

Spring 49.9 29.3 4.1 6.6 80 64 

Summer 44.2 24.0 3.8 6.3 77 63 

Autumn 41.5 21.8 3.9 5.8 76 62 

Winter 52.1 34.5 2.1 5.5 81 72 

ANNUAL 46.6 27.1 3.5 6.1 79 65 

Northern Rocky Mountains

Spring 48.2 26.4 3.6 8.2 79 77 

Summer 49.0 25.1 5.7 8.2 80 71 

Autumn 67.6 39.7 4.9 13.1 85 80 

Winter 67.0 46.1 1.9 8.9 85 86 

ANNUAL 57.2 33.6 4.0 9.6 83 79 

Pacific Coast

Spring 55.4 33.6 5.9 5.8 82 73 

Summer 55.5 34.5 5.6 5.4 82 72 

Autumn 62.8 39.3 5.3 8.2 84 71 

Winter 56.5 36.4 3.7 6.5 82 75 

ANNUAL 58.4 36.8 5.2 6.4 83 73 

Sierra-Humboldt

Spring 32.9 15.3 2.9 4.6 70 67 

Summer 37.6 18.1 3.0 6.4 73 71 

Autumn 31.0 13.5 2.3 5.2 68 55 

Winter 24.2 9.6 1.2 3.5 59 66 

ANNUAL 31.6 14.2 2.4 5.0 68 65 

Sierra Nevada

Spring 44.8 23.8 3.9 7.0 78 63 

Summer 48.9 23.9 4.4 10.6 80 44 

Autumn 39.7 18.6 3.6 7.4 75 45 

Winter 23.7 9.6 2.1 2.1 58 56 

ANNUAL 40.0 19.8 3.5 6.7 75 52 
5-9

Table 5.2 Continued 

Season Total 

Extinction 

Fine 

Scattering 

Coarse 

Scattering 

Absorption Percent 

Aerosol 

Relative 

Humidity 

Sonoran Desert

Spring 35.9 14.0 5.0 6.9 72 37 

Summer 39.8 17.6 4.9 7.2 75 43 

Autumn 35.5 15.7 3.4 6.4 72 45 

Winter 32.5 14.9 2.7 4.9 69 56 

ANNUAL 36.2 15.8 4.0 6.4 72 45 

Southern California

Spring 102.3 73.6 6.2 12.5 90 55 

Summer 80.3 47.9 7.4 15.0 88 45 

Autumn 54.6 27.5 8.2 8.9 82 41 

Winter 35.6 19.8 1.9 3.9 72 51 

ANNUAL 69.7 43.8 5.8 10.2 86 48 

Washington D.C.

Spring 155.1 102.8 5.5 36.8 94 62 

Summer 216.6 160.7 5.4 40.4 95 68 

Autumn 188.8 131.6 5.3 41.8 95 68 

Winter 161.1 101.9 5.5 43.7 94 62 

ANNUAL 182.5 126.5 5.4 40.6 95 65 

West Texas

Spring 41.0 18.1 5.6 7.3 76 41 

Summer 51.2 26.3 6.7 8.2 80 54 

Autumn 39.7 19.2 4.8 5.7 75 53 

Winter 37.1 18.3 3.7 5.1 73 53 

ANNUAL 42.3 20.5 5.2 6.6 76 50 
5-10

Table 5.3 Seasonal and annual averages of reconstructed aerosol light extinction coefficient 

(Mm-1) for the 21 regions in the IMPROVE network. Also shown are the light 

extinction coefficients (Mm-1) resulting from sulfate, nitrate, organic carbon, light 

absorption, and coarse particles/fine soil.

Season Aerosol 

Extinction 

Sulfate Nitrate Organics Absorption Soil and 

Coarse 

Alaska

Spring 13.4 5.3 0.5 3.1 2.5 2.0 

Summer 16.0 3.8 0.4 6.4 3.5 1.9 

Autumn 10.9 4.5 0.5 2.9 1.7 1.3 

Winter 11.2 4.8 0.8 2.8 1.7 1.1 

ANNUAL 13.2 4.9 0.6 3.8 2.4 1.6 

Appalachian

Spring 97.8 60.6 7.9 10.7 14.8 3.8 

Summer 170.7 129.1 3.8 14.7 18.1 4.9 

Autumn 114.9 78.5 6.9 12.1 14.5 2.9 

Winter 78.3 44.0 12.1 9.2 10.8 2.2 

ANNUAL 118.0 80.7 7.6 11.7 14.6 3.4 

Boundary Waters

Spring 42.7 23.4 4.1 6.1 6.7 2.5 

Summer 44.0 22.5 1.4 11.1 6.6 2.5 

Autumn 43.3 22.7 6.4 6.7 5.5 2.0 

Winter 51.3 22.6 15.1 5.9 5.9 1.8 

ANNUAL 46.0 23.4 6.7 7.5 6.2 2.2 

Cascade Mountains

Spring 60.4 30.3 5.9 11.9 9.6 2.8 

Summer 77.4 46.7 6.8 11.5 10.0 2.5 

Autumn 59.6 29.9 4.5 12.6 10.1 2.5 

Winter 35.3 14.4 3.6 9.2 6.5 1.6 

ANNUAL 60.5 32.4 5.4 11.3 9.0 2.3 

Central Rocky Mountains

Spring 23.9 9.1 2.2 4.4 4.3 3.9 

Summer 24.0 5.4 0.9 7.4 6.1 4.3 

Autumn 19.5 5.6 1.1 5.6 4.4 2.9 

Winter 12.9 3.6 1.0 4.0 2.4 1.9 

ANNUAL 19.8 5.6 1.2 5.4 4.3 3.2 
5-11

Table 5.3 Continued 

Season Aerosol 

Extinction 

Sulfate Nitrate Organics Absorption Soil and 

Coarse 

Colorado Plateau

Spring 20.9 6.5 1.4 4.7 4.8 3.4 

Summer 23.6 6.9 1.0 5.9 5.9 3.9 

Autumn 21.6 6.6 1.0 5.9 5.3 2.8 

Winter 19.3 7.1 2.0 4.4 3.8 2.0 

ANNUAL 21.4 6.9 1.3 5.2 4.9 3.0 

Florida

Spring 105.3 65.5 6.7 13.3 15.2 4.7 

Summer 102.1 61.2 5.8 11.8 14.1 9.1 

Autumn 94.5 60.1 5.8 11.6 12.7 4.2 

Winter 92.0 50.3 8.3 13.4 16.0 4.0 

ANNUAL 100.6 61.1 6.8 12.5 14.6 5.5 

Great Basin

Spring 18.3 4.0 1.0 4.8 4.2 4.2 

Summer 22.1 3.4 0.6 7.0 5.5 5.6 

Autumn 17.9 3.7 0.7 5.9 4.2 3.4 

Winter 13.7 3.5 1.4 4.4 2.7 1.8 

ANNUAL 17.9 3.7 0.9 5.5 4.1 3.7 

Lake Tahoe

Spring 35.1 4.5 2.1 10.5 12.8 5.2 

Summer 32.6 4.3 1.2 10.4 12.6 4.1 

Autumn 42.6 3.7 1.9 15.5 17.7 3.8 

Winter 52.0 2.3 2.9 20.0 20.7 6.1 

ANNUAL 40.3 3.9 2.0 13.9 15.8 4.7 

Mid Atlantic

Spring 82.5 42.9 9.2 9.5 14.3 6.5 

Summer 118.2 72.5 6.5 13.9 17.6 7.7 

Autumn 78.5 36.2 7.3 12.3 17.1 5.6 

Winter 80.2 29.4 14.4 13.2 18.6 4.6 

ANNUAL 88.8 44.1 9.7 12.2 16.9 5.9 

Mid South

Spring 113.8 66.7 12.6 13.0 17.9 3.7 

Summer 153.9 107.1 5.6 15.5 18.2 7.5 

Autumn 116.5 74.2 8.5 14.4 15.7 3.8 

Winter 117.0 60.8 24.7 12.5 16.0 3.0 
5-12

ANNUAL 127.0 78.8 12.9 13.8 17.0 4.5 

Table 5.3 Continued 

Season Aerosol 

Extinction 

Sulfate Nitrate Organics Absorption Soil and Coarse 

Northeast 

Spring 51.7 30.4 4.4 6.5 7.4 2.9 

Summer 92.7 65.6 4.1 10.3 9.7 3.0 

Autumn 69.7 45.2 6.5 7.1 8.2 2.6 

Winter 55.8 29.5 8.1 7.5 8.2 2.6 

ANNUAL 67.3 42.4 5.8 7.9 8.4 2.7 

Northern Great Plains

Spring 39.9 18.2 5.7 5.4 6.6 4.1 

Summer 34.2 14.5 1.3 8.2 6.3 3.8 

Autumn 31.5 11.1 4.0 6.7 5.8 3.9 

Winter 42.1 17.5 11.8 5.2 5.5 2.1 

ANNUAL 36.6 15.3 5.4 6.4 6.1 3.5 

Northern Rocky Mountains

Spring 38.2 13.3 2.8 10.3 8.2 3.6 

Summer 39.0 11.4 1.5 12.2 8.2 5.7 

Autumn 57.6 16.0 4.9 18.8 13.1 4.9 

Winter 57.0 21.9 12.7 11.5 8.9 1.9 

ANNUAL 47.2 15.5 4.8 13.2 9.6 4.0 

Pacific Coast

Spring 45.4 18.6 8.9 6.1 5.8 5.9 

Summer 45.5 21.8 7.0 5.7 5.4 5.6 

Autumn 52.8 19.0 10.3 10.0 8.2 5.3 

Winter 46.5 10.4 18.4 7.6 6.5 3.7 

ANNUAL 48.4 18.1 11.4 7.3 6.4 5.2 

Sierra-Humboldt

Spring 22.9 7.1 2.7 5.5 4.6 2.9 

Summer 27.6 8.0 1.7 8.4 6.4 3.0 

Autumn 21.0 4.7 1.6 7.1 5.2 2.3 

Winter 14.2 3.6 1.8 4.1 3.5 1.2 

ANNUAL 21.6 5.9 1.9 6.4 5.0 2.4 

Sierra Nevada

Spring 34.8 10.7 5.6 7.6 7.0 3.9 

Summer 38.9 7.1 2.1 14.7 10.6 4.4 

Autumn 29.7 5.9 2.8 10.0 7.4 3.6 
5-13

Winter 13.7 3.3 2.0 4.3 2.1 2.1 

ANNUAL 30.0 7.5 3.2 9.0 6.7 3.5 

Table 5.3 Continued 

Season Aerosol 

Extinction 

Sulfate Nitrate Organics Absorption Soil and 

Coarse 

Sonoran Desert

Spring 25.9 6.5 1.4 6.0 6.9 5.0 

Summer 29.8 9.7 1.1 6.8 7.2 4.9 

Autumn 25.5 8.0 0.9 6.8 6.4 3.4 

Winter 22.5 7.8 1.8 5.4 4.9 2.7 

ANNUAL 26.2 8.3 1.3 6.2 6.4 4.0 

Southern California

Spring 92.3 13.7 47.2 12.6 12.5 6.2 

Summer 70.3 11.5 20.3 16.1 15.0 7.4 

Autumn 44.6 6.4 12.5 8.6 8.9 8.2 

Winter 25.6 4.6 10.8 4.5 3.9 1.9 

ANNUAL 59.7 9.8 23.5 10.5 10.2 5.8 

Washington D.C.

Spring 145.1 64.8 21.0 17.0 36.8 5.5 

Summer 206.6 128.4 11.9 20.4 40.4 5.4 

Autumn 178.8 84.7 25.3 21.6 41.8 5.3 

Winter 151.1 46.8 31.5 23.6 43.7 5.5 

ANNUAL 172.5 83.0 22.9 20.6 40.6 5.4 

West Texas

Spring 31.0 10.3 1.1 6.6 7.3 5.6 

Summer 41.2 17.2 1.9 7.2 8.2 6.7 

Autumn 29.7 12.2 1.2 5.8 5.7 4.8 

Winter 27.1 11.1 2.0 5.2 5.1 3.7 

ANNUAL 32.3 12.8 1.5 6.2 6.6 5.2 
5-14

Table 5.4 Seasonal and annual averages of percentage contributions to the reconstructed aerosol 

light extinction coefficient (light extinction budget) for the 21 regions in the IMPROVE 

network for sulfate, nitrate, organic carbon, absorption, and coarse particle/fine soil. 

Season Sulfate Nitrate Organics Absorption Soil and 

Coarse 

Alaska

Spring 39.5 3.8 23.2 18.9 14.7

Summer 24.0 2.2 40.1 21.6 12.1

Autumn 41.3 4.8 26.6 15.5 11.8

Winter 42.8 7.0 24.8 15.4 9.9

ANNUAL 37.0 4.3 29.0 17.8 12.0

Appalachian

Spring 62.0 8.1 10.9 15.2 3.9

Summer 75.7 2.2 8.6 10.6 2.9

Autumn 68.3 6.0 10.5 12.6 2.5

Winter 56.2 15.5 11.8 13.8 2.8

ANNUAL 68.4 6.4 9.9 12.4 2.9

Boundary Waters

Spring 54.8 9.6 14.2 15.6 5.7

Summer 51.1 3.1 25.2 14.9 5.7

Autumn 52.5 14.7 15.4 12.8 4.7

Winter 44.1 29.4 11.6 11.4 3.5

ANNUAL 50.9 14.5 16.4 13.4 4.8

Cascade Mountains

Spring 50.2 9.7 19.6 15.8 4.6

Summer 60.3 8.8 14.8 12.9 3.2

Autumn 50.1 7.6 21.2 16.9 4.2

Winter 40.7 10.1 26.1 18.5 4.5

ANNUAL 53.5 9.0 18.7 14.9 3.9

Central Rocky Mountains

Spring 38.1 9.2 18.6 17.9 16.3

Summer 22.3 3.9 30.7 25.2 17.8

Autumn 28.7 5.7 28.6 22.4 14.7

Winter 28.2 8.0 30.8 18.5 14.5

ANNUAL 28.6 6.3 27.1 21.7 16.4 
5-15

Table 5.4 Continued 

Season Sulfate Nitrate Organics Absorption Soil and 

Coarse

Colorado Plateau

Spring 31 2 6 8 22 4 23 1 16 4

Summer 29.3 4.1 24.9 25.1 16.5

Autumn 30.3 4.8 27.2 24.5 13.2

Winter 37.0 10.1 22.8 19.7 10.3

ANNUAL 32.3 6.1 24.3 23.1 14.2

Florida

Spring 62.2 6.3 12.6 14.4 4.4

Summer 60.0 5.7 11.6 13.9 8.9

Autumn 63.6 6.1 12.3 13.5 4.5

Winter 54.6 9.0 14.5 17.4 4.4

ANNUAL 60.8 6.8 12.5 14.5 5.5

Great Basin

Spring 21.9 5.5 26.3 23.2 23.0

Summer 15.2 2.8 31.5 25.1 25.4

Autumn 20.5 3.9 33.1 23.4 19.1

Winter 25.6 9.9 32.1 19.6 12.7

ANNUAL 20.7 4.9 30.7 23.0 20.8

Lake Tahoe

Spring 12.9 6.0 30.0 36.4 14.7

Summer 13.1 3.7 32.0 38.6 12.5

Autumn 8.6 4.4 36.5 41.6 8.8

Winter 4.5 5.6 38.4 39.9 11.7

ANNUAL 9.7 4.9 34.5 39.2 11.8

Mid Atlantic

Spring 52.1 11.1 11.6 17.4 7.9

Summer 61.4 5.5 11.8 14.8 6.5

Autumn 46.2 9.3 15.6 21.8 7.1

Winter 36.7 17.9 16.5 23.2 5.8

ANNUAL 49.7 10.9 13.8 19.0 6.7

Mid South

Spring 58.6 11.0 11.4 15.7 3.3

Summer 69.6 3.6 10.0 11.8 4.9

Autumn 63.6 7.3 12.3 13.5 3.2
5-16

ANNUAL 62.0 10.2 10.9 13.4 3.5 

Table 5.4 Continued 

Season Sulfate Nitrate Organics Absorption Soil/Coarse 

Northeast

Spring 58.9 8.5 12.6 14.3 5.7 

Summer 70.7 4.4 11.1 10.5 3.3 

Autumn 64.9 9.4 10.2 11.8 3.7 

Winter 52.8 14.5 13.4 14.7 4.6 

ANNUAL 63.1 8.7 11.7 12.5 4.1 

Northern Great Plains

Spring 45.6 14.3 13.5 16.5 10.2 

Summer 42.5 3.9 23.9 18.5 11.2 

Autumn 35 4 12 6 21 2 18 6 12 3

Winter 41.6 28.0 12.3 13.0 5.1

ANNUAL 41.7 14.9 17.4 16.6 9.5 

Northern Rocky Mountains

Spring 34 8 7 3 27 0 21 4 9 4

Summer 29 3 3 9 31 2 21 0 14 6

Autumn 27 7 8 5 32 6 22 7 8 4

Winter 38 4 22 3 20 2 15 7 3 4

ANNUAL 32 9 10 3 28 0 20 3 8 6

Pacific Coast

Spring 41 0 19 7 13 4 12 9 13 1

Summer 47 9 15 4 12 5 11 8 12 3

Autumn 36 0 19 5 19 0 15 5 10 0

Winter 22 4 39 4 16 3 13 9 8 0

ANNUAL 37 3 23 6 15 1 13 3 10 7

Sierra-Humboldt

Spring 31 2 11 7 24 1 20 3 12 8

Summer 29 1 6 2 30 5 23 4 10 9

Autumn 22 6 7 8 33 8 25 0 10 7

Winter 25 5 12 7 28 9 24 4 8 4

ANNUAL 27 3 9 0 29 4 23 2 11 1

Sierra Nevada

Spring 30 7 16 0 21 7 20 2 11 3

Summer 18 2 5 4 37 9 27 3 11 3

Autumn 19 7 9 3 33 8 25 1 12 1
5-17

Winter 24 1 14 2 31 2 15 1 15 4

ANNUAL 25.0 10.8 30.1 22.4 11.7 

Table 5.4 Continued 

Season Sulfate Nitrate Organics Absorption Soil and 

Coarse

Sonoran Desert

Spring 25.2 5.5 23.2 26.7 19.4

Summer 32.7 3.6 22.8 24.3 16.6

Autumn 31.3 3.6 26.5 25.2 13.4

Winter 34.8 7.8 23.8 21.7 11.9

ANNUAL 31.5 5.0 23.8 24.3 15.4

Southern California

Spring 14.9 51.2 13.7 13.5 6.8

Summer 16.3 28.9 22.9 21.3 10.6

Autumn 14.4 27.9 19.3 20.0 18.4

Winter 17.8 42.1 17.4 15.3 7.4

ANNUAL 16.4 39.3 17.6 17.1 9.7

Washington D.C.

Spring 44.7 14.5 11.7 25.4 3.8

Summer 62.2 5.8 9.9 19.6 2.6

Autumn 47.4 14.2 12.1 23.4 3.0

Winter 31.0 20.8 15.7 28.9 3.6

ANNUAL 48.1 13.3 11.9 23.6 3.1

West Texas

Spring 33.3 3.6 21.5 23.5 18.2

Summer 41.7 4.7 17.4 19.9 16.3

Autumn 41.3 4.0 19.4 19.1 16.2

Winter 41.0 7.3 19.2 18.9 13.6

ANNUAL 39.6 4.7 19.2 20.3 16.2 
5-18

 Cascade Mountains. This region is represented by two sites, Mount Rainier National Park 

southeast of Seattle, and Columbia River Gorge on the Hood River National Forest. The site at 

Columbia River Gorge has operated for one year out of the last six and only Mount Rainier is 

reported here. The average annual extinction for this region is 70.5 Mm-1, of which 86% is due to 

aerosols. The seasonality is significant and ranges from a high in the summer of 87.4 Mm-1 then 

drops to a low in the spring of 45.3 Mm-1. The seasonality is driven primarily by sulfate. Sulfate 

extinction ranges from a summer high of 46.7 Mm-1 then drops to 14.4 Mm-1 in the summer. 

Organics show very little variance between seasons and has an annual average value of 11.3 Mm-1. 

The largest contributor to aerosol extinction is sulfate (53.5%), followed by organics (18.7%), and 

absorption (14.9%). Nitrates account for 9% of aerosol extinction and coarse extinction accounts 

for 3.9%. 

 Central Rocky Mountains. The measurements in this region are made at five locations in the 

mountainous Class I areas of Colorado and Wyoming, including the Bridger and Weminuche 

Wilderness Areas, Rocky Mountain and Yellowstone National Parks, and Great Sand Dunes 

National Monument. All five sites have been operated for six years and show an annual average 

total extinction for the three-year period of 29.8 Mm-1, of which 66% is due to aerosol extinction. 

The seasonal variation is significant and has a maximum in the summer of 34 Mm-1 and decreases 

to 22.9 Mm-1 during the winter. The seasonal variance is driven primarily by organic extinction 

and absorption. Organic extinction peaks at 7.4 Mm-1 in the summer and drops in the winter to 4 

Mm-1, absorption ranges for 6.1 Mm-1 in the summer and drops to 2.4 Mm-1 in the winter. Sulfates 

(28.6%) contribute the most to extinction annually followed by organics (27.1%), absorption 

(21.7%), coarse mass (16.4%), and nitrate is the smallest contributor (6.4%). During the summer 

sulfate is the third largest contributor at 22.8% with organics contributing the most at 30.7% 

followed by absorption at 25.2%. 

 Coastal Mountains. This region includes three Class I areas along and near the coast of northern 

California: Pinnacles National Monument, Point Reyes National Seashore, and Redwoods National 

Park. The average annual extinction during the three-year period for this area is 58.4 Mm-1 with 

83% due to aerosol extinction. The annual variance is very slight and only ranges between 55.4 

Mm-1 during the spring and 62.8 Mm-1 in the autumn. However, extinction due to sulfate and 

nitrate show large seasonal variances that are opposed to each other. Sulfate extinction obtains its 

maximum in the summer at 21.8 Mm-1 when nitrate extinction is at its minimum of 7 Mm-1. When 

nitrate extinction obtains its maximum of 18.4 Mm-1 during the winter sulfate extinction is at its 

minimum of 10.4 Mm-1. Organic extinction and absorption obtain their maxima in the autumn of 

10 Mm-1 and 8.2 Mm-1, respectively. On an annual basis, the largest contributor to aerosol 

extinction is sulfate (37.3%), followed by nitrate (23.6%), organics (15.1%), absorption (13.3%), 

and coarse particles (10.7%). The contribution from sulfate shows considerable variation ranging 

from a high in the summer of 47.9% to 22.4% in the winter when its contribution is eclipsed by 

nitrate, which contributes 39.4%. 

 Colorado Plateau. This region in the Four Corners' states of the Southwest is the most 

intensively monitored in the IMPROVE network. There are six sites, most of them within the socalled Golden Circle of national parks: Bandelier, Bryce Canyon, Canyonlands, Grand Canyon, 

Mesa Verde, and Petrified Forest National Parks. The three-year annual average for total extinction 
5-19

is relatively low at 31.4 Mm-1, 68% of which is aerosol extinction. There is a very slight variance 

between seasons of total extinction ranging from 29.3 Mm-1 in the winter to as high as 33.6 Mm-1

during the winter. The peaking of extinction in the winter is unlike most other regions. Here 

sulfate extinction obtains its maximum of 7.1 Mm-1 and is lowest in the spring at 6.5 Mm-1, and is 

at its next lowest in the autumn at 6.6 Mm-1. However, the seasonality of nitrate extinction is 

typically high during the winter at 2.0 Mm-1 and lowest during the summer at 1.0 Mm-1. The 

largest contribution to annual aerosol extinction is sulfate (32.3%) followed by organics (24.3%), 

absorption (23.1%), coarse particles (14.2%), and nitrate (6.1%). However, during the summer, 

extinction contributions from sulfate (29.3%), organics (24.9%), and absorption (25.1%) are about 

on par with each other. 

 Florida. This region now consists of two sites, Chassahowitzka National Wildlife Refuge north 

of Tampa and Okefenokee National Wildlife Refuge on the Georgia-Florida border. Previously, this 

site was represented by Everglades National Park, which has been downgraded to a channel A only 

monitoring site. The annual total extinction for this region is 111 Mm-1, 91% is due to aerosol 

extinction. Very little seasonal variance exists here, with spring having the most extinction of 115 

Mm-1 and winter the least at 102 Mm-1. The largest contributor to aerosol extinction is from sulfates 

(60.8%) followed by absorption (14.5%), organics (12.5%), nitrate (6.8%), and coarse particles 

(5.5%). 

 Great Basin. The Great Basin of Nevada is represented by two sites. The site at Jarbidge 

Wilderness Area in northeastern Nevada was implemented in March of 1988, and the other site at 

Great Basin National Park began operating in May of 1992. The annual average extinction during 

the three-year period for this region is quite low at 27.9 Mm-1, with 64% from aerosol extinction, 

the only region with less extinction is Alaska. A slight seasonal variation exists between 32.1 Mm-1

during the summer and 23.7 Mm-1 during the winter. On an annual basis the largest contributor to 

extinction is organics (30.7%) followed by absorption (23%), soil and coarse particles (20.8%), and 

sulfate (20.7%). This region is unique in that sulfate is the fourth largest contributor to extinction. 

This holds for two out of the four seasons (spring and summer). During the other seasons, sulfate 

extinction is larger than extinction from soil and coarse making sulfate the third largest contributor. 

 Lake Tahoe. Two sites represent the Lake Tahoe region: one is located in Bliss State Park, the 

other is close to the south end of the lake. The average extinction for this area is 50.3 Mm-1 with a 

modest seasonality with winter being the maximum season at 62 Mm-1, and summer being the 

clearest at 42.6 Mm-1. The seasonality is driven by organics and absorption, whose winter values of 

20.6 Mm-1 and 20.7 Mm-1, respectively, are about twice their summer levels. The dominant 

contributors to aerosol extinction are absorption (39.2%) and organics (34.5%), followed by soil 

and coarse particles (11.8%), sulfate (9.7%), and nitrate (4.9%). 

 Mid Atlantic. This region, represented by the Edmond B. Forsythe Wildlife Refuge, just west of 

Atlantic City, New Jersey, has an average annual reconstructed extinction of 98.8 Mm-1. There is a 

significant seasonality, with extinction moving from a high during the summer of 128 Mm-1, to 

88.5 Mm-1 in the autumn. Sulfates move between 72.5 Mm-1 in the summer and decreases to 29.4 

Mm-1 during winter and are responsible for the seasonality. Nitrate has an average winter value of 

14.4 Mm-1, about twice of all other seasons. Sulfates contribute about half (47.5%) of the aerosol 
5-20

extinction, followed by absorption (19.0%), organics (13.8%), nitrate (10.9%), and soil and coarse 

particles the least (6.7%). 

 Mid South. Three sites represent this region: Sipsey Wilderness Area in northern Mississippi, 

Upper Buffalo Wilderness Area in northern Arkansas, and Mammoth Cave National Park in 

Kentucky. This region has the highest levels of reconstructed extinction for a rural area. The only 

exception is Washington, D.C., which is an urban area. The average annual reconstructed 

extinction is 137 Mm-1 with a significant seasonal variation of 164 Mm-1 between the summer high 

and the spring low of 124 Mm-1. Sulfate dominates the aerosol extinction and is responsible for 

much of the seasonality observed. Sulfate extinction is highest in the summer at 107 Mm-1 and 

lowest in the spring at 60.8 Mm-1. Organics, and elemental carbon all have seasonal trends that peak 

in the summer but are lowest in the winter for organics and autumn for absorption. On an annual 

average, sulfate contributes 62% of the aerosol extinction peaking in the summer (69.6%) and is 

least in the winter (52%). The next largest contributor annually is absorption (13.4%) followed by 

organics (10.9%), and nitrate (10.2%). 

 Northeast. The northeastern United States is represented by measurements at two sites: Acadia 

National Park on the coast of Maine, which began operating in March of 1988; and, Lye Brook 

Wilderness Area in Vermont, which began operations in September of 1991. The average annual 

extinction during the three-year period for the Northeast is 77.3 Mm-1 of which aerosol extinction 

accounts for 87%. There is a significant seasonal variation of 61.7 Mm-1 with the spring being the 

least and the highest occurs during the summer at 102.7 Mm-1. Sulfates and organics are 

responsible for most of the seasonal variation with sulfates varying from 29.5 Mm-1 to 65.6 Mm-1

between winter and summer, and similarly organics vary between 6.5 Mm-1 in the spring to 10 

Mm-1 in the summer. Nitrate extinction obtains its maximum during the winter at 8.1 Mm-1 and its 

minimum at 4.1 Mm-1 during the summer. The largest contributor to extinction is from sulfates at 

63.1% annually. The next highest contributor is absorption (12.5%), followed by organics (11.7%), 

nitrate (8.7%), and soil and coarse particles (4.1%). 

 Northern Great Plains. Only one set of aerosol measurements was made in this region, at 

Badlands National Monument in South Dakota, where reconstructed light extinction averaged 46.6 

Mm-1. Unlike most other regions extinction was highest in spring and lowest in autumn. This 

seasonality is driven primarily by sulfate and nitrate extinction. Sulfate extinction obtains a 

maximum of 18.2 Mm-1 in the spring and has its seasonal minimum of 11.1 Mm-1 in the autumn. 

Nitrate extinction in the spring, at 5.7 Mm-1, is more than four times its summer extinction of 1.3 

Mm-1. The maximum nitrate extinction of 11.8 Mm-1 occurs in the winter. The main contributor to 

annual extinction is sulfate, which accounts for 41.7% of the extinction. The next highest 

contributor is absorption at 16.6% followed by organics at 17.4%, nitrate (14.9%), and coarse mass 

(9.5%). 

 Northern Rocky Mountains. This region is represented by one site at Glacier National Park 

close to the Canada border. Here, the reconstructed light extinction coefficient is 57.2 Mm-1 for an 

annual average of 83% due to aerosols. There is a modest seasonality ranging between 67.6 Mm-1

in the autumn down to 48.2 Mm-1 during the spring. The seasonality is driven by sulfate and nitrate 

extinction. Sulfate and nitrate extinctions peak during the winter at 22.9 Mm-1 and 12.7 Mm-1, 
5-21

respectively. The largest contributor to aerosol extinction is sulfate (32.9%) followed by organics 

(28%), and absorption (20.3%). 

 Sierra-Humboldt. The region further north in the Sierra Nevada and Humboldt Mountain 

Ranges was measured at Crater Lake National Park in Oregon and Lassen Volcanoes National Park 

in northern California. For this region, total reconstructed light extinction averaged 31.6 Mm-1 with 

maximum extinction in summer (37.6 Mm-1) and minimum extinction in winter (23.2 Mm-1). The 

seasonality is primarily variations from sulfate and organic extinctions and absorption. Organic 

carbon, sulfate, and elemental carbon contribute almost equally to annual extinction at 29.4%, 

27.3%, and 23.2%, respectively. 

 Sierra Nevada. The aerosol in the Sierra Nevada region is monitored at two sites: Yosemite 

National Park has been monitored since March 1988, monitoring at Sequoia-Kings Canyon began 

in March of 1992. The average reconstructed light extinction is 40 Mm-1 with a strong seasonal 

component that has a winter minimum of 23.7 Mm-1 and a summer maximum of 48.9 Mm-1. The 

seasonality is driven primarily by organics and absorption with both species peaking during the 

summer at 14.7 Mm-1 and 10.3 Mm-1, then dropping to 4.3 Mm-1 and 2.1 Mm-1 their minimum 

during the winter. Sulfate, to a lesser extent, is responsible for the seasonality, while its maximum 

occurs in the spring at 10 Mm-1. Its summer extinction drops off to 7.1 Mm-1 and obtains its 

seasonal low in the winter of 3.3 Mm-1. 

 Sonoran Desert. This region in southeastern Arizona was measured at two sites: Chiracahua and 

Tonto National Monuments. The three-year average reconstructed extinction is 36.2 Mm-1 and 

varies from a summer high of 39.8 Mm-1 to a winter low of 32.5 Mm-1. The seasonality is due to 

changes in extinction from sulfate, organics, and absorption. Sulfate and absorption obtain their 

seasonal maxima of 9.7 Mm-1 and 7.2 Mm-1 during the summer. The largest contributor to 

extinction is sulfate (31.5%) followed by absorption (24.3%), and organics (23.8%). 

 Southern California. Measurements in this region were made in San Gorgonio National 

Monument, east of the Los Angeles metropolitan area. Total reconstructed light extinction 

averaged over the three-year period was 69.7 Mm-1 and varied from a seasonal high of 102 Mm-1 in 

the spring to as little as 35.6 Mm-1 in the winter. The seasonality is driven primarily by nitrates and 

to a lesser extent sulfate, organics, and absorption, all of which obtain their maximum in the spring 

and their minimum in the winter. This region is unique in that nitrates are by far the largest 

contributor to annual extinction (39.3%) followed by absorption (17.1%), and organics (17.6%), 

sulfate (16.4%), and soil and coarse particles (9.7%). 

 Washington D.C. The highest light extinction coefficient, reconstructed from aerosol 

concentration, was found in Washington. It averaged 182 Mm-1 over the three-year period. 

Extinction was highest in the summer (216 Mm-1) and lowest in the spring (155 Mm-1). Most of 

the seasonality is due to sulfate. In the summer, sulfate extinction averaged 128 Mm-1, much higher 

than other seasons. Except for nitrate, the other species were fairly constant between seasons. 

Sulfate is the dominate contributor to light extinction, contributing nearly half (48.1%), followed by 

absorption (23.6%), nitrate (13.3%), organics (11.5%), and soil and coarse particles (3.1%). 
5-22

 West Texas. Total light extinction reconstructed from the aerosol measurements at Big Bend 

and Guadalupe Mountains National Parks averaged 42.3% over the three-year period. A modest 

seasonality is evident with the highest extinction in the summer (51.2 Mm-1) and the least during 

the winter (37.1 Mm-1). The seasonality is primarily due to sulfate, which is the largest contributor 

to aerosol extinction (39.6%) followed distantly by absorption (20.3%), organics (19.2%), soil and 

coarse particles (16.2%), and nitrate (4.7%). 

5.2.2 Spatial Trends in Reconstructed Light Extinction in the United States

 Figure 5.2 shows the sulfate light extinction coefficient averaged over the three-year period of 

IMPROVE (March 1992 - February 1995). Note that the highest sulfate extinction occurs in the 

eastern United States, and the lowest sulfate extinction occurs in Oregon, Nevada, Idaho, and 

Wyoming. The major gradient in sulfate light extinction is from the eastern United States to the 

nonurban West. However, there is also a gradient from the San Francisco Bay Area and from the 

Pacific Northwest to the nonurban west. Sulfate extinction is more than half of the total aerosol 

light extinction in the eastern and north central United States. In the Appalachians, Middle Atlantic 

states, and the Northeast, sulfate contributes about two thirds of aerosol light extinction. In the 

worst season for sulfate (summer), sulfate's share is even higher, reaching three quarters in the 

eastern United States. 

 Figure 5.3 shows the nitrate light extinction. There is a gradient from east to west, with 

relatively high nitrate contributions in the Washington D.C. area. However, the strongest gradient 

is from the urban areas of California, especially the Los Angeles metropolitan area, to the California 

desert. Nitrate contributions to aerosol light extinction are generally less than 10%, except in 

California, where nitrate can contribute as much as 40% and the upper midwest where nitrate 

extinction contributes in excess of 15%. 

 Figure 5.4 shows isopleths of the light extinction due to organics throughout the United States, 

averaged over the three-year period. Note that extinction caused by organic carbon is largest in the 

eastern United States and in the Pacific Northwest, and lowest in the Golden Circle of parks in 

southern Utah and northern Arizona. The fraction of aerosol light extinction contributed by organic 

carbon ranges from a high of more than 30% in the Great Basin Region to less than 20% in the 

urban areas of California and in much of the eastern United States. The reason that organic carbon 

is a smaller share of aerosol extinction in the East is the much larger contribution of sulfate 

extinction there. 

 Figure 5.5 shows isopleths of the extinction caused by absorption. Absorption is highest in the 

Pacific Northwest and in the eastern United States and lowest in the nonurban west. However, the 

greatest contribution by absorption is in the nonurban west, Great Basin region, and the Sonoran 

Desert, with more than 20% of extinction from absorption being routine. Except for the coastal 

regions of northern California, Oregon, and Washington, most of the western United States has a 

contribution from absorption in excess of 18%. 

 Figure 5.6 shows isopleths of light extinction due to coarse material throughout the United 

States, averaged over the three-year period. Extinction caused by coarse material is highest in the 
5-23

Coastal Mountains, West Texas, Mid South, Florida, Appalachian, and Mid Atlantic regions. The 

least contribution occurs in the Northeast, Colorado Plateau, and portions of the Central Rockies. 

The fraction of aerosol extinction contributed by coarse material shows an east-west dichotomy 

with the eastern United States having the lowest percentages with the Mid South and Appalachian 

regions at about 3%. In the West, there is a large region that encompasses the Central Rockies, 

Sonoran Desert, West Texas, and the Great Basin that routinely exceeds 15%. 
5-24

Figure 5.2 Three-year averages of reconstructed sulfate light extinction coefficient in Mm-1 (top) 

and sulfate fraction in percent of aerosol light extinction (bottom), for each of the sites 

in the IMPROVE network reported for the United States. 
5-25

Figure 5.3 Three-year averages of reconstructed nitrate light extinction coefficient in Mm-1 (top) 

and nitrate fraction in percent of aerosol light extinction (bottom), for each of the sites 

in the IMPROVE network reported for the United States. 
5-26

Figure 5.4 Three-year averages of reconstructed organic carbon light extinction coefficient in 

Mm-1 (top) and organic carbon fraction in percent of aerosol light extinction (bottom), 

for each of the sites in the IMPROVE network reported for the United States. 
5-27

Figure 5.5 Three-year averages of reconstructed absorption coefficient in Mm-1 (top) and 

absorption fraction in percent of aerosol light extinction (bottom), for each of the sites 

in the IMPROVE network reported for the United States. 
5-28

Figure 5.6 Three-year averages of reconstructed light extinction due to coarse material in Mm-1

(top) and percent of aerosol extinction (bottom), for each of the sites in the 

IMPROVE network reported for the United States. 
5-29

5.2.3 Spatial Trends in Visibility in the United States

 To show the effect on visibility of aerosol extinction the deciview (dv) scale is applied to the 

total (Rayleigh included) aerosol extinction (see Chapter 1). By utilizing the dv scale the effect of 

aerosol extinction on the human visual system is portrayed as a linear scale of visibility degradation. 

 Pristine or Rayleigh conditions have a dv of zero. A one or two dv change is usually associated 

with the minimal or just noticeable change (JNC) in visibility perceived by the average individual. 

 Figure 5.7 shows isopleths of deciviews averaged over the three-year period. There is a broad 

region that includes the Great Basin, most of the Colorado Plateau and portions of the Central 

Rockies that has visibility impairment of less than 10 dv or better visibility. Moving in any 

direction from this region generally results in a gradient of increasing dv. West of the Sierra Range 

and including southern California have dv values in excess of 15. To the north a maximal value of 

20 dv occurs at Mount Rainier. The northwest United States and all of the eastern half of the United 

States have in excess of 15 dv of impaired visibility and the region east of the Mississippi, and 

south of the Great Lakes have impairment in excess of 24 dv with the Appalachian region 

exceeding 26 dv. The highest annual dv is reported at Washington D.C. with an impairment of 29 

dv followed by Sipsey at 28 dv.

Figure 5.7 Average visibility impairment in deciviews calculated from total (Rayleigh included) 

reconstructed light extinction for the three-year period, March 1992 through February 

1995, of IMPROVE. 
5-30

 Isopleths of dv for the winter, spring, summer, and autumn are shown in Figure 5.8 through 

Figure 5.11, respectively. The general spatial trend noted above for the annual average generally 

holds true for each season's average dv trend. Specifically, the least impairment or lowest dv's 

generally occur in all or part of the Great Basin, Colorado Plateau, and Central Rockies with 

gradients of increasing dv in any direction. One interesting exception to this occurs in the winter 

(Figure 5.8), which shows an "island" of impaired visibility in the middle of the Colorado Plateau 

region at Canyonlands with a dv of 12. It is also of interest to note that the eastern United States is 

almost uniformly above 20 dv of impairment for all four seasons. 

 The best visibility in the West occurs during the winter (Figure 5.8) with a minimum dv of 7 

being reported at Bridger Wilderness followed by 8 dv at Jarbidge. The region of 10 or less dv's 

encompasses a broad expanse that covers the Sierra-Humboldt, Sierra Nevada, Great Basin, Central 

Rockies, and the northwestern half of the Colorado Plateau. In the eastern half of the United States 

the season of best visibility is split between winter and spring. In the Northeast and Florida, the 

winter is best for visibility, while the Appalachian and Mid-West are variable between sites. 

However, all sites east of the Mississippi and south of the Great Lakes site have impairment in 

excess of 20 dv's for both the spring and winter. 

 Summertime visibilities (Figure 5.10), except for the Coastal Range, are generally the worst. 

Only small portions of the Great Basin, Central Rockies, and Colorado Plateau regions have 

impaired visibilities slightly below 12 dv. In the East, including the Ozark Plateau, there is a broad 

region east of the Mississippi with more than 26 dv of impairment in visibility. Moreover, 

Washington, Shenandoah, and Sipsey exceed 30 dv's in impairment. 

 Visibility impairment in the spring (Figure 5.9) and autumn (Figure 5.11) are quite 

comparable. The exceptions to this are in the East where extinction is higher in the autumn, while 

in the intermountain west, autumn is generally less impaired, particularly in the Central Rockies and 

the Sierra-Humboldt regions. Southern California has better visibility in the autumn. 

5.3 Summary

 The following are the major patterns in light extinction reconstructed from aerosol 

measurements and relative humidity during the three-year period of IMPROVE (March 1992-

February 1995): 

1. Spatial Patterns. Following the patterns observed in fine aerosol concentrations, reconstructed 

light extinction is highest in the eastern United States and in urban California and lowest in the 

nonurban west. 

2. Major Contributors to Light Extinction. Fine aerosols are the most effective in scattered light 

and are the major contributors to light extinction. In most cases, the sulfate component of fine 

aerosol is the largest single contributor to light extinction. This is because sulfate, being 

hygroscopic, generally has a higher light extinction efficiency than other species due to 

associated liquid water. This is especially true in the eastern United States, where relative 

humidity is high. In the Appalachian Mountains (Shenandoah and Great Smoky Mountains), 
5-31

sulfate accounts for 2/3 of the total aerosol light extinction throughout the year, and 3/4 of the 

total in summer. Sulfate is the largest single contributor to light extinction in 14 of the 21 

regions, and is comparable with organics as the most significant contributor in three additional 

regions (Northern Rockies, Central Rockies, and Sierra-Humboldt). Organic carbon is the 

largest single contributor to light extinction in three of the 21 regions (Great Basin, Sierra 

Nevada, and Lake Tahoe) and is a major contributor in the two previously mentioned regions. 

Smaller contributions come from wind-blown dust (coarse particles and fine soil) and nitrate. 

Nitrate is the single largest contributor to light extinction only in southern California. 

3. Smaller Contributors. After sulfate and organic carbon, nitrate, and wind-blown dust (coarse 

particles and fine soil) generally contribute equal amounts. Light-absorbing carbon is generally 

the smallest contributor. 

4. Seasonality. Generally, reconstructed light extinction is highest in summer and lowest in 

winter; however, there are many exceptions to this general rule. Higher extinction occurs in 

summer generally because of relatively elevated sulfate and carbonaceous aerosol 

concentrations. 

Figure 5.8 Average winter visibility impairment in deciviews calculated from total (Rayleigh 

included) reconstructed light extinction for the three-year period, March 1992 through 

February 1995, of IMPROVE. 
5-32

Figure 5.9 Average spring visibility impairment in deciviews calculated from total (Rayleigh 

included) reconstructed light extinction for the three-year period, March 1992 through 

February 1995, of IMPROVE. 

 

Figure 5.10 Average summer visibility impairment in deciviews calculated from total (Rayleigh 

included) reconstructed light extinction for the three-year period, March 1992 

through February 1995, of IMPROVE. 
5-33

Figure 5.11 Average autumn visibility impairment in deciviews calculated from total (Rayleigh 

included) reconstructed light extinction for the three-year period, March 1992 through 

February 1995, of IMPROVE. 
6-1

CHAPTER 6 

TEMPORAL TRENDS AND INTERRELATIONSHIPS OF 

AEROSOL CONCENTRATIONS

 The IMPROVE aerosol monitoring network, established in March 1988, initially consisted of 

36 sites instrumented with aerosol sampling modules A through D [Sisler et al., 1993]. Many of 

the IMPROVE sites are successors to sites where aerosol monitoring with stacked filter units (SFU) 

was carried out as early as 1979 [Sisler and Malm, 1989]. The IMPROVE module A is identical in 

many aspects to the second stage of the SFU sampler. Both methods measured PM2.5 samples of 

ambient aerosol on Teflon filters and were subjected to the same assay techniques (see Table 2.1). 

In this discussion, three measured values will be examined in some detail: gravimetric fine mass 

(FM), sulfur as measured by Proton Induced X-ray Emission (PIXE), and absorption (babs) 

measured by the Laser Integrating Plate Method (LIPM) [Eldred et al., 1988, Cahill et al., 1986]. 

Assuming an absorption efficiency of 10 m2/gm, babs is expressed as a mass in ng/m3. 

 The IMPROVE sites that can be paired with antecedent SFU sites have an almost unbroken 

record of sulfur and fine mass (and other elements measured by PIXE) from as early as 1979 and 

babs from 1983. Table 6.1 lists the sites and time periods that IMPROVE or SFU samplers were 

operated. These data provide an excellent opportunity to look for evidence of long-term trends in 

aerosol concentrations. 

 Two distinct temporal trends are considered here: seasonal, and long-term trends of statistical 

measures such as maxima, minima, percentiles, and standard deviations. For the sake of 

completeness, Appendix 1 has time lines of FM, sulfur, and babs for every IMPROVE/SFU site. 

Presented here for discussion are data that demonstrate identifiable trends and differences between 

sites. 

6.1 Protocol Induced Trends of Sulfur Concentrations and babs

 Two significant changes in sampling protocol have occurred since sampling began in 1979. In 

June 1986, the SFU sampling schedule was changed from two 72-hour duration samples per week, 

with start times alternating between midnight and noon, to two 24-hour samples per week, with 

both start times at midnight. The IMPROVE network has maintained the new schedule. In March 

1988, the IMPROVE network succeeded the SFU network. There was a three month hiatus from 

December 1987 through February 1988 when almost no samples were obtained while equipment 

was changed.
6-2

Table 6.1 Sites and time periods for IMPROVE and SFU. 

Acronym Full Name SFU Start SFU End IMPROVE 

Start 

IMPROVE 

End 

ACAD Acadia NP 9/21/85 11/28/87 3/01/88 Present

ARCH Arches NP 9/28/79 11/28/87 3/01/88 5/92 

BAND Bandelier NM 10/02/82 2/09/85 3/01/88 Present

BIBE Big Bend NP 7/27/82 11/28/87 3/01/88 Present

BRCA Bryce Canyon NP 9/21/79 12/02/87 3/01/88 Present

BRLA Brooklyn Lake 3/01/91 7/31/93 7/31/93 Present

CANY Canyonlands NP 9/21/79 11/28/87 3/01/88 Present

CHIR Chiricahua NM 6/8/82 5/31/86 3/01/88 Present

CRLA Crater Lake NP 10/12/82 11/28/87 3/01/88 Present

CRMO Craters of the Moon 7/17/82 3/29/86 5/12/92 Present

DENA Denali NP & 9/10/86 11/25/87 3/01/88 Present

DEVA Death Valley NP 6/01/82 3/29/86 10/18/93 Present

GLAC Glacier NP 9/28/82 12/5/87 3/01/88 Present

GICL Gila NF 10/1/79 8/31/81 3/28/94 Present

GRBA Great Basin NP 10/12/82 3/29/86 5/00/88 Present

GRCA Grand Canyon NP 8/03/79 11/28/87 3/01/88 Present

GRSA Great Sand Dunes 9/15/80 8/31/81 5/04/88 Present

GRSM Great Smoky Mtns 1/31/84 11/28/87 3/01/88 Present

GUMO Guadalupe Mtns NP 2/19/83 12/02/87 3/01/88 Present

LAVO Lassen Volcanic NP 6/29/82 5/29/84 3/01/88 Present

MEVE Mesa Verde NP 10/30/82 12/05/87 3/01/88 Present

MORA Mount Rainier NP 7/23/83 12/16/87 3/01/88 Present

PEFO Petrified Forest NP 7/30/79 11/25/87 3/01/88 Present

ROMO Rocky Mountain NP 9/21/79 12/02/87 9/15/90 Present

SAGU Saguaro NM 7/2/85 8/31/88 3/1/88 Present

SALM Salmon NF 9/01/90 11/13/93 11/09/93 Present

SHEN Shenandoah NP 7/13/82 11/28/87 3/01/88 Present

TONT Tonto NM 8/3/79 11/29/83 3/01/88 Present

VOYA Voyageurs NP 7/13/85 Present 3/01/88 Present

YELL Yellowstone NP 9/29/79 12/05/87 3/01/88 Present

YOSE Yosemite NP 9/25/82 10/28/87 3/01/88 Present

NP = National Park 

NM = National Monument 

NF = National Forest 

 Both changes in protocol are relatively close to each other in time. Therefore, it is difficult to 

separate the effects of one change from the other using the data. Since there are no monitoring sites 

where SFU samplers and IMPROVE samplers were operated side by side, any changes due to 

protocol must be hunted for in the data. The purpose of this chapter is not to put this issue to rest by 

exhaustive statistical analysis but rather to alert the reader to the possibility. However, since the 
6-3

changes in protocol affect all sampling sites, the affects should be systematic across the network. 

 Two changes in the data that are most probable are a smoothing effect due to the change in the 

sampling duration and a bias in elemental concentrations, absorption and fine mass due to the 

change from SFU samplers to IMPROVE samplers. One would expect a smoothing effect for data 

collected over 72 hours compared to data collected over 24 hours. Smoothing of the data would 

show a tighter distribution about the mean resulting in a smaller standard deviation and less 

extreme maximum and minimum values. Bias in the data, resulting from switching the equipment 

from SFU samplers to IMPROVE samplers, comes from the actual sampling methodology. For 

example, the SFU fine mass (PM2.5) is a sequential filter that sites behind a filter that collects coarse 

material, while the IMPROVE module A filter has a cyclone inlet that is calibrated to 2.5 microns. 

Any discrepancy in cutpoint efficiency and derivative, as a function of aerodynamic radius between 

the two samplers, could generate a bias in seasonal mean values. If there is a long-term trend in the 

data, this bias could either enhance the trend or mask it. 

 It appears, based on a cursory inspection of the data as presented here, that a systematic effect 

associated with changes in protocols is not evident for fine mass and sulfur concentrations. Most 

clearly identifiable changes in the data can be explained by other physical causes. One such notable 

change occurred at Mount Rainier where the sampling site was moved from a high altitude to a low 

altitude location. Other explanations are related to changes in emissions. In general, the expected 

changes due to smoothing did not materialize, instead the changes in data behavior appear random 

and slight at best. No systematic bias in the data between SFU samplers and IMPROVE samplers 

was noted, suggesting that any bias at one particular site must be due to circumstances unique to 

that site such as equipment calibration, or characteristics of the ambient aerosol and meteorology 

that would affect sampler performance, or actual location/orientation of the equipment. 

 In the case of absorption, Figure 6.1 shows time lines for babs for five sites that demonstrate a 

clear change before and after the IMPROVE network was initialized. The sites included are 

Acadia, Glacier, Great Smoky Mountains, Mount Rainier, and Shenandoah National Parks. It is 

clear by inspection of Figure 6.1 that a significant change occurred after March 1988. Almost all 

sites for all seasons show significant increases in babs between sampling regimes with the 

IMPROVE values being larger than the SFU. As with sulfur, it should be noted that increases of 

babs at Mount Rainier are likely related to changing of the sampler location. Reasons for the 

changes at the other sites are not known and it should be noted that these five sites are exceptions as 

most sites show little if any change by inspection. 

6.2 Seasonal Trends of Sulfur

 Sulfur concentrations often have a readily identifiable seasonal trend [Day et al., 1996, Malm 

et al., 1994; Sisler et al., 1993; Sisler and Malm, 1989; Trijonis and Yuan 1987; Flocchini et al., 

1981]. These trends have been related to a number of factors including meteorology, 

photochemistry, and long-range transport with sulfur concentrations being the highest during the 

summer and lowest during the winter. 
me line of absorption (babs) at five sites that demonstrate a clear change before and after the IMPROVE network 

s initiated. 
6-5

 Figure 6.2 shows the time lines of sulfur at seven sites: Shenandoah, Great Smoky Mountains, 

Yosemite, Rocky Mountain, Canyonlands, Yellowstone, and Glacier National Parks. Table 6.2 has 

seasonal statistics for these sites. At two sites, Yellowstone and Glacier, one extreme value was 

discarded from the time lines presented in Figure 6.2. This value, 1364 ng/m3 at Yellowstone on 

May 31, 1989 is a factor of 10 higher than the mean and 200% higher than the next highest value. 

Similarly, one value of 1326 ng/m3

 at Glacier on June 2, 1993, was discarded. 

 These sites demonstrate a range in amplitude of seasonal variation. The two sites with the 

highest sulfur concentrations, Great Smoky Mountains and Shenandoah, are in the East. The 

maximum sulfur concentrations for these sites, 8700 and 6900 ng/m3

 at Shenandoah and Great 

Smoky Mountains, respectively, occurred during the summer. 

 At Shenandoah, Great Smoky Mountains, Yosemite, and Rocky Mountain National Parks, 

sulfur concentrations have clear seasonal patterns. The pattern is less clear at Canyonlands, while at 

Yellowstone and Glacier a seasonal pattern is not apparent. These seven sites represent the range of 

seasonal variability of sulfur in the data set. 

 It is notable that even at sites with strong seasonal trends there are some sampling periods that 

have zero or near zero concentrations in any season. 

 Yosemite is interesting as the maximum sulfur is only about 1400 ng/m3, yet a seasonal pattern 

is clearly evident from the minimums, which are much greater during the summer months. At 

Rocky Mountain, the seasonality is much weaker than at Yosemite as evidenced by the variability 

in time that yearly maximum values occur. However, it is clear from the minimum values that a 

seasonal trend exists with higher minimums occurring during the summer. 

 

 The three remaining sites shown in Figure 6.2, Canyonlands, Yellowstone, and Glacier have 

much lower sulfur concentrations. None of these sites exhibit obvious seasonal trends as displayed 

by the other sites. Their maximum values are quite a bit less than the other sites and about equal to 

each other. 

6.3 Seasonal Trends of Absorption (babs)

 Absorption, like sulfur, has a strong seasonal trend at many sites with highest concentrations 

usually occurring during the summer and early autumn months. Figure 6.3 shows time lines of babs

at six sites across the United States: Acadia, Glacier, Great Smoky Mountains, Rocky Mountain, 

and Yosemite National Parks, and Saguaro National Monument. This ensemble demonstrates the 

range of the strength of the seasonal signature that varies from none at Acadia and Saguaro, to 

moderate at Glacier and Great Smoky Mountains, to strong at Rocky Mountain and Yosemite. 

 Table 6.3 has seasonal statistics for absorption at these six sites. Acadia, a site with minimal 

seasonality, has a mean value that varies from 750 ng/m3 in the spring to 950 ng/m3 in the winter. 

Acadia's maximum concentrations are similar between seasons at about 3000 ng/m3

, except during 

the winter when the maximum value of 3562 ng/m3

 was obtained. 
Time lines of sulfur concentration at seven sites that demonstrate a range of seasonal behavior with the 

strongest seasonality at the top and the weakest at the bottom. 
6-7

Table 6.2. Seasonal statistics (in ng/m3) for particulate sulfate at seven sites. 

Site N Mean Std Dev Minimum Maximum 

SPRING 

 SHEN 299 1415 809 131 5199 

 GRSM 267 1508 838 175 6789 

 YOSE 284 261 156 11 964 

 ROMO 354 270 140 0 921 

 CANY 374 257 127 36 1202 

 YELL 220 177 120 0 1365 

 GLAC 308 257 146 36 866 

SUMMER 

 SHEN 304 2495 1286 11 8665 

 GRSM 267 2407 1250 393 6928 

 YOSE 283 374 178 55 1339 

 ROMO 354 319 130 0 963 

 CANY 374 306 130 66 1206 

 YELL 237 148 73 0 432 

 GLAC 301 216 96 26 669 

AUTUMN 

 SHEN 283 1413 1059 15 7722 

 GRSM 237 1374 892 27 5610 

 YOSE 259 260 182 16 1008 

 ROMO 348 236 148 0 877 

 CANY 343 282 175 0 1679 

 YELL 192 149 92 0 558 

 GLAC 274 231 132 39 862 

WINTER 

 SHEN 255 827 441 62 2588 

 GRSM 214 831 462 110 3152 

 YOSE 256 86 69 12 525 

 ROMO 327 168 143 14 1146 

 CANY 329 280 194 0 1339 

 YELL 205 135 89 0 488 

 GLAC 261 259 181 13 1326 

Saguaro shows even less variability in the mean with a low of 826 ng/m3

 in the spring to a high of 

927 ng/m3

 in the winter. 

 Glacier and Great Smoky Mountains have relatively stable means with Glacier obtaining its 

low of 743 ng/m3

 during spring and its high of 1085 ng/m3 in the autumn. Great Smoky Mountains 

obtains its lowest during the winter at 1156 ng/m3

 and highest in the summer with 1585 ng/m3. The 

seasonality at Great Smoky Mountains and Glacier is more readily observed 
6-8

lines of absorption at six sites that demonstrate a range of seasonal behavior with the weakest seasonality at 

p and the strongest at the bottom. 
6-9

Table 6.3 Seasonal statistics for absorption (in ng/m3) at six sites. 

Site N Mean Std Dev Minimum Maximum 

SPRING 

 ACAD 211 750 418 0 3041 

 SAGU 167 826 295 98 1799 

 GLAC 305 743 361 0 1994 

 GRSM 265 1332 567 0 4195 

 ROMO 290 480 255 36 1633 

 YOSE 282 661 357 0 2305 

SUMMER 

 ACAD 196 942 589 0 2932 

 SAGU 210 843 299 21 2239 

 GLAC 297 812 371 38 2852 

 GRSM 266 1585 887 391 5104 

 ROMO 272 713 281 37 1990 

 YOSE 278 1155 617 148 6295 

AUTUMN 

 ACAD 218 779 509 116 2963 

 SAGU 186 868 369 0 2087 

 GLAC 255 1085 571 152 3284 

 GRSM 235 1275 599 132 3292 

 ROMO 263 449 256 0 1729 

 YOSE 239 867 588 56 3655 

WINTER 

 ACAD 195 950 455 0 3563 

 SAGU 161 927 468 198 2883 

 GLAC 259 911 461 20 2347 

 GRSM 209 1156 627 51 4564 

 ROMO 262 308 245 23 1566 

 YOSE 242 300 264 0 1773 

by the extreme values. The minimum for Great Smoky Mountains varies from 0 (below detection) 

to 381 ng/m3

 in the summer when the maximum of 5104 ng/m3 is obtained as well. Glacier exhibits 

similar though not as extreme behavior where the largest minimum of 152 ng/m3 and largest 

maximum of 3284 ng/m3

 are in the winter. 

 Yosemite and Rocky Mountain, which have the strongest seasonal variation, have means of 

300 ng/m3

 and 308 ng/m3 in the winter, and 1155 ng/m3 and 713 ng/m3in the summer. 

6.4 Long-Term Variability

 Because of seasonal variability, long-term trends can more easily be explored by examining 
6-10

trends in seasonally-averaged data over a number of years. Seasonal statistics by year are 

graphically portrayed at each site for both fine mass concentrations, sulfur concentrations, and 

absorption. Appendix 2 has plots for every site and season. The box icon used for each season portrays the 

minimum, the mean minus one standard deviation, the 25th percentile, 50th percentile (median), mean, 75th 

percentile, mean plus one standard deviation, and maximum. The percentiles are connected by a solid line. 

Presented here are representative examples for sites that demonstrate trends and the lack of trends.

6.4.1 Bryce Canyon National Park

Bryce Canyon in the autumn (Figures 6.4a, 6.4b, 6.4c) is an example showing an apparent change in 

fine mass concentrations (Figure 6.4a). Excluding the fall of 1979, there appears to be a step increase in 

fine mass concentrations beginning in 1987. All percentiles, means, maxima and standard deviations 

increase noticeably after 1987. It is tempting to associate this with a bias caused by changing the equipment 

from an SFU sampler to an IMPROVE module A; however, the changeover did not occur until after the 

autumn of 1987 when the 75th percentile and mean are greater than all succeeding years. 

 Sulfur concentrations (Figure 6.4b) at Bryce Canyon show no apparent trend. The sulfur concentrations 

from year to year are variable with the median hovering around 250 ng/m3

. However, the highest median 

value occurs at the start of the data record in the winter of 1979 at about 400 ng/m3

, which exceeds the 75th 

percentile for all other years. This season has been analyzed by a number of researchers and has been 

associated with transport from the smelters in Arizona. 

 Absorption is somewhat greater than sulfur at around 400 ng/m3 and displays a variable pattern 

between years. The first year of the absorption record is notable in that the 75th percentile is greater than the 

maxima for all subsequent years except 1993; similarly, the mean for 1983 at about 600 ng/m3

 is on par with 

the 75th percentile for all years after and including 1988. 

6.4.2 Rocky Mountain National Park

 Fine mass concentrations at Rocky Mountain National Park (Figure 6.5a) during winter, the season of 

best visibility, shows no trend for the 25th and 50th percentile, which vary around 900 ng/m3

 and 1500 

ng/m3

, respectively. There is a most interesting block of years beginning in 1986 

running through 1991 that demonstrate inflated variability marked by increased standard deviations caused 

by high maximum values and 75th percentiles driving the mean values up. Association of this behavior with 

the decrease in sampling time from 72 hours to 24 hours is at first tempting. This explanation seems 

doubtful noting the dramatic quieting that occurs after 1991 when the standard deviations, maximums, and 

75th percentiles dropped sharply. Also, it is worth noting that the change in protocol did not occur until the 

summer of 1986 after the start of the period of inflation in the winter of 1986. 

 Sulfur concentrations (Figure 6.5b) have a fairly constant median level of sulfur during the winters of 

about 150 ng/m3

. The pattern of variability is mixed and the median sulfur concentration never moves in the 

same direction for more than two seasons. 

 A clear downward trend in absorption (Figure 6.5c) is readily seen. The median absorption in the 

winter of 1982-1983 is about 400 ng/m3

 then drops to about 125 ng/m3 by the winter of 
ly statistics for fine mass concentration (ng/m3) at Bryce Canyon National Park in the autumn. 
ly statistics for sulfur concentration (ng/m3) at Bryce Canyon National Park in the autumn. 
ly statistics for absorption (ng/m3) at Bryce Canyon National Park in the autumn. 
ly statistics for fine mass concentration (ng/m3) at Rocky Mountain National Park in the winter. 
ly statistics for sulfur concentration (ng/m3) at Rocky Mountain National Park in the winter. 
ly statistics for absorption (ng/m3) at Rocky Mountain National Park in the winter. 
6-17

1994-1995. Since 1991-1992 the median absorption has never exceeded 200 ng/m3. The trend of 

the 75th percentile is even more impressive, the maximum occurs in the winter 1982-1983 at about 

800 ng/m3 then drops to less then 200 ng/m3 in recent years. 

6.4.3 Guadalupe Mountains National Park

 Fine mass concentrations at Guadalupe Mountains (Figure 6.6a) in the autumn have been quite 

variable. Concentrations decreased steadily the first four years. The 75th percentile and 50th 

percentile decrease every year from 6500 ng/m3

 and 5200 ng/m3, respectively in 1982 to 4200 

ng/m3

 and 3500 ng/m3, respectively, in 1986. The 25th percentile obtains its minimum as well in 

1986 of 2000 ng/m3

. After 1986 there is a precipitous rise in the 50th and 75th percentile in 1990 

to almost 6000 ng/m3

 and 9000 ng/m3, respectively. Then a quick recovery to almost 1986 levels 

occurs by 1993 followed by an upturn in 1994. 

 Sulfur at Guadalupe Mountains in the autumn (Figure 6.b) does not display the gyrations of fine 

mass and appears to be trending downward. The median concentration is highest (650 ng/m3

) 

during autumn 1984, then drops to about 400 ng/m3

 the next year. After a slight increase in 1986 to 

500 ng/m3

 the median sulfur never exceeds that level again and trends downward. The last four 

years show a steady decline of the median to 350 ng/m3

 in 1994. 

Absorption, on the other hand (Figure 6.6c), while trending down at first, increases to a high of 700 

ng/m3

 for the median in autumn 1990, then steadily declines. Similarly, the 75th percentile trends up to its 

maximum in 1988 at almost 1000 ng/m3

 then steadily declines to about 600 ng/m3 in the autumn of 1994. 

The large increase in babs, coincident with the change from SFU to IMPROVE between 1987 and 1988, is 

suspicious and should be further investigated to confirm that the apparent trend is not a measurement 

artifact. 

6.4.4 Crater Lake National Park

 Fine mass concentrations at Crater Lake during the winters (Figure 6.7a) appear to have trended down 

slightly. During the first five winters the 75th percentile has trended down from 3400 ng/m3

 to 2500 ng/m3. 

With the exception of the winter of 1990-1991, which shows a significant up tick in all measures (except the 

minimum), all winters after 1989 the 75th percentile is below 2000 ng/m3 for five out of six winters. 

On the other hand, during the winter, sulfur appears to be holding steady at around 60 ng/m3 for the 

median (Figure 6.7b). The winters of 1984-85, 1985-86, and 1986-87 are interesting due the very large 

maximums with concentrations as much as a factor of 10 larger than the medians. 

 Crater Lake in the winter displays a strong absorption trend (Figure 6.7c). In the winter of 1982-83, the 

75th percentile value was about 900 ng/m3

. During the next five out of six winters of record all percentiles 

decline with the 75th percentile obtaining a minimum of less than 400 ng/m3 during the winter of 1989-90. 

The remaining winters, until the last, have a very steady 25th percentile at about 150 ng/m3

. The other 

percentiles are variable and obtain their global minimum during the winter of 1993-94. The last winter of 

1994-95 shows a dramatic increase in all measures, with the 75th percentile exceeding 1400 ng/m3

.
ly statistics for fine mass concentration (ng/m3) at Guadalupe Mountains National Park in the autumn. 
ly statistics for sulfur concentration (ng/m3) at Guadalupe Mountains National Park in the autumn. 
ly statistics for absorption (ng/m3) at Guadalupe Mountains National Park in the autumn. 
ly statistics for fine mass concentration (ng/m3) at Crater Lake National Park in the winter. 
ly statistics for sulfur concentration (ng/m3) at Crater Lake National Park in the winter. 
ly statistics for absorption (ng/m3) at Crater Lake National Park in the winter. 
6-24

6.4.5 Great Smoky Mountains National Park

 Great Smoky Mountains in the autumn (Figure 6.8a) displays constant levels of fine mass 

concentrations for the 50th percentile at about 9000 ng/m3

. The 75th percentile obtains its maximum in 

1990 at about 21000 ng/m3

 then drops steadily to 13000 ng/m3 by 1995. There is a large decline in medium 

concentration coincident with the SFU to IMPROVE change in 1987-1988. However, because this type of 

change is not seen at other sites or at this site in other seasons, it is believed to be coincidental.

 The median sulfur concentration (Figure 6.8b) is high in 1986 at about 1500 ng/m3 then drops to a low 

of about 800 ng/m3

 in 1988. After 1988, with the exception of a sharp decrease in 1992, the median sulfur 

concentration increases to its maximum of about 1700 ng/m3

 in 1993 then pulls back to about 1100 ng/m3 by 

1994. 

 Absorption (Figure 6.8c) demonstrates an increasing trend. The median value, with slight variability, 

increased from about 900 ng/m3

 in the autumn of 1985 to greater than 1500 ng/m3 in 1994. The 75th 

percentile shows a similar rise from about 1200 ng/m3

 in 1985 to almost 2000 ng/m3 in 1991 and 1993. A 

similar trend is displayed by the 25th percentile, rising from about 600 ng/m3

 to almost 1000 ng/m3. In 

1994, the 75th and 50th percentiles decrease sharply from the high in 1993 to about 1600 ng/m3

 and 1200 

ng/m3

, respectively, but only a slight decrease is seen for the 25th percentile.

6.4.6 Mesa Verde National Park

 Fine mass concentrations during the summer at Mesa Verde (Figure 6.9a) demonstrates an interesting 

trend. The 25th percentile, beginning in 1986, increased dramatically from around 2200 ng/m3

 to 5000 

ng/m3

 in 1990, then dropped off sharply to 3000 ng/m3 by 1992. The same trend is closely mirrored by the 

50th percentile and to a lesser extent by the 75th percentile, which rose from about 4000 ng/m3

 in 1985 to 

almost 8500 ng/m3

 in 1990 and then drops back to 4000 ng/m3 in 1992. Since 1992 all percentiles have 

increased significantly; the 75th from 4000 ng/m3

 to 5000 ng/m3; the 50th from about 3300 ng/m3 to almost 

4000 ng/m3

; and the 25th rose from about 3000 ng/m3 to almost 3400 ng/m3.

 Median concentrations of sulfur at Mesa Verde (Figure 6.9b) are highest during the first two 

measurement summers of 1983 and 1984 at about 400 ng/m3

, then decrease to their minimum in 1987 at less 

than 200 ng/m3

. This same pattern is shown by the 25th percentiles and 75th percentiles, with the 25th 

percentile decreasing from 340 ng/m3

 to about 120 ng/m3. After 1987 the 25th percentile increased every 

year except two, obtaining a level of about 280 ng/m3

 in 1994. The 50th percentile and 75th percentile rise 

to about 320 ng/m3

 and 440 ng/m3 by 1990, respectively. After 1990 the 50th percentile essentially hovers 

about 320 ng/m3

 and the 75th percentile drops by to around 380 ng/m3. 

 Absorption (Figure 6.9c) shows a similar trend as sulfur with its maximum median of about 650 ng/m3

in 1983 and minimum median of 400 ng/m3

 in 1987. Median absorption then increases with sulfur to 

another high in 1990 of about 550 ng/m3

 then generally decreases to 450 ng/m3. The 25th percentile 

decreased from 480 ng/m3

 in 1983 to 280 ng/m3 in 1986 increased to 
ly statistics for fine mass concentration (ng/m3) at Great Smoky Mountains National Park in the autumn. 
ly statistics for sulfur concentration (ng/m3) at Great Smoky Mountains National Park in the autumn. 
ly statistics for absorption (ng/m3) at Great Smoky Mountains National Park in the autumn. 
ly statistics for fine mass concentration (ng/m3) at Mesa Verde National Park in the summer. 
ly statistics for sulfur concentration (ng/m3) at Mesa Verde National Park in the summer. 
ly statistics for absorption (ng/m3) at Mesa Verde National Park in the summer. 
6-31

almost 460 ng/m3 by 1989. The 75th percentile has a similar pattern with a high value of about 800 ng/m3, 

then decreasing to 480 ng/m3

 in 1987 before rising again to almost 700 ng/m3 in 1990. After 1990 the 75th 

percentile drops steadily to about 460 ng/m3

, the 50th and 25th percentile are trending down as well but not 

as dramatically. 

6.4.7 Chiricahua National Monument

 Fine mass concentrations at Chiricahua (Figure 6.10a) during the summer show little change. The only 

notable feature is the spike in 1990 for the 75th percentile at over 11000 ng/m3

, which then drops sharply to 

little more than 5000 ng/m3

 by 1992, followed by an increase to about 7500 ng/m3 in 1994. The 25th and 

50th percentiles do not demonstrate any trends. The 75th percentile varies between its high of almost 7000 

ng/m3

 in the summer of 1981 and its low of about 4500 ng/m3, while the 25th percentile varies between 

5500 ng/m3

 in 1981 and 3000 ng/m3 in 1987.

 A particular point of interest is to what extent are changes in emissions reflected by changes in sulfur 

concentrations. Large fluctuations in smelter emissions have occurred in the desert southwest during the 

1980s, providing an opportunity to study the relationship between emissions and aerosol sulfur 

concentrations. In the intermountain region between the continental divide and the Sierra Nevada, 90% of 

United States emissions were from 15 power plants and 12 smelters. Seven of the smelters were located in 

southern Arizona. Since the late 1980s, four of the seven Arizona smelters were shut down and the rest 

were controlled [Sisler and Malm, 1989; Oppenheimer, 1987; Epstein and Oppenheimer, 1986]. The 

reduction in smelter emissions is evident from the change in sulfur distributions at Chiricahua during the 

summer as shown in Figure 6.10b. Beginning in 1987 the variance drops considerably. There is no 

appreciable change in the minima and 25th percentile values, so the reduction in variance is attributed to 

reduced medians, means, 75th percentiles, and maxima. From 1988 through 1990 median concentrations 

increase from 350 ng/m3

 to about 650 ng/m3 then drop to 400 ng/m3 in 1991. Since 1991 there has been a 

steady increase to about 650 ng/m3

 by the median. 

Absorption shows no consistent trend (Figure 6.10c). The median, from its high in 1983 of about 700 

ng/m3

, decreases for the next three out of four years to its low in 1987 of 400 ng/m3, then rises again to a 

high value of 700 ng/m3

 in 1989. Since 1989, there appears to have been a slight decline in median 

absorption. 

6.4.8 Grand Canyon National Park - Winter

 Figure 6.11a shows the winter distributions at Hopi Point in Grand Canyon for fine mass 

concentrations. Winter distributions are notable for the very high maxima in 1980 and 1987 at almost 

25000 ng/m3

 and 18000 ng/m3, respectively. There has been a significant increase in the 25th percentile, 

which starts out less than 1000 ng/m3

 then rising to almost 2000 ng/m3 by 1989. After falling back slightly 

to 1500 ng/m3

, the 25th percentile essentially stays flat until 1994 when it drops back to about 1000 ng/m3. 

The same behavior, although somewhat more variable, is displayed by the 50th percentile and 75th 

percentile, a maximum of about 4000 ng/m3

 and 2500 ng/m3 are obtained in 1989, respectively. After 1989 

the 75th and 50th percentiles drop to about 2000 ng/m3

 and 1500 ng/m3, respectively. 
ly statistics for fine mass concentration (ng/m3) at Chiricahua National Monument in the summer. 
ly statistics for sulfur concentration (ng/m3) at Chiricahua National Monument in the summer. 
ly statistics for absorption (ng/m3) at Chiricahua National Monument in the summer. 
ly statistics for fine mass concentration (ng/m3) at Grand Canyon National Park in the winter. 
6-36

 Sulfur concentrations show two distinct trends for the 25th percentile (Figure 6.11b). For the 

first four years the 25th percentile concentration is essentially flat at about 50 ng/m3

 then rises to 

over 120 ng/m3

 for 1991, 1992 and 1993. By 1995 it has dropped back to about 50 ng/m3. This 

same pattern is displayed with more variance by the 50th percentile rising from its lowest value of 

about 60 ng/m3

 in 1982 to almost 200 ng/m3 by 1992, then dropping off to 100 ng/m3 by 1995. No 

trend is apparent for the 75th percentile. 

 Absorption (Figure 6.11c) shows a similar trend as sulfate. Levels start out low in 1983 and 

1984 with concentrations about 100 ng/m3, 200 ng/m3, and 300 ng/m3 for the 25th, 50th and 75th 

percentiles, respectively. By 1989 the 25th and 75th percentiles reach their maxima of 250 ng/m3

and 550 ng/m3

, respectively, the 50th percentile obtains its maximum of about 400 ng/m3 in 1991. 

After 1991 concentration levels drop significantly and steadily until 1995 to about 75 ng/m3

, 125 

ng/m3, and 200 ng/m3 for the 25th, 50th and 75th percentile, respectively. 

6.4.9 Grand Canyon National Park - Summer

 Fine mass concentrations have significantly increased during the summers since 1980 (Figure 

6.12a). It is particularly obvious by the rise in the 25th percentile almost doubling from about 1500 

ng/m3

 in 1981 to almost 3000 ng/m3 in 1994. A similar trend is seen for the 50th percentile, which 

has increased from about 2500 ng/m3

 in 1981 to about 4500 ng/m3 in 1994. The 75th percentile, 

after some initial variance, has increased from 4000 ng/m3 in 1984 to about 6000 ng/m3 in 1994. 

This trend is also played out by the minima. In 1980 and 1981 the minima were about 500 ng/m3

, 

however during the last three years (1993-1995) the minima concentrations are about 3000 ng/m3

. 

 Figure 6.12b shows the summer distributions of sulfur at Hopi Point. Beginning in 1980, sulfur 

has median values that trend up from about 210 ng/m3

 to about 400 ng/m3 in 1985, then fall to 200 

ng/m3

 in 1987. From 1988 through 1993 the median for sulfur is quite stable and ranges between 

250 ng/m3

 to about 325 ng/m3. In 1994, the median increases to about 375 ng/m3. The 25th 

percentile shows two clear trends; from a low of about 100 ng/m3

 in 1980 it increased to around 

350 ng/m3

 in 1985, then drops sharply to 150 ng/m3 in 1987. After 1987 the concentrations of the 

25th percentile increase five years out of seven to more than 300 ng/m3

. A slight trend towards 

decreasing variability is evidenced by a decrease in standard deviations attributed to a decrease of 

maxima and an increase of minima. 

 The median value of absorption (Figure 6.12c) is lowest in 1984 at about 300 ng/m3

 then 

doubles to 600 ng/m3 in 1985. From 1985 through 1994 the median remains relatively stable, 

ranging from a high of 700 ng/m3

 in 1989 to a low of 500 ng/m3 in 1990, then dropping in 1995 to 

about 400 ng/m3

. The 25th and 75th percentiles essentially track the median rising and falling 

almost in lockstep. The 25th and 75th percentiles reach their maxima in 1989 of about 600 ng/m3

and 900 ng/m3

, respectively then fall off by 1995 to about 500 ng/m3 and 300 ng/m3. 

 
ly statistics for sulfur concentration (ng/m3) at Grand Canyon National Park in the winter. 
ly statistics for absorption (ng/m3) at Grand Canyon National Park in the winter. 
ly statistics for fine mass concentration (ng/m3) at Grand Canyon National Park in the summer. 
ly statistics for sulfur concentration (ng/m3) at Grand Canyon National Park in the summer. 
ly statistics for absorption (ng/m3) at Grand Canyon National Park in the summer. 
6-42

6.4.10 Grand Canyon National Park - Autumn

Fine mass concentrations (Figure 6.13a) have been trending upwards in the autumn at Hopi Point since 

obtaining their minimum in 1980. This is particularly evidenced by the 25th percentile and minima. In 

1980, the minimum concentration was 200 ng/m3

, by 1993 it had increased to its maximum in excess of 

1600 ng/m3

 before falling back to about 1000 ng/m3 in 1994. The 25th percentile in 1980 was about 700 

ng/m3

 and increased to almost 3000 ng/m3 in 1992, then falls back to about 2000 ng/m3 in 1994. Similar 

behavior by the 50th percentile is present with its minimum of 1200 ng/m3

 occurring in 1980; however, the 

50th percentile obtains its maximum of about 4200 ng/m3

 in 1987 then remains relatively steady. 

 As with fine mass, sulfur has increased since 1980 when the 25th percentile obtained its minimum of 

about 60 ng/m3

 as shown by Figure 6.13b. By 1983 sulfur concentrations for the 25th percentile increase 

sharply to about 240 ng/m3

 then fall off to about 125 ng/m3 in 1985. By 1990 the 25th percentile increases to 

almost 200 ng/m3

 and remains at this level with some variability through 1994. There is a trend towards 

decreased variability as evidenced by the standard deviation and is attributed to decreased maxima and 

increased minima. 

 Absorption displays little or no long-term trend (Figure 6.13c). Beginning in 1983 all three percentiles 

drop sharply by 1984; the 75th percentile moves from over 600 ng/m3

 to about 350 ng/m3; the 50th 

percentile decreases from about 550 ng/m3

 to less than 250; and the 25th percentile drops from about 300 

ng/m3

 to 200 ng/m3. After 1984 all percentiles show steady increases by 1987 to their 1983 levels. From 

1987 until 1993 the three percentiles are essentially steady with some variability at about 650 ng/m3

, 550 

ng/m3

, and 350 ng/m3 for the 75th, 50th, and 25th percentiles, respectively. 

6.5 Interrelationships of Fine Mass, Sulfur and Absorption

 Matrix scatter plots provide a useful tool for understanding the correlation of daily fine mass, babs, and 

sulfur, as well as for distinguishing differences between sites and seasons. Some correlation between fine 

mass and its constituents is expected, particularly in the case of sulfur where ammonium sulfate aerosol 

comprises a large fraction of the mass at many sites. By the same argument a limited amount of correlation 

between babs, sulfur, nitrate, organic carbon, and fine soil by virtue of their association with fine mass would 

not be unexpected. Sulfur and babs demonstrate the greatest amount of correlation between the constituent 

species. The strength of the correlation is variable and relatively strong at certain sites. Strong correlations 

suggest several possibilities including common anthropogenic sources or transport pathways and internally 

mixed aerosol. On the other hand, lack of correlation is indicative of different sources and externally mixed 

aerosols. 

 In the determination of babs a correction for Ashadowing@ is made. This is because as the filter becomes 

loaded with particles, the observed proportion of absorption to fine mass decreases. This is believed to be 

the case because some of the particles shadow others from the light source. Thus, a correction must be 

applied. If it is correct, then any correlation of babs with fine mass would be due to physical reasons. On the 

other hand, an over correction for fine mass would artificially increase absorption and the correlation of babs

with fine mass [Campbell et al., 1995]. 
ly statistics for fine mass concentration (ng/m3) at Grand Canyon National Park in the autumn. 
ly statistics for sulfur concentration (ng/m3) at Grand Canyon National Park in the autumn. 
ly statistics for absorption (ng/m3) at Grand Canyon National Park in the autumn. 
6-46

6.6.1 Daily Scatter Plots 

Appendix 3 has matrix scatter plots of fine mass, sulfur, and babs by season for all IMPROVE 

monitoring sites. Presented here are a representative subset of sites that demonstrate the variability, range, 

and character of the correlations with emphasis on differences between sites. Overlaid on each scatter plot 

are two sets of concentric ellipses. The major axis lies on the first principal component and the ellipse center 

lies on the mean value of both species. The ellipses define contours that enclose points that fall within the 

50% and 90% of a bivariate normal distribution. A perfectly round ellipse indicates no correlation, and the 

oblateness indicates the degree of correlation. Perfect correlation would result in the ellipse collapsing into a 

straight line. 

6.6.1 Shenandoah National Park

The scatter plots for Shenandoah (Figure 6.14) display many of the qualities expected for a site 

impacted by numerous anthropogenic sources. Sulfate constitutes a major fraction of the fine mass and 

accordingly the correlation of sulfur to fine mass is high, particularly in the autumn followed by the spring. 

Absorption is surprisingly correlated with fine mass even though babs composes a much smaller fraction of 

the mass. The correlation of babs with sulfur, although significant, especially in the winter, appears to be the 

weakest of the three relationships. Autumn is interesting because of the similarity of the scatter of babs

against sulfur and fine mass. There is a readily identifiable subpopulation at lower concentrations and the 

appearance of a hard edge applies to both scatters. A hard edge is indicative of a strong influence from one 

source type or source area as evidenced by one ratio of babs to sulfur. The scatter away from the hard edge 

indicates occasional influx additional sulfur with proportionately less babs from other sources.

6.6.2 Glacier National Park

 Glacier (Figure 6.15) is an interesting contrast to Shenandoah. The strongest correlations with fine 

mass are with babs rather than sulfur. The correlation of sulfur to babs is quite weak as evidenced by the 

roundness of the ellipse. This is especially evident during the spring and autumn with the strongest babs

sulfur correlation occurring in the winter. The scatter plots of babs vs sulfur and sulfur vs fine mass suggest 

two types of days are being observed. One group of days has high babs and low sulfur. The other group has 

low babs and high sulfur. 

6.6.3 Denali National Park

 At Denali (Figure 6.16), many interesting features are evident. During the winter and spring 

correlations of babs with sulfur are the strongest of any site in the IMPROVE network. During the summer 

and autumn, when the correlations are lowest, the hard edges indicate two groups of days or Apopulations@

dominated either by sulfur or by babs (similar to those discussed above for Glacier). Each population would 

most likely be associated with a distinct source type and/or region. The possibility of two populations 

during autumn and summer are also suggested by the scatter of sulfur against babs. The strongest correlation 

between babs and fine mass occurs during the summer. During the winter all three aerosol measures are 

relatively well correlated with each other. 
6-47

Figure 6.14 Matrix scatter plots of absorption (babs) sulfur (S) and gravimetric fine mass (FM) 

for the four seasons at Shenandoah National Park. Assuming an absorption 

efficiency of 10 m2

/gm all units are ìg/m3.

Figure 6.15 Matrix scatter plots of absorption (babs) sulfur (S) and gravimetric fine mass (FM) 

for the four seasons at Glacier National Park. Assuming an absorption efficiency of 

10 m2

/gm all units are ìg/m3. 
6-48

Figure 6.16 Matrix scatter plots of absorption (babs) sulfur (S) and gravimetric fine mass (FM) 

for the four seasons at Denali National Park. Assuming an absorption efficiency of 

10 m2

/gm all units are ìg/m3. 

6.6.4 Bridger Wilderness Area

 At Bridger (Figure 6.17), there are relatively moderate to strong correlations of all three aerosol 

measures with each other. The correlation of babs with sulfur is especially strong during the winter 

followed by the summer. The strongest correlations of babs with fine mass are during the spring and 

autumn. In the scatter of babs vs fine mass during the summer, two populations are evident. There 

is a population that appears very tight and then another group of days with elevated fine mass. This 

pattern is also seen to a lesser extent in the scatter between sulfur and fine mass. 

6.7 Conclusions

 Changes in sampling protocol, whether by sample duration (24-hour vs 72-hour) or sampler 

type (SFU vs IMPROVE) appear to have a minimal affect on observed concentrations of fine mass, 

sulfur or absorption. This is especially the case for sulfur where the noted changes were slight and 

variable between sites. The only site with a clear change between protocols was at Mount Rainier, 

which is coincident with a change in sampler location and altitude. For the case 
6-49

Figure 6.17 Matrix scatter plots of absorption (babs) sulfur (S) and gravimetric fine mass (FM) 

for the four seasons at Bridger Wilderness. Assuming an absorption efficiency of 10 

m

2

/gm all units are ìg/m3. 

of absorption, five sites demonstrate clear changes between sampler type, one of those sites is 

again at Mount Rainier, while the other sites show notable increases in absorption during most 

seasons. 

 Demonstrated long-term trends fall into three categories: increases, decreases, and variable. 

Sites that demonstrated decreases are at Crater Lake and Rocky Mountain National Parks where 

absorption dropped dramatically, and at Guadalupe Mountains National Park where sulfur is 

decreasing in the autumn. A clear demonstration of decreased sulfur concentrations as a result of 

emission reductions is in the desert southwest at Chiricahua. Two sites where increases have been 

observed are at the Grand Canyon in the autumn where the 25th percentile of sulfur concentrations 

have increased steadily since 1980, and at Great Smoky Mountains National Park where autumn 

concentrations of sulfur and absorption have increased. Other sites that demonstrate little or 

variable changes in sulfur concentrations are at Bryce Canyon, Rocky Mountain, and Crater Lake 

National Parks. Variable or little change in absorption was noted at the Grand Canyon National 

Park in the winter, and Chiricahua National Monument in the summer. 

 The most notable observation from a national perspective is the lack of a clear uniform trend of 

sulfur concentration or absorption. There are local success stories related to emission controls, and 
6-50

there are failures most likely associated with increased local emissions or long-range transport. The 

bulk of the sites show little or variable trends in the long run. 

 The matrix scatter plots demonstrate correlations ranging between slight to strong between 

gravimetric fine mass, babs, and sulfur. Some of the strongest correlations are between fine mass and 

babs even though light-absorbing material is a small fraction of fine mass suggesting an internal 

mixture of carbon with the primary constituents of the fine mass. The exceptions to this are sites in 

the eastern United States where sulfur is a large fraction of the fine mass; here sulfur shows strong 

correlations with fine mass indicative of strong sources. Weak correlations are usually manifested 

by 'fan shaped' scatters, some with hard edges, which suggest multiple sources with variable ratios 

of babs or sulfur. 

6.8 References

Cahill, T.A., R.A. Eldred, and P.J. Feeney, Particulate monitoring and data analysis for the National 

Park Service 1982-1985, University of California, Davis, 1986. 

Campbell, D., S. Copeland, and T. Cahill, Measurement of aerosol absorption coefficient from 

Teflon Filters Using Integrating Plate and Integrating Sphere Techniques, Aerosol Sci. and 

Tech., 22, 287-292, 1995. 

Day, D., W.C. Malm, S.M. Kreidenweis, Seasonal variations in aerosol acidity estimated from 

IMPROVE data, Proc. International Specialty Conference Aerosols and Atmospheric Optics: 

Radiative Balance and Visual Air Quality, Air & Waste Management Association (AWMA), 

Pittsburgh, PA, 1994. 

Eldred, R.A., T.A. Cahill, M. Pitchford, and W.C. Malm, IMPROVE-a new remote area particulate 

monitoring system for visibility studies, Proc. Air Pollution Control Association (APCA) 81st 

Annual Meeting, Pittsburgh, PA, Paper No. 88-54.3:1-16, 1988. 

Epstein, C., and M. Oppenheimer, Empirical relation between sulfur dioxide emissions and acid 

deposition derived from monthly data, Nature, 323, 1986. 

Flocchini, R.G., T.A. Cahill, L. Ashbaugh, R.A. Eldred, and M. Pitchford, Seasonal behavior of 

particulate matter at three rural Utah sites, Atmos. Environ., 35, 315-320, 1981. 

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

particle concentration and optical extinction in the United States, J. Geophys. Res., 99(D1), 

1347-1370, 1994. 

Oppenheimer, M., Empirical source receptor relations for sulfate in the western U. S., Proc. 80th 

Annual Meeting of the Air Pollution Control Association (APCA), Pittsburgh, PA, 1987. 

Sisler, J.F., and W.C. Malm, Relationship of trends in regional sulfur dioxide emissions to 

particulate sulfate concentrations in the southwestern U. S., Transactions of the International 

Specialty Conference on Visibility and Fine Particles, Air & Waste Management Association 
6-51

(AWMA), Pittsburgh, PA, 1989. 

Sisler, J.F., D. Huffman, and D.A. Latimer, Spatial and temporal patterns and the chemical 

composition of the haze in the United States: an analysis of data from the IMPROVE network, 

1988-1991, Report by Cooperative Institute for Research in the Atmosphere (CIRA), Colorado 

State University, Ft. Collins, CO 80523, ISSN: 0737-5352-26, 1993. 

Trijonis, J.C., and K. Yuan, Empirical studies of the relationship between emission and visibility in 

the southwest, EPA-450/5-79-009, Research Triangle Park, NC, 1987.
6-52